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

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University431 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

431

Conditions

Colorectal Liver Metastasis (CRLM)Histopathological Growth Patterns (HGPs)Artificial Intelligence (AI) in Diagnosis

Interventions

CRLM surgeryPROCEDURE

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.

Eligibility

Sex: ALLMin age: 18 YearsMax age: 75 Years

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Classification Accuracy (%) of the COFFEE AI Model in Binary Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images

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.

Time frame: 6 months post-surgery (for prospective cohort)

Secondary Outcomes

Classification Accuracy (%) of the COFFEE AI Model in Multi-Class Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images

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.