Premium
Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy
Author(s) -
Pella Andrea,
Cambria Raffaella,
Riboldi Marco,
JereczekFossa Barbara Alicja,
Fodor Cristiana,
Zerini Dario,
Torshabi Ahmad Esmaili,
Cattani Federica,
Garibaldi Cristina,
Pedroli Guido,
Baroni Guido,
Orecchia Roberto
Publication year - 2011
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3582947
Subject(s) - receiver operating characteristic , support vector machine , radiation therapy , prostate cancer , machine learning , acute toxicity , artificial intelligence , artificial neural network , toxicity , medicine , computer science , cancer
Purpose : The goal of this study is to investigate the advantages of large scale optimization methods vs conventional classification techniques in predicting acute toxicity for urinary bladder and rectum due to prostate irradiation. Methods : Clinical and dosimetric data of 321 patients undergoing prostate conformal radiotherapy were recorded. Gastro‐intestinal and genito‐urinary acute toxicities were scored according to the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer (RTOG/EORTC) scale. Patients were classified in two categories to separate mild (Grade<2) from severe toxicity levels (Grade>2). Machine learning methods at different complexity were implemented to predict toxicity as a function of multiple variables. The first approach consisted of a large scale optimization method, based on genetic algorithms (GAs) and artificial neural networks (ANN). The second approach was a binary classifier based on support vector machines (SVM). Results : The ANN and SVM‐based solutions showed comparable prediction accuracy, exhibiting an area under the receiver operating characteristic (ROC) curve of 0.7. Different sensitivity and specificity features were measured for the two approaches. The ANN algorithm showed enhanced sensitivity if combined with appropriate classification criteria. Conclusions : The results demonstrate that high sensitivity in toxicity prediction can be achieved with optimized ANNs, that are put forward to represent a valuable support in medical decisions. Future studies will be focused on enlarging the available patient database to increase the reliability of toxicity prediction algorithms and to define optimal classification criteria.