ESTRO 36 Abstract Book

S458 ESTRO 36 2017 _______________________________________________________________________________________________

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%).

A comprehensive, radiobiologically consistent Poisson- based TCP model of the response to post-prostatectomy RT was validated for the first time on a completely independent data set. A more extensive validation on a larger population is actually in progress to further corroborate its generalizability. PO-0853 A method for automatic selection of parameters in NTCP modelling D. Christophides 1 , A.L. Appelt 2 , J. Lilley 3 , D. Sebag- Montefiore 2 1 Leeds CRUK Centre and Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom 2 Leeds Institute of Cancer and Pathology - University of Leeds and Leeds Cancer Centre, St James’s University Hospital, Leeds, United Kingdom 3 Leeds Cancer Centre, St James’s University Hospital, Leeds, United Kingdom Purpose or Objective The use of multivariate models in predicting NTCP has the potential of improving predictive accuracy compared to univariate models 1 . However the large numbers of clinical parameters and dose metrics involved can make the selection of the optimal multivariate model inconsistent and time consuming. In this study a genetic algorithm based method is utilised to automatically generate ordinal logistic regression models; subsequently the quality of the parameter selection process is evaluated by comparison with published results on the same patient cohort 2 . Material and Methods A general method for selecting optimal models for outcome prediction in radiotherapy was developed (Fig.1). The method was tested on data from 345 rectal cancer patients, used in a previously published study 2 , to generate ordinal logistic regression models for the prediction of acute urinary toxicity during chemoradiotherapy. Principal component analysis (PCA) was used to derive principal components (PCs) that summarise the variance in the DVH data. Overall 25 clinical parameters were considered in the analysis including demographics, treatment regime, plan parameters and stage of disease; as well as 8 PCs that explained >95% of the variance in the DVHs. Urinary toxicity was categorised as grade 0, 1 and 2≥ cystitis, according to the CTCAE v3.0. The method (Fig.1) for optimising the models was implemented in Python and the entire procedure was repeated 100 times, using bootstrap sampling from the whole data set, to evaluate the stability of the parameter selection. Confidence intervals for the Akaike information criterion (AIC) of the final models selected were estimated using

Conclusion