UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000039009 |
|---|---|
| Receipt number | R000044471 |
| Scientific Title | A deep learning-based automated diagnostic system for classifying mammographic lesions |
| Date of disclosure of the study information | 2019/12/26 |
| Last modified on | 2023/06/30 21:10:57 |
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Basic information
Public title
A deep learning-based automated diagnostic system for classifying mammographic lesions
Acronym
DLADS
Scientific Title
A deep learning-based automated diagnostic system for classifying mammographic lesions
Scientific Title:Acronym
DLADS
Region
| Japan |
Condition
Condition
Breast cancer
Classification by specialty
| Hematology and clinical oncology |
Classification by malignancy
Malignancy
Genomic information
NO
Objectives
Narrative objectives1
The aim of this study is to construct a deep learning-based AI system to detect breast cancer on mammograms with high specificity, and to evaluate the performance of the AI system.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
The sensitivity and specificity of the AI system to detect breast cancer with the test image set.
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
| 20 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Female
Key inclusion criteria
Digital mammography images fulfilling all the following criteria are collected.
1.Taken after 2010
2.Images meeting either one of the following criteria
>Visible breast cancer or benign lesions on images
>Normal breast
3.If cancer or benign lesions are visible on images, their outlines can be traced manually
4.Images from patients aged 20 or older
5.Available mediolateral oblique (MLO) view with or without cranial-caudal view (CC)
6.No visible axillary lymph node metastasis from breast cancer
7.Images from patients with no previous history of chemotherapy, endocrine therapy or radiotherapy.
8.Images from patients who have not received any previous surgical breast procedure including partial resection, breast reconstruction, incisional biopsy, vacuum-assisted biopsy and mammoplasty
9.Read by readers ranked A according to the Japan Central Organization on Quality Assurance of Breast Cancer Screening
10.Benign lesions, breast cancer and normal breast on images are confirmed by the following criteria
(Benign lesions)
Meeting one of the following criteria
>Confirmed by histopathology
>Without malignancy development over at least 2 years of follow-up
>Findings clearly indicating a simple cyst by mammmography and other imaging modalities
(Breast cancer)
>Confirmed by histopathology
(Normal breast)
Meeting either one of the following criteria
>In addition to the findings of mammography, ultrasonography and MRI do not detect any lesions.
>Without malignancy development over at least 2 years of follow-up when no other imaging modalities except mammography are performed.
Key exclusion criteria
Digital mammography images fulfilling any of the following criteria are not collected.
1.Tomosynthesis and synthetic 2D mammographic images
2.Spot compression views
3.Poor image quality
4.Inappropriate images as judged by the local investigators
Target sample size
16000
Research contact person
Name of lead principal investigator
| 1st name | Hirofumi |
| Middle name | |
| Last name | Mukai |
Organization
National Cancer Center Hospital East
Division name
Division of Breast and Medical oncology
Zip code
277-8577
Address
6-5-1, Kashiwanoha, Kashiwa-shi, Chiba
TEL
04-7133-1111
hrmukai@east.ncc.go.jp
Public contact
Name of contact person
| 1st name | Hirofumi |
| Middle name | |
| Last name | Mukai |
Organization
National Cancer Center Hospital East
Division name
Division of Breast and Medical oncology
Zip code
277-8577
Address
6-5-1, Kashiwanoha, Kashiwa-shi, Chiba
TEL
04-7133-1111
Homepage URL
http://cspor-bc.or.jp/
hrmukai@east.ncc.go.jp
Sponsor or person
Institute
Comprehensive Support Project for Oncology Research for Breast Cancer (CSPOR-BC)
Institute
Department
Personal name
Funding Source
Organization
Comprehensive Support Project for Oncology Research for Breast Cancer (CSPOR-BC)
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
National Cancer Center Hospital East Certified Review Board
Address
6-5-1 Kashiwanoha, Kashiwa-shiChiba-ken, 277-8577 Japan
Tel
04-7133-1111
ncche-irb@east.ncc.go.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
| 2019 | Year | 12 | Month | 26 | Day |
Related information
URL releasing protocol
https://journals.lww.com/md-journal/Fulltext/2020/07020/A_deep_learning_based_automated_diagnostic_s
Publication of results
Published
Result
URL related to results and publications
unpublished
Number of participants that the trial has enrolled
20000
Results
The constructed AI showed comparable ability to humans in reading mammograms.
Results date posted
| 2023 | Year | 06 | Month | 30 | Day |
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Mammographic images of breast cancer, benign lesions and normal breasts were collected.
Participant flow
The images were collected from 63 institutions.
Adverse events
Not applicable.
Outcome measures
Both the sensitivity and specificity of the AI exceeded the target performance of 80%.
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Completed
Date of protocol fixation
| 2019 | Year | 05 | Month | 10 | Day |
Date of IRB
| 2019 | Year | 07 | Month | 04 | Day |
Anticipated trial start date
| 2019 | Year | 09 | Month | 01 | Day |
Last follow-up date
| 2021 | Year | 08 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
The aim of this study is to construct a deep learning-based AI system to detect breast cancer on mammograms with high specificity, and to evaluate the performance of the AI system.
Management information
Registered date
| 2019 | Year | 12 | Month | 26 | Day |
Last modified on
| 2023 | Year | 06 | Month | 30 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000044471
