ESTRO 2020 Abstract Book

S405 ESTRO 2020

Conclusion This study confirmed ERI TCP as a potentially powerful biomarker able to early predict the pCR after radio- chemotherapy for LARC. This performance was confirmed with MR images obtained during MRgRT, acquired with different imaging sequences and magnetic field strength than those used in the original training cohort. These results, if confirmed in larger cohorts, could facilitate the clinical implementation of ERI TCP in personalized protocols for LARC PH-0716 Radiomics pCR predictive model in rectal cancer: an intercontinental validation on real world data L. Boldrini 1 , J. Lenkowicz 1 , L.C. Orlandini 2 , N. Dinapoli 1 , G. Yin 2 , D. Cusumano 1 , C. Casà 1 , Q. Peng 1 , G. Chiloiro 1 , M.A. Gambacorta 1 , J. Lang 2 , V. Valentini 1 1 Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Diagnostica per Immagini- Radioterapia Oncologica ed Ematologia, Roma, Italy ; 2 Sichuan Cancer Hospital & Institute- Sichuan Cancer Center- School of Medicine- University of Electronic Science and Technology of China, Department of Radiation Oncology, Chengdu, China Purpose or Objective Pathological complete response (pCR) prediction in locally advanced rectal cancer (LARC) undergoing neoadjuvant (NAD) chemoradiotherapy (CRT) represents a promising field of investigation. Unfortunately, the predictive models published so far often lack of replicability and reliable external validations. Aim of this study was to evaluate the replicability of an already published multivariable pCR predictive model in LARC, using a third external independent extraeuropean validation cohort. Material and Methods The original multivariable model was based on 4 covariates: clinical T and N staging and 2 radiomics features extracted from vendor independent staging 1.5 T MRI (entropy of the image and skewness of the ROI grey level histogram filtered). The considered binary outcome was pCR achievement. The model was realized using a single center training set of 162 patients (pts) and 2 external validation sets for a total of 59 pts provided by other 2 European centers. All the pts received long course NAD CRT. The first validation of the model confirmed its stability on both internal and external validation, with AUC values of 0.73 and 0.75 respectively. In order to test model’s replicability, a third extraeuropean validation cohort has been enrolled using real world data of a Chinese institution including both 1.5 and 3 T staging MRI images, and pts receiving both long and short course NAD CRT. Validation was performed via Receiving Operator Characteristics (ROC) curve analysis and classification matrix metrics at the ROC Youden index cut-off point. ROC AUC and classifications matrix accuracy, specificity, sensitivity, negative predictive value, positive predictive value, and Kappa statistics were taken as validation metrics. Results 60 Chinese pts were enrolled in the extraeuropean validation cohort, showing a pCR occurrence of 16% (10 cases). 27 and 33 image datasets were collected from 1.5 T and 3 T scanners. 21.9% and 78.1% of the pts received short and long course NAD treatment respectively. Proper Laplacian of Gaussian sigma and pixel spacing

values have been taken into account for model applicability. A ROC AUC of 0.83 was achieved on the whole Chinese validation dataset (see fig. 1). Classification matrix on the testing dataset best cut-off point according to Youden index: 0.85 accuracy, 0.90 specificity, 0.60 sensitivity, 0.92 negative predictive value, 0.55 positive predictive value, 0.48 Kappa statistics.

Conclusion Despite the introduction of significant different variables (i.e. ethnic origin, MRI field strength, type of NAD treatment) the proposed model appeared to be replicable and stable on a real world data extraeuropean patients cohort. The obtained promising results encourage to further investigate the application of radiomics modeling in the frame of multivariable decision support systems for LARC. PH-0717 Locally Advanced Pancreatic Cancer: Robust Radiomic based model of outcome after radio- chemotherapy M. Mori 1 , P. Passoni 2 , E. Incerti 3 , S. Broggi 1 , G.M. Cattaneo 1 , M. Reni 4 , E. Spezi 5 , N. Slim 2 , E.G. Vanoli 3 , V. Bettinardi 3 , L. Gianolli 3 , M. Picchio 3 , N.G. Di Muzio 2 , C. Fiorino 1 1 IRCCS San Raffaele Scientific Institute, Medical Physics, Milan, Italy ; 2 IRCCS San Raffaele Scientific Institute, Radiotherapy, Milan, Italy ; 3 IRCCS San Raffaele Scientific Institute, Nuclear Medicine, Milan, Italy ; 4 IRCCS San Raffaele Scientific Institute, Oncology, Milan, Italy ; 5 Cardiff University, School of Engineering, Cardiff, United Kingdom Purpose or Objective PET Radiomics is a promising tool to predict outcome after Radiotherapy (RT) although no reliable results exist for Locally Advanced Pancreatic Cancer (LAPC). The aim of the study was to develop robust radiomic based predictive models for LAPC treated with exclusive chemo-RT. Poorly repeatable/reproducible PET Radiomic Features (RF) were previously identified and withdrawn. Material and Methods The study involved a single-center cohort of 120 LAPC patients (pts) treated with chemo-RT within a study investigating moderate hypo-fractionation (44.25 Gy in 15 fr, with/without boosting PET positive volumes to 48-54 Gy). The IBSI (International Biomarker Standardization Initiative) compliant software SPAARC was used to extract RF. ROC and COX regressions were performed to detect the predictive power of the most robust RF for Overall, Local and Distant-Relapse Free Survival (OFS, LRFS and DRFS). COX regression multi-variable models including RFs with/without including available clinical parameters were