ESTRO 2023 - Abstract Book

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ESTRO 2023

trained on 157 patients and cross-validated on an independent test set of 52 patients. The models’ performance in predicting PFS was quantified using the area under the receiver operating characteristic curve (AUC). Results The median (range) age at cycle one day one of therapy was 64 (56-72) years. Median ECOG performance status was 1 (IQR = 0). 179 (85.6%) patients had adenocarcinoma histology; 113 (54.1%) were female; 186 (89%) were white; median (range) BMI was 25.8 (22-29); 49 (23.4%) had high baseline PD-L1 expression (tumor proportion score ≥ 50%), and the top decile for TMB was ≥ 32 mutations/Mb. Following dimension reduction of the radiographic feature vector (4096 to 117), SSL representations alone predicted PFS with AUC = 0.66. This predictive performance increased when combined with age and performance status (AUC = 0.73). Age and performance status alone had AUC = 0.52. PD-L1 expression and TMB had a combined AUC = 0.53. Other combinations of clinical variables did not significantly change model performance. Driver mutation status was not predictive. Conclusion SSL representations of CT are predictive of PFS in treatment naïve advanced NSCLC patients receiving chemo- immunotherapy, particularly when these features are combined with clinical and demographic risk factors. They offer a predictive performance benefit over existing methods based on PD-L1 expression and TMB. Automated high-throughput analysis of lung cancer images can aid stratified personalized precision medicine. 1 The Hong Kong Polytechnic University, Department of Health Technology and Informatics, Hong Kong, Hong Kong (SAR) China; 2 Hong Kong Sanatorium & Hospital, Department of Radiotherapy, Hong Kong, Hong Kong (SAR) China Purpose or Objective Prostate-only radiotherapy (PORT) has been used for treating high-risk prostate cancer (HRPCa), with the objective of balancing disease control and quality of life. However, over half of these patients suffer from disease progression within 5 years after PORT. Currently, conventional clinical factors are incapable to identify HRPCa patients who may benefit more from PORT than other aggressive treatments. In precision medicine, radiomics facilitates prognostic prediction in many cancers. In this study, we investigated the ability of pre-treatment planning computed tomography (pCT)-based radiomic features combined with clinical factors in predicting 5-year progression-free survival (PFS) of HRPCa patients following PORT. Materials and Methods Clinical data and pCT of 100 biopsy-confirmed HRPCa patients treated by PORT at the Hong Kong Princess Margaret Hospital were retrospectively analysed. Based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guideline, temporal stratification by the PORT start date was performed, with patients divided into the training (n = 75) and independent validation cohort (n = 25). Radiomic (R) features were extracted from gross tumour volume (GTV) on pCT according to the Image Biomarker Standardisation Initiative protocol. Clinical (C) features used for building the C model included prostate specific antigen (PSA), Gleason score (GS), Roach score (RS) and GTV volume. Mann- Whitney U test was used for selecting radiomic features with high clinical relevance for R model building. The radiomic- clinical (RC) model was generated by combining the two models. Ridge regression with 5-fold cross-validation was iterated 100 times in the training cohort of each model, with a model score (i.e. coefficient * feature + intercept) rendered by each model for each individual patient. The classification performance of the three models on 5-year PFS was evaluated in the independent validation cohort using the area under receiver-operating-characteristics curve (AUC), and the Delong’s test for model comparisons. PO-2126 Integrative Radiomic-Clinical Model Improves Long-term Prognostication of High-risk Prostate Cancer C.F. CHING 1 , C.F. Ching 2 , C.H. Lam 1 , O.Y. Lui 1 , C.K. Kwong 1 , Y.H. Lo 1 , W.H. Chan 1 , W.S. Leung 1 , S.W. Lee 1