Predictive Cancer ModelNCT06140823
Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models
An observational study of Predictive Cancer Model, run by Beth Israel Deaconess Medical Center at 1 site in United States.
Trial summary
The goal of this prospective observational cohort study is to validate previously developed Hepatocellular Carcinoma (HCC) risk prediction algorithms, the Liver Risk Computation (LIRIC) models, which are based on electronic health records. Read more about this study
- Who is it for?
- Ages 40 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Predictive Cancer Model
- What is being tested?
- Liver Risk Computation Model (LIRIC), Liver Risk Computation Model (LIRIC)_cirrhosis and Liver Risk Computation Model (LIRIC)_no_cirrhosis
- How many people
- 6,000,000 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Apr 2023to 31 Mar 2027
- 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 40 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The cohort will be selected from 44 eligible HCOs comprised of community hospitals, outpatient clinics and academic medical centers from across the US.
Who may be able to join
- Male and females age ≥40 years from all US HCOs available on the platform
- at least at least 2 clinical encounters to the HCO, within the last year, before the study start date
Who cannot join
- Personal history of HCC or current HCC (ICD-9: 155.0; ICD-10: C22.0)
- Age below 40. The same dataset will be utilized for the non-cirrhosis validation, with exclusion of all cases with cirrhosis (ICD-9: 571.2, 571.5; ICD-10: K70, K70.3, K70.30, K70.31, K74, K74.0, K74.6, K74.60, K74.69). For the cirrhosis validation, the investigators will include only patients with the above cirrhosis codes.
Trial locations and contacts
1 site in 1 country.
United States1 site
Beth Israel Deaconess Medical Center
Boston, Massachusetts, 02115
About this study
Official title: Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models on Multicenter EHR Data
The goal of this prospective observational cohort study is to validate previously developed Hepatocellular Carcinoma (HCC) risk prediction algorithms, the Liver Risk Computation (LIRIC) models, which are based on electronic health records.
The main questions it aims to answer are:
Read more
- Will our retrospectively developed general population LIRIC models, developed on routine EHR data, perform similarly when prospectively validated, and reliably and accurately predict HCC in real-time?
- What is the average time from model deployment and risk prediction, to the date of HCC development and what is the stage of HCC at diagnosis?
The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.
Read the detailed description
The investigators will conduct a prospective observational cohort study, separately deploying three separate LIRIC models (the general population, cirrhosis, and no_cirrhosis models) on retrospective de-identified EHR data of 44 HCOs in the USA, using the TriNetX federated network platform. LIRIC will generate a risk score for each individual. All risk-stratified individuals will be prospectively, electronically followed for up to 3-years to assess the primary end-point of HCC development. At the end of this period, model discrimination will be assessed, using the following metrics: AUROC, sensitivity, specificity, PPV/NPV. Risk scores generated by the model will be divided into quantiles. For each quantile, the investigators will evaluate the following: number of individuals in each quantile, number of HCC cases, PPV, NNS, SIR. Model calibration will be used for assessing the accuracy of estimates, based on the estimated to observed number of events. The model will dynamically re-evaluate all individual data every 6 months, re-classifying individuals (as needed).
- Conditions
- Predictive Cancer Model
- Being tested
- Liver Risk Computation Model (LIRIC), Liver Risk Computation Model (LIRIC)_cirrhosis, Liver Risk Computation Model (LIRIC)_no_cirrhosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Liver Risk Computation Model (LIRIC)Other
A neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the general population
Liver Risk Computation Model (LIRIC)_cirrhosisOther
A neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the population with liver cirrhosis
Liver Risk Computation Model (LIRIC)_no_cirrhosisOther
neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the population without liver cirrhosis
| Study group | Role | What they receive |
|---|---|---|
| prospective general population cohort | Males and females age >= 40 years, without a personal history of HCC or current HCC and at least two clinical visits to their HCO, within the last year, before the study start date. Receives: Other: Liver Risk Computation Model (LIRIC) | |
| Prospective cirrhosis population cohort | Males and females age >= 40 years, with liver cirrhosis and without a personal history of HCC or current HCC, that have at least two clinical visits to their HCO, within the last year, before the study start date. Receives: Other: Liver Risk Computation Model (LIRIC)_cirrhosis | |
| Prospective no_cirrhosis population cohort | Males and females age >= 40 years, without a personal history of HCC or current HCC and without a diagnosis of liver cirrhosis, that have at least two clinical visits to their HCO, within the last year, before the study start date. Receives: Other: Liver Risk Computation Model (LIRIC)_no_cirrhosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified
To assess the discriminatory performance of LIRIC for prospective identification of high-risk individuals for HCC development. ROCs and AUROC numbers will be calculated for the whole population and groups stratified by age, sex, race, and geographical location.
Measured: 6 months from index date, at 1 year, 2 years and 3 years
Calibration of LIRIC for all groups stratified
To assess how well the risk prediction by LIRIC aligns with observed risk without recalibration. Calibration plots will be created for the whole population and groups stratified by age, sex, race, and geographical location.
Measured: 6 months from index date, at 1 year, 2 years and 3 years
Performance metrics for LIRIC model risk quantiles
To evaluate the sensitivity, specificity, number of individuals/number of HCC cases, PPV, NNS in each predicted risk quantile for multiple risk thresholds
Measured: 6 months from index date, at 1 year, 2 years and 3 years
Secondary outcomes (2)
Timing of incident HCC occurrence
To evaluate how long in advance before HCC occurrence should be expected for LIRIC models to make high-risk predictions based on different thresholds for high-risk. Distribution plots of the date of HCC incidence for multiple risk thresholds will be created.
Measured: 6 months from index date, at 1 year, 2 years and 3 years
Tumor stage at HCC diagnosis
TNM staging at HCC diagnosis
Measured: 6 months from index date, at 1 year, 2 years and 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Nov 2023
- Study start1 Apr 2023
- Primary completion31 Mar 2027 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Predictive Cancer ModelStudies 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
- Prospective
- Enrolment
- 6,000,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Beth Israel Deaconess Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Massachusetts Institute of Technology, TriNetX, LLC
- Responsible party
- Principal investigator, Limor Appelbaum, Beth Israel Deaconess Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06140823
- Sponsor's protocol ID
- Nov2023Trial
- Status last verified
- Apr 2026
- First submitted
- 15 Nov 2023
- Last updated
- 22 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Validation of Liver Cancer Risk Computation (LIRIC) Models on Multicenter EHR Data
- ClinicalTrials.gov ID
- NCT06140823
- Acronym / sponsor ID
- Nov2023Trial
- Conditions
- Predictive Cancer Model
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 6,000,000 participants (actual)
- Lead sponsor
- Beth Israel Deaconess Medical CenterUniversity, hospital or other
- Collaborators
- Massachusetts Institute of Technology, TriNetX, LLC
- Study start
- 1 Apr 2023 (actual)
- Primary completion
- 31 Mar 2027 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 20 Nov 2023 (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.
The goal of this prospective observational cohort study is to validate previously developed Hepatocellular Carcinoma (HCC) risk prediction algorithms, the Liver Risk Computation (LIRIC) models, which are based on electronic health records.
The main questions it aims to answer are:
- Will our retrospectively developed general population LIRIC models, developed on routine EHR data, perform similarly when prospectively validated, and reliably and accurately predict HCC in real-time?
- What is the average time from model deployment and risk prediction, to the date of HCC development and what is the stage of HCC at diagnosis?
The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.
Detailed description
The investigators will conduct a prospective observational cohort study, separately deploying three separate LIRIC models (the general population, cirrhosis, and no_cirrhosis models) on retrospective de-identified EHR data of 44 HCOs in the USA, using the TriNetX federated network platform. LIRIC will generate a risk score for each individual. All risk-stratified individuals will be prospectively, electronically followed for up to 3-years to assess the primary end-point of HCC development. At the end of this period, model discrimination will be assessed, using the following metrics: AUROC, sensitivity, specificity, PPV/NPV. Risk scores generated by the model will be divided into quantiles. For each quantile, the investigators will evaluate the following: number of individuals in each quantile, number of HCC cases, PPV, NNS, SIR. Model calibration will be used for assessing the accuracy of estimates, based on the estimated to observed number of events. The model will dynamically re-evaluate all individual data every 6 months, re-classifying individuals (as needed).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| prospective general population cohort | – | Other: Liver Risk Computation Model (LIRIC)Males and females age >= 40 years, without a personal history of HCC or current HCC and at least two clinical visits to their HCO, within the last year, before the study start date. |
| Prospective cirrhosis population cohort | – | Other: Liver Risk Computation Model (LIRIC)_cirrhosisMales and females age >= 40 years, with liver cirrhosis and without a personal history of HCC or current HCC, that have at least two clinical visits to their HCO, within the last year, before the study start date. |
| Prospective no_cirrhosis population cohort | – | Other: Liver Risk Computation Model (LIRIC)_no_cirrhosisMales and females age >= 40 years, without a personal history of HCC or current HCC and without a diagnosis of liver cirrhosis, that have at least two clinical visits to their HCO, within the last year, before the study start date. |
Who may qualify
- Ages
- 40 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The cohort will be selected from 44 eligible HCOs comprised of community hospitals, outpatient clinics and academic medical centers from across the US.
Key inclusion criteria (2 listed)
- Male and females age ≥40 years from all US HCOs available on the platform
- at least at least 2 clinical encounters to the HCO, within the last year, before the study start date
Key exclusion criteria (2 listed)
- Personal history of HCC or current HCC (ICD-9: 155.0; ICD-10: C22.0)
- Age below 40. The same dataset will be utilized for the non-cirrhosis validation, with exclusion of all cases with cirrhosis (ICD-9: 571.2, 571.5; ICD-10: K70, K70.3, K70.30, K70.31, K74, K74.0, K74.6, K74.60, K74.69). For the cirrhosis validation, the investigators will include only patients with the above cirrhosis codes.
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 |
|---|---|---|
| Liver Risk Computation Model (LIRIC)A neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the general population | Other | prospective general population cohort |
| Liver Risk Computation Model (LIRIC)_cirrhosisA neural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the population with liver cirrhosis | Other | Prospective cirrhosis population cohort |
| Liver Risk Computation Model (LIRIC)_no_cirrhosisneural network model (LIRIC-NN) and a logistic regression model (LIRIC-LR) that use routinely collected EHR data to stratify individuals into HCC risk groups for the population without liver cirrhosis | Other | Prospective no_cirrhosis population cohort |
Primary outcome
Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified
Time frame: 6 months from index date, at 1 year, 2 years and 3 years
To assess the discriminatory performance of LIRIC for prospective identification of high-risk individuals for HCC development. ROCs and AUROC numbers will be calculated for the whole population and groups stratified by age, sex, race, and geographical location.
Calibration of LIRIC for all groups stratified
Time frame: 6 months from index date, at 1 year, 2 years and 3 years
To assess how well the risk prediction by LIRIC aligns with observed risk without recalibration. Calibration plots will be created for the whole population and groups stratified by age, sex, race, and geographical location.
Performance metrics for LIRIC model risk quantiles
Time frame: 6 months from index date, at 1 year, 2 years and 3 years
To evaluate the sensitivity, specificity, number of individuals/number of HCC cases, PPV, NNS in each predicted risk quantile for multiple risk thresholds
2 secondary outcomes
Timing of incident HCC occurrence
Time frame: 6 months from index date, at 1 year, 2 years and 3 years
Tumor stage at HCC diagnosis
Time frame: 6 months from index date, at 1 year, 2 years and 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 22 Apr 2026.
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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.
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 Predictive Cancer Model, run by Beth Israel Deaconess Medical Center at 1 site in United States.
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 2 inclusion and 2 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: under way, not looking for participants, as of the sponsor's last update on 22 Apr 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 United States. 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?
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?
Area under the receiver operating characteristic curve (AUROC) of LIRIC for all groups stratified (time frame: 6 months from index date, at 1 year, 2 years and 3 years). The study lists 3 primary outcomes in all. 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 22 Apr 2026.