NCT06936098

Deep Learning-Based Analysis of Colorectal Cancer Pathology Images: An Innovative Approach for Predicting Colorectal Cancer Subtypes

AI-Powered Copilots for Precision Diagnosis and Surgical Assessment of Histological Growth Patterns in Resectable Colorectal Liver Metastases: A Prospective Study

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 431 (actual)
• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

Colorectal cancer (CRC) is a leading cause of mortality in China, with metastasis significantly contributing to poor outcomes. Histopathological growth patterns (HGPs) in colorectal liver metastasis (CRLM) provide vital prognostic insights, yet the limited number of pathologists highlights the need for auxiliary diagnostic tools. Recent advancements in artificial intelligence (AI) have demonstrated potential in enhancing diagnostic precision, prompting the development of specialized AI models like COFFEE to improve the classification and management of HGPs in CRLM patients. This study aims to develop and validate a Transformer-based deep learning model, COFFEE, for the classification of colorectal cancer subtypes using whole slide images (WSIs) from patients diagnosed with colorectal cancer liver metastasis. The model is pre-trained using self-supervised learning (DINO) on WSIs from the TCGA-COAD cohort, utilizing a Vision Transformer (ViT) architecture to extract 384-dimensional feature vectors from 256×256 pixel patches. The COFFEE model integrates a Transformer-based Multiple Instance Learning (TransMIL) framework, incorporating multi-head self-attention and Pyramid Position Encoding Generator (PPEG) modules to aggregate spatial and morphological information. The study includes training, testing, and prospective validation cohorts and evaluates the performance of the model in both binary and multi-class classification settings, as well as its potential to assist pathologists in clinical workflows.

Conditions

• Colorectal Liver Metastasis (CRLM)
• Histopathological Growth Patterns (HGPs)
• Artificial Intelligence (AI) in Diagnosis
• Vision Transformer (ViT)
• Desmoplastic Classification

Interventions

Type	Name	Description
PROCEDURE	CRLM surgery	Surgical resection of colorectal cancer liver metastasis (CRLM) involves the removal of metastatic lesions from the liver. This procedure is aimed at improving survival rates and reducing tumor burden in patients diagnosed with CRLM. The resection is performed to treat liver metastasis, and clinical outcomes, such as progression-free survival (PFS) and overall survival (OS), are assessed post-surgery to determine treatment efficacy.

Timeline

• Start date: 2023-05-22
• Primary completion: 2024-03-06
• Completion: 2024-03-06
• First posted: 2025-04-20
• Last updated: 2025-04-20

Locations

1 site across 1 country: China