Clinical trial NCT03871140: Utility of Ultrasound Imaging for Diagnosis of Focal Liver Lesions: A Radiomics Analysis
Official title: Intelligent Diagnosis of Focal Liver Lesions and Thermal Ablation Zone of Liver Cancer Based on Ultrasound Imaging
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Focal Liver Lesions
Ultrasonics
Intelligent Diagnosis
Ablation
Radiomics
Lead sponsor: Chinese PLA General Hospital
Sponsor class: OTHER
First posted: 2019-03-12
Enrollment: 10000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: patients with focal liver lesions

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.


Eligibility criteria: 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

Primary outcomes:
measure: AUC value
timeFrame: through study completion, an average of 3 year
measure: specificity
timeFrame: through study completion, an average of 3 year
measure: sensitivity
timeFrame: through study completion, an average of 3 year
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Chinese PLA General Hospital
state: Beijing
Study officials:
References: