Calculated and measured noise amplitude after pulse instability compensation at different extrapolated sampling rates.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"17468",title:"Ultra-Wideband Pulse-Based Microwave Imaging for Breast Cancer Detection: Experimental Issues and Compensations",doi:"10.5772/19613",slug:"ultra-wideband-pulse-based-microwave-imaging-for-breast-cancer-detection-experimental-issues-and-com",body:'Recent research based on numerical modeling (Bond, 2003; Xu, 2001) that ignored hardware characteristics and simple experiments using homogenous breast phantoms (Sill, 2005; Xu, 2004) have shown the potential of ultra-wideband to detect early stage breast cancer. However, clutter interference from heterogeneous breast tissues and hardware characteristics like pulse jitter, finite dynamic range and precision for signal acquisition can severely degrade the detectability of breast tumors. This chapter discusses the experimental issues encountered and compensation methods used to improve the detectability of tumor.
In order to bridge the gap between numerical simulations and experiments, it is important to identify the experimental issues before conducting experiments with more realistic breast phantoms so that the source of imaging artifacts can be identified and compensated. An ideal imaging scenario is first created where the simple sum-and-delay algorithm (Xu, 2001) is working perfectly. In this ideal scenario, the breast phantom is round and symmetrical such that the averaging method can perfectly remove the skin reflection. The breast medium is homogeneous such that propagation of signal in the medium is constant with accurate delay time estimation. Skin is approximated by a single interface (air to breast phantom) because its thickness is small compared to UWB pulse width in space.
In this chapter, several important experimental issues are discussed.
Impulse Generator – Pulse Jitter Artifact
Real Time Oscilloscope – Limited Dynamic Range
Breast Phantom Positioning Error – Ring Artifact
Signal Loss Compensation – Noise Amplification
Filtering and Correlation – Noise Reduction
Averaging and Antenna Number – Signal SNR vs Image SNR
To facilitate the discussion, the experimental setup will first be described in the following subsection 1.1. Experiments discussed in this chapter are conducted in time domain using an impulse generator and a real-time oscilloscope.
A breast phantom is fabricated using soy bean oil (εr = 2.6, σ = 0.05 S/m), contained in a cylindrical polypropylene container (diameter 16 cm, height 12 cm). Tumor is simulated with a small cylindrical shape jelly (εr = 8, σ = 0.4 S/m) with 4 mm diameter and 1 cm length made of tissue-mimicking phantom material (Lazebnik, 2005).
The excitation signal is generated using the Picosecond Pulse Labs 3500D impulse generator, which produces gaussian pulses with full width at half maximum (FWHM) of 80 picoseconds. Agilent DSO81204B real-time oscilloscope with 40 GHz sampling rate is used for recording the backscattered signals from the breast phantom.
Thales UWB antennas (Chua, 2005) are used as the transmitter and receiver of the UWB signals. The antennas dimension is 3 cm width and 4 cm height. The antennas gain is 11 dB with azimuth beamwidth of 60 degrees and elevation beamwidth of 40 degrees. The antennas return loss measured with Agilent N5230A vector network analyzer is lower than -10 dB from 2.4 to 12 GHz.
Breast phantom is placed on a rotary stage with antennas scanning at the side to simulate the human breast in prone position. Breast phantom is rotated for 360 degrees relative to the stationary antennas to simulate a circular antenna array around the breast circumference. The overall experimental setup is shown in Figure 1.
The collected signals are processed with averaging method (Xu, 2001) to remove the signal artifacts, which includes incident pulse, boundary reflection and multipath. The processing is also known as calibration in the literature. Delay-and-sum beamforming (Xu, 2001) algorithm is used to generate the image as in confocal imaging technique. Breast image is formed by synthetically focusing the signals received from the antenna array to every point within the region of interest.
Overall experimental setup.
As mentioned in the introduction, averaging method is applied to remove artifacts in the received signals before delay-and-sum beamforming. An average of all received signals from different antennas is calculated. The averaged signal is used as a template artifact and is subtracted from individual received signals. Clean tumor responses can be obtained if the system is free of noise. The processing is also known as calibration in the literature.
However, averaging method will not work well if the received signals are not aligned perfectly and have unequal amplitudes. Pulse delay jitter is caused by the impulse generator being unable to maintain a constant delay time between trigger signal and the output UWB pulse. The maximum delay timing jitter measured with oscilloscope in the experiment is 31 ps. Pulse amplitude instability is caused by the impulse generator which is unable to maintain constant amplitude of the output UWB pulse.
Figures 2 and 3 show that pulse instability causes phase shift of 31 ps or 20 sample points in the signals. The resultant artifact would be large if the signals are not first compensated. For phase jitter compensation, all received signals are aligned by finding the zero-crossing point between maximum and minimum peaks and phase shifting the signals to the same zero-crossing point. For amplitude instability, compensation is done by normalizing all the signals peak-to-peak amplitude to one unit.
Signals before and after pulse jitter compensation.
Zoom in view of signals given in Figure 2.
To further minimize the phase error, the received signals are extrapolated to higher sampling rates. Table 1 shows the phase error for received signals and resultant signal artifact after pulse instability compensation at different extrapolated sampling rates. Measured noise amplitude is obtained after applying averaging method to a set of data collected from a tumor-free breast phantom. Simulated noise amplitude is calculated with Matlab by subtracting two identical signals, one signal is phase shifted by one sample time from another signal.
The noise amplitude shown in Table 1 is normalized to the incident pulse amplitude. The measured noise amplitude does not further decrease with higher sampling rates greater than 1 THz because the other contributors of noise such as environmental noise becomes significant, whereas the simulated noise amplitude continues to decrease with higher sampling rate as expected.
Sampling Rate | Worst Phase Error | Simulated Noise Amplitude | Measured Noise Amplitude |
No compensation | 31.25 ps | – | 0.7866 |
40 GHz | 25.00 ps | 0.5437 | 0.5217 |
80 GHz | 12.50 ps | 0.2816 | 0.2781 |
160 GHz | 6.25 ps | 0.1684 | 0.1734 |
320 GHz | 3.13 ps | 0.0815 | 0.0942 |
640 GHz | 1.56 ps | 0.0533 | 0.0565 |
1 THz | 1.00 ps | 0.0344 | 0.0436 |
2 THz | 0.50 ps | 0.0173 | 0.0435 |
Calculated and measured noise amplitude after pulse instability compensation at different extrapolated sampling rates.
Figure 4 shows the effects of pulse instability compensation on images formed by delay-and-sum beamforming. The tumor is located at 3 cm to the right from the center, or at coordinate (130, 100). The pulse jitter artifact located at (150,130) is significantly suppressed.
The effects of pulse instability compensation on breast phantom images. Left: without compensation. Right: with compensation.
This section discusses the dynamic range of Agilent DSO-081204B real-time oscilloscope used in the experiment. The information of the dynamic range will determine whether the pulse reflected from a tumor is too small to be detected. In this discussion, dynamic range is defined as the ratio of the oscilloscope vertical-scale range and the amplitude of the smallest possible digitized pulse.
Agilent DSO-081204B real time oscilloscope has analog-to-digital converters (ADC) with 8-bit resolution. After averaging and interpolation, the oscilloscope is able to increase the vertical resolution and store the data in 16-bit resolution. Due to quantization of the signal, at least 4-bit resolution is needed to construct the pulse shape of tumor response as shown in Figure 5. So the remaining 12-bit range is the maximum dynamic range of the oscilloscope.
Ideal tumor response constructed with 2-bit, 4-bit, and 6-bit vertical resolutions showing that the minimum required resolution is 4 bits.
An experiment is conducted to determine the dynamic range of the oscilloscope by investigating its ability to construct a pulse. Due to the presence of noise, a pulse cannot be observed from a single signal. Instead, 360 sets of signals are collected and an averaged signal is obtained. The random noise will be averaged out, and the pulse can be observed if it is within the dynamic range.
The incident pulse is set very small so that its amplitude is equal to the expected smallest peak for different possible dynamic range as shown in the Table 2. The recorded signals are averaged to determine at which dynamic range the pulse can still be constructed.
From the experiment,, the pulse cannot be seen from the averaged signals if its amplitude is set to 2 mV, while the maximum noise amplitude is 5.9 mV. Whereas for a pulse amplitude of 4mV, the pulse is merely noticeable at sample time 1600 as shown in Figure 6. From this experiment which considers the system noise, the dynamic range is estimated to be 11-bits, which has 2048 values available for the whole range. Thus, the maximum detectable ratio between incident pulse and tumor response is 2048.
Dynamic Range 2^ | Smallest peak can be detected | Recorded peak |
8bits | 31mV | 33.2mV |
9bits | 16mV | 14.9mV |
10bits | 8mV | 7.8mV |
11bits | 4mV | 6.1mV |
12bits | 2mV | 5.9mV |
Expected and recorded peak of incident pulse for different dynamic range.
Constructed pulse to test dynamic range of 211 bits. The pulse is just noticeable at sample 1600. Upper trace shows the 360 signals and lower trace shows the averaged signal.
If the experiments use a breast phantom with higher permittivity, ring artifacts will appear as shown in Figure 7. The phantom is fabricated using tissue mimicking phantom material (Lazebnik, 2005) with 80% oil, contained in a polypropylene cylindrical container (diameter 16cm, height 12cm). The material is able to closely simulate the dielectric properties of human tissues.
Tumor simulant is an 8 mm cube made of phantom material with 10% oil buried at 25 mm right from center of the phantom. The measured dielectric constant at 5 GHz for phantom medium is 9 whereas for tumor is 50 which is representative of normal and malignant human breast tissues. Ring artifacts have not been reported previously by other researchers because their breast phantoms use only lower dielectric constant materials.
a. Images of breast phantom with 1 mm off-center positioning error (left) and breast phantom less than 0.5 mm off-center error (right). b. Same Images from Figure 7a with correlation applied.
Ring artifact arises from positioning error (off-center) of the breast phantom. Ideally, averaging method works perfectly with round phantom. Ring artifact appears when the phantom is not positioned perfectly on the rotary axis of the experimental stage. This causes small displacement error of the phantom boundary relative to the antennas.
Ring artifact is caused by the coherent adding of the residue boundary reflections after delay-and-sum beamforming. The ring-to-ring distance is proportional to the wavelength of the incident signal. Ring artifact also indicates the direction of phantom off-center displacement. For instance, when the phantom is displaced to right side, the ring artifact will appear on right indicating positive x-axis off-center displacement and left indicating negative x-axis off-center displacement.
In experiments with oil medium which has lower dielectric constant, ring artifact is not noticeable because the tumor response is large enough to dominate the ring artifacts in the image. The correlation method is not able to improve the image quality when ring artifacts arise as illustrated in Figure 7b. This is due to the high similarity between tumor response and the residue incident pulse ringing.
Adjusting the phantom to the best position using visual inspection will result in a positioning error of 1 mm to 3 mm. Better placement can be achieved by placing a reference object on the antenna to measure the antenna to phantom boundary distance and adjusting the phantom position until the error is smaller than 0.5 mm. The resulting ring artifact is reduced significantly.
An experiment is conducted to test the amplitude of the ring artifact for different displacement errors of a breast phantom without tumor. The phantom is adjusted to the best position with error less than 0.5 mm. Measurements are taken for the phantom at best position, then with off-center displacements of 1 mm, 2 mm, and 3 mm from the best position. The resultant signal artifact shown in Table 3 is computed after applying averaging method. When the breast phantom is perfectly positioned, the signal artifact should be zero.
Position error | Artifact RMS Amplitude (x10-3) | Artifact P-P Amplitude (x10-3) |
<0.5 mm | 1.0 | 2.3 |
1 mm | 3.3 | 5.7 |
2 mm | 5.5 | 9.9 |
3 mm | 8.7 | 15.0 |
Averaged RMS and peak-to-peak amplitude of signal artifact after averaging method for different off-center positioning errors.
This section discusses the power loss during propagation of UWB pulse in breast medium, and the loss models used for compensation. Loss can be contributed by the radial spreading of UWB pulse originating from the antenna as well as attenuation caused by the breast medium. Loss compensation is a signal processing step to equalize all the received signals originating from different locations such that the whole scanning region has unity gain.
Most studies approximate the propagating signal as a uniform cylindrical wave and thus the radial spreading loss equal to 1/r, where r is distance from antenna to the particular scanning point. Considering both transmit and receive paths make the loss proportional to distance square. Compensation is done by multiplying the signals by r2. Figure 8 shows the decrease of reflected signal amplitude from a tumor considering only radial spreading loss. The tumor is located nearest to the antenna at 0 degree and furthest at 180 degrees.
Simulated received pulse amplitude considering only radial spreading loss.
To see the effects of loss compensation, compensation is applied on experiment data to compare the results obtained without compensation applied.
Imaging results in Figure 9 show that loss compensation tends to amplify noise near the phantom boundary. The compensation applied here is only considering radial spreading loss. Worse results will be expected if other loss factors are incorporated, since the signals are multiplied by larger factors.
Radial spreading compensation is a commonly used signal processing step in breast cancer detection algorithms in numerical noise-free modeling. In view of the deteriorating effects of radial spreading compensation on image quality, it is recommended not to apply the compensation.
Image with radial spreading compensation (left) and without radial spreading compensation (right).
This section describes two signal processing methods applied to improve the signal-to-noise ratio (SNR) of the breast phantom images.
The ultra-wideband (UWB) antenna used in the experiments has a bandwidth of 1.8 to 6.3 GHz. Two significant narrowband interferences for the experiments conducted are cell phone noise at 1.8 GHz and wireless local area network (LAN) noise at 2.4 GHz. Thus, digital notch filters at 1.8 GHz and 2.4 GHz are applied to reduce the interferences. The signals and power spectra before and after filtering applied are given in Figure 10. The image quality has been improved with filtering as shown in Figure 11.
a. Signals (upper trace) and power spectra (lower trace) before filtering. b. Signals (upper trace) and power spectra (lower trace) after filtering.
Images formed without filtering (left) and with filtering applied (right). The tumor is located at 3 cm to the right of the center.
A tumor response template is created in Matlab as shown in Figure 12. Correlation is applied by multiplying the tumor response with the filtered signals after the delay-and-sum operation at each pixel. Then the signals are windowed and summed to give a value for every pixel in the breast phantom image given in Figure 13. The image quality has been enhanced with correlation.
Tumor response template.
Comparison for filtered images without correlation (left) and with correlation (right).
The discussion above focuses on only a single experiment data. The result may not be representative as the occurrences of artifacts in images are random due to coherence alignment of noise at certain points. Thus large scale experiments are performed to study the effect of applying filtering and correlation on breast phantom images. Ten experiments are conducted with the same experimental setup and breast phantom. Tumor simulant is at position (130, 100) for all experiments. Experiments are repeated with different antennas array of 6, 12, 24, and 36 elements in one phantom rotation. Forty sets of data are collected and processed. A total of 80 images are formed for SNR analysis. Figure 14 show 20 images from experiments conducted with 24 antennas array.
SNR is calculated from the ratio of the pixel value at tumor location (130, 100) over the highest value of noisy pixels 6 mm radius outside the tumor location. Since the SNR value from individual experiment is highly variable, due to random occurrences of artifacts, an averaged SNR value is calculated using ten SNR values from ten experiments.
Detectability is the ability to observe the tumor in the images although the tumor may not appear as the strongest pixel. Detectability is defined as one when the tumor pixel value is above half of the maximum scale and above twice of the adjacent region pixel values, otherwise zero is given for that image. Table 4 shows that detectability is at least 80% when SNR is positive.
Figure 15 show the SNR versus number of antennas per phantom. There is a significant improvement on SNR with correlation applied. Increasing the number of antennas improves the SNR but cost and complexity of implementation increases. To archive positive SNR, the minimum number of antennas needed without correlation is 12 compared to 10 for correlation.
a. Ten filtered images without correlation applied. b. Ten filtered images with correlation applied.
SNR | Array of 6 | Array of 12 | Array of 24 | Array of 36 |
Delay & Sum | -8.7 dB | -3.6 dB | -0.3 dB | 4.3 dB |
Filtering | -6.3 dB | 0.5 dB | 6.1 dB | 8.2 dB |
Correlation | -5.7 dB | 3.5 dB | 11 dB | 13 dB |
Detectability | Array of 6 | Array of 12 | Array of 24 | Array of 36 |
Delay & Sum | 0/10 | 5/10 | 8/10 | 10/10 |
Filtering | 1/10 | 8/10 | 10/10 | 10/10 |
Correlation | 2/10 | 10/10 | 10/10 | 10/10 |
SNR and detectability for different antennas arrays.
SNR versus number of antennas.
To study the robustness of the correlation method, the tumor stimulant is positioned at different distances, 1 cm to 6 cm, from the center of the breast phantom with the experimental results given in Figure 16. Images in Figure 16 show that correlation improves the image quality.
Images formed without correlation (top) and with correlation (bottom), with tumor located 1 cm to 6 cm from center.
This section discusses the effects of improving the image SNR by increasing the averaging number and antenna number. The tradeoff of increasing these two factors is the increase of acquisition time.
In most other studies, an array of only a few antennas are considered as more antennas do not improve the image resolution but at higher simulation time. However, the advantage of more antennas is noise reduction. Better image SNR can be obtained by increasing the averaging number and antenna number.
Some terms used in the discussion are defined as follows:
Averaging number is the number of waveforms acquired by the oscilloscope to produce an averaged waveform for each acquisition at one antenna position.
Antenna number is the number of antennas in the synthetic array around the circumference of the phantom. It is also the number of steps for a complete phantom rotation relative to a stationary antenna.
Signal SNR is the ratio of the root mean square (RMS) value of averaged tumor response to the RMS value of the averaged noise.
Image SNR is the ratio of the tumor pixel intensity to the highest-value artifact pixel intensity. The definition differs from SCR which is Signal-to-Clutter Ratio, since there is no clutter considered in this discussion. Artifact pixels are caused by coherent summation of noise and occur randomly whereas clutters remain at their positions and averaging is unable to remove them.
Signal SNR can be improved by using larger averaging number, whereas image SNR can be improved by using larger antenna number.
To measure the noise level, antenna is placed stationary without the presence of breast phantom or any moving object. Incident pulse amplitude is set to different attenuation setting from 0 dB (8 V) to 24 dB (0.5 V). A total of 360 measurements with only incident pulse are taken as in breast scanning. Noise is calculated by subtracting the individual measurement with the average measurements. The RMS and peak-to-peak noise is shown in Table 5 and Figure 17, which is the average of 360 noise amplitudes relative to the incident pulse amplitude.
Averaging | 1 | 4 | 16 | 64 | 256 | 1024 | 4096 |
RMS noise | 4.8 mV | 1.7mV | 0.83 mV | 0.45 mV | 0.24 mV | 0.15 mV | 0.10 mV |
RMS amplitude of noise for different averaging number with maximum pulse amplitude of 8V (attenuation setting 0 dB).
RMS noise versus averaging number for different pulse attenuation settings.
Matlab simulations are conducted to determine the image SNR. Ten sets of UWB noise are created by applying a bandpass filter to white noise with all the resultant noise RMS amplitudes set to 0.5 mV. The noise is scaled to the noise RMS amplitude of different averaging numbers as shown in Table 5 and added to the ideal tumor response as shown in Figure 12. The tumor response has peak-to-peak amplitude of 1 mV and RMS of 0.2567 mV.
The signals are delayed as though they are received from the 360 antennas spaced regularly around the breast phantom. The same delay factors are used in creating the signals and in subsequent beamforming. Thus there is no error caused by delay estimation. A total of 120 sets of data are created for confocal imaging for each antenna number of 12, 24, 45, 90, 180, and 360.
Since the image SNR is random due to random occurrences of artifacts, ten SNR values are obtained and averaged based on simulation with ten sets of data for each averaging number and antenna number. A total of 720 images are produced from the simulations. The results are summarized in Table 6 and Figure 18.
Antenna No. | 12 | 24 | 45 | 90 | 180 | 360 | |
Averaging No. | Signal SNR | Image SNR | |||||
2 | -18.66 | -2.8 | -2.8 | -2.2 | -2.4 | -2.0 | -1.3 |
4 | -15.89 | -2.8 | -2.8 | -2.1 | -2.1 | -1.4 | 0.0 |
8 | -13.4 | -2.7 | -2.7 | -1.9 | -1.7 | -0.2 | 1.7 |
16 | -10.09 | -2.6 | -2.2 | -1.3 | -0.2 | 1.9 | 4.0 |
32 | -7.08 | -2.3 | -1.3 | -0.2 | 1.9 | 4.0 | 5.8 |
64 | -4.68 | -1.9 | -0.2 | 1.3 | 3.8 | 5.5 | 6.8 |
128 | -2.69 | -1.2 | 1.1 | 2.6 | 5.1 | 6.5 | 7.5 |
256 | -0.11 | 0.3 | 2.8 | 4.4 | 6.4 | 7.4 | 8.1 |
512 | 2.17 | 1.9 | 4.3 | 5.7 | 7.2 | 8.0 | 8.6 |
1024 | 3.58 | 2.9 | 5.2 | 6.4 | 7.7 | 8.3 | 8.8 |
2048 | 6.6 | 4.8 | 6.5 | 7.5 | 8.4 | 8.8 | 9.1 |
4096 | 8.19 | 5.6 | 7.1 | 8.0 | 8.7 | 9.0 | 9.2 |
Image SNR for different averaging number and antenna number. Signal SNR is calculated with tumor response RMS of 0.2567 mV.
Images from the simulations are shown in Figures 19, 20, and 21 for different sets of noise, noise amplitudes, and antenna numbers. As observed from Figure 18, the cost to increase the image SNR is getting higher when the signal SNR is positive. When the signal SNR is negative, the cost to increase the image SNR is lower. In other word, it is worth using array of more antennas when the tumor response received is weaker than the noise.
In this simulation, the amplitude of the tumor response is fixed at 1 mV, with RMS value of 0.2567 mV. The tumor response is 1000 times smaller than the 1V incident pulse. In this case, we could use antenna number of 12 and averaging number of 1024 to produce reasonably good results.
Image SNR versus Signal SNR for different antenna numbers.
Images obtained with ten sets of noise processed with antenna number of 360 and RMS noise level of 2.2 mV.
Images obtained with the same set of noise processed with antenna number of 360 and RMS noise level from 2.2 mV (top left) to 0.10 mV (bottom right).
Images obtained with the same set of noise processed with antenna number of 12 (top left), 24, 45, 90, 180, 360 (bottom right) and RMS noise level of 0.45 mV.
In this chapter, experimental study of UWB breast cancer detection in time domain and several important experimental issues are discussed. These include pulse jitter artifact, dynamic range of oscilloscope, ring artifact caused by positioning error, noise amplification caused by radial spreading loss compensation, noise reduction after applying filtering and correlation, as well as the improvement on signal SNR and image SNR by using larger averaging and antenna number. The identified issues and compensation methods will facilitate the future experiments in UWB breast cancer detection with more realistic breast phantoms.
Breast cancer is the one of the three most frequent human neoplastic disease worldwide and is the most common female cancer, remaining with considerable impact on general mortality. Worldwide, in the last 10 years, the incidence is growing up, with approximately 2.1 million of new cases per year and estimated mortality of 15%, at about 300.000 per year [1, 2].
Breast cancer remains as an heterogeneous group of disease from the point of view of biological behavior, therapeutic issues and prognostic features, determining different tracks of overall and free of disease survivals [3, 4]. Thus, the clinicopathologic classification of breast cancer has been challenging over the last years, since the isolated simple morphologic classification of the tumor on histology examination is not necessarily related to the precise biologic behavior of the disease [5, 6].
In this way, especially over the last two decades, important researches revealing novel molecular markers expressed by cancer cells has been published in the literature. The new discoveries have improved the breast cancer classification, which has been progressed from a purely morphologic classification, based on histologic patterns, to a molecular classification, based on expression of oncoproteins and hormonal receptors, detected mainly by immunohistochemical techniques, in paraffin-embedded tumoral specimens [6, 7].
The novel molecular classification of breast cancer seems to exhibit more accurated correlation to the clinicopathological aspects of the tumor, as proliferative index, invasiveness and potential to metastatic spread. Furthermore, some of these molecular markers allowed the development of new drugs with specific actions on populations of cancer cells with specific genes alterations, improving considerably the therapeutic, prognostic and survival issues [7].
Instead of the recent advances on new therapeutic protocols under a new molecular perspective, early breast cancer on clinicopathological classification still remains the single one potencially curative [8]. The management of advanced clinicopathologic stage tumors and some established molecular groups of cancer, especially the ‘triple negative’ disease, remains with lacks of consensus. Anyway, the molecular markers have just improved the pathophysiology pathways knowledge, with potential future development of promising new drugs for target therapy of breast cancer [8, 9, 10] .
In the beginning of the 21st century, breast cancer was classified mainly on histologic basis. The WHO current histologic classification of breast cancer is demonstrated in Table 1. Photomicrographies of the most frequent histologic subtypes of invasive breast cancer are represented on Figure 1. The hormonal status receptors (estrogen and progesterone) expression by the neoplastic cells was just evaluated by immunohistochemistry on paraffin-embedded specimens of tumor (core needle biopsy or the surgical excision specimen) [6, 7] .
WHO classification of epithelial breast tumors (5th edition, 2019) | |
Benign epithelial proliferations and precursors
| Invasive breast carcinoma
|
Current histologic (morphologic) classification of epithelial breast tumors (WHO, 2019, 5th edition). This classification considers the tumors histologic patterns of tumors. The most common histologic breast cancer subtype is the infiltrating duct carcinoma NOS (or invasive ductal carcinoma non-special type), accounting for 65–80% of all breast cancers. Invasive lobular carcinoma corresponds to around 5% of all breast malignancies.
Photomicrographies of hematoxylin & eosin (H&E) slides illustrating the most frequent histologic subtypes of infiltrating (invasive) breast cancer. (A) Infiltrating duct carcinoma (Invasive ductal carcinoma NOS) is the most frequent histologic subtype of breast cancer (nearly 75–80% of all invasive breast cancer), constituted of cohesive cancer cells forming infiltrative ductal and ribbons structures (4×); (B) Lobular invasive carcinoma is the second most frequent invasive breast cancer (5–15% of all invasive breast cancer), composed of infiltrating cancer cells with diffuse single-file pattern (10×). In this subtype, the cancer cells lose the cohesion (e-cadherin, an immunomarker important for cell adhesion evaluation, is negative on immunohistochemistry); (C) Mucinous carcinoma represents approximately 2% of breast invasive cancer, composed of groups of cancer cells outling ductal structures, immersed in mucin pools, with delicate fibrous strands containing capilaries (10×); (D) Tubular carcinoma represents around 2% of invasive breast cancer, composed of haphazard arrangement of small well-differentiated duct structures, forming tubules (4×). The other listed invasive breast cancers are uncommon, with each one histologic subtype representing 1% or less (figures extracted from [11]).
Breast cancer is known for its heterogeneous behavior [3, 4]. The histologic classification has been satisfactory for malignancy determination [6]. Though, the clinical division based on hormonal status was not enough for accurate prediction of the prognosis and of clinical response to the therapy [5]. Thus, until the last decade of 20th century, the clinical treatment of breast cancer was based on unespecific chemotherapy and hormonal therapy with drugs like tamoxifen, a known hormonal receptor antagonist [12].
The hormone positive breast cancer is more “differentiated” than the negative one, as the cancer cells maintain the epithelial original cell feature of hormonal receptor expression and, therefore, the hormonal antagonist drugs are effective against these tumors [8]. On the other hand, the approaching of hormonal negative cancers were variable, since it was forming a kindly heterogeneous group, with different aggressiveness potentials, imprecise therapeutic response and doubtful prognosis [6, 8, 10].
In the first decade of the current century, it was emerged a promising classification of breast cancer, proposing a division of the disease in 3 molecular subtypes: luminal, HER-2 overexpressed and “triple negative” (Table 2). This new classification has demonstrated better correlation with the breast cancer behavior. Thus, it was adopted on diagnostic routine of breast cancer. Since this study was published, besides of evaluate the histologic patterns and report the pathologic tumor stage, the pathologist has been required to determine the molecular cancer profile, which has became indispensable to therapy planning [12, 13].
Molecular subtype | Biomarkers profile | Incidence |
---|---|---|
Luminal
| Hormone receptors positive (ER+ and/or PR+)
| 50–70% 35–50% 5–15% |
HER-2 overexpressed | Hormone receptors negative (ER and PR negative) and HER-2 positive | 10–20% |
“Triple negative” | Hormone receptors negative (ER and PR negative) and HER-2 negative | 15–30% |
Molecular subtypes of breast carcinoma. The reported absolute incidences of each molecular subtype of breast carcinoma are variable among several studies.
The luminal subtype cancer is the hormonal positive tumors. This kind of cancer is frequently well or moderately differentiated on histology, formed by lower grades of cells, with lower proliferative index, which is evaluated by antibody Ki-67/MIB-1 on immunohistochemistry. The majority of breast cancers are classified as this subtype (Figure 2). Eventually, luminal cancer can overexpress or amplify at the same time the protein called human epithelial growth factor receptor 2 (HER-2), codified by the oncogen ERBB2 [14, 15].
Photomicrographies of immunohistochemical assessment of invasive breast cancer hormonal expression, in an example of infiltrating duct carcinoma (Invasive ductal carcinoma NOS, WHO 2019), which is the most frequent histologic subtype of breast cancer, constituted by ductal and ribbons structures of cancer cells infiltrating the breast stroma (A). Any kind of nuclear positivity of estrogen receptor (B) and progesterone receptor (C) allows to consider the tumor as positive to hormonal receptor on immunohistochemistry, even when rare cells are positive (C). The hormonal receptors positivities on immunohistochemistry are evaluated for intensity (mild, moderate or strong) and percentages of positive cells (0–100%). Examples of mild positivity (black arrow, C), moderate positivity (red arrow, B) and strong positivity (green arrow, C). Ki-67/ MIB-1 assesses the tumor proliferative index (D), its positivity is nuclear and is expressed in percentages of positive cells (0–100%).
ERBB2 is a oncogen localized in chromosome 17, which codifies the HER-2 protein, a type I transmembrane protein with an extracellular and an intracellular domains, activating signaling pathways from extracellular signals. In last instance, the overexpression/amplification of HER-2 overactivates the intracellular protein kinases, dysregulating the cell cycle, disrupting the cell adhesion and cell polarity and promoting the invasive phenotype [16].
The breast cancer classified as HER-2 subtype is necessarily negative for hormonal receptors and is featured by overexpression or amplification of HER-2. This subtype is frequently less differentiated than the luminal ones on histology, constituted by high grades of cancer cells, with high proliferative index. The presence of elevated concentration of intratumoral lymphocytes (TIL) is not an uncommon finding in these tumors [17].
This new receptor was one of the pioneers for target therapy in molecular era of breast cancer approaching, as it was developed a new class of drug, called trastuzumab, with selective action against the cancer cells overexpressing/amplifying HER-2. Besides the HER-2 subtype tumors, this drug is also recommended for the luminal ones with positive status for HER-2 [18, 19].
The status of HER-2 expression is analyzed through immunohistochemistry of paraffin-embedded specimens of the breast cancer (Figure 3a). The tumor is considered negative for HER-2 if it is not labeled (score zero) or the cell membrane is partially labelled for the HER-2 antibody (score 1+). The tumor is positive for HER-2 if all the cell membranes outlines are strongly labeled for this antibody (score 3+). Finally, in part of the cases, the HER-2 antibody can label totally the cancer cell membrane, but with low intensity or can label partially the cell membrane with high intensity. In these situations, the HER-2 status is considered equivocal (score 2+). The confirmation of overexpression/amplification must be evaluated through fluorescence “in situ” hybridization (FISH) (Figure 3b) [21, 22].
(a) Photomicrographies of immunohistochemical assessment of HER-2 expression status by tumoral cells in histologic paraffinized specimen of breast cancer. Score 0 (negative): none tumoral cell is labeled. Score 1+ (negative): incomplete positivity with low intensity in part of tumoral cells. Score 2+ (equivocal): complete positivity with low intensity in majority of tumoral cells. Score 3+ (positive): complete positivity with strong intensity in majority of tumoral cells. (b) Photomicrographies of amplification of HER-2 gene performed through fluorescence in situ hibridization (FISH) in a HER-2-overexpressed breast carcinoma on immunohistochemistry (Score 3+, E). HER-2 gene copies are the orange signals (B) and chromosome 17 centromeres (CEP17) are the green signals (C). The signals of HER-2 gene and CEP17 are present in tumoral cell nuclei (blue, A and D). CEP17 is an internal control on the same chromosome to compare with HER-2 signals in tumoral cell nucleus. According to American Society of Clinical Oncology/College of American Pathologists (ASCO-CAP) guidelines, a HER-2/CEP17 ratio > 2.0 defines a positive result for amplification of HER-2 gene. If HER-2/CEP17 ratio is < 2.0, an average HER-2 copy number > 6.0 signals/cell defines a positive result for amplification of HER-2 gene, an average HER-2 copy number < 4.0 signals/cell defines a negative result for amplification of HER-2 gene and an average HER-2 copy number > 4.0 and < 6.0 signals/cell defines an equivocal result for amplification of HER-2 gene (extracted from [20]).
The “triple negative” breast cancer is negative for hormonal receptors and HER-2. It is the less differentiated tumor subtype on histology, formed by highest grades cancer cells, with highest proliferative index, presenting the worst prognosis among the 3 molecular subtypes. This tumor still does not present an specific therapy, which is chosen depending on the clinicopathological stage. In metastatic disease, the treatment focuses on quality of life and palliation. In “triple negative” tumors, the evaluation of BRCA status is mandatory [8, 21].
Identified in 1994, BRCA-1/BRCA-2 are tumoral suppressor genes, respectively located in chromosome 17 and 13. Mutations of these genes are related to hereditary breast cancer, estimated in 5–10% of all breast malignancies. BRCA-1/BRCA-2 play a central role in DNA repair [23, 24]. Mutations of these genes increase the susceptibility for DNA damages. “Triple negative” subtypes carry more frequently mutations of BRCA-1 and mutations of BRCA-2 increase the risk for luminal subtypes of breast cancer. HER-2 overexpression is inversely correlated to BRCA mutations [24, 25].
It was observed in some studies that “triple negative” breast cancers with BRCA mutations present more chemosensitivity than the ones without BRCA mutations. Chemotherapy with DNA-damaging drugs, like the alkylating agents and anthracycline, can prolong the free of disease survival for tumors of triple negative phenotypes. This found is expected, since BRCA mutation prejudices the DNA repair and, consequently, increase the sensibility to DNA damages of cancer cells by these drugs. Neither therapeutic response nor free of disease survival of luminal subtypes of breast cancer seems to be influenced by BRCA mutations [8, 24, 26].
Regarding on prognosis, multiples studies present conflicting results. The prognosis depends on tumor features, especially the molecular subtypes and the clinicopathologic stage. The predictive value depends on the administrated therapy. Thus, BRCA-1 mutated breast cancer probably present worse prognosis than the BRCA-2 mutated ones, since BRCA-1 mutated tumors are mainly of “triple negative” phenotype, therefore intrinsically more aggressive than the luminal subtypes harboring BRCA-2 mutations [24, 27].
The tumoral suppressor proteins codified by BRCA-1/BRCA-2 act on homologous recombination repair of double stranded DNA breaks. Homologous recombination mechanism protect the integrity of genome in proliferating cells. BRCA-1 recognize DNA damage and recruit DNA repair proteins. BRCA-2 mediates the recruitment of another protein, called RAD51, to double stranded DNA breaks, allowing for homologous recombination repair [24, 28].
In BRCA-mutant breast tumors, the base excision repair pathway is important for cancer cell survival, in response to single stranded DNA breaks. Polyadenosine diphosphate-ribose (PARP) is a family of DNA repair enzymes, playing a key role in base excision repair mechanism. These enzymes are recruited to the site of DNA damage and add ADP-ribose to target nuclear proteins, causing post-translational modifications and restarting stalled DNA replication. BRCA-mutant breast cancer presents deficiency of homologous recombination repair, with overactivated PARP, leading the cancer cell to avoid apoptosis [24, 26, 28].
The inhibition of PARP cause persistance of single stranded break, resulting in stalled replication and double strand breaks. This mechanism leads to accumulation of DNA damage, causing cell cycle arrest and apoptosis. The PARP inhibitors form an emerging class of drugs, which have been recommended to chemotherapy for BRCA-mutant breast cancer and empirically for metastatic breast cancer, with promising results [24, 25, 28].
In the last two decades, experimental evidences in several studies of neoplastic tissues have revealed a population of cancer cell with properties of self-renewal, differentiation to multiple lineages ability and low proliferative index. These properties have been considered cancer stem-cell like features and attributed to a possible cancer stem-cell lineage present in the tumor bulk [29, 30].
Cancer stem-cell has awaked interest in the context of breast cancer because of its characteristic heterogeneity of biological behavior and therapeutic response. It has been hypothesized that cancer stem-cell might be one of the causes of the high variability of biological and prognostic spectrum of breast cancer. Cancer-stem cells might play an important role on therapeutic resistance and progression of disease, affecting the overall and free of disease survival [31, 32].
Thus, an important feature which allows possible cancer stem-cell resistance to chemotherapy is its low expression of surface proteins. Because of its self-renewal properties, cancer stem-cell does not depends on signaling from other cells to proceed its functions in tumoral tissues. Furthermore, for its low antigenicity and low proliferation index, there are few alternatives for drug interactions. DNA damage agents are poor effective against these cells possibly for a lack of proliferation, as well new classes of drugs, like PARP inhibitors, which better act on cells in proliferative phase [31, 33].
One of possible pathways for breast cancer therapeutic resistance acquired along the time might be explained by populations of cancer stem-cells not eliminated, selected by multiple chemotherapy cycles. Tumoral cells in active proliferation phase are more hitten, increasing the proportion of indolent cells with stem-like features in cancer cell population. Through the capacity of multilineage differentiation, cancer stem cells might generate new daughter cells with more aggressiveness and chemoresistance [32, 34].
The identification of cancer stem-cells is challenging. First, because of its irregular distribution in selected tumor amounts. Second, for definition, these cells are frequently scarces in tumor bulk. In this way, these cells are better identified through “in vitro” methods, like cellular cultures. However, the mainly disadvantage of this technique is the fact of stem cells behave in a different fashion in artificial environment, since the cell phenotype expression depends on their interactions [32, 35].
Thus, several studies with cancer stem-cells in different neoplastic tissues have been accomplished with conflicting results. An interesting method to identify these cells in their original environment is the immunohistochemistry performed on amounts of paraffin-embedded neoplastic tissues, with the advantages to allow the evaluation of phenotype expression next to the reality and to be easily performed and cost-effectiveness in diagnostic routine [35].
In the last years, some putative stem-cell markers detected by immunohistochemistry have been tested in paraffinized tissues of breast cancer. Multiple studies have demonstrated that expression of putative stem-cell markers by tumoral cells seems to worse the prognosis and survival in breast cancer. The most frequent studied stem-cell markers are CD24, CD44, CD133 and EPCAM, with two identified putative stem-cell phenotypes: CD24 low/CD44 enriched and co-expression of CD133 and EPCAM (Figure 4). Besides of the scarcity of stem-cells in neoplastic tissues, the conflictous results of these studies might be explained by a necessity to qualitative analysis of these markers expression, exactly for the rarity of stem-cells [32, 36].
Photomicrographies of double-labeled simple stained putative CSC antibodies (400×, original magnification, immunoperoxidase and DAB). (A) CD133: cytoplasm positivity (immunoperoxidase); (B) EPCAM: membrane positivity (DAB); (B) CD133+/EPCAM+: CSC profile (black arrow: membrane positivity to DAB and cytoplasm positivity to immunoperoxidase at the same cell); (C) CD24: cytoplasm positivity (immunoperoxidase); (D) CD24−/CD44+: CSC profile (black arrow: membrane positivity only to DAB).
In some studies, identification of a stem-cell like phenotype CD24 low/CD44 enriched have prejudiced the free of disease survival, especially in cases of early stages of breast cancer, with more occurrence of distant metastasis and cancer recurrence after surgical and adjuvant treatments. The presence of cancer cells with positivity for cancer stem-cell phenotype CD133/EPCAM is has been related to poor overall survival in breast cancer, with more adjuvant therapeutic fail [32].
For the moment, these putative stem-cell phenotypes seems to be independent prognostic factors in breast cancer. “Triple negative” breast cancer and BRCA-1 mutant breast cancer have been associated to stem-cell like phenotype CD24 low/CD44 enriched. These putative stem-cell markers may become possible future targets for new drugs in the future [30, 32].
In the context of cancer, the immune system can suppress the tumor growth by the destruction of cancer cells or inhibition of their outgrowth. On the other hand, immune system can play a role on tumor progression by the selection of tumor cells which are adapted to survive in an immunocompetent host or modifying the tumor environment to facilite the tumor outgrowth [37] .
Elevated levels of CD4+ regulatory T lymphocytes (Tregs) found in many cancers are associated to poor prognosis. Tregs create a favorable immunosuppressive microenvironment to the outgrowth and progression of the tumor. On this way, FOXP3 is expressed by the Tregs and can be detected by immunohistochemistry. FOXP3 is responsible for induction and maintenance of tolerance to self antigens in normal cells, as well this immunotolerance can be performed by the Tregs with cancer cell antigens [37, 38].
Another example of cancer cell escape mechanism from the immune system is caspase-8 mutations present in “triple negative” breast cancers and other solid malignant tumors. These mutations abolish the death induced by cytotoxic lymphocytes CD8+ in tumoral cells [37, 39].
The activation of T lymphocytes by foreign antigens occurs by concomitant major histocompatibility complex (MHC) antigen presentation and co-expression of T-cell receptor (TCR). At the same time, a family of T-cell transmembrane proteins CD28/B7, called “immune checkpoints”, produces co-inhibitory or co-stimulatory signals. The immune checkpoints regulates the T-cell immunotolerance to protect the tissues from undesirable damages. Cancer cells may produce signals to inhibit T-cell action, through cytotoxic T-lymphocyte associated antigen-4 (CTLA-4), programmed cell death-1 (PD-1) and its ligands (PDL-1) [37, 40].
PD-1 is an inhibitory “immune checkpoint” expressed on the surface of T-cells, B-cells and NK-cells. When T-cells have been activated by their TCR, the cells express at the same time PD-1, which is a possibility to the attacked cell to escape from the immune reaction (Figure 5). Cancer cells express the ligand PDL-1 on their surfaces, activating PD-1 of T-cells, escaping from the attack [37, 40].
Simplified schematic illustration of PD-1/PDL-1 interactions in immune responses against cancer cell. Tumoral antigens (Ag) are presented via T-cell by major histocompatibility complex (MHC) of dendritic cells. T-cell recognize tumoral Ag via TCR (T-cell receptor). Interaction Ag-TCR induces an positive immune response against tumoral Ag. Though, there is a scape mechanism of cancer cell from the T-cell attack: interaction of programmed death cell ligands (PDL-1/2) expressed by cancer cell with PD-1 expressed by T-cell inhibit the T-cell action. This scape mechanism of cancer cell mimics the regulation action to avoid immune responses of T-cell against self antigens. The principle of immune therapy is the inhibition of PD-1/PDL-1 (extracted from [40]).
PD-L1 expression has been associated with large tumor size, high grade, high proliferation, estrogen receptor (ER)-negative status, and human epidermal growth factor receptor-2 (HER2)-positive status in breast cancer. Survival in breast cancer is inversely related to PD-1/PDL-1 levels. PDL-1 expression increases tumor aggressiveness, stimulating tumorigenesis, invasiveness and ability to escape from cytotoxic T CD8+ lymphocytes attacks [39, 41]. The immunohistochemical evaluation of PDL-1 is shown in Figure 6.
Examples of PDL-1 expression in breast cancer using 3 different antibodies: Dako 22C3 (D,E and F), Ventana SP263 (G,H and I) and BioCare RbM CAL10 (A, B and C). PDL-1 scoring is divided into 3 groups: zero staining is negative, 1–49% of positive cells are considered “low PDL-1 expression” and 50% of more positive cells are considered “high PDL-1 expression”. Examples of negative, low and high PDL-1 expression are represented on A, B and C for BioCare antibody (extracted from [42]).
Immune therapies with anti-CTLA-4 and anti-PD1/anti-PDL-1 agents have been promising for treating several cancers. In breast cancer, some researches reported positive results around 20% of breast tumors on treatment with these agents, mainly the “triple negative” and HER-2 subtypes, for their higher antigenicity. In general, breast cancer present lower immunogenicity than other cancers and breast cancer cells frequently create an immunosuppressor tumor microenvironment by signaling [37, 43].
The presence of tumor infiltrating lymphocytes (TIL) in some breast cancers has been related to a favorable prognosis, especially in “triple negative” and HER-2 subtypes. TIL are formed mainly by T-cells CD3+/CD56 negative, which are either CD4+ or CD8+. A minority component of B-cells CD20+ and NK-cells may be present. The attraction of TIL by cancer cells have been related to their expression of some chemokines, like CXCL9 and CXCL13 [37, 44].
In “triple negative” and HER-2 subtypes of breast cancer, the presence of TIL is related to a better response to neoadjuvant therapy, as well neoadjuvant treatment may modify the tumor microenvironment to attract TIL to tumor site. Furthermore, when the TIL are not attracted instead of neoadjuvance, it is indicative for bad prognosis [44].
Metastatic breast cancer is considered incurable nowadays with currently therapies. Therapy of metastatic disease aims to guarantee quality of life, palliation of symptoms and prolongation of the patient survival. Advanced stage disease is becoming increasingly chronic, controlled by sequencial therapies, with more personalized approach than the early stage breast cancer [8].
Systemic therapy is frequently the first choice of metastatic disease. Before the new therapeutic decision, it is necessary to consider the previous treatments. If possible, it is recommended to re-evaluate the histologic features and molecular subtype status of the metastatic lesion through a new biopsy, with new immunohistochemical study for hormonal receptor and HER-2 status. Some studies reported until 40% of discrepances of metastatic lesion histologic features and molecular subtype status versus primary tumor histologic and immunohistochemical aspects [45].
The metastatic disease therapeutic choices search for positive targets to hit more effectively the neoplastic cells. Thereby, expression of hormonal receptors by the metastatic lesion is elective for endocrine therapy. Endocrine drugs include tamoxifen, aromatase inhibitors, fulvestrant and progestins. The use of these drugs in metastasis with hormone receptor positive status have demonstrated increase of free of disease survival in several studies [8, 45].
Furthermore, new generation of drugs which inhibit the cyclin dependant kinase (CDK) have been successful in prolongation of free of disease survival in luminal subtype HER-2 negative metastatic disease. CDK4/6 is a holoenzyme responsible for several extracellular signaling pathways to cell cycle transitions. CDK4/6 fosforilates and inactivates retinoblastoma tumor supressor protein (Rb). Extracellular signals regulate the expression of cyclins and CDK inhibitors, like p16INK4a [46].
In human cancer, this circuit is dysregulated by either overexpression of cyclin D1, loss of p16Ink4a, the mutation of CDK4 to an Ink4-refractory state, or the loss of Rb itself. The primary target of CDK4 is the Rb protein, though this holoenzyme either can phosphorylate factors involved in cell differentiation affecting their transcriptional activity, apoptotic factors affecting their activity and other factors that can directly affect mitochondrial function [8, 46, 47].
Therefore, CDK inhibitors act in tumor microenvironment, blocking Rb phosphorilation and leading to cell cycle exit. Moreover, CDK have kinase activity towards SPOP, an ubiquitin protein that interacts with PDL-1. CDK inhibitors lead to inhibition of SPOP phosphorilation with blockade of PDL-1 and stimulus to PD-1 expression by T-cells, attracting T-cell infiltration to the tumor. In this way, the combined use of CDK inhibitors and PDL-1/PD-1 inhibitors may be promising, requiring more future studies [46, 47, 48].
For the moment, hormonal receptors and HER-2 status are the few validated molecular targets of clinical importance on metastatic breast cancer approaching through chemotherapy and endocrine therapy. For HER-2 positive metastatic disease, anti-HER-2 treatment with trastuzumab is well established and is recommended as soon as possible. Immune therapy is not standardized for metastatic breast cancer, since metastatic breast disease is highly heterogeneous. Though, it is a promising therapy for the future, as well the target molecular therapies, which become more effective with discovery of novel pathways and mutations by new studies to be developed [8].
A resume of main biomarkers of clinicopathologic importance for breast cancer management is shown in Table 3 and a proposal of a algorithm for clinicopathologic evaluation of breast cancer is presented in Table 4.
Biomarker | Detection technique | Nature | Clinicopathologic importance |
---|---|---|---|
Hormonal receptors/HER2 | IHC1/FISH | Biomarkers of molecular subtypes of breast cancer | Targets for endocrine and anti-HER2 therapies; prognostic predictors |
BRCA1/BRCA2 | PCR sequencing | Biomarker of hereditary breast cancer | Target for PARP inhibitors; indication for other malignancies screening |
CD24, CD44, CD133, EPCAM | IHC | Putative stem-cell biomarkers | Prediction of poor prognosis, risk of tumor progression and reduction of survival |
PD-1/PDL-1 | IHC | Biomarker of possible inhibited immune response of T-cell against cancer cell | Target for immune therapy with PD-1/PDL-1 antagonists |
TILs2 | Histologic assessment and IHC | Marker of better cellular mediated immune response against cancer cell | Prediction of better therapeutic responses, mainly of neoadjuvant therapies |
Resume of main biomarkers of clinicopathologic importance for breast cancer management.
IHC = Immunohistochemistry.
TILs = Tumoral infiltrating lymphocytes.
Proposal of an algorithm for clinicopathologic evaluation of breast cancer.
In the 21st century, breast cancer classification and diagnosis advanced considerably from a purely morphologic/histologic approaching to a immune and molecular basis, with remarkable improvement of the correlation between classification and prediction of biological behavior and prognosis.
The adoption of a clinicopathologic classification based on molecular subtypes of breast cancer in the last decade has modified decisively the management of the disease in the way of molecular era, opening new ways to discovering of multiple targets for novel therapies.
Innovative concepts related to immune reactions related to human cancers, which have been unveiled in the recent years, particularly the immune checkpoints, have offered new treatment tools for several human cancers with promising results, although not still established for breast cancer.
In the molecular era of cancer, the integration of novel knowledges in a direction of more accurated diagnosis and prediction of prognosis to allow personalized therapies is the key to future human cancer management, including the breast cancer.
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\n\nCHANGES IN AUTHORSHIP
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\n\nAFFILIATION
\n\nAuthors are responsible for ensuring all addresses and emails provided are correct. Under affiliation(s) all Authors should indicate where the research was conducted. Please note that no changes to the affiliation(s) can be made after the chapter has been published.
\n\nPolicy last updated: 2017-05-29
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