Colorectal Liver Metastasis (CRLM)NCT06936098
Deep Learning-Based Analysis of Colorectal Cancer Pathology Images: An Innovative Approach for Predicting Colorectal Cancer Subtypes
An observational study of Colorectal Liver Metastasis (CRLM) and Histopathological Growth Patterns (HGPs), run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Colorectal Liver Metastasis (CRLM), Histopathological Growth Patterns (HGPs), Artificial Intelligence (AI) in Diagnosis
- What is being tested?
- CRLM surgery
- Type of treatment
- Surgery or procedure
- How many people
- 431 participants
- Where
- 1 siteChina
- Study dates
- 22 May 2023to 6 Mar 2024
- How long will I take part?
- 10 Years
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Tissue sections obtained from biopsy specimens;
- Absence of viable tumor tissue in metastatic lesions;
- Lesions previously treated with ablation followed by surgical resection, resulting in inadequate tissue slide quality.
Trial locations and contacts
1 site in 1 country.
China1 site
Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510655
About this study
Official title: 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.
- Conditions
- Colorectal Liver Metastasis (CRLM), Histopathological Growth Patterns (HGPs), Artificial Intelligence (AI) in Diagnosis, Vision Transformer (ViT), Desmoplastic Classification
- Being tested
- CRLM surgery
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CRLM surgeryProcedure or 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Procedure: CRLM surgery | |
| 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. Receives: Procedure: CRLM surgery | |
| 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. Receives: Procedure: CRLM surgery |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification Accuracy (%) of the COFFEE AI Model in Binary Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images
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.
Measured: 6 months post-surgery (for prospective cohort)
Secondary outcomes (1)
Classification Accuracy (%) of the COFFEE AI Model in Multi-Class Identification of Histopathological Growth Patterns (HGPs) in CRLM Using Whole Slide Images
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.
Measured: 6 months post-surgery (for prospective cohort)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Apr 2025
- Study start22 May 2023
- Primary completion6 Mar 2024
- Study completion6 Mar 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal Liver Metastasis (CRLM)Studies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 431 participants (actual)
- Target duration
- 10 Years
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06936098
- Sponsor's protocol ID
- 2023ZSLYEC-256
- Status last verified
- Apr 2025
- First submitted
- 3 Apr 2025
- Last updated
- 20 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Powered Copilots for Precision Diagnosis and Surgical Assessment of Histological Growth Patterns in Resectable Colorectal Liver Metastases: A Prospective Study
- ClinicalTrials.gov ID
- NCT06936098
- Acronym / sponsor ID
- 2023ZSLYEC-256
- Conditions
- Colorectal Liver Metastasis (CRLM), Histopathological Growth Patterns (HGPs), Artificial Intelligence (AI) in Diagnosis, Vision Transformer (ViT), Desmoplastic Classification
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 431 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 22 May 2023 (actual)
- Primary completion
- 6 Mar 2024 (actual)
- Study completion
- 6 Mar 2024 (actual)
- First posted
- 20 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 10 Years
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Surgical pathology slides from the SAHSYSU, 1,994 WSIs from 297 slides dated July 3, 2013. | – | Procedure: CRLM surgeryThis 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. |
| Surgical pathology slides from the SAHSYSU , 972 WSIs from 104 patients dated April 21, 2023. | – | Procedure: CRLM surgeryThis 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. |
| Surgical pathology slides from the SAHSYSU, 114 WSIs from 30 patients dated 2024. | – | Procedure: CRLM surgeryThis 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. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- Tissue sections obtained from biopsy specimens;
- Absence of viable tumor tissue in metastatic lesions;
- Lesions previously treated with ablation followed by surgical resection, resulting in inadequate tissue slide quality.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CRLM surgerySurgical 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. | Procedure or surgery | Surgical pathology slides from the SAHSYSU , 972 WSIs from 104 patients dated April 21, 2023., Surgical pathology slides from the SAHSYSU, 1,994 WSIs from 297 slides dated July 3, 2013., Surgical pathology slides from the SAHSYSU, 114 WSIs from 30 patients dated 2024. |
Primary outcome
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.
1 secondary outcome
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)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Apr 2025.
- Individual participant dataSharing planned: Undecided
Related trials for Colorectal Liver Metastasis (CRLM)
RecruitingPhase 2
Circulating Tumor DNA Methylation-Guided Adjuvant Therapy Decision-Making in Patients With Resectable Colorectal Cancer Liver Metastases After Resection
Colorectal Cancer Liver Metastases (CRLM), ctDNA
Not yet recruitingPhase 2
HRX215, A First Generation MKK4 Inhibitor Drug, for the Treatment of Patients With Colorectal Liver Metastasis After Undergoing a Portal Vein Embolization
Metastatic Colorectal Carcinoma, Metastatic Malignant Neoplasm in the Liver
RecruitingPhase 2
PULSAR Combined With PD-1 Ab and Chemotherapy Plus Bev. for CRLM
Colorectal Cancer Liver Metastasis
Active, not recruitingObservational study
The Use of Artificial Intelligence for the Prediction of Recurrence After Resection of Colorectal Liver Metastases
Colorectal Liver Metastasis (CRLM), Liver Resection
Not yet recruitingPhase 2
A Single-Arm, Phase II Study of Retlirafusp Alfa in Combination With XELOX and Bevacizumab for the First-Line Treatment of Advanced RAS-Mutated Colorectal Cancer With Liver Metastases (CRLM)
Advanced RAS-Mutated Colorectal Cancer With Liver Metastases (CRLM)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Liver Metastasis (CRLM) and Histopathological Growth Patterns (HGPs), run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Apr 2025.