UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000055069 |
|---|---|
| Receipt number | R000062911 |
| Scientific Title | Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning |
| Date of disclosure of the study information | 2024/07/25 |
| Last modified on | 2024/07/25 01:56:52 |
* This page includes information on clinical trials registered in UMIN clinical trial registed system.
* We don't aim to advertise certain products or treatments
Basic information
Public title
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Acronym
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Scientific Title
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Scientific Title:Acronym
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Region
| Japan |
Condition
Condition
cervical cancer
Classification by specialty
| Radiology |
Classification by malignancy
Malignancy
Genomic information
NO
Objectives
Narrative objectives1
Aiming to establish a method for predicting the therapeutic effects of heavy-ion therapy for cervical cancer.
Basic objectives2
Safety,Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Check the extent to which the results of the treatment effect prediction model created and the actual treatment effect match the following items for consideration.
Items for consideration: local control rate, recurrence-free survival rate, distant metastasis incidence rate, overall survival rate, etc.
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
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Female
Key inclusion criteria
The study subjects are patients who underwent heavy ion therapy for cervical cancer at QST Hospital between June 1995 and May 2023.
Key exclusion criteria
Patient data for which the subject of the research has requested that it not be used shall be excluded.
Target sample size
250
Research contact person
Name of lead principal investigator
| 1st name | Kazutoshi |
| Middle name | |
| Last name | Murata |
Organization
National Institutes for Quantum Science and Technology QST hospital
Division name
Department of Diagnostic Radiology and Radiation Oncology
Zip code
2638555
Address
4-9-1 Anagawa, Inage-ku, Chiba City
TEL
0432063306
murata.kazutoshi@qst.go.jp
Public contact
Name of contact person
| 1st name | Kazutoshi |
| Middle name | |
| Last name | Murata |
Organization
National Institutes for Quantum Science and Technology QST hospital
Division name
Department of Diagnostic Radiology and Radiation Oncology
Zip code
2638555
Address
4-9-1 Anagawa, Inage-ku, Chiba City
TEL
0432063306
Homepage URL
murata.kazutoshi@qst.go.jp
Sponsor or person
Institute
National Institutes for Quantum Science and Technology
Institute
Department
Personal name
Kazutoshi Murata
Funding Source
Organization
National Institutes for Quantum Science and Technology
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 Institutes for Quantum Science and Technology Certified Review Board
Address
4-9-1 Anagawa, Inage-ku, Chiba City
Tel
0432063306
murata.kazutoshi@qst.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
| 2024 | Year | 07 | Month | 25 | 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
Preinitiation
Date of protocol fixation
| 2023 | Year | 09 | Month | 06 | Day |
Date of IRB
Anticipated trial start date
| 2023 | Year | 09 | Month | 06 | Day |
Last follow-up date
| 2023 | Year | 09 | Month | 06 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
This is an observational study.
Cases that meet the conditions of this study and for which the research subject or other party does not request refusal by November 30, 2023, will be registered as cases for this study. Data collection and analysis will be conducted on or after December 1, 2023.
The following clinical data will be collected from the Heavy Ion Therapy Medical Database (TMS), the clinical database (AMIDAS), and medical records.
Clinical data necessary for predicting treatment efficacy
Data indicating treatment efficacy
Using past treatment results from heavy ion therapy for cervical cancer, we will establish a mathematical model to predict the effects of heavy ion therapy based on multiple clinical data such as patient background and treatment details.
In addition, a machine learning model for predicting treatment efficacy will be constructed using supervised learning, with clinical data as the input. In order to efficiently learn from clinical data that is partially missing for each case, missing information will be supplemented and data pre-processed. Furthermore, by analyzing the computational behavior of the trained model and the importance of the input data, the types and order of the clinical data required for predicting treatment efficacy for each case will be calculated. From these results, we aim to establish a prognosis prediction model using machine learning and develop a more intuitive and effective mathematical model for predicting treatment efficacy.
Management information
Registered date
| 2024 | Year | 07 | Month | 25 | Day |
Last modified on
| 2024 | Year | 07 | Month | 25 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000062911
