Clinical trial NCT06936098: Deep Learning-Based Analysis of Colorectal Cancer Pathology Images: An Innovative Approach for Predicting Colorectal Cancer Subtypes
Official title: AI-Powered Copilots for Precision Diagnosis and Surgical Assessment of Histological Growth Patterns in Resectable Colorectal Liver Metastases: A Prospective Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colorectal Liver Metastasis (CRLM)
Histopathological Growth Patterns (HGPs)
Artificial Intelligence (AI) in Diagnosis
Vision Transformer (ViT)
Desmoplastic Classification
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
First posted: 2025-04-20
Enrollment: 431
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Classification Accuracy (%) of the COFFEE AI Model in Binary Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images
timeFrame: 6 months post-surgery (for prospective cohort)
Secondary outcomes:
measure: Classification Accuracy (%) of the COFFEE AI Model in Multi-Class Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images
timeFrame: 6 months post-surgery (for prospective cohort)
Other outcomes:
measure: Progression-Free Survival (PFS, in months) in Colorectal Cancer Liver Metastasis (CRLM) Patients Stratified by AI-based Histopathological Growth Pattern (HGP) Classification
timeFrame: Up to 3 years post-surgery
measure: Overall Survival (OS, in months) in Colorectal Cancer Liver Metastasis (CRLM) Patients Stratified by AI-based Histopathological Growth Pattern (HGP) Classification
timeFrame: Up to 3 years post-surgery
measure: Time to Diagnosis (in minutes) by Pathologists With and Without AI-Assisted COFFEE Model in CRLM HGP Classification
timeFrame: During the prospective trial period (6 months)
measure: Diagnostic Accuracy (percentage of correct classifications) of Pathologists With and Without AI-Assisted COFFEE Model in CRLM HGP Classification
timeFrame: During the prospective trial period (6 months)
Locations:
city: Guangzhou
country: China
facility: Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University
state: Guangdong
Study officials:
References: