Clinical Decision Biological Biomarker and Prognosis Prediction of Hepatocellular Carcinoma by Deep Learning

A Deep Learning Model Based on Contrast-enhanced Ultrasound to Aid Clinical Decisions and Predict Biological Biomarker and Prognosis of Hepatocellular Carcinoma

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05257694

Enrollment

Registered

2022-02-25

Start date

2022-01-01

Completion date

2025-12-31

Last updated

2022-10-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ablation, Contrast-enhanced Ultrasound, Hepatocellular Carcinoma, Prognosis, Surgery

Brief summary

Developing a deep learning model based on contrast-enhanced ultrasound (CEUS) to predict the prognosis of hepatocellular carcinoma (HCC) and aid choose operation decisions

Detailed description

Collecting CEUS and clinical data of HCC from different institutions retrospectively. Developing a deep learning model based on CEUS to predict the prognosis of HCC. Developing a deep learning model based on CEUS to choose a better operation (ablation or surgery) of HCC patients. Then, validating the deep learning model in the prospective data.

Interventions

PROCEDURESurgery

hepatectomy

PROCEDUREAblation (Microwave ablation or Radiofrequency ablation)

image-guided ablation

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

* patients with HCC (Ia, Ib, IIa stage) China liver cancer staging who underwent resection or ablation * without macro-vascular invasion * Child-Pugh A/B grade * HCC is proved by pathological examination or two enhanced imaging * CEUS (Sonovue or Sonozoid) images are performed two weeks before the operation * Invasive biomarker or prognosis of HCC available * CEUS images are included in at least three stages (Arterial phase, Portal phase, and Late phase)

Exclusion criteria

* postop follow-up loss or expired less than 3 months * patients with co-malignancy * poor images quality for analyzing

Design outcomes

Primary

Measure	Time frame	Description
Recurrence-free survival (RFS)	Immediately after the surgery or ablation	Recurrence-free survival is defined as the time elapsed between a predefined point in time (the date of diagnosis, randomization or the intervention) and any recurrence (local, regional, or distant) or death due to any cause (death is an event).

Secondary

Measure	Time frame	Description
Recurrence	Immediately after the surgery or ablation	Recurrence included local tumor progression, regional recurrence, or distant recurrence.

Countries

China

Contacts

Primary ContactPing Liang, Dr.

liangping301@hotmail.com+86 10 66939530

Backup Contactjiapeng wu, Dr.

wjpdabao@126.com+86 13079692188

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026