Ablation, Focal Liver Lesions, Intelligent Diagnosis, Radiomics, Ultrasonics
Conditions
Brief summary
Ultrasound (US) as first-line imaging technology in detecting focal liver lesions,also plays a crucial role in evaluating image and guiding ablation which is the main treatment for liver lesions. However, the effect of US in diagnosing liver lesions is challenged by several factors including being highly dependent on doctor's experience, low signal-to-noise ratio, low resolution for lesion feature,large error from thermal field evaluation during the process of ablation and so on. Therefore, it is of great significance to construct an intelligent US analysis system depending on the digital information technology. Basing on these problems,the following research will be involved in our project: 1) US database of liver lesions with seamless connection to Picture Archiving and Communication Systems (PACS) will be developed, with the aim to provide standard data for intelligent US analysis. 2) Deep learning model for accurate segmentation, detection and classification of liver lesions on US images will be studied. Then automatic extraction, selection and analysis of liver lesion ultrasound features and the intelligent US diagnosis for liver lesions will be realized. 3) Proposing a clustering model with deep image features, and depicting the similarity measurement of liver cancer, which can be furthered used to link the liver cancer feature to optimal ablation parameters. The intelligent decision-making system for quantifying thermal ablation will be established. 4) Regression algorithm and Generative Adversarial Nets will be developed to extract the image features of liver cancer which will predict risk factors after US-guided thermal ablation.Based on the above researches, it is of great value to establish an intelligent focal liver lesion US diagnosis system involving intelligent diagnosis,personalized ablation strategy and accurate prognosis evaluation, improving the level of accurate diagnosis and treatment of liver lesions.
Interventions
OTHERdiagnosis
therre is no intervention diagnosis or treatment for patients
Sponsors
Chinese PLA General Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
1. clear ultrasound imaging of focal liver lesions including malignant liver tumors such as hepatocellular carcinoma, metastatic liver cancer and benigh liver tumors such as hemangioma and focal nodular hyperplasia and so on can be acquired. 2. clear ultrasound imaging of liver tissues backgroud without lesions can be acquired. 3. disease history and pathological diagnosis of the lesions can be acquired.
Exclusion criteria
1. patients unsuitable for ultrasound san 2. patients counldn't provide disease history such as hepatitis, alcohol intake and so on 3. patients without pathological results
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC value | through study completion, an average of 3 year | Area under the receiver operating characteristic (ROC) curve (AUC） |
| specificity | through study completion, an average of 3 year | diagnosis specificity of intelligent ultrasound analysis |
| sensitivity | through study completion, an average of 3 year | diagnosis sensitivity of intelligent ultrasound analysis |
Countries
China
Contacts
Primary ContactJie Yu, Dr
Outcome results
None listed