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AI Multimodal Model for Liver Cancer Diagnosis and Prognosis (AIM-LCAP)

28. juni 2026 opdateret af: Fubo Wang, Guangxi Medical University

A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data

This study aims to develop a comprehensive artificial intelligence model system integrating preoperative multimodal data (CT/MRI imaging, clinical laboratory data, and radiology report text) to achieve two core objectives. First, to develop a multimodal fusion diagnostic model for non-invasive and accurate preoperative differentiation of liver cancer subtypes, including distinguishing benign from malignant lesions and differentiating hepatocellular carcinoma from intrahepatic cholangiocarcinoma. Second, to develop a prognostic prediction model for patients with confirmed liver cancer undergoing radical surgery to assess postoperative progression-free survival and overall survival. This is a multicenter retrospective cohort study with an anticipated sample size of ≥600 patients. Model performance will be evaluated using AUC, accuracy, sensitivity, specificity, C-index, and calibration curves. Subgroup analysis will be conducted based on whether patients received neoadjuvant therapy.

Studieoversigt

Status

Aktiv, ikke rekrutterende

Betingelser

Undersøgelsestype

Observationel

Tilmelding (Anslået)

600

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Kina
    • Guangxi
      • Nanning, Guangxi, Kina
        • Guangxi Medical University First Affiliated Hospital

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

(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.

Beskrivelse

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

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Diagnostic
Diagnostic Model Cohort: Patients with suspected liver space-occupying lesions who underwent preoperative contrast-enhanced CT or MRI and have definite pathological diagnosis (surgical or biopsy) as gold standard.
Prognostic
Prognostic Prediction Model Cohort: Patients selected from the diagnostic cohort who were pathologically diagnosed with liver cancer, received radical hepatectomy, and have complete postoperative follow-up data (minimum 24 months) to determine progression-free survival and overall survival endpoints.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification
Tidsramme: At the time of initial diagnosis
The diagnostic performance of the multimodal AI model in differentiating benign from malignant liver lesions and distinguishing hepatocellular carcinoma from intrahepatic cholangiocarcinoma, evaluated using pathology results as the gold standard. Performance metrics include area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
At the time of initial diagnosis
Prognostic Performance of the Multimodal AI Model for Postoperative Survival Prediction
Tidsramme: minimum follow-up of 24 months
The prognostic performance of the multimodal AI model in predicting postoperative progression-free survival (PFS) and overall survival (OS) in patients with pathologically confirmed liver cancer who underwent radical hepatectomy. Performance metric includes the concordance index (C-index). Calibration curves are also assessed.
minimum follow-up of 24 months

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Guangxi Medical University

Publikationer og nyttige links

Den person, der er ansvarlig for at indtaste oplysninger om undersøgelsen, leverer frivilligt disse publikationer. Disse kan handle om alt relateret til undersøgelsen.

Generelle publikationer

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. december 2025

Primær færdiggørelse (Anslået)

1. december 2028

Studieafslutning (Anslået)

1. december 2028

Datoer for studieregistrering

Først indsendt

14. juni 2026

Først indsendt, der opfyldte QC-kriterier

14. juni 2026

Først opslået (Faktiske)

22. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

1. juli 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

28. juni 2026

Sidst verificeret

1. juni 2026

Mere information

Begreber relateret til denne undersøgelse

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • KY20260007

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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