UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000029996 |
|---|---|
| Receipt number | R000034243 |
| Scientific Title | Multicenter study on computer-aided detection from mammography using convolutional neural network |
| Date of disclosure of the study information | 2017/11/16 |
| Last modified on | 2020/01/20 17:10:59 |
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Basic information
Public title
Multicenter study on computer-aided detection from mammography using convolutional neural network
Acronym
KBOG1701 trial
Scientific Title
Multicenter study on computer-aided detection from mammography using convolutional neural network
Scientific Title:Acronym
KBOG1701 trial
Region
| Japan |
Condition
Condition
Breast cancer
Classification by specialty
| Breast surgery |
Classification by malignancy
Malignancy
Genomic information
NO
Objectives
Narrative objectives1
Computer-aided detection of mammography using deep learning
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Accuracy
Sensitivity
Specificity
Key secondary outcomes
Base
Study type
Observational
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
| 20 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Female
Key inclusion criteria
biopsy-proven lesion, either benign or malignant
apparently markable on mammography
for multiple lesions, markable at each lesion
Key exclusion criteria
treated by neoadjuvant therapy
treated by surgery
breast tomosynthesis
PET mammography
mammography with severely low resolution
Target sample size
15000
Research contact person
Name of lead principal investigator
| 1st name | Yutaka |
| Middle name | |
| Last name | Tokuda |
Organization
Tokai University
Division name
Dept. of Endocrine & Breast Surgery
Zip code
259-1292
Address
4-1-1 Kitakaname, Hiratsuka, Kanagawa 259-1291
TEL
0463-58-1211
tokuda@is.icc.u-tokai.ac.jp
Public contact
Name of contact person
| 1st name | Kenichi |
| Middle name | |
| Last name | Inoue |
Organization
Shonan Memorial Hospital
Division name
Breast Cancer Center
Zip code
248-0027
Address
2-2-60 Fueda, Kamakura, Kanagawa 248-0027
TEL
0467-32-3456
Homepage URL
kinoue@syonankinenhp.or.jp
Sponsor or person
Institute
Kanagawa Breast Oncology Group (KBOG)
Institute
Department
Personal name
Funding Source
Organization
Kanagawa Breast Oncology Group (KBOG)
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Shonan Memorial Hospital
Address
2-2-60 Fueda, Kamakura, Kanagawa 248-0027
Tel
0467-32-3456
info@syonankinenhp.or.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
| 2017 | Year | 11 | Month | 16 | 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
9
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
No longer recruiting
Date of protocol fixation
| 2017 | Year | 11 | Month | 02 | Day |
Date of IRB
| 2017 | Year | 11 | Month | 02 | Day |
Anticipated trial start date
| 2019 | Year | 06 | Month | 30 | Day |
Last follow-up date
| 2019 | Year | 12 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Automatically cropped images from mammography are labeled whether breast cancer patterns are drawn. Images are used as input for the supervised learning using deep learning.
Management information
Registered date
| 2017 | Year | 11 | Month | 16 | Day |
Last modified on
| 2020 | Year | 01 | Month | 20 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000034243
