NIH grant: Multimodal computational models for early prediction of peritoneal recurrence in gastric cancer (1R37CA304421-01A1)

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NIH grant 1R37CA304421-01A1: Multimodal computational models for early prediction of peritoneal recurrence in gastric cancer
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: Yuming  Jiang. Award: $513915.
Start: 2026-06-18T00:00:00.
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.

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