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

Launched by PING LIANG · Feb 24, 2022

Keywords

ClinConnect Summary

This clinical trial is focused on using advanced technology called deep learning to analyze images from a special type of ultrasound (contrast-enhanced ultrasound) to help predict how well patients with liver cancer, known as hepatocellular carcinoma (HCC), will do after treatment. The goal is to assist doctors in making better decisions about surgeries or other treatments for patients with early-stage liver cancer.

To be eligible for this trial, participants need to have a specific stage of liver cancer (stages Ia, Ib, or IIa) and should not have certain complications, such as large blood vessel invasion. They must also have undergone diagnostic imaging and have a good liver function score. If you decide to participate, you will undergo imaging about two weeks before your surgery, and the researchers will collect and analyze your ultrasound images to help improve treatment predictions. This trial is currently looking for participants and is open to all genders aged 65 and older.

Gender

ALL

Eligibility criteria

• 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