Focal Liver LesionsNCT03871140
Utility of Ultrasound Imaging for Diagnosis of Focal Liver Lesions: A Radiomics Analysis
An observational study of Focal Liver Lesions and Ultrasonics, run by Chinese PLA General Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Focal Liver Lesions, Ultrasonics, Intelligent Diagnosis
- What is being tested?
- diagnosis
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2017to 30 Dec 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: patients with focal liver lesions
Who may be able to join
- 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.
- clear ultrasound imaging of liver tissues backgroud without lesions can be acquired.
- disease history and pathological diagnosis of the lesions can be acquired.
Who cannot join
- patients unsuitable for ultrasound san
- patients counldn't provide disease history such as hepatitis, alcohol intake and so on
- patients without pathological results
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Chinese PLA General Hospital
Beijing, Beijing Municipality, 100853
Jie Yu, Doctor, 8610-66939530, yu-jie301@hotmail.com
About this study
Official title: Intelligent Diagnosis of Focal Liver Lesions and Thermal Ablation Zone of Liver Cancer Based on Ultrasound Imaging
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.
- Conditions
- Focal Liver Lesions, Ultrasonics, Intelligent Diagnosis, Ablation, Radiomics
- Being tested
- diagnosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
diagnosisOther
therre is no intervention diagnosis or treatment for patients
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC value
Area under the receiver operating characteristic (ROC) curve (AUC）
Measured: through study completion, an average of 3 year
specificity
diagnosis specificity of intelligent ultrasound analysis
Measured: through study completion, an average of 3 year
sensitivity
diagnosis sensitivity of intelligent ultrasound analysis
Measured: through study completion, an average of 3 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2019
- Study start1 Jan 2017
- Primary completion30 Dec 2020 (estimated)
- Study completion30 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Focal Liver LesionsStudies of the same condition, open and under way.
- More trials run by Chinese PLA General HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese PLA General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ping Liang, Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03871140
- Sponsor's protocol ID
- 301jrcsk3
- Status last verified
- Apr 2018
- First submitted
- 29 Apr 2018
- Last updated
- 12 Mar 2019
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Du Z, Fan F, Ma J, Liu J, Yan X, Chen X, Dong Y, Wu J, Ding W, Zhao Q, Wang Y, Zhang G, Yu J, Liang P. Development and validation of an ultrasound-based interpretable machine learning model for the classification of </=3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study. EClinicalMedicine. 2025 Feb 13;81:103098. doi: 10.1016/j.eclinm.2025.103098. eCollection 2025 Mar.
PubMed 40034568Yang Y, Cairang Y, Jiang T, Zhou J, Zhang L, Qi B, Ma S, Tang L, Xu D, Bu L, Bu R, Jing X, Wang H, Zhou Z, Zhao C, Luo B, Liu L, Guo J, Nima Y, Hua G, Wa Z, Zhang Y, Zhou G, Jiang W, Wang C, De Y, Yu X, Cheng Z, Han Z, Liu F, Dou J, Feng H, Wu C, Wang R, Hu J, Yang Q, Luo Y, Wu J, Fan H, Liang P, Yu J. Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study. Lancet Digit Health. 2023 Aug;5(8):e503-e514. doi: 10.1016/S2589-7500(23)00091-2.
PubMed 37507196
Study at a glance
From the registry record, last updated by the sponsor on 12 Mar 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intelligent Diagnosis of Focal Liver Lesions and Thermal Ablation Zone of Liver Cancer Based on Ultrasound Imaging
- ClinicalTrials.gov ID
- NCT03871140
- Acronym / sponsor ID
- 301jrcsk3
- Conditions
- Focal Liver Lesions, Ultrasonics, Intelligent Diagnosis, Ablation, Radiomics
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Chinese PLA General HospitalUniversity, hospital or other
- Study start
- 1 Jan 2017 (actual)
- Primary completion
- 30 Dec 2020 (estimated)
- Study completion
- 30 Dec 2021 (estimated)
- First posted
- 12 Mar 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients with focal liver lesions
Key inclusion criteria (3 listed)
- 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.
- clear ultrasound imaging of liver tissues backgroud without lesions can be acquired.
- disease history and pathological diagnosis of the lesions can be acquired.
Key exclusion criteria (3 listed)
- patients unsuitable for ultrasound san
- patients counldn't provide disease history such as hepatitis, alcohol intake and so on
- patients without pathological results
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| diagnosistherre is no intervention diagnosis or treatment for patients | Other | – |
Primary outcome
AUC value
Time frame: through study completion, an average of 3 year
Area under the receiver operating characteristic (ROC) curve (AUC）
specificity
Time frame: through study completion, an average of 3 year
diagnosis specificity of intelligent ultrasound analysis
sensitivity
Time frame: through study completion, an average of 3 year
diagnosis sensitivity of intelligent ultrasound analysis
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Mar 2019.
- Derived from this study: PubMed 40034568Du Z, Fan F, Ma J, Liu J, Yan X, Chen X, Dong Y, Wu J, Ding W, Zhao Q, Wang Y, Zhang G, Yu J, Liang P. Development and validation of an ultrasound-based interpretable machine learning model for the classification of </=3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study. EClinicalMedicine. 2025 Feb 13;81:103098. doi: 10.1016/j.eclinm.2025.103098. eCollection 2025 Mar.
- Derived from this study: PubMed 37507196Yang Y, Cairang Y, Jiang T, Zhou J, Zhang L, Qi B, Ma S, Tang L, Xu D, Bu L, Bu R, Jing X, Wang H, Zhou Z, Zhao C, Luo B, Liu L, Guo J, Nima Y, Hua G, Wa Z, Zhang Y, Zhou G, Jiang W, Wang C, De Y, Yu X, Cheng Z, Han Z, Liu F, Dou J, Feng H, Wu C, Wang R, Hu J, Yang Q, Luo Y, Wu J, Fan H, Liang P, Yu J. Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study. Lancet Digit Health. 2023 Aug;5(8):e503-e514. doi: 10.1016/S2589-7500(23)00091-2.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Focal Liver Lesions and Ultrasonics, run by Chinese PLA General Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Mar 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jie Yu, Dr (15901417963, jiemi301@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC value (time frame: through study completion, an average of 3 year). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03871140, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Mar 2019.