Colorectal Cancer Liver Metastases (CRLM)NCT07399236
AI-Based Prediction of Liver Metastasis in Colorectal Cancer (A Retrospective Study)
An observational study of Colorectal Cancer Liver Metastases (CRLM), run by Tongji Hospital at 1 site in China.
Trial summary
This multicenter, retrospective study aims to develop and validate a multimodal deep learning model for predicting the risk of metachronous liver metastasis in patients with stage I-III colorectal cancer following curative resection. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Colorectal Cancer Liver Metastases (CRLM)
- What is being tested?
- Multimodal Deep Learning Model Analysis
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2015to 30 Jan 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: Adult patients (aged 18-75) with stage I-III primary colorectal cancer who underwent curative resection at participating medical centers between 2015 and 2025, and for whom complete preoperative imaging, postoperative pathological data, and follow-up records are available for retrospective analysis.
Who may be able to join
- Age 18-75 years, any gender.
- Histologically confirmed primary colon or rectal adenocarcinoma.
- Underwent curative radical resection (R0 resection) for colorectal cancer.
- Preoperative contrast-enhanced abdominal/pelvic CT scan performed within 1 month before surgery, with acceptable image quality.
- No evidence of distant metastasis (including synchronous liver metastasis) on preoperative or intraoperative exploration.
Who cannot join
- History of other malignant tumors.
- Previous history of liver surgery or liver transplantation.
- Missing clinical, imaging, or pathological data required for the study.
- Death within the perioperative period (within 30 days after surgery).
- Lack of regular follow-up information.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Tongji Hospital
Wuhan, Hubei
Yang WU, M.D., 13636076910, 255001907@qq.com
About this study
Official title: A Multicenter, Retrospective, Observational Study to Develop and Validate a Multimodal Deep Learning Model for Predicting Metachronous Liver Metastasis in Colorectal Cancer Patients After Curative Resection
This multicenter, retrospective study aims to develop and validate a multimodal deep learning model for predicting the risk of metachronous liver metastasis in patients with stage I-III colorectal cancer following curative resection. The model will integrate preoperative contrast-enhanced CT imaging, digitized histopathological whole-slide images, and standard clinical-pathological data.
The primary objective is to assess the model's discriminatory performance, measured by the area under the receiver operating characteristic curve (AUC), and to compare its predictive accuracy against traditional prognostic factors such as TNM staging and serum carcinoembryonic antigen levels. This research utilizes existing archival data; no direct patient contact or intervention is involved. The ultimate goal is to provide a robust, data-driven tool for improved risk stratification, which could potentially guide personalized surveillance strategies and adjuvant therapy decisions in the future.
- Being tested
- Multimodal Deep Learning Model Analysis
- Keywords
- Colorectal cancer liver metastases (CRLM), deep learning, multimodal, predictive model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal Deep Learning Model AnalysisOther
This is a non-interventional study. The primary study procedure is the application of a multimodal deep learning model to retrospectively analyze existing clinical data (contrast-enhanced CT images, digitized pathology slides, and structured clinical variables) for the purpose of predicting the risk of metachronous liver metastasis. No therapeutic or diagnostic interventions are administered to participants as part of this research protocol.
| Study group | Role | What they receive |
|---|---|---|
| Colorectal Cancer Resection Cohort | A retrospective cohort of adult patients (aged 18-75) with stage I-III primary colorectal adenocarcinoma who underwent curative (R0) resection. This cohort is defined for the purpose of developing and validating a multimodal deep learning model to predict the risk of metachronous liver metastasis. All data, including preoperative contrast-enhanced CT scans, postoperative digitized pathology slides, and clinical records, were collected retrospectively from routine clinical practice. No interventions were administered as part of this study. Receives: Other: Multimodal Deep Learning Model Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUC)
The discriminatory performance of the multimodal deep learning model for predicting the 3-year risk of metachronous liver metastasis. The model integrates preoperative contrast-enhanced CT images, digitized whole-slide pathology images, and clinical data. The AUC will be calculated on the held-out independent test set. The assessment is based on data collected from the date of curative surgery (baseline) to the date of first imaging-confirmed liver metastasis or last follow-up.
Measured: up to 3 years
Secondary outcomes (1)
Liver Metastasis-Free Survival (LMFS) by Risk Group
The difference in liver metastasis-free survival between the high-risk and low-risk groups as stratified by the multimodal model. LMFS is defined as the time from surgery to the first radiological diagnosis of liver metastasis. From the date of surgery until the date of first documented liver metastasis or last follow-up.
Measured: up to 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Feb 2026
- Study start1 Jan 2015
- Primary completion30 Jan 2026
- Study completion30 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal Cancer Liver Metastases (CRLM)Studies of the same condition, open and under way.
- More trials run by Tongji HospitalThe 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tongji Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wan-Guang Zhang, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07399236
- Sponsor's protocol ID
- TJ-IRB202512239
- Status last verified
- Jan 2026
- First submitted
- 30 Jan 2026
- Last updated
- 10 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter, Retrospective, Observational Study to Develop and Validate a Multimodal Deep Learning Model for Predicting Metachronous Liver Metastasis in Colorectal Cancer Patients After Curative Resection
- ClinicalTrials.gov ID
- NCT07399236
- Acronym / sponsor ID
- TJ-IRB202512239
- Keywords
- Colorectal cancer liver metastases (CRLM), deep learning, multimodal, predictive model
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Tongji HospitalUniversity, hospital or other
- Study start
- 1 Jan 2015 (actual)
- Primary completion
- 30 Jan 2026 (actual)
- Study completion
- 30 Jan 2026 (estimated)
- First posted
- 10 Feb 2026 (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.
This multicenter, retrospective study aims to develop and validate a multimodal deep learning model for predicting the risk of metachronous liver metastasis in patients with stage I-III colorectal cancer following curative resection. The model will integrate preoperative contrast-enhanced CT imaging, digitized histopathological whole-slide images, and standard clinical-pathological data.
The primary objective is to assess the model's discriminatory performance, measured by the area under the receiver operating characteristic curve (AUC), and to compare its predictive accuracy against traditional prognostic factors such as TNM staging and serum carcinoembryonic antigen levels. This research utilizes existing archival data; no direct patient contact or intervention is involved. The ultimate goal is to provide a robust, data-driven tool for improved risk stratification, which could potentially guide personalized surveillance strategies and adjuvant therapy decisions in the future.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Colorectal Cancer Resection Cohort | – | Other: Multimodal Deep Learning Model AnalysisA retrospective cohort of adult patients (aged 18-75) with stage I-III primary colorectal adenocarcinoma who underwent curative (R0) resection. This cohort is defined for the purpose of developing and validating a multimodal deep learning model to predict the risk of metachronous liver metastasis. All data, including preoperative contrast-enhanced CT scans, postoperative digitized pathology slides, and clinical records, were collected retrospectively from routine clinical practice. No interventions were administered as part of this study. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients (aged 18-75) with stage I-III primary colorectal cancer who underwent curative resection at participating medical centers between 2015 and 2025, and for whom complete preoperative imaging, postoperative pathological data, and follow-up records are available for retrospective analysis.
Key inclusion criteria (5 listed)
- Age 18-75 years, any gender.
- Histologically confirmed primary colon or rectal adenocarcinoma.
- Underwent curative radical resection (R0 resection) for colorectal cancer.
- Preoperative contrast-enhanced abdominal/pelvic CT scan performed within 1 month before surgery, with acceptable image quality.
- No evidence of distant metastasis (including synchronous liver metastasis) on preoperative or intraoperative exploration.
Key exclusion criteria (5 listed)
- History of other malignant tumors.
- Previous history of liver surgery or liver transplantation.
- Missing clinical, imaging, or pathological data required for the study.
- Death within the perioperative period (within 30 days after surgery).
- Lack of regular follow-up information.
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 |
|---|---|---|
| Multimodal Deep Learning Model AnalysisThis is a non-interventional study. The primary study procedure is the application of a multimodal deep learning model to retrospectively analyze existing clinical data (contrast-enhanced CT images, digitized pathology slides, and structured clinical variables) for the purpose of predicting the risk of metachronous liver metastasis. No therapeutic or diagnostic interventions are administered to participants as part of this research protocol. | Other | Colorectal Cancer Resection Cohort |
Primary outcome
Area Under the Receiver Operating Characteristic Curve (AUC)
Time frame: up to 3 years
The discriminatory performance of the multimodal deep learning model for predicting the 3-year risk of metachronous liver metastasis. The model integrates preoperative contrast-enhanced CT images, digitized whole-slide pathology images, and clinical data. The AUC will be calculated on the held-out independent test set. The assessment is based on data collected from the date of curative surgery (baseline) to the date of first imaging-confirmed liver metastasis or last follow-up.
1 secondary outcome
Liver Metastasis-Free Survival (LMFS) by Risk Group
Time frame: up to 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Feb 2026.
- Individual participant dataSharing planned: No
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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.
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 Cancer Liver Metastases (CRLM), run by Tongji Hospital 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 5 inclusion and 5 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: looking for participants now, as of the sponsor's last update on 10 Feb 2026. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yang wu, M.D. (13636076910, 255001907@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUC) (time frame: up to 3 years). 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 10 Feb 2026.