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Predictive Atlas of Tumor Shrinkage for Predictive Treatment Planning of Lung Radiotherapy

P Zhang

P Zhang1*, E Yorke1 , G Mageras1 , A Rimner1, J Sonke2 , J Deasy1 , (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Netherlands Cancer Institute, Amsterdam, NH,


SU-K-605-10 (Sunday, July 30, 2017) 4:00 PM - 6:00 PM Room: 605

Purpose: To develop a predictive atlas that can estimate patterns of lung tumor shrinkage during radiotherapy, integrate it in predictive treatment planning (PTP), and enhance dose escalation and tumor control.

Methods: Data from 22 patients at a collaborating institution were obtained to expand an atlas originally created with 12 patients for predicting tumor shrinkage. Geometric patterns of tumor shrinkage from pre- to post-radiotherapy CT scans of each patient were extracted using principle component analysis. A study patient was selected via a leave-one-out scheme and matched with a subgroup in the 34-patients atlas based on a similarity measure of tumor location. The spatial distribution of residual tumor was estimated by transforming the study tumor with the shrinkage pattern of each patient in the subgroup, and subsequently defining a binary map by thresholding the superposition of individual predictions. On contrast, the 12-patients atlas used all available patients for prediction. In a retrospective PTP study, the predicted residual tumors were escalated to the highest achievable dose, while maintaining the original prescription dose to the remainder of the tumor and without compromising toxicity limits to adjacent organs at risk. The PTP approach was compared isotoxically to an adaptive approach that replans with mid-course imaging.

Results: The 34-patients atlas improved predictive accuracy (true positive plus true negative ratios based on predicted and actual residual tumor), 0.732 vs. 0.718 of the 12-patients atlas (p<0.01) due to the patient matching process. Tumor mean dose and tumor control probability were on average 6Gy and 8% higher with PTP compared to the adaptive replanning approach.

Conclusion: A predictive atlas benefits from more diversified shrinkage patterns and tumor locations. PTP, elevating tumoricidal dose to the residual tumor throughout the entire treatment course with a single optimized plan, could potentially improve the efficacy and efficiency compared to the adaptive replanning approach.

Funding Support, Disclosures, and Conflict of Interest: Supported by Varian Medical System

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