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AI could spot liver cancer types and predict survival without needle biopsy

NCT ID NCT07658586

Diagnosis Sponsor: Guangxi Medical University Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing This study
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jul 02, 2026 · Updated 2 times

Summary

This study is developing an artificial intelligence system that combines CT/MRI scans, lab results, and radiology reports to help diagnose liver cancer and predict how patients will fare after surgery. The AI aims to distinguish benign from malignant liver lesions and differentiate between two common liver cancer types—hepatocellular carcinoma and intrahepatic cholangiocarcinoma—without needing a biopsy. It will also estimate how long patients might live without the cancer returning after tumor removal. The research involves at least 600 patients and uses past medical data to train and test the AI model.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
Artificial intelligence model
What this could lead to
If successful, this AI system could help doctors diagnose liver cancer subtypes more accurately without invasive biopsies and better predict patient outcomes after surgery.
What could go wrong
This is a retrospective study using existing data, not a prospective trial. The AI model may not perform as well in real-world settings or on diverse patient populations.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 600 people

The number the study aims to enrol. It can still change while the study runs.

Started

Dec 2025

Expected to finish

Dec 2028

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

(1) Key clinical, imaging, or pathological data severely missing or incomplete; (2) Preoperative CT or MRI images of poor quality or missing sequences, unable to perform reliable image analysis; (3) Prior local treatment for the target liver lesion, unless clearly recorded as neoadjuvant therapy before surgery; (4) Concurrent other malignant tumors; (5) Lost to follow-up or follow-up data cannot meet endpoint determination requirements.

Ages

18 to 80 years

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: -Diagnostic Model Cohort: * Age ≥18 years * Underwent preoperative contrast-enhanced CT or MRI for clinically suspected liver space-occupying lesion * Have complete preoperative clinical laboratory data * Have complete original CT/MRI imaging data and radiology reports * Have definite pathological diagnosis from surgery or biopsy as gold standard Prognostic Prediction Model Cohort (selected from diagnostic cohort): * Meet all diagnostic cohort inclusion criteria * Pathologically confirmed liver cancer * Underwent radical hepatectomy * Have complete preoperative multimodal data (CT/MRI imaging, clinical laboratory data, radiology reports) * Have complete postoperative follow-up data to determine progression-free survival and overall survival endpoints and time (minimum follow-up of 24 months) Exclusion Criteria: * · Key clinical, imaging, or pathological data severely missing or incomplete * Preoperative CT or MRI images of poor quality or missing sequences, unable to perform reliable image analysis * Prior local treatment for the target liver lesion, unless clearly recorded as neoadjuvant therapy before surgery * Concurrent other malignant tumors * Lost to follow-up or follow-up data cannot meet endpoint determination requirements

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Conditions

The condition(s) this trial relates to.

cholangiocarcinoma Cirrhosis, Familial, with Pulmonary Hypertension hepatocellular carcinoma intrahepatic cholangiocarcinoma liver cancer Liver Neoplasms

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Hepatocellular carcinoma Intrahepatic cholangiocarcinoma (ICC) Liver cancer

Contacts and locations

Locations

  • Guangxi Medical University First Affiliated Hospital

    Nanning, Guangxi, China

More trials for these conditions

Other studies related to the condition(s) this trial covers.