Hepatocellular CarcinomaGENDER-HCC-METNCT07386639
Sex-Specific Machine Learning Models to Predict Distant Metastasis in Liver Cancer
An observational study of Hepatocellular Carcinoma and Liver Cancer, run by Tongji University.
Trial summary
This study looked at whether male and female patients with liver cancer (hepatocellular carcinoma, HCC) have different risks of the cancer spreading to distant parts of the body (distant metastasis). Liver cancer is much more common in men than in women, and women often have better survival rates. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Hepatocellular Carcinoma, Liver Cancer, Neoplasm Metastasis
- How many people
- 19,019 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2004to Dec 2025
- 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 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study is a retrospective analysis of a pre-existing, de-identified population-based cancer registry (SEER). The study population consists of all adult patients meeting the above eligibility criteria who were diagnosed with hepatocellular carcinoma (HCC) within the specified period. Participants were not prospectively recruited for this study.
Who may be able to join
- Pathologically confirmed diagnosis of Hepatocellular Carcinoma (HCC).
- Diagnosis year between 2004 and 2022, inclusive.
- Case identified within the 22 registries of the Surveillance, Epidemiology, and End Results (SEER) database.
Who cannot join
- Missing information on race, marital status, tumor grade, or surgical status.
- Non-first primary malignancy or presence of multiple primary tumors.
- Incomplete TNM staging data.
About this study
Official title: Gender-Specific Prediction Models for Hepatocellular Carcinoma Metastasis: A Machine Learning-Based Retrospective Cohort Study
This study looked at whether male and female patients with liver cancer (hepatocellular carcinoma, HCC) have different risks of the cancer spreading to distant parts of the body (distant metastasis). Liver cancer is much more common in men than in women, and women often have better survival rates. However, it was unclear if the factors that predict this spread are the same for both sexes.
To answer this question, researchers analyzed information from a large, national cancer database (SEER) from 2004 to 2022, including 19,019 patients diagnosed with liver cancer. They studied factors like age, race, tumor stage, treatment received, and where patients lived. The team used advanced computer models (machine learning) to build separate prediction tools for men and women to estimate their risk of distant metastasis at the time of diagnosis.
Read the detailed description
Study Design and Objective:
This was a retrospective, population-based cohort study utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database. The primary objective was to systematically compare the incidence and identify sex-specific determinants of distant metastasis in patients with hepatocellular carcinoma (HCC). A secondary objective was to develop and validate separate, high-performance machine learning (ML) prediction models for distant metastasis risk tailored to male and female patients.
Data Source and Participants:
Data were extracted from 22 SEER registries covering patients diagnosed with HCC between 2004 and 2022. Inclusion required a pathological diagnosis of HCC. Key exclusion criteria were: missing data on race, marital status, tumor grade, or surgical status; non-first primary malignancy; and incomplete TNM staging data. After applying criteria, 19,019 patients were included in the final analysis (14,575 males, 4,444 females).
Variables and Definitions:
The outcome variable was distant metastasis status at diagnosis, dichotomized as M0 (no metastasis) or M1 (metastasis) based on consistent AJCC criteria. Predictor variables included: age, sex, race, tumor grade, marital status, surgical treatment (categorized as non-surgery, local therapy, surgical resection, or liver transplantation), radiotherapy, chemotherapy, annual household income, and residential location (based on population size). To ensure comparability across different editions of the AJCC staging manual, T stage was grouped as T0-2 (localized) vs. T3-4 (locally advanced), and N stage as N0 vs. N1.
Statistical and Machine Learning Analysis:
Univariate and multivariable logistic regression analyses were performed to identify factors independently associated with distant metastasis, stratified by sex.
For predictive modeling, the dataset was randomly split into a training set (80%) and an internal testing set (20%). Eight machine learning algorithms were developed and compared: Logistic Regression, Random Forest, XGBoost, LightGBM, AdaBoost, Decision Tree, Gradient Boosting Decision Tree (GBDT), and Multilayer Perceptron. Model hyperparameters were optimized using 10-fold cross-validation on the training set. The final models were evaluated on the independent testing set. Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration curves, and Decision Curve Analysis (DCA). The interpretability of the best-performing model was enhanced using Shapley Additive Explanations (SHAP).
- Conditions
- Hepatocellular Carcinoma, Liver Cancer, Neoplasm Metastasis
- Keywords
- Machine Learning, Prediction Model, Sex Factors, SEER Program, Retrospective Cohort Study
- Medical subject headings
- Carcinoma, Hepatocellular, Liver Neoplasms, Neoplasm Metastasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| MALE and Female HCC Patients | A cohort of male patients (n=14,575) diagnosed with hepatocellular carcinoma (HCC) between 2004 and 2022, identified from the SEER database. This group was analyzed separately to identify sex-specific determinants and build a prediction model for distant metastasis.；A cohort of female patients (n=4,444) diagnosed with hepatocellular carcinoma (HCC) between 2004 and 2022, identified from the SEER database. This group was analyzed separately to identify sex-specific determinants and build a prediction model for distant metastasis. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Presence of Distant Metastasis at Diagnosis
The area under the receiver operating characteristic curve (AUC) of the best-performing machine learning model for predicting distant metastasis in the male cohort, evaluated on the internal testing set.
Measured: At completion of data analysis (2024).
Secondary outcomes (1)
Presence of distant metastasis at initial diagnosis
The primary outcome is the occurrence of distant metastasis (coded as AJCC M1 stage) at the time of initial hepatocellular carcinoma (HCC) diagnosis, as recorded in the Surveillance, Epidemiology, and End Results (SEER) database.
Measured: At diagnosis (Data from SEER registries covering years 2004-2022).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Feb 2026
- Study start1 Jan 2004
- Primary completionJan 2023
- Study completionDec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hepatocellular CarcinomaStudies of the same condition, open and under way.
- 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
- 19,019 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tongji University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Linlin Liu, Tongji University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07386639
- Sponsor's protocol ID
- SEER-HCC-DM-Gender-2024
- Other IDs
- 82504454
- Acronym
- GENDER-HCC-MET
- Status last verified
- Jan 2026
- First submitted
- 23 Jan 2026
- Last updated
- 4 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 4 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Gender-Specific Prediction Models for Hepatocellular Carcinoma Metastasis: A Machine Learning-Based Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT07386639
- Acronym / sponsor ID
- GENDER-HCC-MET / SEER-HCC-DM-Gender-2024
- Conditions
- Hepatocellular Carcinoma, Liver Cancer, Neoplasm Metastasis
- Keywords
- Machine Learning, Prediction Model, Sex Factors, SEER Program, Retrospective Cohort Study
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 19,019 participants (actual)
- Lead sponsor
- Tongji UniversityUniversity, hospital or other
- Study start
- 1 Jan 2004
- Primary completion
- Jan 2023 (actual)
- Study completion
- Dec 2025 (actual)
- First posted
- 4 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 study looked at whether male and female patients with liver cancer (hepatocellular carcinoma, HCC) have different risks of the cancer spreading to distant parts of the body (distant metastasis). Liver cancer is much more common in men than in women, and women often have better survival rates. However, it was unclear if the factors that predict this spread are the same for both sexes.
To answer this question, researchers analyzed information from a large, national cancer database (SEER) from 2004 to 2022, including 19,019 patients diagnosed with liver cancer. They studied factors like age, race, tumor stage, treatment received, and where patients lived. The team used advanced computer models (machine learning) to build separate prediction tools for men and women to estimate their risk of distant metastasis at the time of diagnosis.
Detailed description
Study Design and Objective:
This was a retrospective, population-based cohort study utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database. The primary objective was to systematically compare the incidence and identify sex-specific determinants of distant metastasis in patients with hepatocellular carcinoma (HCC). A secondary objective was to develop and validate separate, high-performance machine learning (ML) prediction models for distant metastasis risk tailored to male and female patients.
Data Source and Participants:
Data were extracted from 22 SEER registries covering patients diagnosed with HCC between 2004 and 2022. Inclusion required a pathological diagnosis of HCC. Key exclusion criteria were: missing data on race, marital status, tumor grade, or surgical status; non-first primary malignancy; and incomplete TNM staging data. After applying criteria, 19,019 patients were included in the final analysis (14,575 males, 4,444 females).
Variables and Definitions:
The outcome variable was distant metastasis status at diagnosis, dichotomized as M0 (no metastasis) or M1 (metastasis) based on consistent AJCC criteria. Predictor variables included: age, sex, race, tumor grade, marital status, surgical treatment (categorized as non-surgery, local therapy, surgical resection, or liver transplantation), radiotherapy, chemotherapy, annual household income, and residential location (based on population size). To ensure comparability across different editions of the AJCC staging manual, T stage was grouped as T0-2 (localized) vs. T3-4 (locally advanced), and N stage as N0 vs. N1.
Statistical and Machine Learning Analysis:
Univariate and multivariable logistic regression analyses were performed to identify factors independently associated with distant metastasis, stratified by sex.
For predictive modeling, the dataset was randomly split into a training set (80%) and an internal testing set (20%). Eight machine learning algorithms were developed and compared: Logistic Regression, Random Forest, XGBoost, LightGBM, AdaBoost, Decision Tree, Gradient Boosting Decision Tree (GBDT), and Multilayer Perceptron. Model hyperparameters were optimized using 10-fold cross-validation on the training set. The final models were evaluated on the independent testing set. Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration curves, and Decision Curve Analysis (DCA). The interpretability of the best-performing model was enhanced using Shapley Additive Explanations (SHAP).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| MALE and Female HCC Patients | – | –A cohort of male patients (n=14,575) diagnosed with hepatocellular carcinoma (HCC) between 2004 and 2022, identified from the SEER database. This group was analyzed separately to identify sex-specific determinants and build a prediction model for distant metastasis.；A cohort of female patients (n=4,444) diagnosed with hepatocellular carcinoma (HCC) between 2004 and 2022, identified from the SEER database. This group was analyzed separately to identify sex-specific determinants and build a prediction model for distant metastasis. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study is a retrospective analysis of a pre-existing, de-identified population-based cancer registry (SEER). The study population consists of all adult patients meeting the above eligibility criteria who were diagnosed with hepatocellular carcinoma (HCC) within the specified period. Participants were not prospectively recruited for this study.
Key inclusion criteria (3 listed)
- Pathologically confirmed diagnosis of Hepatocellular Carcinoma (HCC).
- Diagnosis year between 2004 and 2022, inclusive.
- Case identified within the 22 registries of the Surveillance, Epidemiology, and End Results (SEER) database.
Key exclusion criteria (3 listed)
- Missing information on race, marital status, tumor grade, or surgical status.
- Non-first primary malignancy or presence of multiple primary tumors.
- Incomplete TNM staging data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Presence of Distant Metastasis at Diagnosis
Time frame: At completion of data analysis (2024).
The area under the receiver operating characteristic curve (AUC) of the best-performing machine learning model for predicting distant metastasis in the male cohort, evaluated on the internal testing set.
1 secondary outcome
Presence of distant metastasis at initial diagnosis
Time frame: At diagnosis (Data from SEER registries covering years 2004-2022).
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 4 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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Hepatocellular Carcinoma and Liver Cancer, run by Tongji University.
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 4 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
Presence of Distant Metastasis at Diagnosis (time frame: At completion of data analysis (2024).). 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 4 Feb 2026.