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Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model

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dc.contributor.authorChun, Seok-Joo-
dc.contributor.authorJang, Bum-Sup-
dc.contributor.authorChoi, Hyeon Seok-
dc.contributor.authorChang, Ji Hyun-
dc.contributor.authorShin, Kyung Hwan-
dc.date.accessioned2024-08-08T12:00:33Z-
dc.date.available2024-08-08T12:00:33Z-
dc.date.issued2024-04-
dc.identifier.issn2072-6694-
dc.identifier.issn2072-6694-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/21895-
dc.description.abstractSimple 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.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)-
dc.titlePrediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/cancers16081494-
dc.identifier.scopusid2-s2.0-85191394012-
dc.identifier.wosid001210140600001-
dc.identifier.bibliographicCitationCancers, v.16, no.8, pp 1 - 10-
dc.citation.titleCancers-
dc.citation.volume16-
dc.citation.number8-
dc.citation.startPage1-
dc.citation.endPage10-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaOncology-
dc.relation.journalWebOfScienceCategoryOncology-
dc.subject.keywordPlusRADIOTHERAPY-
dc.subject.keywordPlusMETAANALYSIS-
dc.subject.keywordPlusRECONSTRUCTION-
dc.subject.keywordPlusIRRADIATION-
dc.subject.keywordPlusMASTECTOMY-
dc.subject.keywordPlusSURGERY-
dc.subject.keywordPlusRISK-
dc.subject.keywordAuthorBayesian network-
dc.subject.keywordAuthordisease burden-
dc.subject.keywordAuthordisability weights-
dc.subject.keywordAuthorbreast cancer-
dc.subject.keywordAuthorradiotherapy-
dc.subject.keywordAuthorde-escalation-
dc.subject.keywordAuthorin silico-
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