Shawn Kirby

By Linda Ding, Carla D. Bradford, Kenneth Ulin, Koren Smith, I-Lin Kuo, Yankhua Fan, Abdulnasser Khalifeh, Fenghong Liu, Suhong Lu, Harry Bushe, Salvatore Larosa, Camelia Bunaciu, Jonathan Saleeby, Shannon Higgins, Julie Trifone, Maureen Britton, Joshua Taylor, Marious Croos, Katie Figura, Thomas Quinn, Linda O’Connor, Kathleen Briggs, Sherri Suhl, Jean Quigley, Heather Reifler, Shawn Kirby, Fred Prior, Joel Saltz, Maryann Bishop-Jodoin and Thomas J. FitzGerald

Technology and computational analytics are moving forward at an extraordinary rate with changes in patient care and department workflows. This rapid pace of change often requires initiating and maintaining the educational support at multiple levels to introduce technology to radiation oncology staff members. Modern physics quality assurance and dosimetry treatment planning now require expertise beyond traditional skill based in computational algorithms and image management including quality assurance of the process of image acquisition and fusion of image datasets. Expertise in volumetric anatomy and normal tissue contouring are skills now performed by physics/dosimetry in collaboration with physicians and these skills are required in modern physics dosimetry training programs. In this chapter, challenges of modern radiation planning are reviewed for each disease site. Skills including future applications of image integration into planning objects and the future utility of artificial intelligence in modern radiation therapy treatment planning are reviewed as these issues will need to be added to modern training programs.

Part of the book: Dosimetry