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Model Based Classification for Optimal Position Selection for Left-Sided Breast Radiotherapy: Free Breathing, DIBH, Or Prone

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H Lin

H Lin1*, T Liu1 , C Shi2 , S Petillion3 , I Kindts3 , X Tang4 , X Xu1 , (1) Rensselaer Polytechnic Institute, Troy, NY, (2) Saint Vincent Medical Center, Bridgeport, CT, (3) University Hospitals Leuven, Leuven, Vlaams-Brabant, (4) Memorial Sloan Kettering Cancer Center, West Harrison, NY


SU-G-BRC-13 (Sunday, July 31, 2016) 4:00 PM - 6:00 PM Room: Ballroom C

Purpose: There are clinical decision challenges to select optimal treatment positions for left-sided breast cancer patients—supine free breathing (FB), supine Deep Inspiration Breath Hold (DIBH) and prone free breathing (prone). Physicians often make the decision based on experiences and trials, which might not always result optimal OAR doses. We herein propose a mathematical model to predict the lowest OAR doses among these three positions, providing a quantitative tool for corresponding clinical decision.

Methods: Patients were scanned in FB, DIBH, and prone positions under an IRB approved protocol. Tangential beam plans were generated for each position, and OAR doses were calculated. The position with least OAR doses is defined as the optimal position. The following features were extracted from each scan to build the model: heart, ipsilateral lung, breast volume, in-field heart, ipsilateral lung volume, distance between heart and target, laterality of heart, and dose to heart and ipsilateral lung. Principal Components Analysis (PCA) was applied to remove the co-linearity of the input data and also to lower the data dimensionality. Feature selection, another method to reduce dimensionality, was applied as a comparison. Support Vector Machine (SVM) was then used for classification. Thirty-seven patient data were acquired; up to now, five patient plans were available. K-fold cross validation was used to validate the accuracy of the classifier model with small training size.

Results: The classification results and K-fold cross validation demonstrated the model is capable of predicting the optimal position for patients. The accuracy of K-fold cross validations has reached 80%. Compared to PCA, feature selection allows causal features of dose to be determined. This provides more clinical insights.

Conclusion: The proposed classification system appeared to be feasible. We are generating plans for the rest of the 37 patient images, and more statistically significant results are to be presented.

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