UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000053096 |
|---|---|
| Receipt number | R000060591 |
| Scientific Title | Development of an artificial intelligence system to support early diagnosis of pancreatic cancer using multiparametric MRI |
| Date of disclosure of the study information | 2023/12/14 |
| Last modified on | 2025/01/11 23:06:05 |
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Basic information
Public title
Development of an artificial intelligence system to support early diagnosis of pancreatic cancer using multiparametric MRI
Acronym
Development of an artificial intelligence system to support early diagnosis of pancreatic cancer using multiparametric MRI
Scientific Title
Development of an artificial intelligence system to support early diagnosis of pancreatic cancer using multiparametric MRI
Scientific Title:Acronym
Development of an artificial intelligence system to support early diagnosis of pancreatic cancer using multiparametric MRI
Region
| Japan |
Condition
Condition
Pancreatic cancer
Classification by specialty
| Hepato-biliary-pancreatic medicine |
Classification by malignancy
Malignancy
Genomic information
NO
Objectives
Narrative objectives1
Development of an artificial intelligence system to support early diagnosis of pancreatic cancer using multiparametric MRI
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Sensitivity, specificity
Key secondary outcomes
Base
Study type
Others,meta-analysis etc
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
None
Key exclusion criteria
None
Target sample size
1000
Research contact person
Name of lead principal investigator
| 1st name | NOBUHIKO |
| Middle name | |
| Last name | FUKUBA |
Organization
Shimane University
Division name
Internal medicine II
Zip code
6938501
Address
89-1,Enya-cho, Izumo, Shimane,Japan
TEL
0853-23-2111
nfukuba@med.shimane-u.ac.jp
Public contact
Name of contact person
| 1st name | NOBUHIKO |
| Middle name | |
| Last name | FUKUBA |
Organization
Shimane University
Division name
Internal medicine II
Zip code
6938501
Address
89-1,Enya-cho, Izumo, Shimane,Japan
TEL
0853232111
Homepage URL
nfukuba@med.shimane-u.ac.jp
Sponsor or person
Institute
Shimane University
Institute
Department
Personal name
Funding Source
Organization
None
Organization
Division
Category of Funding Organization
Other
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Medical Research Ethics Committee,Shimane University Faculty of Medicine
Address
89-1,Enya-cho, Izumo, Shimane,Japan
Tel
0853-20-2515
kenkyu@med.shimane-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 12 | Month | 14 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Enrolling by invitation
Date of protocol fixation
| 2023 | Year | 12 | Month | 13 | Day |
Date of IRB
| 2023 | Year | 12 | Month | 13 | Day |
Anticipated trial start date
| 2023 | Year | 12 | Month | 13 | Day |
Last follow-up date
| 2025 | Year | 12 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
We will develop an AI diagnostic support model using deep learning using MRI image data from Shimane University and affiliated hospitals, and evaluate sensitivity, specificity, positive predictive value, and negative predictive value using a test set.
Management information
Registered date
| 2023 | Year | 12 | Month | 13 | Day |
Last modified on
| 2025 | Year | 01 | Month | 11 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000060591
