NCT06936098Completed
Deep Learning-Based Analysis of Colorectal Cancer Pathology Images: An Innovative Approach for Predicting Colorectal Cancer Subtypes
AI-Powered Copilots for Precision Diagnosis and Surgical Assessment of Histological Growth Patterns in Resectable Colorectal Liver Metastases: A Prospective Study
Source: ClinicalTrials.gov · Last updated 20 Apr 2025
Brief summary (registry, in English)
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.
- Status (registry)
- Completed
- Study type
- Observational
- Enrollment
- 431 (actual)
- First submitted
- 3 Apr 2025
- Start date
- 22 May 2023
- Primary completion date
- 6 Mar 2024
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Diseases
- Colorectal cancer
- NCT number
- NCT06936098
What you commit to
- Study type: Observational
- Participants enrolled: 431
- From 22 May 2023 to the primary completion date, 6 Mar 2024
- 1 site in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- Lesions previously treated with ablation followed by surgical resection, resulting in inadequate tissue slide quality.
Other
- Tissue sections obtained from biopsy specimens;
- Absence of viable tumor tissue in metastatic lesions;
Who the study is looking for
- 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.
Age in the registry: 18 years to 75 years · Sex: All · Does not accept healthy volunteers
Sites (1)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University Coordinator of this site? Confirm its status | Guangzhou | China | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.