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

Sun Yat-sen University

Status: Completed

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

Treatments: Procedure: CRLM surgery

Study type: Observational

Funder types: Other

Identifiers

NCT06936098

2023ZSLYEC-256

Details and patient eligibility

About

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.

Enrollment: 431 patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.

Trial design

431 participants in 3 patient groups

Surgical pathology slides from the SAHSYSU, 1,994 WSIs from 297 slides dated July 3, 2013.

Description:

This group includes 297 patients with colorectal cancer liver metastasis (CRLM), from which 1,994 whole slide images (WSIs) were collected. These slides were used for developing and testing the COFFEE AI model for histopathological growth pattern (HGP) classification, providing valuable insights for tumor characterization and prognosis.

Treatment:

Procedure: CRLM surgery

Surgical pathology slides from the SAHSYSU , 972 WSIs from 104 patients dated April 21, 2023.

Description:

This cohort contains 104 patients diagnosed with CRLM. 972 WSIs were collected to validate the COFFEE model on a more recent dataset, evaluating the model's performance in both binary and four-class HGP classifications.

Treatment:

Procedure: CRLM surgery

Surgical pathology slides from the SAHSYSU, 114 WSIs from 30 patients dated 2024.

Description:

This prospective cohort consists of 30 patients with CRLM, from which 114 WSIs were obtained in 2024. The cohort was used to assess the clinical applicability of the COFFEE AI model through a prospective trial, comparing the diagnostic performance of pathologists with and without AI assistance.

Treatment:

Procedure: CRLM surgery