Hepatocellular CarcinomaNCT04299919
The AI Prognostic Assessment and Pathological Basis Research of Early HCC After Minimally Invasive Treatment
An observational study of Hepatocellular Carcinoma, run by The First Affiliated Hospital of Dalian Medical University at 1 site in China.
Trial summary
The study evaluates artificial intelligence method based on multimodal magnetic resonance imaging (MRI) images and clinical data in preoperative prediction of prognosis in early hepatocellular carcinoma (HCC) patients treated with minimally invasive treatment. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Hepatocellular Carcinoma
- What is being tested?
- Minimally invasive treatment and Hepatectomy
- Type of treatment
- Surgery or procedure
- How many people
- 1,200 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2007to 30 Jun 2024
- 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
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients underwent MRI examination of the abdomen in our hospital.
Who may be able to join
- Hepatocellular carcinoma patients received minimally invasive treatment (transcatheter arterial chemoembolization, radiofrequency ablation, or combined) or hepatectomy;
- Patients received MRI examination within 1 month before treatment;
- Complete post-treatment prognosis information.
Who cannot join
- local or systemic treatment before MR examination;
- Incomplete clinical and pathological data;
- Heavy image artifacts.
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital of Dalian Medical University
Dalian, Liaoning, 116000
About this study
Official title: The Artificial Intelligent Prognostic Assessment and Pathological Basis Research of Early Primary Hepatocellular Carcinoma After Minimally Invasive Treatment Based on Multimodal MRI and Clinical Big Data
The study evaluates artificial intelligence method based on multimodal magnetic resonance imaging (MRI) images and clinical data in preoperative prediction of prognosis in early hepatocellular carcinoma (HCC) patients treated with minimally invasive treatment. The correlation between prognosis-related MRI features and pathological features was studied through artificial intelligence method, so as to provide the interpretability of image features for predicting the prognosis of HCC patients treated with minimally invasive treatment.
Read the detailed description
The prognosis prediction of early stage hepatocellular carcinoma (HCC) after minimally invasive treatment involves clinical decision of treatment and follow-up. Magnetic resonance imaging (MRI) has become the main approach for monitoring and following up of HCC, however it's difficult to predict HCC prognosis before surgery. We found the following limitations among previous researches: multimodal MRI using different sequences shows uncertain boundaries of HCC, which makes precise segmentation more difficult, and also leads to an additional workload for extracting high throughput radiomics features, which are limited in quantity and repeatability. Regarding to prognosis aspect, the MRI images, clinical data, and follow up information have not been fully exploited yet. In addition, the prognosis result obtained by radiomics workflow is difficult to be explained and applied to clinical application. Therefore, we conduct a study to solve the problems mentioned above: (1) To explore an effective deep learning neural network method and a pre-training model for improving tumor segmentation accuracy. (2) To establish a method for extracting high-throughput multi-dimensional and multimodal MRI radiomics features related to HCC prognosis. (3) To explore a correlation between "multimodal MRI based pathological features of early stage HCC" and the results of "multimodal MRI based prognosis depth network of early stage HCC after minimally invasive treatment". Based on above approaches, we aim to establish "multimodal MRI based prognosis model of early stage HCC after minimally invasive treatment" in different clinical application scenarios guiding to clinical decision-making. Moreover, we also aim to explore the correlation between MRI radiomics features and pathology, which provides theoretical foundations for the MRI radiomics based pathological researches.
- Conditions
- Hepatocellular Carcinoma
- Being tested
- Minimally invasive treatment, Hepatectomy
- Keywords
- Precision medicine information processing, Lesion recognition, Feature extraction, Deep learning, Diagnosis model
- Medical subject headings
- Carcinoma, Hepatocellular
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Minimally invasive treatmentProcedure or surgery
All hepatocellular carcinoma (HCC) patients received minimally invasive treatment, including transcatheter arterial chemoembolization (TACE), radiofrequency ablation (RFA) or combined.
HepatectomyProcedure or surgery
All hepatocellular carcinoma (HCC) patients received hepatectomy.
| Study group | Role | What they receive |
|---|---|---|
| Recurrence | All hepatocellular carcinoma (HCC) patients have been regularly monitored for recurrence via contrast CT or contrast-enhanced MRI after minimally invasive treatment or hepatectomy. The recurrence status included new intrahepatic lesions and/or extrahepatic metastasis. Receives: Procedure: Minimally invasive treatment; Procedure: Hepatectomy | |
| Non-recurrence | All hepatocellular carcinoma (HCC) patients have been regularly monitored for recurrence via contrast CT or contrast-enhanced MRI after minimally invasive treatment or hepatectomy. The recurrence status included new intrahepatic lesions and/or extrahepatic metastasis. Receives: Procedure: Minimally invasive treatment; Procedure: Hepatectomy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Three-month recurrence
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least three months.
Measured: Three months
Six-month recurrence
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least six months.
Measured: Six months
One-year recurrence
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least one year.
Measured: One year
Two-year recurrence
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least two years.
Measured: Two years
Three-year recurrence
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least three years.
Measured: Three years
Secondary outcomes (1)
Progression-free survival
The time between the tumor progression and initial treatment was recorded.
Measured: Three months, six months, one year, two years, and three years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Mar 2020
- Study start1 Apr 2007
- Primary completion30 Jun 2023 (estimated)
- Study completion30 Jun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (21)Papers the sponsor has linked to this study.
- Other trials for Hepatocellular CarcinomaStudies of the same condition, open and under way.
- 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 1,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Dalian Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ying Zhao, MD, Study chair, The First Affiliated Hospital of Dalian Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04299919
- Sponsor's protocol ID
- PJ-KS-KY-2019-167
- Status last verified
- Mar 2020
- First submitted
- 5 Mar 2020
- Last updated
- 9 Mar 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Partial de-identified individual participant data for primary and secondary outcome measures will be made available.
Publications
Bloomston M, Binitie O, Fraiji E, Murr M, Zervos E, Goldin S, Kudryk B, Zwiebel B, Black T, Fargher S, Rosemurgy AS. Transcatheter arterial chemoembolization with or without radiofrequency ablation in the management of patients with advanced hepatic malignancy. Am Surg. 2002 Sep;68(9):827-31.
PubMed 12356160Ye JZ, Chen JZ, Li ZH, Bai T, Chen J, Zhu SL, Li LQ, Wu FX. Efficacy of postoperative adjuvant transcatheter arterial chemoembolization in hepatocellular carcinoma patients with microvascular invasion. World J Gastroenterol. 2017 Nov 7;23(41):7415-7424. doi: 10.3748/wjg.v23.i41.7415.
PubMed 29151695Li S, Zhang L, Huang ZM, Wu PH. Transcatheter arterial chemoembolization combined with CT-guided percutaneous thermal ablation versus hepatectomy in the treatment of hepatocellular carcinoma. Chin J Cancer. 2015 Jun 10;34(6):254-63. doi: 10.1186/s40880-015-0023-9.
PubMed 26063407Pang Q, Zhang JY, Xu XS, Song SD, Chen W, Zhou YY, Miao RC, Qu K, Liu SS, Dong YF, Liu C. The prognostic values of 12 cirrhosis-relative noninvasive models in patients with hepatocellular carcinoma. Scand J Clin Lab Invest. 2015 Jan;75(1):73-84. doi: 10.3109/00365513.2014.981759. Epub 2014 Dec 3.
PubMed 25465804Kim NH, Lee T, Cho YK, Kim BI, Kim HJ. Impact of clinically evident portal hypertension on clinical outcome of patients with hepatocellular carcinoma treated by transarterial chemoembolization. J Gastroenterol Hepatol. 2018 Jul;33(7):1397-1406. doi: 10.1111/jgh.14083. Epub 2018 Mar 12.
PubMed 29314222Yasaka K, Akai H, Abe O, Kiryu S. Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. Radiology. 2018 Mar;286(3):887-896. doi: 10.1148/radiol.2017170706. Epub 2017 Oct 23.
PubMed 29059036Ibragimov B, Toesca D, Chang D, Koong A, Xing L. Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning. Phys Med Biol. 2017 Nov 10;62(23):8943-8958. doi: 10.1088/1361-6560/aa9262.
PubMed 28994665Song W, Yu X, Guo D, Liu H, Tang Z, Liu X, Zhou J, Zhang H, Liu Y, Liu X. MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization. J Magn Reson Imaging. 2020 Aug;52(2):461-473. doi: 10.1002/jmri.26977. Epub 2019 Nov 1.
PubMed 31675174Hui TCH, Chuah TK, Low HM, Tan CH. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study. Clin Radiol. 2018 Dec;73(12):1056.e11-1056.e16. doi: 10.1016/j.crad.2018.07.109. Epub 2018 Sep 10.
PubMed 30213434Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, Zhu S, Wang M, Dou S, Shi D. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2019 Jun;29(6):2802-2811. doi: 10.1007/s00330-018-5787-2. Epub 2018 Nov 7.
PubMed 30406313Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, Li B. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017 Jul 13;17(1):42. doi: 10.1186/s12880-017-0212-x.
PubMed 28705145Kim J, Choi SJ, Lee SH, Lee HY, Park H. Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics. AJR Am J Roentgenol. 2018 Nov;211(5):1026-1034. doi: 10.2214/AJR.18.19507. Epub 2018 Sep 21.
PubMed 30240304Zhou Y, He L, Huang Y, Chen S, Wu P, Ye W, Liu Z, Liang C. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY). 2017 Jun;42(6):1695-1704. doi: 10.1007/s00261-017-1072-0.
PubMed 28180924Huang YL, Chen JH, Shen WC. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Acad Radiol. 2006 Jun;13(6):713-20. doi: 10.1016/j.acra.2005.07.014.
PubMed 16679273Tang H, Bai HX, Su C, Lee AM, Yang L. The effect of cirrhosis on radiogenomic biomarker's ability to predict microvascular invasion and outcome in hepatocellular carcinoma. Hepatology. 2016 Aug;64(2):691-2. doi: 10.1002/hep.28620. Epub 2016 May 31. No abstract available.
PubMed 27113641
Study at a glance
From the registry record, last updated by the sponsor on 9 Mar 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Artificial Intelligent Prognostic Assessment and Pathological Basis Research of Early Primary Hepatocellular Carcinoma After Minimally Invasive Treatment Based on Multimodal MRI and Clinical Big Data
- ClinicalTrials.gov ID
- NCT04299919
- Acronym / sponsor ID
- PJ-KS-KY-2019-167
- Conditions
- Hepatocellular Carcinoma
- Keywords
- Precision medicine information processing, Lesion recognition, Feature extraction, Deep learning, Diagnosis model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,200 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Dalian Medical UniversityUniversity, hospital or other
- Study start
- 1 Apr 2007 (actual)
- Primary completion
- 30 Jun 2023 (estimated)
- Study completion
- 30 Jun 2024 (estimated)
- First posted
- 9 Mar 2020 (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.
The study evaluates artificial intelligence method based on multimodal magnetic resonance imaging (MRI) images and clinical data in preoperative prediction of prognosis in early hepatocellular carcinoma (HCC) patients treated with minimally invasive treatment. The correlation between prognosis-related MRI features and pathological features was studied through artificial intelligence method, so as to provide the interpretability of image features for predicting the prognosis of HCC patients treated with minimally invasive treatment.
Detailed description
The prognosis prediction of early stage hepatocellular carcinoma (HCC) after minimally invasive treatment involves clinical decision of treatment and follow-up. Magnetic resonance imaging (MRI) has become the main approach for monitoring and following up of HCC, however it's difficult to predict HCC prognosis before surgery. We found the following limitations among previous researches: multimodal MRI using different sequences shows uncertain boundaries of HCC, which makes precise segmentation more difficult, and also leads to an additional workload for extracting high throughput radiomics features, which are limited in quantity and repeatability. Regarding to prognosis aspect, the MRI images, clinical data, and follow up information have not been fully exploited yet. In addition, the prognosis result obtained by radiomics workflow is difficult to be explained and applied to clinical application. Therefore, we conduct a study to solve the problems mentioned above: (1) To explore an effective deep learning neural network method and a pre-training model for improving tumor segmentation accuracy. (2) To establish a method for extracting high-throughput multi-dimensional and multimodal MRI radiomics features related to HCC prognosis. (3) To explore a correlation between "multimodal MRI based pathological features of early stage HCC" and the results of "multimodal MRI based prognosis depth network of early stage HCC after minimally invasive treatment". Based on above approaches, we aim to establish "multimodal MRI based prognosis model of early stage HCC after minimally invasive treatment" in different clinical application scenarios guiding to clinical decision-making. Moreover, we also aim to explore the correlation between MRI radiomics features and pathology, which provides theoretical foundations for the MRI radiomics based pathological researches.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Recurrence | – | Procedure: Minimally invasive treatment, Procedure: HepatectomyAll hepatocellular carcinoma (HCC) patients have been regularly monitored for recurrence via contrast CT or contrast-enhanced MRI after minimally invasive treatment or hepatectomy. The recurrence status included new intrahepatic lesions and/or extrahepatic metastasis. |
| Non-recurrence | – | Procedure: Minimally invasive treatment, Procedure: HepatectomyAll hepatocellular carcinoma (HCC) patients have been regularly monitored for recurrence via contrast CT or contrast-enhanced MRI after minimally invasive treatment or hepatectomy. The recurrence status included new intrahepatic lesions and/or extrahepatic metastasis. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients underwent MRI examination of the abdomen in our hospital.
Key inclusion criteria (3 listed)
- Hepatocellular carcinoma patients received minimally invasive treatment (transcatheter arterial chemoembolization, radiofrequency ablation, or combined) or hepatectomy;
- Patients received MRI examination within 1 month before treatment;
- Complete post-treatment prognosis information.
Key exclusion criteria (3 listed)
- local or systemic treatment before MR examination;
- Incomplete clinical and pathological data;
- Heavy image artifacts.
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 |
|---|---|---|
| Minimally invasive treatmentAll hepatocellular carcinoma (HCC) patients received minimally invasive treatment, including transcatheter arterial chemoembolization (TACE), radiofrequency ablation (RFA) or combined. | Procedure or surgery | Non-recurrence, Recurrence |
| HepatectomyAll hepatocellular carcinoma (HCC) patients received hepatectomy. | Procedure or surgery | Non-recurrence, Recurrence |
Primary outcome
Three-month recurrence
Time frame: Three months
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least three months.
Six-month recurrence
Time frame: Six months
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least six months.
One-year recurrence
Time frame: One year
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least one year.
Two-year recurrence
Time frame: Two years
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least two years.
Three-year recurrence
Time frame: Three years
All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least three years.
1 secondary outcome
Progression-free survival
Time frame: Three months, six months, one year, two years, and three years.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Mar 2020.
- Background: PubMed 12356160Bloomston M, Binitie O, Fraiji E, Murr M, Zervos E, Goldin S, Kudryk B, Zwiebel B, Black T, Fargher S, Rosemurgy AS. Transcatheter arterial chemoembolization with or without radiofrequency ablation in the management of patients with advanced hepatic malignancy. Am Surg. 2002 Sep;68(9):827-31.
- Background: PubMed 29151695Ye JZ, Chen JZ, Li ZH, Bai T, Chen J, Zhu SL, Li LQ, Wu FX. Efficacy of postoperative adjuvant transcatheter arterial chemoembolization in hepatocellular carcinoma patients with microvascular invasion. World J Gastroenterol. 2017 Nov 7;23(41):7415-7424. doi: 10.3748/wjg.v23.i41.7415.
- Background: PubMed 26063407Li S, Zhang L, Huang ZM, Wu PH. Transcatheter arterial chemoembolization combined with CT-guided percutaneous thermal ablation versus hepatectomy in the treatment of hepatocellular carcinoma. Chin J Cancer. 2015 Jun 10;34(6):254-63. doi: 10.1186/s40880-015-0023-9.
- Background: PubMed 25465804Pang Q, Zhang JY, Xu XS, Song SD, Chen W, Zhou YY, Miao RC, Qu K, Liu SS, Dong YF, Liu C. The prognostic values of 12 cirrhosis-relative noninvasive models in patients with hepatocellular carcinoma. Scand J Clin Lab Invest. 2015 Jan;75(1):73-84. doi: 10.3109/00365513.2014.981759. Epub 2014 Dec 3.
- Background: PubMed 29314222Kim NH, Lee T, Cho YK, Kim BI, Kim HJ. Impact of clinically evident portal hypertension on clinical outcome of patients with hepatocellular carcinoma treated by transarterial chemoembolization. J Gastroenterol Hepatol. 2018 Jul;33(7):1397-1406. doi: 10.1111/jgh.14083. Epub 2018 Mar 12.
- Background: PubMed 29059036Yasaka K, Akai H, Abe O, Kiryu S. Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study. Radiology. 2018 Mar;286(3):887-896. doi: 10.1148/radiol.2017170706. Epub 2017 Oct 23.
- Background: PubMed 28994665Ibragimov B, Toesca D, Chang D, Koong A, Xing L. Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning. Phys Med Biol. 2017 Nov 10;62(23):8943-8958. doi: 10.1088/1361-6560/aa9262.
- Background: PubMed 31675174Song W, Yu X, Guo D, Liu H, Tang Z, Liu X, Zhou J, Zhang H, Liu Y, Liu X. MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization. J Magn Reson Imaging. 2020 Aug;52(2):461-473. doi: 10.1002/jmri.26977. Epub 2019 Nov 1.
- Background: PubMed 30213434Hui TCH, Chuah TK, Low HM, Tan CH. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study. Clin Radiol. 2018 Dec;73(12):1056.e11-1056.e16. doi: 10.1016/j.crad.2018.07.109. Epub 2018 Sep 10.
- Background: PubMed 30406313Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, Zhu S, Wang M, Dou S, Shi D. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2019 Jun;29(6):2802-2811. doi: 10.1007/s00330-018-5787-2. Epub 2018 Nov 7.
- Background: PubMed 28705145Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, Li B. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017 Jul 13;17(1):42. doi: 10.1186/s12880-017-0212-x.
- Background: PubMed 30240304Kim J, Choi SJ, Lee SH, Lee HY, Park H. Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics. AJR Am J Roentgenol. 2018 Nov;211(5):1026-1034. doi: 10.2214/AJR.18.19507. Epub 2018 Sep 21.
- Transcatheter arterial chemoembolization.Link provided by the sponsor.
- Efficacy of postoperative in hepatocellular carcinoma patients with microvascular invasion.Link provided by the sponsor.
- Transcatheter arterial chemoembolization.Link provided by the sponsor.
- The prognostic values.Link provided by the sponsor.
- Portal hypertension.Link provided by the sponsor.
- Deep Learning.Link provided by the sponsor.
- Individual participant dataSharing planned: Yes. Partial de-identified individual participant data for primary and secondary outcome measures will be made available.
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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.
Ying Zhao, MD study chair
The First Affiliated Hospital of Dalian Medical University
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 Hepatocellular Carcinoma, run by The First Affiliated Hospital of Dalian Medical University 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 9 Mar 2020. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Three-month recurrence (time frame: Three months). The study lists 5 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 NCT04299919, which also lists 21 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 9 Mar 2020.