Unique ID issued by UMIN | UMIN000055069 |
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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 |
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Predicting the treatment effect of heavy ion particle therapy for cervical cancer: Mathematical modeling and machine learning
Japan |
cervical cancer
Radiology |
Malignancy
NO
Aiming to establish a method for predicting the therapeutic effects of heavy-ion therapy for cervical cancer.
Safety,Efficacy
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.
Observational
Not applicable |
Not applicable |
Female
The study subjects are patients who underwent heavy ion therapy for cervical cancer at QST Hospital between June 1995 and May 2023.
Patient data for which the subject of the research has requested that it not be used shall be excluded.
250
1st name | Kazutoshi |
Middle name | |
Last name | Murata |
National Institutes for Quantum Science and Technology QST hospital
Department of Diagnostic Radiology and Radiation Oncology
2638555
4-9-1 Anagawa, Inage-ku, Chiba City
0432063306
murata.kazutoshi@qst.go.jp
1st name | Kazutoshi |
Middle name | |
Last name | Murata |
National Institutes for Quantum Science and Technology QST hospital
Department of Diagnostic Radiology and Radiation Oncology
2638555
4-9-1 Anagawa, Inage-ku, Chiba City
0432063306
murata.kazutoshi@qst.go.jp
National Institutes for Quantum Science and Technology
Kazutoshi Murata
National Institutes for Quantum Science and Technology
Other
National Institutes for Quantum Science and Technology Certified Review Board
4-9-1 Anagawa, Inage-ku, Chiba City
0432063306
murata.kazutoshi@qst.go.jp
NO
2024 | Year | 07 | Month | 25 | Day |
Unpublished
Preinitiation
2023 | Year | 09 | Month | 06 | Day |
2023 | Year | 09 | Month | 06 | Day |
2023 | Year | 09 | Month | 06 | Day |
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.
2024 | Year | 07 | Month | 25 | Day |
2024 | Year | 07 | Month | 25 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000062911
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