Feasibility study of individualized optimal positioning selection for left-sided whole breast radiotherapy: DIBH or prone

Hui Lin, Tianyu Liu, Chengyu Shi, Saskia Petillion, Isabelle Kindts, Caroline Weltens, Tom Depuydt, Yulin Song, Ziad Saleh, Xie George Xu, Xiaoli Tang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The deep inspiration breath hold (DIBH) and prone (P) position are two common heart-sparing techniques for external-beam radiation treatment of left-sided breast cancer patients. Clinicians select the position that is deemed to be better for tissue sparing based on their experience. This approach, however, is not always optimum and consistent. In response to this, we develop a quantitative tool that predicts the optimal positioning for the sake of organs at risk (OAR) sparing. Sixteen left-sided breast cancer patients were considered in the study, each received CT scans in the supine free breathing, supine DIBH, and prone positions. Treatment plans were generated for all positions. A patient was classified as DIBH or P using two different criteria: if that position yielded (1) lower heart dose, or (2) lower weighted OAR dose. Ten anatomical features were extracted from each patient's data, followed by the principal component analysis. Sequential forward feature selection was implemented to identify features that give the best classification performance. Nine statistical models were then applied to predict the optimal positioning and were evaluated using stratified k-fold cross-validation, predictive accuracy and receiver operating characteristic (AUROC). For heart toxicity-based classification, the support vector machine with radial basis function kernel yielded the highest accuracy (0.88) and AUROC (0.80). For OAR overall toxicities-based classification, the quadratic discriminant analysis achieved the highest accuracy (0.90) and AUROC (0.84). For heart toxicity-based classification, Breast volume and the distance between Heart and Breast were the most frequently selected features. For OAR overall toxicities-based classification, Heart volume, Breast volume and the distance between ipsilateral lung and breast were frequently selected. Given the patient data considered in this study, the proposed statistical model is feasible to provide predictions for DIBH and prone position selection as well as indicate important clinical features that affect the position selection.

Original languageEnglish
Pages (from-to)218-229
Number of pages12
JournalJournal of applied clinical medical physics
Volume19
Issue number2
DOIs
StatePublished - Mar 2018

Keywords

  • Breast cancer
  • DIBH
  • Machine learning
  • OAR sparing
  • Prone

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