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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

431 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-05-22

Study Completion Date

2024-03-06

Brief Summary

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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.

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Detailed Description

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Conditions

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

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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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.

CRLM surgery

Intervention Type 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.

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.

CRLM surgery

Intervention Type 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, 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.

CRLM surgery

Intervention Type 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.

Interventions

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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.

Intervention Type PROCEDURE

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No