Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

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2019-01

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Abstract

Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively (P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction (P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario (P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.

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10.1177/1533033819874788

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Sheng, Yang, Jiahan Zhang, Chunhao Wang, Fang-Fang Yin, Q Jackie Wu and Yaorong Ge (2019). Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study. Technology in cancer research & treatment, 18. p. 1533033819874788. 10.1177/1533033819874788 Retrieved from https://hdl.handle.net/10161/19363.

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Scholars@Duke

Sheng

Yang Sheng

Assistant Professor of Radiation Oncology

My research interest focuses on machine learning and AI application in radiation oncology treatment planning, including prostate cancer, head-and-neck cancer and pancreatic cancer etc.

Wang

Chunhao Wang

Assistant Professor of Radiation Oncology
  • Deep learning methods for image-based radiotherapy outcome prediction and assessment
  • Machine learning in outcome modelling
  • Automation in radiotherapy planning and delivery




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