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

Completed

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

• Registration Number: NCT06936098
• Lead Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-05-22
• Primary Completion: 2024-03-06
• Study Completion: 2024-03-06
• Status Verified: 2025-04
• Study First Submitted: 2025-04-03
• Study First Submitted QC: 2025-04-13
• Last Update Submitted: 2025-04-13
• Study First Posted: 2025-04-20
• Last Update Posted: 2025-04-20

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 431

Inclusion Criteria

1. Patients diagnosed with colorectal cancer liver metastasis (CRLM) undergoing surgical treatment;
2. The maximum diameter of resected metastatic lesions should be ≥ 2 cm;
3. Availability of pathology slides along with baseline clinical, biological, and pathological features.

Exclusion Criteria

1. Tissue sections obtained from biopsy specimens;
2. Absence of viable tumor tissue in metastatic lesions;
3. Lesions previously treated with ablation followed by surgical resection, resulting in inadequate tissue slide quality.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Classification Accuracy (%) of the COFFEE AI Model in Binary Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images	6 months post-surgery (for prospective cohort)	This outcome measures the diagnostic classification accuracy of the COFFEE AI model in detecting histopathological growth patterns (HGPs) in patients with colorectal cancer liver metastasis (CRLM). Accuracy is defined as the proportion of correctly predicted HGP labels compared to the ground truth labels determined by consensus of expert pathologists. The analysis includes binary classification (desmoplastic vs. non-desmoplastic). Accuracy will be calculated as: Accuracy = Total number of predictions / Number of correct predictions×100%. The outcome will be assessed using digital whole slide images obtained from liver metastasis specimens collected during surgery. Model performance will be evaluated 6 months post-surgery in a prospective validation cohort.

Secondary Outcome Measures

Name	Time	Method
Classification Accuracy (%) of the COFFEE AI Model in Multi-Class Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images	6 months post-surgery (for prospective cohort)	This outcome measures the diagnostic classification accuracy of the COFFEE AI model in detecting histopathological growth patterns (HGPs) in patients with colorectal cancer liver metastasis (CRLM). Accuracy is defined as the proportion of correctly predicted HGP labels compared to the ground truth labels determined by consensus of expert pathologists. The analysis includes four-class classification (desmoplastic, replacement, pushing, and mixed). Accuracy will be calculated as: Accuracy = Total number of predictions / Number of correct predictions×100%. The outcome will be assessed using digital whole slide images obtained from liver metastasis specimens collected during surgery. Model performance will be evaluated 6 months post-surgery in a prospective validation cohort.

Trial Locations

Locations (1)

Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China