ESTRO 36 Abstract Book

S457 ESTRO 36 2017 _______________________________________________________________________________________________

(gamma=0.7Gy/day), thus taking count of a possible consequential effect between acute and late damage. The figure reports calibration plot for all cases.

Artificial neural networks (ANNs) were used in the last years for the development of models for the prediction of radiation-induced toxicity following RT. In fact, ANNs are powerful tools for pattern classification in light of their ability to model extremely complex functions and huge numbers of data. However, their major counterpoint is that in some specific cases they might not deliver realistic results due to their missing critical capacity. The objective of this study was to develop a method for assessing reliability of ANNs response over the entire range of possible input variables. In particular, in this study the method was applied to the selection of an ANN for the prediction of late faecal incontinence (LFI) following prostate cancer RT. Material and Methods The analysis was carried out on 664 patients (pts) of two multicentre trials. The following information was available for each pt: i) self completed pt reported questionnaire (PRO) for LFI determination, ii) clinical data (co-morbidity, previous abdominal surgery and use of drugs), iii) dosimetric data (DVH and mean dose). Several feed-forward ANNs with a proper balance between complexity and number of training cases were developed, with input variables and hidden neurons ranging between 3 and 5. Once the best ANNs were obtained, a method was developed and applied to verify the reliability of their response over the entire range of possible input variables. The method consists in the development of a virtual library of variables covering all the possible ranges/permutations of continuous/discrete inputs. These are all classified and penalties (pen) are assigned if ANN outputs are not coherent with the real world expectance (i.e., decreasing LFI probability with increasing dose to the rectum). Results More than 1,000,000 different ANN configurations (i.e., architecture and internal weights and thresholds) were developed. For the 200 ANNs showing the best performance, area under the ROC curve (AUC), sensitivity (Se), specificity (Sp) and pen were quantified. The best ANN in terms of classification capability (i.e. AUC=0.79, Se=74%, Sp=72%) was an ANN with 5 inputs (i.e., mean dose, use of antihypertensive, previous presence of haemorrhoids, previous colon disease, hormone therapy) and 5 hidden neurons. However, the application of the method to investigate its coherence with the real life classification expectancy resulted in pen=3, indicating that this wasn’t the most 'intelligent” ANN to select. The best ANN with pen=0 was a less complex ANN (i.e. 3 inputs, 5 hidden neurons), resulting in AUC=0.67, Se=70%, Sp=57%. Conclusion A new method consisting in the development of a virtual library of cases was established to evaluate ANN reliability after its training process. Application of this method to the development of an ANN for LFI prediction following prostate cancer RT allowed us to select an ANN with the best generalization capability. PO-0852 External validation of a TCP model predicting PSA relapse after post-prostatectomy Radiotherapy S. Broggi 1 , A. Galla 2 , B. Saracino 3 , A. Faiella 3 , N. Fossati 4 , D. Gabriele 5 , P. Gabriele 2 , A. Maggio 6 , G. Sanguineti 3 , N. Di Muzio 7 , A. Briganti 4 , C. Cozzarini 7 , C. Fiorino 1 1 IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy 2 Candiolo Cancer Center -FPO- IRCCS, Radiotherapy, Candiolo Torino, Italy 3 Regina Elena National Cancer Institute, Radiotherapy, Roma, Italy 4 IRCCS San Raffaele Scientific Institute, Urology, Milano, Italy 5 University of Sassari, Radiotherapy, Sassari, Italy 6 Candiolo Cancer Institute -FPO- IRCCS, Medical Physics, Candiolo Torino, Italy

Conclusion The study confirms formerly published results on effect of abdominal surgery and dose to large rectal volumes as potential risk factors for late FI. Calibrations highlight a possible role of HF beyond linear quadratic correction. Inclusion of time recovery correction improved calibrations, but a further separate effect of HF was still detectable. This might be related to the selected alpha/beta (5Gy), which is currently accepted for late rectal toxicity. A more suitable value could be found for the longitudinal definition used in these trials (i.e., toxicity starting in acute phase and persisting during follow-up), instead of using the assumption settled in studies focusing on incidence of late peak events (such as rectal bleeding). PO-0851 Artificial neural networks for toxicity prediction in RT: a method to validate their “intelligence” E. Massari 1 , T. Rancati 2 , T. Giandini 1 , A. Cicchetti 2 , V. Vavassori 3 , G. Fellin 4 , B. Avuzzi 5 , C. Cozzarini 6 , C. Fiorino 7 , R. Valdagni 2 , M. Carrara 1 1 Fondazione IRCCS Istituto Nazionale dei Tumori, Diagnostic Imaging and Radiotherapy- Medical Physics Unit, Milan, Italy 2 Fondazione IRCCS Istituto Nazionale dei Tumori, Prostate Cancer Program, Milan, Italy 3 Cliniche Humanitas-Gavazzeni, Radiotherapy, Bergamo, Italy 4 Ospedale Santa Chiara, Radiotherapy, Trento, Italy 5 Fondazione IRCCS Istituto Nazionale dei Tumori, Radiation Oncology 1, Milan, Italy 6 San Raffaele Scientific Institute, Radiotherapy, Milan, Italy 7 San Raffaele Scientific Institute, Medical Physics, Milan, Italy

Purpose or Objective