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

NCT06936098 | Completed

• 1 other identifier: 2023ZSLYEC-256
• Study Type: observational
• Target: 431
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Outcome Measures

Primary Outcomes (1)

• 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.

  6 months post-surgery (for prospective cohort)

Secondary Outcomes (1)

• 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)

Other Outcomes (4)

• Progression-Free Survival (PFS, in months) in Colorectal Cancer Liver Metastasis (CRLM) Patients Stratified by AI-based Histopathological Growth Pattern (HGP) Classification

  Up to 3 years post-surgery

• Overall Survival (OS, in months) in Colorectal Cancer Liver Metastasis (CRLM) Patients Stratified by AI-based Histopathological Growth Pattern (HGP) Classification

  Up to 3 years post-surgery

• Time to Diagnosis (in minutes) by Pathologists With and Without AI-Assisted COFFEE Model in CRLM HGP Classification

  During the prospective trial period (6 months)

Study Arms (3)

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

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.

Procedure: CRLM surgery

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

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.

Procedure: CRLM surgery

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

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.

Procedure: CRLM surgery

Interventions

CRLM surgery PROCEDURE

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.

Surgical pathology slides from the SAHSYSU , 972 WSIs from 104 patients dated April 21, 2023.; Surgical pathology slides from the SAHSYSU, 1,994 WSIs from 297 slides dated July 3, 2013.; Surgical pathology slides from the SAHSYSU, 114 WSIs from 30 patients dated 2024.

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study involved 431 patients with colorectal cancer liver metastasis, all undergoing surgery at the Sixth Affiliated Hospital of Sun Yat-sen University. The cohort consisted of 297 patients in the training set and 104 patients in the testing set.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510655, China

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

This study recruited 431 colorectal cancer liver metastasis patients who underwent surgery at the Sixth Affiliated Hospital of Sun Yat-sen University (SAHSYSU), with liver metastasis samples from the hospital's archives. The training dataset included 1,994 whole slide images (WSIs) from 297 patients (batch dated July 3, 2013), and the testing dataset contained 972 WSIs from 104 patients (batch dated April 21, 2023). In 2024, two prospective experiments were conducted with 114 WSIs from 30 patients. One experiment involved a human-AI competition, where nine pathologists and AI independently interpreted WSIs for binary and quaternary classifications. The other assessed AI-assisted classification, where nine pathologists used AI support for the same tasks. Both experiments aimed to evaluate the model's performance and clinical applicability.

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 10 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Attending Physician

Study Record Dates

• First Submitted: April 3, 2025
• First Posted: April 20, 2025
• Study Start: May 22, 2023
• Primary Completion: March 6, 2024
• Study Completion: March 6, 2024
• Last Updated: April 20, 2025

Record last verified: 2025-04

Locations

CN (1)