Gevaert, Olivier

e16457 Background: Patients with inoperable stage II-III pancreatic cancer commonly undergo sequential stereotactic body radiation therapy (SBRT) and chemotherapy. However, there is variability in clinical outcomes, and the predictors of high-risk patients with rapid tumor progression (within 3 months) and poor overall survival (OS) despite treatment are not well characterized. We developed a radiomic (imaging-derived features) signature (RS) that predicts rapid progression. We then investigated clinical characteristics, including pathologic features and treatments received, and RS in a combined analysis to develop and validate the highest-performing model to predict OS from the time of SBRT initiation. We identified the most predictive feature from the combined model that best predicts OS. Methods: In our retrospective study, we examined a cohort of 124 stage II-III pancreatic cancer patients who underwent sequential SBRT and systemic chemotherapy and had pre-treatment pancreatic protocol computed tomography (CT) imaging. We examined 10 clinical features and extracted 900 radiomic features from each segmented tumor per patient using PyRadiomics. Dividing our cohort into training and test sets (60:40), we built a prediction model for rapid tumor progression using radiomic data on the training set (n = 74), applying a LASSO-based algorithm for feature selection and 5-fold cross-validation for parameter optimization. We validated the model performance on the held-out test set (n = 50) and generated the RS for predicting rapid progression. We examined the performance of clinical features and RS in predicting OS in univariate and multivariate Cox regression models. Results: Analysis on our cohort (57 men, 67 women; mean age 67 ± 11 years) generated a 43-feature RS that predicted rapid tumor progression (AUC 0.83, 95% CI: 0.70–0.94) in the test set. High RS was a significant predictor of mortality with hazard ratio (HR) 2.22 (95% CI: 1.32–3.73, p = 0.003) within the first year and 2.85 (95% CI: 1.35–6.03, p = 0.006) thereafter. Non-intensive chemotherapy increased only early mortality risk (HR 1.95, 95% CI: 1.08–3.53, p = 0.03), while older age was significant in later years (HR 1.80, 95% CI: 1.02–3.15, p = 0.04). Conclusions: CT-derived RS accurately predicted rapid tumor progression in stage II-III pancreatic cancer patients undergoing SBRT sequentially with chemotherapy. High RS was the strongest prognostic indicator for increased mortality risk, suggesting its utility for guiding treatment or selection for clinical trials.

706 Background: We sought to assess the ability of a computed tomography (CT)-derived radiomic signature (RS) feature set to predict rapid tumor progression and overall survival (OS) in patients diagnosed with stage II-III pancreatic cancer undergoing stereotactic body radiation therapy (SBRT) in sequence with chemotherapy. Methods: We conducted a retrospective study on a cohort of 240 stage II-III pancreatic cancer patients who underwent SBRT in sequence with chemotherapy at a single institution (Stanford Hospital and Clinics). Among them, 83 had pre-SBRT contrast-enhanced CT images with segmented tumors, forming the imaging set, from which we extracted 900 radiomic (quantitative pixel-level imaging characteristic) features from delineated tumor regions-of-interest. For RS development, we divided the imaging set into a training set (n = 53) for model development and a test set (n = 30) for evaluation. We built a binary prediction model on the training set to identify patients at risk of rapid tumor progression within three months after SBRT. We utilized logistic regression with the Least Absolute Shrinkage and Selection Operator algorithm for feature selection and classification. To fine-tune parameters, we performed five-fold cross-validation on the training set, repeating each set of parameters five times. Finally, we assessed the model's performance on the test set (n = 30) using the area under the curve (AUC) value. We selected the model with the best AUC, while also generating the predictive radiomic feature set. For OS prognostication, we conducted both univariate and multivariate Cox proportional-hazards analyses using RS and clinical features (age, sex, stage, vessel involvement, tumor location, performance status, body mass index, biological equivalent dose of radiation) as potential predictors. Results: The enrolled cohort consisted of 127 men (mean age, 69 years ± 12 [SD]) and 113 women (mean age, 69 years ± 13 [SD]). Seventeen textural radiomic features were identified as the RS, which demonstrated a high AUC in the test set for the prediction of rapid progression (AUC 0.850; 95% CI: 0.725, 0.975). Age was associated with OS in the multivariate model while high RS (>0.5) was independently associated with OS in both univariate and multivariate models. Conclusions: CT-derived RS, combined with age, represent the most prognostic factors for stage II-III pancreatic cancer. RS, which likely reflects underlying biology or molecular alterations, contributes to the highest HR of poor OS and provide the strongest indicator of outcome following commonly used treatments in this patient population. [Table: see text]