Multimodal computational models for early prediction of peritoneal recurrence in gastric cancer

The National Cancer Institute awarded Wake Forest University Health Sciences $513,915 on June 18, 2026, under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to develop and validate predictive computational models for early detection of peritoneal recurrence in gastric cancer.

The recipient will develop novel deep learning multimodal models integrating clinical, radiological (CT), and histopathological (hematoxylin and eosin) data guided by genomics knowledge to predict peritoneal recurrence likelihood and timing in advanced gastric cancer patients. Aim 1 focuses on creating a rational deep learning approach that combines deep learning-extracted features and traditional hand-crafted radiomics features with clinical data to improve prediction accuracy. Aim 2 develops a robust prediction model using a pre-trained foundation model trained on large-scale H&E image data. Aim 3 combines CT, H&E, and clinical data in an integrated framework. The work addresses a critical clinical gap: nearly 50 percent of patients with advanced-stage gastric cancer develop peritoneal recurrence after radical surgery, resulting in median survival of only 3 to 6 months, and traditional imaging techniques cannot reliably detect the micro-metastases that characterize early peritoneal recurrence.

Performance occurs in Winston-Salem, North Carolina. The period of performance runs through May 31, 2031. The assistance type is a Project Grant, an R37 mechanism typically supporting established, highly productive investigators continuing research programs.

Abstract

Gastric cancer represents a significant disease burden and is a leading cause of cancer-related deaths in the united states and globally. Approximately 80% of gastric cancer patients are diagnosed at an advanced stage, with the peritoneum being the most common site of relapse (peritoneal recurrence) after radical surgery. Nearly 50% of patients with advanced-stage gastric cancer develop peritoneal recurrence post-surgery, resulting in a median survival of only 3-6 months and a markedly reduced quality of life. Early peritoneal recurrence is primarily characterized by micro-metastasis, which traditional imaging techniques struggle to detect due to the small size of metastatic nodules. Predicting the likelihood and timing of peritoneal recurrence is crucial for identifying at- risk patients, enabling timely interventions that could improve survival rates and quality of life. Unfortunately, reliable predictive biomarkers and models for peritoneal recurrence in gastric cancer are lacking in clinical practice, highlighting an urgent need for innovative predictive tools. This proposal aims to develop and validate novel predictive models for early peritoneal recurrence in gastric cancer, leveraging advanced deep learning techniques and multimodal integration of clinical, radiological (CT), and histopathological (hematoxylin and eosin, h & e) data. In aim 1, we will develop a rational approach for predicting peritoneal recurrence by creating a novel deep learning multimodal method guided by genomics knowledge. Additionally, we will integrate both deep learning-extracted features and traditional hand-crafted radiomics features with clinical data to improve prediction accuracy. Aim 2 focuses on developing a robust prediction model of peritoneal recurrence utilizing a pre-trained foundation model from large-scale h & e image data. Aim 3 will combine CT, h & e, and clinical data to further enhance predictive capabilities, employing an innovative cross-modal collaborative optimization approach for multimodal data integration. All models will be trained and internally validated using a retrospective cohort from atrium health wake forest baptist comprehensive cancer center and externally validated in two independent cohorts from additional institutions to ensure robustness across populations and imaging protocols. Additionally, we will compare our models with existing methods, including clinical staging and alternative fusion strategies. If successful, these models will enhance risk stratification and prediction of peritoneal recurrence in gastric cancer patients, significantly improving survival rates and quality of life by identifying those likely to develop peritoneal recurrence post-surgery and facilitating timely intervention. Furthermore, they can help avoid the risk of complications and extra medical costs associated with overtreatment. Since the information is derived from routinely examined CT, h & e and clinical data, they could be seamlessly integrated into current clinical workflows. The AI technology developed through this project has the potential to benefit underserved populations in low- resource settings and reduce healthcare disparities in the U.S.