Artificial Intelligence (AI) in Diagnosis, Colorectal Liver Metastasis (CRLM), Desmoplastic Classification, Histopathological Growth Patterns (HGPs), Vision Transformer (ViT)
Conditions
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.
Interventions
PROCEDURECRLM 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.
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Classification Accuracy (%) of the COFFEE AI Model in Binary Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images | 6 months post-surgery (for prospective cohort) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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) | 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 four-class classification (desmoplastic, replacement, pushing, and mixed). 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. |
Other
| Measure | Time frame | Description |
|---|---|---|
| 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 | This outcome evaluates the association between AI-based HGP classification (desmoplastic and non-desmoplastic) and progression-free survival (PFS) in patients with colorectal cancer liver metastasis (CRLM) following curative-intent resection. PFS is defined as the time from surgery to disease progression or death from any cause. Kaplan-Meier analysis will be used to estimate PFS for each HGP group, with comparisons by log-rank test. Multivariate Cox regression models will assess the prognostic value of HGPs, adjusting for clinical covariates (e.g., age, sex, metastasis number/size, chemotherapy, margin status, tumor burden score). Hazard ratios with 95% confidence intervals will be reported. Model assumptions will be tested and adjusted if necessary. |
| 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 | This outcome evaluates the association between AI-based HGP classification (desmoplastic and non-desmoplastic) and overall survival (OS) in patients with colorectal cancer liver metastasis (CRLM) following curative-intent resection. OS is defined as the time from surgery to death from any cause. Kaplan-Meier analysis will be used to estimate OS for each HGP group, with comparisons by log-rank test. Multivariate Cox regression models will assess the prognostic value of HGPs, adjusting for clinical covariates (e.g., age, sex, metastasis number/size, chemotherapy, margin status, tumor burden score). Hazard ratios with 95% confidence intervals will be reported. Model assumptions will be tested and adjusted if necessary. |
| 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) | This outcome assesses the impact of the AI-assisted COFFEE model on diagnostic efficiency by comparing the time required by pathologists to classify histopathological growth patterns (HGPs) of colorectal cancer liver metastasis (CRLM), with and without COFFEE assistance. The metric is the time (minutes) from slide review start to final diagnosis, measured for each pathologist using a standardized digital whole slide image platform. The comparison includes two arms: the AI-assisted diagnosis arm, where junior pathologists use COFFEE as a decision-support tool, and the conventional diagnosis arm, where pathologists perform manual classification based on visual histopathological assessment. All participants review the same set of slides in randomized order, and diagnostic time is logged by the viewing software. Descriptive statistics (median, IQR) will be reported. |
| Diagnostic Accuracy (percentage of correct classifications) of Pathologists With and Without AI-Assisted COFFEE Model in CRLM HGP Classification | During the prospective trial period (6 months) | This outcome evaluates the diagnostic accuracy of pathologists in classifying histopathological growth patterns (HGPs) of colorectal cancer liver metastasis (CRLM), comparing AI-assisted versus conventional diagnostic workflows. Accuracy is defined as the proportion of correctly classified whole slide images (WSIs) relative to a gold-standard consensus diagnosis by expert gastrointestinal pathologists. Each pathologist will independently classify the same set of CRLM WSIs under two conditions: with AI assistance (COFFEE model) and without AI assistance (manual assessment). Classification will be evaluated for both binary HGP categories (desmoplastic vs. non-desmoplastic) and four-class HGP categories (desmoplastic, replacement, pushing, mixed). Accuracy will be calculated as: Accuracy = Total number of predictions / Number of correct predictions×100%. |
Countries
China
Outcome results
None listed