Deep Learning Predicts Imminent Tumor Progression in Advanced Pancreatic Adenocarcinoma Using Serial CT Scans During Chemotherapy.
Authors
Affiliations (15)
Affiliations (15)
- National-Regional Key Technology Engineering Laboratory For Medical Ultrasound Guangdong Key Laboratory For Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Thoracic Surgery Department of the First Affiliated Hospital Shenzhen University Medical School Shenzhen University Shenzhen China.
- Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China.
- Department of Pancreatobiliary Surgery State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou China.
- Department of Radiology Xiangya Hospital Central South University Changsha Hunan China.
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital) Central South University Changsha Hunan China.
- The First Affiliated Hospital of Jinan University Guangzhou China.
- The First Affiliated Hospital of Bengbu Medical University Bengbu China.
- Department of Radiology State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou China.
- Department of Radiology, Affiliated Dongguan Hospital Southern Medical University Dongguan China.
- School of Artificial Intelligence Shenzhen University Shenzhen China.
- National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China.
- School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China.
- Department of Thoracic Surgery the Affiliated Wuxi People's Hospital of Nanjing Medical University Wuxi China.
- Wuxi College of Clinical Medicine Nanjing Medical University Wuxi China.
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou China.
Abstract
Advanced pancreatic ductal adenocarcinoma (PDAC) often progresses rapidly during chemotherapy despite initial assessments of stable disease or partial response by Response Evaluation Criteria in Solid Tumors (RECIST 1.1), underscoring the limitations of the current methods for predicting short-term progressive disease (PD). To address this, the study developed a spatiotemporal deep learning framework that integrates convolutional and long short-term memory (LSTM) neural networks to dynamically predict PD at the next follow-up visit using serial computed tomography (CT) scans and baseline clinical variables. The model was trained on a retrospective cohort of 243 patients (415 predicted events, defined as temporal sequences for the next follow-up PD prediction) and evaluated across internal, external, and prospective cohorts. The model achieved area under the curve (AUC) values of 0.77, 0.76, and 0.74, respectively. Performance remained robust across chemotherapy regimens (AG or Gemcitabine-based, FOLFIRINOX, and SOXIRI; AUC 0.68-0.79), PD subtypes (target lesion growth vs. new metastases; AUC 0.72 vs. 0.77), and baseline disease stages (locally advanced vs. metastatic; AUC 0.85 vs. 0.71). This framework enables the noninvasive, real-time prediction of imminent PD in advanced PDAC, facilitating timely treatment modification. Its validated generalizability and reliance on routine clinical data underscore its potential for seamless integration into chemotherapy management.