ESTRO 2023 - Abstract Book

S1884

Digital Posters

ESTRO 2023

Sciences, Pavia, Italy; 8 University of Pavia, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences and Fondazione IRCCS Policlinico San Matteo, Radiology, Pavia, Italy; 9 University of Pavia, Department of Internal Medicine and Medical Therapeutics and Fondazione IRCCS Policlinico San Matteo, Medical Oncology, Pavia , Italy; 10 University of Pavia, Department of Internal Medicine and Medical Therapeutics and Fondazione IRCCS Policlinico San Matteo, Respiratory Medicine , Pavia, Italy; 11 Spedali Civili and University of Brescia, Radiation Oncology, Brescia , Italy; 12 Spedali Civili and University of Brescia, Radiation Oncology , Brescia , Italy Purpose or Objective In the observational, multi-center "Blue Sky Radiomics” study (NCT04364776), we aim to investigate the prognostic role of radiomic features in predicting progression-free survival (PFS) in a series of stage III, unresectable, PD-L1 positive NSCLC patients undergoing chemoradiotherapy (CRT) and maintenance durvalumab. This is a preliminary report on the first evaluable patients (n=57). Materials and Methods Patients were all affected with stage 3 unresectable NSCLC, and received either sequential or concurrent CRT (Rt dose 60 Gy) followed by durvalumab maintenance (median of 22 doses). Median PFS for the whole cohort was 20.6 months. Median follow-up time 19.3 months. For radiomic analysis we identified the primary lung tumor on computed tomography (CT) images acquired with i.v. contrast medium, and with different scanners. CT images have been collected at 2 time points: at the diagnosis (T0), after CRT (T1). Tumor segmentation was performed by two specialists (thoracic radiologist and radio- oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To harmonize radiomic features we used the Combat tool. We compared Elastic Net (EN), Random Forest (RF) and Support Vector Machine (SVM) models performances for classifying PFS (Leave-One-Out Cross Validation method) at T0 and T1 time points. Moreover, we assessed the change in radiomic features between T0 and T1. We used the Cox and Survival Random Forest models to evaluate the performance of radiomic features, clinical factors, and both, for stratifying for PFS (k-Fold Cross Validation method, k=5). Results In the first Table, we reported the accuracy and the AUC of the 3 different models tested, at different time points. The addition of radiomic features to clinical features alone (stage A vs. B-C, histology, durvalumab start <42 days vs. > 42 days, PD-L1 level <50% or > 50%, sequential vs. concurrent CRT) improved the AUC by 4% for EN, and by 2% for both RF and SVM. In the second Table, we reported the performances in predicting PFS of each patient in a time-to-event setting.

Conclusion Our preliminary study underlines the importance of the development of a robust analysis pipeline for small-datasets. In this preliminary analysis on 57 patients out of 100 (target sample size), we exploited the application of Machine Learning methods for PFS prediction. With the use of radiomic features integrated with clinical factors, the AUC was improved by 4% for the EN, and 2% for both RF and SVM, respectively. A larger sample size should be tested to confirm this favorable trend and to hopefully reach better results.

PO-2101 Added Value Of MRI Radiomics To Predict Pathological Status Of Prostate Cancer Patients

M.G. Vincini 1 , G. Marvaso 2,3 , L.J. Isaksson 4 , M. Zaffaroni 4 , M. Pepa 4 , G. Corrao 4 , P.E. Summers 5 , M. Repetto 6 , G.C. Mazzola 4 , M. Rotondi 4 , S. Raimondi 7 , S. Gandini 7 , S. Volpe 4 , Z. Haron 8 , S. Alessi 5 , P. Pricolo 5 , F.A. Mistretta 9 , S. Luzzago 10 , F. Cattani 11 , G. Musi 10 , O. De Cobelli 10 , M. Cremonesi 12 , R. Orecchia 13 , D. La Torre 14 , G. Petralia 15 , B.A. Jereczek-Fossa 4,3 1 IEO, European Institute of Oncology IRCCS, Division of Radiation Oncology, Milan, Italy; 2 IEO, European Institute of Oncology, IRCSS, Division of Radiation Oncology, Milan, Italy; 3 University of Milan, Department of Oncology and Hemato- Oncology, Milan, Italy; 4 IEO European Institute of Oncology IRCCS, Division of Radiation Oncology, Milan, Italy; 5 IEO European Institute of Oncology IRCCS, Division of Radiology, Milan, Italy; 6 University of Milan-Bicocca, Department of Economics, Management and Statistics, Milan, Italy; 7 IEO European Institute of Oncology IRCCS, Department of Experimental Oncology, Milan, Italy; 8 National Cancer Institute, Radiology Department, Putrajaya, Malaysia; 9 Department of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy; 10 IEO European Institute of Oncology IRCCS, Department of Urology, Milan, Italy; 11 IEO European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy; 12 IEO European Institute of Oncology IRCCS, Radiation Research Unit, Milan, Italy; 13 IEO European Institute of Oncology IRCCS, Scientif Directorate, Milan, Italy; 14 SKEMA Business School, Université Côte d'Azur, SKEMA Business School, Université Côte d'Azur,,