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

April 13, 2025 updated by: Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

AI-Powered Copilots for Precision Diagnosis and Surgical Assessment of Histological Growth Patterns in Resectable Colorectal Liver Metastases: A Prospective Study

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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Completed

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Actual)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
431

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510655
        • Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.
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 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.
Procedure: 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.
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
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.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Classification Accuracy (%) of the COFFEE AI Model in Binary Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images
Time Frame: 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.
6 months post-surgery (for prospective cohort)

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Classification Accuracy (%) of the COFFEE AI Model in Multi-Class Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images
Time Frame: 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.
6 months post-surgery (for prospective cohort)

Other Outcome Measures

Other Outcome Measures

For clinical trials, a planned measurement described in the protocol that is used to determine the effect of an intervention/treatment on participants. For observational studies, a measurement or observation that is used to describe patterns of diseases or traits, or associations with exposures, risk factors, or treatment. Types of outcome measures include primary outcome measure and secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Progression-Free Survival (PFS, in months) in Colorectal Cancer Liver Metastasis (CRLM) Patients Stratified by AI-based Histopathological Growth Pattern (HGP) Classification
Time Frame: 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.
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
Time Frame: 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.
Up to 3 years post-surgery
Time to Diagnosis (in minutes) by Pathologists With and Without AI-Assisted COFFEE Model in CRLM HGP Classification
Time Frame: 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.
During the prospective trial period (6 months)
Diagnostic Accuracy (percentage of correct classifications) of Pathologists With and Without AI-Assisted COFFEE Model in CRLM HGP Classification
Time Frame: 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%.
During the prospective trial period (6 months)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
May 22, 2023

Primary Completion (Actual)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
March 6, 2024

Study Completion (Actual)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
March 6, 2024

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
April 3, 2025

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
April 13, 2025

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
April 20, 2025

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
April 20, 2025

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
April 13, 2025

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
April 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • 2023ZSLYEC-256

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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