ESTRO 2020 Abstract Book

S143 ESTRO 2020

Conclusion We present a novel concept of SBRT-PATHY to treat unresectable bulky NSCLC patients which showed improved RT therapeutic ratio compared to standard of care in this phase 2 study with 60 patients. A larger prospective trial is ongoing to provide additional hope and promise for bulky, unresectable NSCLC patients, especially in the context of emerging IT combinations.

Poster Highlights: Poster highlights 10 PH: Toxicity modelling

PH-0283 Machine learning methods to predict rectal bleeding after prostate cancer radiotherapy M. Ibrahim 1 , E. Mylona 2 , N. Boussion 1 , O. Acosta 2 , R. De Crevoisier 2 , M. Hatt 1 1 University of Western Brittany, LaTIM- INSERM- UMR 1101, Brest, France ; 2 University of Rennes, LtSI-INSERM- UMR 1099, Rennes, France Purpose or Objective The goal of this work was to predict rectal bleeding (RB) following prostate cancer (PC) radiotherapy (RT) exploiting dose volume histograms (DVH) and clinical variables in a multicentric setting using 4 ML machine learning algorithms and 3 deep learning (ML, DL) techniques as well majority voting. A specific issue associated with multicentric data was the covariate shift issue, i.e., variables from each center could have strongly different distributions, which hampers the ability to efficiently train and validate multiparametric models using ML. An additional challenge was the high imbalance in the The records of 591 patients with more than 3 years follow up (including DVH, clinical data and rectal bleeding events) who underwent RT for localized PC were collected prospectively. The target volume was defined as the prostate and seminal vesicles. The mean dose delivered to the prostate was 79.3 Gy (range: 76–80) at 2 Gy per fraction, with 46 Gy delivered to the seminal vesicles. The cohort was split into a training set from 2 centers (n=337, 27 events) and a validation set (3rd center, n=254, 22 events). The classification task was prediction of RB at 3 years after RT. An ML framework was developed consisting of 3 modules to efficiently process multicentric data: 1. covariate shift and imbalance adaptation module relying on SMOTE(EN) [Synthetic Minority Over-sampling Technique -Edited Nearest Neighbours], density estimation ratio and normalization, 2. classification module (implementing 4 ML algorithms: Random Forest, Xtreme Gradient Boosting, LightGBM, CatBoost and 3 DL classifiers: Deep Neural Network, Deep Autoencoder+RF, Deep Variational Autoencoder+RF, as well as majority voting) and 3. Pseudo-labeling module (figure 1). The prediction capability of the proposed method was compared to the prediction capability using “standard” logistic regression using area under the ROC curve (AUC). data (i.e., few events). Material and Methods