Clinico-radiographic predictors of overall survival in stage II-III pancreatic cancer.

Q Qinmei Xu D Diego Toesca (Mayo Clinic College of Medicine, Phoenix, AZ) L Lucas Vitzthum (Stanford University School of Medicine, Stanford, CA) A Arash Jamalian (Stanford University School of Medicine, Stanford, CA) E Emil Schueler (The University of Texas MD Anderson Cancer Center, Houston, TX) E Emel Alkim (Stanford University School of Medicine, Stanford, CA) J J. Richelcyn Baclay (Stanford University, Stanford, CA) D Daniel Tandel Chang (University of Michigan, Ann Arbor, MI) O Olivier Gevaert G George A. Fisher (Stanford University School of Medicine, Stanford, CA) H Haruka Itakura (Stanford University School of Medicine, Stanford, CA)

Abstract

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.

Article Details

Volume / Issue Vol. 43, Issue 16_suppl
Published June 01, 2025
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (11)

Q

Qinmei Xu

D

Diego Toesca

Mayo Clinic College of Medicine, Phoenix, AZ

L

Lucas Vitzthum

Stanford University School of Medicine, Stanford, CA

A

Arash Jamalian

Stanford University School of Medicine, Stanford, CA

E

Emil Schueler

The University of Texas MD Anderson Cancer Center, Houston, TX

E

Emel Alkim

Stanford University School of Medicine, Stanford, CA

J

J. Richelcyn Baclay

Stanford University, Stanford, CA

D

Daniel Tandel Chang

University of Michigan, Ann Arbor, MI

O

Olivier Gevaert

G

George A. Fisher

Stanford University School of Medicine, Stanford, CA

H

Haruka Itakura

Stanford University School of Medicine, Stanford, CA