Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study
DC Field | Value | Language |
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dc.contributor.author | Kim, Kwang Hyeon | - |
dc.contributor.author | Lee, Suk | - |
dc.contributor.author | Shim, Jang Bo | - |
dc.contributor.author | Chang, Kyung Hwan | - |
dc.contributor.author | Cao, Yuanjie | - |
dc.contributor.author | Choi, Suk Woo | - |
dc.contributor.author | Jeon, Se Hyeong | - |
dc.contributor.author | Yang, Dae Sik | - |
dc.contributor.author | Yoon, Won Sup | - |
dc.contributor.author | Park, Young Je | - |
dc.contributor.author | Kim, Chul Yong | - |
dc.date.accessioned | 2021-09-03T05:34:10Z | - |
dc.date.available | 2021-09-03T05:34:10Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-06 | - |
dc.identifier.issn | 1460-3969 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/83277 | - |
dc.description.abstract | Purpose: The aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system. Materials and methods: We analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapy(TM) treatment plans for prostate cancer were established, and a hundred modelled plans were generated to analyse the toxicity prediction for bladder and rectum. Results: The toxicity prediction algorithm analysis showed 91.0% accuracy in the training process. A scatter plot for bladder and rectum was obtained by 100 modelled plans and classification result derived. OAR complication level was analysed and risk factor for 25% bladder and 50% rectum was detected by decision tree. Therefore, it was shown that complication prediction of patients using big data-based clinical information is possible. Conclusion: We verified the accuracy of the tested algorithm using prostate cancer cases. Side effects can be minimised by applying this predictive modelling algorithm with the planning decision support system for patient-specific radiotherapy planning. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | CAMBRIDGE UNIV PRESS | - |
dc.subject | TOMOTHERAPY | - |
dc.subject | OUTCOMES | - |
dc.title | Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Suk | - |
dc.contributor.affiliatedAuthor | Yang, Dae Sik | - |
dc.contributor.affiliatedAuthor | Yoon, Won Sup | - |
dc.contributor.affiliatedAuthor | Kim, Chul Yong | - |
dc.identifier.doi | 10.1017/S1460396916000583 | - |
dc.identifier.scopusid | 2-s2.0-85011296331 | - |
dc.identifier.wosid | 000401797400006 | - |
dc.identifier.bibliographicCitation | JOURNAL OF RADIOTHERAPY IN PRACTICE, v.16, no.2, pp.161 - 170 | - |
dc.relation.isPartOf | JOURNAL OF RADIOTHERAPY IN PRACTICE | - |
dc.citation.title | JOURNAL OF RADIOTHERAPY IN PRACTICE | - |
dc.citation.volume | 16 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 161 | - |
dc.citation.endPage | 170 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | TOMOTHERAPY | - |
dc.subject.keywordPlus | OUTCOMES | - |
dc.subject.keywordAuthor | predictive modelling | - |
dc.subject.keywordAuthor | prostate cancer | - |
dc.subject.keywordAuthor | radiation treatment planning decision support program (PDSS) | - |
dc.subject.keywordAuthor | radiation treatment planning (RTP) system | - |
dc.subject.keywordAuthor | toxicity | - |
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