\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Unlike sympathy, which only requires someone to understand another person’s feelings on an intellectual level, empathy is unique in that it is also thought to involve knowing someone’s feelings by connecting with those same feelings in one’s self. In other words, empathy means having a first-person understanding or emotional response to someone else’s experience. This shared understanding can occur in various ways by employing different emotional and cognitive mechanisms such as affect sharing, simulation, imagination, and theory of mind [1]. Researchers categorize subtypes of empathy as cognitive empathy or emotional empathy. Cognitive empathy refers to perspective taking, while emotional or affective empathy refers to shared emotions and feelings. It is thought that the capacity to empathize requires the activation of different neural networks depending on the type of empathy evoked. Despite the rapid growth in empathy research since Robert Vischer’s first English translation of the word, einfühlung, meaning “in-feeling” or “feeling-into,” over 150 years ago [2], there are still many questions that remain regarding the neuroscience of the phenomenon.
\nSocial and behavioral psychologists have conducted the majority of empathy research in the last few decades [3]. With the advent of modern neuroimaging techniques, researchers now have the ability to study the neural processes involved in empathy and how different factors such as individual traits, situational context, and even personal experience modulate related neural network activity. State-of-the-art neuroimaging methods and techniques such as functional magnetic resonance imaging (fMRI) allow scientists to investigate how these networks activate when sharing the feelings of another person in controlled experimental environments. Functional MRI studies typically measure blood-oxygen-level dependent (BOLD) changes that occur as a result of precisely timed experimental designs that elicit empathy in participants. These designs allow scientists to compare BOLD responses during different subtypes of empathy and compare them to control conditions that do not require empathy. A link between empathy and brain regions is most often found by statistically correlating the BOLD response evoked in an empathy task with individual differences in empathy traits as measured by questionnaires. The present chapter highlights current neuroimaging research on neural networks thought to enable empathy and the attributes that modulate network activation. Specifically, this chapter will cover prevailing theories of empathy, how it is generated, i.e., through “shared” or “mirroring” networks, and the limitations of current study designs.
\nThe first question to ask when investigating the neuroscience of empathy is “What parts of the brain are active when people are acting empathically or feeling empathy?” Core brain regions thought to underlie empathy include regions within the “pain matrix,” namely the anterior insula (AI) and the middle anterior cingulate cortex (mACC) [4–6]. In numerous fMRI studies, these core regions respond both when experiencing first-hand as well as when observing in others an emotional response or feeling (mostly conducted with the feeling of pain [7, 8]). Therefore, it is thought that we understand the emotions and perspectives of others by utilizing and processing in brain regions that are active both when we ourselves have a sensory, affective, or bodily experience, and when someone else has a similar experience [9]. This theory has become known as the “Shared Network Hypothesis” [10]. For this reason, the AI and mACC, as well as other brain areas that have similar properties, are often referred to as “shared” or sometimes “mirroring” brain regions. Indeed, the activation of many shared brain networks, such as the mirror neuron system (MNS) as well as the pain matrix and some emotion-related brain regions, has been linked either directly or indirectly to the genesis of empathy. These shared networks have repeatedly been proposed to underlie the mechanisms that allow people to mentally “share” the feelings of another. The sections below discuss previous and current research related to empathy across these networks.
\nNeurons in motor regions of the brain that respond both to action execution and action observation are called mirror neurons. Mirror neurons were first discovered in macaque monkey’s F5 premotor region in the 1990s when scientists noticed the same neuron responded not only when the monkey was performing an action but also when he observing another person performing a similar action (e.g., cracking a peanut or watching someone else crack a peanut). In humans, the mirror neuron system (MNS), first proposed by Gallese et al. [11], is a network of brain areas that respond similarly (see \nFigure 1\n) [11]. It is composed of the inferior frontal gyrus (IFG; thought to be homologous to F5 area in the macaque monkey), in addition to sensorimotor regions (i.e., the lower part of the precentral gyrus, the rostral part of the inferior parietal lobule). Many models of the MNS suggest that mirror neurons provide a mechanism for automatically translating the actions of others onto our own motor system. In other words, whenever one perceives an action of another person, we automatically and covertly “mirror” those actions onto our motor system. Thus, we might understand other people’s actions and intentions by covertly simulating them in our motor regions as if we were performing the action ourselves. This form of putting ourselves in someone else’s shoes and taking their perspective is thought to be an important component of cognitive empathy [12]. Indeed, several studies have related empathic traits to neural activity in the MNS, indicating that individuals who have higher activity in the MNS also score higher on cognitive aspects of empathy [13–18]. Thus, the MNS may be one neural network involved in perspective taking and cognitive aspects of empathy.
\nHuman mirror neuron on system (MNS). Lateral view of brain with frontal (ventral pre-motor and IFG) and parietal (rostral IPL) labels of the mirror neuron system in addition to the superior temporal sulcus. IFG = inferior frontal gyrus; IPL = inferior parietal lobule; STS = superior temporal sulcus.
Other neural networks are thought to process emotional aspects of empathy. One network that has been indicated to be involved in empathizing with pain experienced by others is called the pain matrix. The pain matrix includes the insula, anterior and middle cingulate gyrus, and somatosensory cortices (SI and SII) [6, 19–21]. Interestingly, like the MNS, the pain matrix is activated both when one experiences pain oneself and when observing another person experiencing pain. In a seminal paper in this area, researchers found significant activity in the pain matrix both when a participant in an MRI scanner experienced pain as well as when someone close to them was experiencing pain (\nFigure 2\n) [6]. Because this network responds so strongly to both physical pain and to processing the pain of others, the AI and mACC are thought to be involved in processing affective components of empathy [6, 8, 22]. The activation of the core regions of the pain matrix for processing self and others’ pain is consistent across numerous studies implementing a variety of experimental paradigms, suggesting that pain mirroring, and perhaps empathy itself, can be investigated with high reliability by social neuroscience [8].
\nHowever, some critics have proposed that shared representations for self and other in the pain matrix might not be related to empathy, but instead merely reflect an evolutionary response carried out to prepare a motor response as a self-protection mechanism when a threat is detected [23]. For example, watching someone grimace in pain from stepping on a nail may activate our pain and sensorimotor network to prepare the actions needed to avoid experiencing pain ourselves (i.e., stepping away from the nail). Furthermore, while there is a consensus regarding the core regions in the brain that process pain, there are discrepancies on when and how input from the pain matrix is linked to empathy. Disparate results in pain research, such as the engagement of primary and secondary somatosensory cortices during the observation of pain [6], suggest that the pain matrix as a whole may not respond to all types of pain and that it depends on the contextual environments (see sections below for more detail). The same is true for the recruitment of the MNS during cognitive empathy. One common explanation for aberrant findings in empathy research is that there are distinct networks or routes for different types of empathy. Many research groups are using new imaging paradigms and methodologies to explore this dynamic network theory. More research is needed to better understand how the different circuits involved during pain empathy interact and modulation by context.
\nShared network for self and others’ pain. Modified figure from Singer et al. [
Mentalizing is a term sometimes used interchangeably with theory of mind (ToM), both typically refer to the cognitive processes involved in understanding the intentions, desires, or beliefs of another person. The “mentalizing network” involves brain regions that have been shown to be activated when someone thinks about another person’s mental states. They include the precuneus, ventral parts of medial prefrontal cortex, posterior superior temporal cortex, temporal parietal junction (TPJ), and the temporal poles [24–26]. For example, this network is active when participants are asked to think about the intentions of another person’s emotions or actions. Some researchers restrict the definition of mentalizing to only cognitive perspectives when the observer is consciously mentalizing about someone’s mental state [27]; however, others include the immediate, automatic, and covert cognitive inference about other people’s emotional states [28]. Neuroimaging studies on ToM often do not make this distinction and typically ask participants to consciously mentalize. However, some neuroimaging research distinguishes between cognitive and affective aspects of ToM. Cognitive ToM is required to understand what someone else may be thinking and affective ToM is required to understand how someone might feel given a specific situation. While cognitive and affective components of ToM may be necessary for a fully functioning ToM, individual regions have been shown to play prominent roles in either cognitive (ventral medial prefrontal cortex) or affective (ventral medial frontal lobe) perspective taking [29].
\nAffective empathy is thought to elicit emotion-related brain regions that are involved in the processing of feelings and emotions. Emotion-related brain regions commonly include the hypothalamus, hippocampus, amygdala, insula, and the cingulate, as well the ventral and medial sectors of the prefrontal cortices [30]. Activity in the insula and amygdala are commonly known to be involved in affective processing and are thought to be related to affective aspects of empathy along with regions of the MNS (i.e., IFG) [31–33]. In particular, the anterior insula, by processing information from the viscera that arise when emotions occur, may be necessary for interpreting body states as affective feelings [34]. Notably, Antonio Damasio et al. have argued that changes in body states and homeostasis (i.e., emotions) are felt as feelings through the representation of these emotions in the brain [35]. So when you feel nervous, this may be because your brain (anterior insula along with other brain areas such as regions of the brainstem) has noticed a quickening in your heartbeat, a clenching in your gut, sweat being produced, and has interpreted that change in body state as nervousness [34]. Damasio has also posited that a physical stimulus is not necessary to experience feelings and that they can be simulated in brain maps when you are empathizing [36]. Indeed, more recent research has been suggested that the insula is involved in integrating subjective feelings, uncertainty, and empathy [10, 37].
\nRecent models of empathy propose that our capacity to understand the affective and cognitive states of others is enabled by different mechanisms or “routes” [9, 38, 39]. Broadly, one route is through the simulating or mirroring networks (e.g., MNS, pain matrix), and the other, the ToM/mentalizing networks (see \nFigure 3\n). Simultaneous core emotion-related brain regions (ACC and AI) and MNS brain regions are thought to be elicited when empathy is triggered in response to various sensory stimuli such as viewing body parts in pain or hearing action sounds [3, 13, 40]. During conscious intention understanding of others, as well as self-referential thought, it is thought that a second route for empathy is elicited through the theory of mind or “mentalizing” brain regions. Processing in this network enables sharing other’s states based upon one’s previous experiences and knowledge (see Ref. [41] for review), and it might be particularly important in situations in which externally provided sensory information about the other’s mental state is lacking. For example, the mentalizing network might be utilized when you listen to lyrics in a pop-song and empathize with the song writer’s experience, or when viewing a painting or photo without knowing the events that led up to the event being captured and empathize with the artist or the subject matter.
\nA similar pattern of dynamic interaction has been proposed for empathic responses in the pain matrix. It is thought that sensory-discriminative attributes (i.e., location, quality, and intensity) and autonomic-affective attributes (i.e., perceived unpleasantness) of a painful experience are coded differently in the network. Singer’s research group demonstrated that only self-pain activated contralateral SI, SII/posterior insula, and caudal ACC, while rostral ACC and AI activation enables both self and others’ pain. The authors suggest that eliciting different networks for self and others’ representations contributes to the better understanding of the subjective feelings. However, contradicting evidence from more recent studies has demonstrated that somatosensory (SI/SII) regions are engaged during both self and others’ processing [21, 43]. These disparate findings may be due to the variations in stimulus representation methodology, which again can trigger different neural routes. Viewing body parts in pain, for example, tends to elicit somatosensory regions compared to more abstract pain cues where body parts are not shown [8, 44]. A study that compares both bodily and psychological pain may help detangle and test a multi-route theory.
\nEmpathetic routes are found to exist at even the smallest levels of distinction. There is evidence of distinct empathic routes that are sensitive to emotions at the level of valence. Since pain elicits a robust response in the brain, empathy research has been skewed to favor pain and negative emotions. It is less known how empathy varies across different emotions in general, and how it is linked to valence (positive or negative feelings). One fMRI study by the Singer group [45] compared pleasant and unpleasant touch in attempts to understand mechanisms for empathy between different valenced emotions. The group found distinct neural pathways for positive and negative valence that were involved in processing empathy. The first-hand and vicarious experience of pleasant touch (e.g., a flower) commonly recruited the medial orbitofrontal cortex (mOFC), while unpleasant stimulation (e.g., rubber maggots) led to shared activation in the right fronto-insular cortex. These findings suggest that different subsystems are engaged when one is mentally sharing positive compared to negative sensations of others. Taken together, these observations indicate that empathy is a dynamic process that elicits multiple subnetworks. Ultimately, further exploration of different emotions in different contexts will bring us closer to understanding the nuances of how empathy is elicited and the subsequent role it plays in influencing one’s experience of the mental and emotional states of others. It will also contribute to our understanding of clinical disorders that are characterized by deficits in social processing, such as psychopathy and ASD.
\nEmpathy network. A modified illustration of Dvash and Shamay-Tsoory [
As discussed above, substantial evidence supports (at least in part) the shared network theory of empathy. However, it is less clear how individual’s traits and past experiences modulate these systems. While empathic neural responses often occur automatically, they can also be modulated tremendously by various individual and situational factors [9, 39, 46–49]. Previous experience with particular actions as well as contextual factors, such as the other belonging to an ingroup or outgroup, can influence network activity as well as which subnetworks are invoked. The sections below discuss experience and individual differences related to empathy in the MNS and pain matrix.
\nTo understand how individual differences relate to neural activity in the MNS, scientists compare brain responses to different actions or changes to the context of an action to individual differences in traits and behavior. For example, one approach has been to try to understand how familiarity with actions modulates MNS activation and empathic processing. One common paradigm is to compare neural activity when observing a familiar action to that of a novel action and correlate the neural activity for each action type to individual differences in trait measures of empathy. Empirical evidence from these studies suggests that while the MNS is engaged for both types of actions (familiar and novel), BOLD signal increases when watching visually or physically familiar actions compared to unfamiliar actions [12, 50–58]. These studies support the general premise of a “like me” hypothesis of action observation function. The “like-me” hypothesis states that we engage with actions that are in our own motor repertoire, or more visually familiar, the more the MNS is engaged [56]. This has led some to propose that experience-driven simulation mechanisms modulate the MNS [54].
\nOn the other hand, some research demonstrates that MNS activity increases when observing novel actions such as a robot dancing [59] or those who are less similar to one’s self (e.g., someone with a different cultural or racial background) [60]. Furthermore, one study found that observing a novel limb (the residual limb of an amputee) elicited more activity than observing a typical hand action. However, this effect was modulated by experience with residual limbs [57]. In addition, in that study, it was found that the more empathic participants were, the more they engage their parietal motor regions when viewing the novel limb, but that this correlation was not significant once they were familiarized with the novel limb. This study underscores the notion that regions of the MNS are modulated by familiarity, experience, and individual differences in empathy traits.
\nVarious individual and contextual factors modulate the pain matrix as well. Social context such as group bias has been studied in the pain matrix, and similar relationships between BOLD signals in pain-related brain regions and empathy have been found. Relationship dynamics between observer and observee, such as social status or group membership, has been reported to modulate the BOLD signal in regions of the pain matrix while observing pain in others [8–10, 22]. For example, a recent study investigating social status and empathy for pain found that activity in the pain matrix is biased toward self-perceived inferior-status individuals compared with superior-status individuals. When painful stimulation (needle from a syringe penetrating cheek) was applied to inferior-status targets, higher activation in empathy-related brain regions (AI and aMCC) was observed, whereas activity in these same regions was attenuated when observing painful stimulation applied to superior-status targets (\nFigure 4\n).
\nIn another study, the pain matrix was found to activate more when pain is inflicted on someone from an individual’s in-group than out-group [22]. Specifically, activity in the ACC and the insula was found to be greater when participants view pained expressions on the faces of a racial in-group member, while receiving injections to the cheek compared to activity when viewing out-group members. These findings suggest that group biases can modulate empathy and sensitivity in pain-related emotions [22]. In concordance with previous studies, Xu et al. [22] found that empathic activity is a function of individual differences. Participants who showed greater empathic neural responses to in-group members also showed stronger empathic neural responses to out-group members, highlighting the significance of individual differences empathy-related activity [22].
\nWhile several of the studies mentioned above found a relationship between activity levels in the pain matrix and empathy traits, it is unclear if activity levels reflect the depth of pain processing rather than empathic processing per se. For example, in a study by Fox et al. [62], it was found that there was more activity in the pain matrix when viewing disliked others rather than liked others. In this study, Jewish participants showed more activity in components of the pain matrix when they viewed neo-nazis in pain than when viewing neutral likable individuals in pain [62]. Given that the participants did not report feeling more empathy for the neo-nazis than the likable individuals, the authors of that study suggested that perhaps activity levels in the pain matrix do not correspond to the degree of empathy one feels, but instead depth of processing. The authors argue in their study that it was more relevant for the Jewish participants to processes the pain level of their enemy (a neo-nazi) than that of a neutral individual, which was reflected in the increased activity the pain matrix. Thus, it might be that while activity in the pain matrix may contribute to empathic processing, it is not sufficient for empathic processing on its own.
\nAnterior insula activity and pain. Modified figure from Feng et al. [
As discussed above, many questions about how, where, and when empathy is generated in the brain remain. Moving forward there are several challenges scientists have to overcome before arriving at a more complete understanding of this dynamic process. Some limitations are the result of neuroimaging techniques. It is difficult to study a complex social phenomenon such as empathy in the sterile, cold, and noisy environment of an MRI. Until a more ecologically valid way of measuring neural activity is found, it is important to keep in mind that findings in the lab may not be generalizable to real, everyday life. Study design, data analysis, and individual differences in samples are also important to consider. The discrepant findings we have examined in this chapter may, in part, be accounted for by differences in these areas. For instance, using on measures of empathy that rely on subjective, self-reports could be problematic. One of the most commonly used empathy measure is a questionnaire called the interpersonal reactivity index (IRI; [1]). Several studies have found that the higher the participant scores on the IRI, the stronger the response in the aMCC and AI when observing the pain of others [6, 9]. Indeed, individuals who have difficulties identifying and describing their emotions (i.e., alexithymia) show attenuated activation during introspection and while empathizing with others [63, 64]. Therefore, it is very important to relate empathic traits in research participants to research findings. However, self-report measures can be problematic if participants have poor introspection and cannot accurately report on their empathic skills thus resulting in inaccurate data. Alexithymia is prevalent in approximately 10% of the general population [65] and is more common in some socially afflicted disorders such as ASD [66, 67]. Since individuals can vary in their ability to perceive emotion, it should be included as a factor when conducting empathy research, especially when studying social disorders where alexithymia is more prevalent. Understanding the differences between deficits in empathy and deficits in introspection is an important distinction. For example, the long history of research suggesting that individuals with ASD have deficits in empathy has recently been disputed by research investigating alexithymia in ASD. A recent study found that deficits in empathy were related to alexithymia and not ASD severity by comparing individuals with ASD with and without Alexithymia and a typical control group [66]. In this study, ASD individuals without alexithymia did not differ in empathy from the control group. Since ToM deficits are consistently reported in ASD, authors of the study suggest that empathy and ToM are dissociable and are part of different streams of social cognition.
\nOn a similar note, experience and other cultural or biological biases should also be taken into consideration when reviewing empathy literature. Surprisingly, not all studies report on potential gender differences. While a few studies have reported no significant differences between males and females [8, 68], Singer et al. [10] found in men, but not in women, empathic responses are shaped by the evaluation of other people’s social behavior, such as participating in fair game play [10]. Gender may potentially be a major factor for some empathy network activation and not others. Relatedly, other biological factors may account for aberrant findings. Oxytocin is a neuropeptide that has been shown to increase social understanding and emotion recognition [69] and may also play an important role in modulating empathy. One large study found that oxytocin receptors were associated with affective empathy, while a second receptor (arginine vasopressin receptor) contributed to cognitive empathy, as measured by the IRI [70]. Genetic variation in oxytocin as well as other receptors may influence empathy-related network activity [71]; therefore, future studies should examine how gender as well as hormones and gene variation modulate empathy.
\nIn addition to individual differences, it is also crucial to note that methodological variation across neuroimaging studies can influence findings. Functional MRI data processing techniques and analysis may affect the robustness, and in some cases, the location of a significant result. For instance, recent studies have found that different segments of the AI respond to various empathic situations [8]. Findings like these require data to be explored with high spatial resolution and thus could be missed or misattributed if the data are preprocessing in such a way that these anatomical boundaries are blurred (i.e., the result of spatial smoothing). Taken together, it is important to consider empathy research in the context of its participants, paradigm, and data analysis before interpreting results.
\nSocial neuroscience has rapidly progressed in its understanding of shared representations in the brain since the discovery of mirror neurons. There is little doubt that research on shared networks has been integral to understanding and mapping empathy in the brain; nevertheless, questions remain regarding how empathy is elicited from the myriad social and contextual situations that generate it. The most current research literature indicates that the human brain engages multiple networks when sharing emotions and perspectives of others. Moreover, these networks vary in activation based on individual differences that make each person unique. As scientists continue to study the many connections between what is perceived and what is felt, it will expand previous models of empathy from a single network engagement to more tailored context-specific network activation. This chapter has discussed how core and complementary systems that respond to self and others play a significant role in our ability to empathize. As future work continues to map empathy in the brain and ultimately improve our understanding of social cognition, we will take one step closer to understanding what makes us uniquely human.
\nClinical trials are an important step to ensuring the safety and efficacy of medical treatment. For radiation therapy, clinical trials have allowed us to look at important questions like dose escalation, fractionation, and new radiotherapy technologies. Much like the use of instensity-modulated radiation therapy (IMRT) was critically reviewed in the early 2000s, proton therapy has come under careful scrutiny over the past decade. Many radiation therapy departments commissioned proton therapy centers and began to integrate protons into their clinical practice.
Most people who work in radiation therapy have seen the striking treatment plan comparisons between proton therapy and traditional photon therapy for a pediatric craniospinal case, noting the marked reduction in dose to organs at risk and normal tissue outside of the target region [1]. These in-silico studies are even more exciting given the potential reduction in secondary cancer for pediatric patients. The potential benefits in these studies come with corresponding risk; if the beam modeling or treatment delivery positioning is not accurate, there is a risk of high overdose to normal tissue or severe underdose of the target. For this reason, the National Cancer Institute (NCI), the American Society for Radiation Oncology (ASTRO), and other groups have encouraged methodical, careful study of the clinical benefits of proton therapy through clinical trials [2].
The potential benefits of proton therapy are also complicated by the higher biological effectiveness of protons as compared with photons. The current clinical practice in the US is to use a relative biological effectiveness (RBE) of 1.1 for protons, but studies have shown that the true biological response is more complicated and variable [3]. While the higher RBE of protons is a potential benefit for killing tumor cells, there is potential increased biological risk to critical organs proximate to the target. Clinical trials with proton therapy can allow us to look at both sides of the coin by analyzing the correlation between RBE and clinical outcomes.
Insurance companies have played a role in driving the development of randomized proton vs. photon clinical trials as well. Due to the higher up-front cost of proton therapy for many disease sites, insurance companies have asked for data showing marked improvement in survival outcomes for patients treated with proton therapy in order to cover treatment costs. As discussed later in the chapter, this presents a bit of a catch-22 in clinical trial accrual, as insurers are waiting for trial data to approve coverage, but trial data is nearly impossible to collect without insurance coverage for patients enrolled on-study.
The largest clinical trial system that supports proton therapy protocols in the US is the National Clinical Trial Network (NCTN), funded by the NCI. The NCTN is made up of four adult and one pediatric clinical trial groups, as well as a partnership with the Canadian Cancer Trials Group. Most of the proton therapy studies run through the NCTN are large-scale, multi-institutional Phase II and Phase III trials. These trials either randomize patients to proton or photon therapy to compare treatment outcomes or imbed proton therapy as a possible treatment modality in a study designed to answer a different clinical question. The NCI has also funded proton clinical trials outside of the NCTN [4, 5, 6, 7]. These are often run by a single proton center “sponsor” in partnership with other proton facilities and funded through NCI grants.
Outside of the NCI, there are several other groups that help sponsor clinical trials for proton therapy. The National Association for Proton Therapy (NAPT) is a nonprofit group that helps facilitate proton therapy research collaborations. Most of the operational proton therapy centers in the US are members of NAPT. The Patient-Centered Outcomes Research Institute (PCORI) provides funding for clinical trials comparing proton vs. photon therapy for prostate and breast treatment. The NCI also has a Childhood Cancer Data Initiative (CCDI) that collects standard patient data, including proton therapy data, in a central repository for data sharing and analysis within the research community.
Outside of the US, several groups in Europe and Asia have proton therapy protocols open or in development. The Japan Clinical Oncology Group (JCOG) is funded by Japan’s National Cancer Center Research and Development Fund and conducts studies with proton therapy [8]. The European Organization for Research and Treatment of Cancer (EORTC) operates clinical trials within Europe and currently has two protocols with proton therapy embedded [9]. The European Society for Radiotherapy (ESTRO) recently established the European Particle Therapy Network (EPTN), which conducts a number of prospective studies looking at proton (and carbon) therapy, and works in concert with the EORTC [10, 11]. Global collaborations on clinical trials have been limited so far. The US has the largest catalog of proton therapy clinical trials and has sought participation of international proton centers, but the many steps to opening the protocols (NCTN membership, state department clearance, baseline approval quality assurance) have slowed down collaboration. The clinical trial groups are working on streamlining these processes to allow for expanded international partnerships in the future.
In order to move past in-silico studies that promise superior dosimetry with proton therapy, clinical evidence is needed. One of the best ways to get these data are through randomized clinical trials. For proton therapy trials, randomization is generally structured with two arms: proton vs. photon. In order to get enough patients for statistical significance, these trials require a lot of patients (usually hundreds) and are typically run as multi-institutional studies. These large randomized studies may be designed to show superiority of proton therapy or to demonstrate non-inferiority [12]. Most NCTN randomized proton vs. photon trials have a primary endpoint of assessing overall survival. Secondary endpoints include progression-free survival, local control, toxicities, cognitive outcomes, symptoms burden, quality of life, cost effectiveness, and cost–benefit economics. While proton therapy generally has a higher up-front cost, it is hypothesized that proton therapy may be more cost-effective for some disease sites due to reduction in acute and long-term toxicities and associated medical costs.
Typically NCTN clinical trial data is only assessed for objectives explicitly listed in the protocol and analysis outside the original scope is only permitted after the trial has been closed several years. For this reason, somewhat indefinite exploratory objectives are written into the protocol to allow for analyses that may not be understood at the time of protocol development. For randomized proton vs. photon trials within the NCTN, exploratory objectives include biospecimen and imaging data collection for the assessment of biomarkers.
Most randomized proton vs. photon studies randomize 1:1, though some protocols have randomize 2:1 in favor of proton therapy. The two arms typically have the same radiobiological dose prescription, though some studies like NRG Oncology/RTOG 1308 have low dose and high dose arms.
Clinical trials can be challenging for a number of reasons - increased personnel effort to coordinate patient enrollment and data submission, increased operational costs, low patient interest, and low physician engagement – but randomized proton vs. photon trials face a number of unique challenges.
One unique aspect of proton vs. photon trials is that it is common to create treatment plans for patients using both modalities to ensure that both can meet the planning dose constraints required by the protocol [13]. This may require increased time on the part of the participating institutions, though many proton centers may already be creating double plans for insurance purposes.
Treatment planning itself is different between proton therapy and photon therapy. The planning target volume (PTV) that is commonly used for photon plans is generally not used in the same way for proton therapy. Instead of uniform expansion from clinical target volume (CTV) to the PTV, proton treatment plans may have one pre-defined lateral margin, and a different margin in the direction of the beam range that depends on the maximum beam energy [14, 15]. In this way, the proton “PTV” is beam-specific. This presents a challenge for clinical trial data analysis, as most protocols are written with historical photon PTV constraints. Future protocols should be designed with this in mind.
Furthermore, proton therapy treatment planning has started to shift away from the standard lateral and range margins in favor of robust optimization of the CTV [16, 17]. There are many different ways to report dose when using robust optimization (e.g. voxel-wise worst-case approach, scenario-wise worst-case approach, delivered dose variance) [18]. Clinical trials should soon consider how robustly optimized treatment planning data will be collected to ensure appropriate data comparison between the proton and photon arm. This highlights the crucial role that physicists and data (i.e. Digital Imaging and Communications in Medicine (DICOM)) experts play in the development of clinical trials.
In addition to the nuances of physical dose, randomized proton vs. photon trials need to consider the implications of radiobiology. The NCTN currently uses an RBE of 1.1, but many proton centers are starting to consider variable RBE in their treatment planning practices [19, 20]. If variable RBE treatment planning becomes standard, clinical trials will need to incorporate it into treatment planning constraints, and determine what patient data needs to be collected to appropriately compare different treatment plans.
One challenge with randomized clinical trials comparing proton therapy with photon therapy is patient preference. This manifests when a patient is randomized to one arm but has a strong desire to be treated on the other arm, and thus goes off protocol. Patients randomized to the photon arm may decide they want proton therapy instead due to an impression gathered through independent online research or a preference for the “latest and greatest” technology. Conversely, some patients randomized to the proton arm may go off protocol to receive photon therapy due to mistrust of a new, “unproven” technology.
Another challenge of proton trial accrual is insurance denial for proton therapy [21]. This is particularly challenging in the case of randomized proton vs. photon trials because it can make it harder to reach accrual goals on the proton arm of the protocol. Insurance denials of proton therapy can also skew the patient demographics of the proton arm. For example, Medicare is significantly more likely to cover proton therapy than private insurers, which can skew the age of the proton cohort toward older participants [22]. This older patient cohort might have comorbidities or other characteristics that make it challenging to compare outcomes data between the two arms. Lastly, the process of appealing insurance denials can lead to delays in the start of radiation treatment [23]. Clinical trial patients may already wait slightly longer for treatment to start due to clinical trial requirements such as pre-treatment reviews of the treatment plan. These delays might result in a patient going off trial to pursue treatment sooner.
One way to counteract the deleterious effect of proton insurance denial on randomization is to use a 2:1 randomization in favor of proton therapy. This gives the trial more opportunities to accrue proton patients, even if insurance challenges persist. But most proton centers choose to challenge insurance denials, and the best way to combat insurance denial is through support networks and sharing of resources. The NAPT offers a guide for patients on steps to deal with insurance denial, many of which are applicable to clinical teams as well [24]. Many proton centers have dedicated personnel to manage insurance appeals. For the NCTN, proton insurance denials are a frequent topic at operations management and proton working group meetings. These forums allow physicians to share successful techniques to overcome insurance barriers. Physicians have banded together to publish pleas for insurance companies to change the insurance approval process for proton therapy [25]. Some proton therapy centers have negotiated with insurance companies to reimburse proton therapy at the cost of IMRT, picking up the rest of the costs themselves [25]. The NCI has also advocated on behalf of proton therapy centers in the context of clinical trial insurance reimbursement for randomized NCTN protocols [26].
Due to the limited number of proton therapy centers, many randomized proton vs. photon trials encourage partnerships between one proton center and any number of photon clinics. There are many considerations when establishing a partnership between two institutions, such as who gets “credit” for the clinical trial accrual, how clinical trial reimbursement is allocated between the institutions, which personnel have rights to upload patient data to the appropriate portals, etc. There is a possibility that a photon clinic might partner with a proton center in another country. In this case, the logistics of travel reimbursement (if provided) should be addressed, as well as clinical trial membership and state approval if the trial is run through the NCTN. This type of partnership may become increasingly common as clinical trials for carbon therapy are being developed, with most carbon centers located in Europe and Eastern Asia. A few concepts have been proposed that randomize IMRT treatment to centers in the US, and carbon therapy to centers abroad [27].
In addition to randomized proton vs. photon clinical trials, there are a number of trials that imbed proton therapy as one of several allowed treatment modalities. This practice was most common this past decade in pediatric trials, such as those conducted by the Children’s Oncology Group (COG), but has been applied to adult trials as well. While the superiority of proton therapy outcomes might not be the primary endpoint of these studies, the hope is that with enough data, secondary analyses can be performed to look at proton patient cohorts compared to others.
To date, the standard method of including proton therapy in pediatric clinical trials has been to imbed protons in the protocols. The strategy recognizes the challenges of accrual to disease-specific radiation therapy protocols in pediatric patients and permits parallel treatment strategies for both photon and proton care to successfully manage the study. Approximately 50% of pediatric malignancies are in the leukemia domain, therefore protocols requiring radiation therapy are directed to tumors of the central nervous system, sarcoma, renal, orbit including retinoblastoma, and lymphoma. Therapy volumes and target dose are uniform between proton and photon care with guidelines imbedded in the study to insure synergistic care for tumor control acknowledging subtle differences in planning target volumes and dose distribution to normal tissue. Both proton and photon patients need to meet the identical dose to tumor and normal tissue. Dose to normal tissue in most situations is more easily achieved with proton therapy. In pediatric studies, outcome analysis including imaging are part of the longitudinal aspect of protocol management, therefore colleagues in the COG and the Imaging and Radiation Oncology Core (IROC) can evaluate normal tissue endpoints with outcome imaging validation to review comparison plans in retrospect to acquire important outcome analysis for secondary study endpoints between proton and photon care.
One challenge pediatric trials have faced is the apparent racial disparities between who receives proton therapy, with non-Hispanic white pediatric patients significantly more likely to be treated with protons than black patients [28]. This presents a challenge to proportional racial representation in clinical trial data.
In the US, adult clinical trial groups have imbedded proton therapy in dozens of clinical trials. At times, proton therapy has been added through clinical trial amendments with the hope of boosting accrual to protocols struggling to accrue patients. For a number of reasons (small number of proton centers, insurance denials, competing proton-specific trials), this has not proven to be the silver bullet, however, and generally it’s not recommended to add proton therapy as an allowable modality solely to improve trial accrual for adult protocols. Despite lower accrual numbers, proton therapy can be a good addition to a trial, adding the possibility of secondary analyses to look at proton therapy outcomes in relation to other treatment modalities.
Outside of prospective clinical trials with proton therapy, there are a number of proton therapy registries. These are generally less structured than Phase II/Phase III trials and allow for more flexibility in which data are analyzed. The Proton Collaborative Group (PCG) is a registry of nearly six thousand proton patients in the US [29]. The PCG looks at survival outcomes and quality of life, and fosters peer review collaboration across centers for clinical trial development. The Pediatric Proton Consortium Registry (PPCR) is a multi-institutional collaborative registry of demographic and clinical data for pediatric patients treated with proton and photon therapy [30]. The goal of the PPCR is to compare benefits of the two radiotherapy techniques, such as disease outcomes and quality of life. Washington University School of Medicine and Radialogica, LLC have a Proton Therapy Registry for adult and pediatric patients that collects clinical and dosimetric data [31].
Proton therapy has great potential and in some cases, proven clinical benefit. The best way to gather evidence to secure proton therapy as a standard of care for cancer treatment is through thoughtful, controlled clinical trials. Much work has already been done to this effect, and with so many clinical trials for proton therapy currently accruing, we will soon have data to answer the myriad questions related to proton therapy treatment outcomes.
The author declares no conflict of interest.
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