Cited 2 time in
Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chun, Seok-Joo | - |
| dc.contributor.author | Jang, Bum-Sup | - |
| dc.contributor.author | Choi, Hyeon Seok | - |
| dc.contributor.author | Chang, Ji Hyun | - |
| dc.contributor.author | Shin, Kyung Hwan | - |
| dc.date.accessioned | 2024-08-08T12:00:33Z | - |
| dc.date.available | 2024-08-08T12:00:33Z | - |
| dc.date.issued | 2024-04 | - |
| dc.identifier.issn | 2072-6694 | - |
| dc.identifier.issn | 2072-6694 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/21895 | - |
| dc.description.abstract | Simple Summary We aimed to develop a Bayesian Network model to predict treatment outcomes and quality of life. Conditional probabilities and disability weights for radiotherapy-related benefit and risk were collected from nationwide expert survey. Overall disease burden (ODB) was defined as sum of conditional probabilities multiplied by disability weights. A Bayesian network model to predict ODB for (y)pN1 breast cancer was constructed. This model evaluated ongoing prospective trials for (y)pN1 breast cancer such as the Alliance A011202, PORT-N1, RAPCHEM, and RT-CHARM trials, validating reported results and assumptions.Abstract Background: We aimed to construct an expert knowledge-based Bayesian network (BN) model for assessing the overall disease burden (ODB) in (y)pN1 breast cancer patients and compare ODB across arms of ongoing trials. Methods: Utilizing institutional data and expert surveys, we developed a BN model for (y)pN1 breast cancer. Expert-derived probabilities and disability weights for radiotherapy-related benefit (e.g., 7-year disease-free survival [DFS]) and toxicities were integrated into the model. ODB was defined as the sum of disability weights multiplied by probabilities. In silico predictions were conducted for Alliance A011202, PORT-N1, RAPCHEM, and RT-CHARM trials, comparing ODB, 7-year DFS, and side effects. Results: In the Alliance A011202 trial, 7-year DFS was 80.1% in both arms. Axillary lymph node dissection led to higher clinical lymphedema and ODB compared to sentinel lymph node biopsy with full regional nodal irradiation (RNI). In the PORT-N1 trial, the control arm (whole-breast irradiation [WBI] with RNI or post-mastectomy radiotherapy [PMRT]) had an ODB of 0.254, while the experimental arm (WBI alone or no PMRT) had an ODB of 0.255. In the RAPCHEM trial, the radiotherapy field did not impact the 7-year DFS in ypN1 patients. However, there was a mild ODB increase with a larger irradiation field. In the RT-CHARM trial, we identified factors associated with the major complication rate, which ranged from 18.3% to 22.1%. Conclusions: The expert knowledge-based BN model predicted ongoing trial outcomes, validating reported results and assumptions. In addition, the model demonstrated the ODB in different arms, with an emphasis on quality of life. | - |
| dc.format.extent | 10 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
| dc.title | Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model | - |
| dc.type | Article | - |
| dc.publisher.location | 스위스 | - |
| dc.identifier.doi | 10.3390/cancers16081494 | - |
| dc.identifier.scopusid | 2-s2.0-85191394012 | - |
| dc.identifier.wosid | 001210140600001 | - |
| dc.identifier.bibliographicCitation | Cancers, v.16, no.8, pp 1 - 10 | - |
| dc.citation.title | Cancers | - |
| dc.citation.volume | 16 | - |
| dc.citation.number | 8 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.endPage | 10 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Oncology | - |
| dc.relation.journalWebOfScienceCategory | Oncology | - |
| dc.subject.keywordPlus | RADIOTHERAPY | - |
| dc.subject.keywordPlus | METAANALYSIS | - |
| dc.subject.keywordPlus | RECONSTRUCTION | - |
| dc.subject.keywordPlus | IRRADIATION | - |
| dc.subject.keywordPlus | MASTECTOMY | - |
| dc.subject.keywordPlus | SURGERY | - |
| dc.subject.keywordPlus | RISK | - |
| dc.subject.keywordAuthor | Bayesian network | - |
| dc.subject.keywordAuthor | disease burden | - |
| dc.subject.keywordAuthor | disability weights | - |
| dc.subject.keywordAuthor | breast cancer | - |
| dc.subject.keywordAuthor | radiotherapy | - |
| dc.subject.keywordAuthor | de-escalation | - |
| dc.subject.keywordAuthor | in silico | - |
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