ESTRO 36 Abstract Book

S462 ESTRO 36 _______________________________________________________________________________________________

7 IRCCS San Raffaele Scientific Institute, Radiotherapy, Milano, Italy Purpose or Objective A Poisson-based TCP model of 5-year biochemical recurrence-free survival (bRFS) after post-prostatectomy radiotherapy (RT) was previously introduced: best parameters values were obtained by fitting a large (n=894 ≥pT2, pN0, hormone-naïve patients) multi-centric population including data from five prospective / Institutional series; a satisfactory internal validation was performed. Current investigation dealt with an independent external validation on a large group of patients pooled from two independent Institutional databases with a minimum follow-up of 3 years. Material and Methods Based on the original model, bRFS may be expressed as: K x (1-exp(-αeff D)) CxPSA where: D is the prescribed dose; αeff is the radiosensitivity factor; C is the number of clonogens for pre-RT PSA=1ng/ml, assuming PSA to be proportional to tumor burden; K (equal to 1-BxPSA) is the fraction of patients who relapse due to clonogens outside the treated volume, depending on pre-RT PSA and Gleason Score (GS). The model works well when grouping patients according to their GS value: best-fit values of αeff (range: 0.23- 0.26), C (10 7 ) and B (0.30-0.50) were separately derived for patients with GS<7, GS=7 and GS>7. For current external validation, data of 352 ≥pT2, pN0, hormone-naïve patients treated with conventionally fractionated adjuvant (175) or salvage (177) intent after radical prostatectomy were available from two Institutions not previously involved in the training data set analysis. The predicted risk of 5-year bRFS was calculated for each patient, taking into account the slope and off-set of the model, as derived from the original calibration plot. Five- year bRFS data were compared against the predicted values in terms of overall performance, calibration and discriminative power. Results The median follow-up time, pre-RT PSA and D were 83 months (range: 36-216 months), 0.28 ng/mL (0.01-9.01 ng/mL) and 70.2Gy (66–80Gy); the GS distribution was: GS<7: 118; GS=7: 185; GS>7: 49. The performances of the model were excellent: the calibration plot showed a satisfactory agreement between predicted and observed rates (slope: 1.02; R 2 =0.62, Figure 1). A moderately high discriminative power (AUC=0.68, 95%CI:0.62-0.73) was found, comparable to the AUC for the original data set (0.69, 95%CI:0.66-0.73). The predicted 5-year bRFS for the whole population assessed as the weighted average of the values referred to the three groups (i.e.: GS<7, =7, >7) was 67%, compared to an observed 5-year bRFS equal to 68% ± 5% (95%CI). The agreement was slightly worse in the GS<7 group (70% vs 79% ± 7%) compared to GS=7 (66% vs 66% ± 7%) and GS>7 (62% vs 51% ± 14%).

Purpose or Objective 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