V. Gopalakrishnan1, B. Feng2, E. Somasundaram2, J. Pelesko3, K. L. Stephans4, J. Piper5, R. L. J. Qiu6, and J. G. Scott4; 1Cleveland Clinic Lerner College of Medicine, Cleveland, OH, 2Case Western Reserve University School of Medicine, Cleveland, OH, 3Department of Physics, Case Western Reserve University, Cleveland, OH, 4Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, 5MIM Software Inc, Cleveland, OH, 6Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
Purpose/Objective(s): Multiple courses of radiation are becoming increasingly common as treatment regimens to extend progression-free and overall survival in patients with oligometastatic disease. However, normal tissue recovery post-radiation has not been well characterized, and there are few objective pathways that clinicians use for determining the risk for toxicity in subsequent radiation treatments. The lack of standardized documentation for radiotherapy history also presents challenges for large-scale studies. To advance our understanding of tissue recovery, we propose adding a novel object to a patient’s medical record that leverages the Digital Imaging and Communications in Medicine (DICOM) standard. This object will serve as a patient model, onto which all radiation plans are mapped at the voxel level. Materials/
Methods: We have developed proof-of-concept software that demonstrates the utility of this DICOM object and how various dose forgiveness algorithms can be applied to the data. We include simple linear, exponential, logarithmic, and Gaussian recovery algorithms as well as complex non-linear algorithms based on the literature currently available. Results: We apply the tool to an anonymized patient, demonstrating the mathematical analysis applied to the data found in the new DICOM object. Noting ease and efficacy, we reason that, in contrast to gathering and structuring information currently distributed across electronic medical records, ready access to prior radiation courses will accomplish two goals. (1) Facilitate data collection and analysis by streamlining access to comprehensive radiotherapy history, enabling researchers to conduct large-scale studies, and ultimately improve our understanding of tissue recovery. (2) Enhance clinical decision-making by enabling clinicians or software tools to leverage this data to personalize treatment plans, minimizing toxicity risks during re-irradiation. Conclusion: A novel DICOM object which keeps track of radiation treatments enables clinicians to factor tissue recovery and response into planning safer multiple radiation therapy courses and facilitates cross-institution research on re-irradiation and dose forgiveness.
