ESTRO 2023 - Abstract Book

S124

Saturday 13 May

ESTRO 2023

specific MDTs are welcomed and should lead to greater consistency in clinical practice, the first step towards improving outcomes.

Poster Discussion: Radiomics and modelling

PD-0163 combining tumor and whole-lung radiomics features to predict prognosis in locally advanced NSCLC Z. Zhang 1 , M. Yan 2 , Z. Wang 1 , A. Dekker 1 , L. Wee 1 , A. Traverso 1 , L. Zhao 2 1 Maastricht University Medical Centre+, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands; 2 Tianjin Medical University Cancer Institute and Hospital, Department of Radiation Oncology, Tianjin, China Purpose or Objective Several studies have shown that lung heterogeneity is associated with overall survival (OS) in lung cancer. However, the predictive value of whole lung-based radiomics for OS in non-small cell lung cancer (NSCLC) is unknown. The purpose of this study is to investigate the predictive value of whole lung-based and tumor-based radiomics for OS in locally advanced- NSCLC treated with curative radiotherapy. Materials and Methods A total of 349 patients were enrolled in the study, with 292 patients in training set, 57 patients from the same hospital as independent test-set-1, and 47 patients from a multi-institutional prospective clinical trial data set (RTOG0617) as test- set-2. Tumor-based radiomics features and total lung-based radiomics features were extracted from planning CT images and radiomics features were selected by least absolute shrinkage embedded with Cox proportional hazards model with 5- fold cross-internal validation, with 1000 bootstrap samples. Radiomics prognostic scores (RS) were calculated by Cox regression based on selected features. Five models based on clinical factors, a tumor RS, and a lung RS separately and their combinations were constructed. The Harrell concordance index (C-index), calibration curves were used to evaluate the discrimination and calibration performance. Patients were stratified into high and low risk groups, and a log-rank test was performed.

Figure 1 : Workflow of the project. Abbreviations: L-RM: COX regression model based on lung signature; TL-RM: COX regression model based on tumor signature and lung signature; CTL-RM: COX regression model based on tumor signature, lung signature and clinical factors; C-index: Harrell concordance index. Results The discrimination ability of lung- and tumor-based radiomics model was similar in terms of C-index, 0.69 vs 0.68 in training set, 0.69 vs 0.66 in test-set-1, 0.63 vs 0.69 in test-set-2. The model that combined clinical factors, tumor and lung RS achieved the best performance (C-index, 0.78 in training set, 0.80 in test-set-1, 0.81 in test-set-2). The calibration curve showed good agreement between predicted and actual values. Patients were well stratified, with statistical significance for all datasets.

Figure 2 : Kaplan-Meier survival curves based on the CTL-RM model in (A) training set, (B) test-set-1, (C) test-set-2. Abbreviations: CTL-RM: COX regression model based on tumor radiomics signature, lung radiomics signature and clinical factors. Conclusion Lung- and tumor-based radiomics features have the potential to predict OS in NSCLC. The combination of clinical and radiomics predictors can achieve optimal performance. PD-0164 The value of post Radiotherapy PSA dynamics for prostate cancer risk stratification models J. Shortall 1 , E. Vasquez-Osorio 1 , A. Green 2 , D. Wong 3 , P. Hoskin 1 , A. Choudhury 1 , M. van Herk 1 , A. McWilliam 1 1 The University of Manchester, Faculty of Biology, Medicine, Division of Cancer Sciences, Manchester, United Kingdom; 2 European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, United Kingdom; 3 The University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom Purpose or Objective One in three men with prostate cancer (PCa) experience biochemical recurrence (BCR), detected via rises in Prostate Specific Antigen (PSA). Although post-radiotherapy (RT) PSA characteristics may be associated with BCR, they are not currently included in prognostic models, missing an opportunity for ‘early’ intervention, e.g increased monitoring