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Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy
DC Field | Value | Language |
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dc.contributor.author | 권혜미 | - |
dc.contributor.author | 김정아 | - |
dc.contributor.author | 손은주 | - |
dc.contributor.author | 육지현 | - |
dc.contributor.author | 은나래 | - |
dc.date.accessioned | 2021-12-28T17:00:37Z | - |
dc.date.available | 2021-12-28T17:00:37Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/186890 | - |
dc.description.abstract | Objectives: To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC). Methods: This retrospective study included 130 women who received NAC and underwent subsequent surgery for breast cancer between January 2012 and August 2017. We assessed common features, including standard morphologic MRI features and clinicopathologic features. We used a commercial software and analyzed texture features from pretreatment and midtreatment MRI. A random forest (RF) method was performed to build a model for predicting recurrence. The diagnostic performance of this model for predicting recurrence was assessed and compared with those of five other machine learning classifiers using the Wald test. Results: Of the 130 women, 21 (16.2%) developed recurrence at a median follow-up of 35.4 months. The RF classifier with common features including clinicopathologic and morphologic MRI features showed the lowest diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.83). The texture analysis with the RF method showed the highest diagnostic performances for pretreatment T2-weighted images and midtreatment DWI and ADC maps showed better diagnostic performance than that of an analysis of common features (AUC, 0.94 vs. 0.83, p < 0.05). The RF model based on all sequences showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers. Conclusions: Texture analysis using an RF model for pretreatment and midtreatment MRI may provide valuable prognostic information for predicting recurrence in patients with breast cancer treated with NAC and surgery. Key points: • RF model-based texture analysis showed a superior diagnostic performance than traditional MRI and clinicopathologic features (AUC, 0.94 vs.0.83, p < 0.05) for predicting recurrence in breast cancer after NAC. • Texture analysis using RF classifier showed the highest diagnostic performances (AUC, 0.94) for pretreatment T2-weighted images and midtreatment DWI and ADC maps. • RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer International | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Breast Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Breast Neoplasms* / drug therapy | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Magnetic Resonance Imaging | - |
dc.subject.MESH | Neoadjuvant Therapy* | - |
dc.subject.MESH | Neoplasm Recurrence, Local / diagnostic imaging | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Na Lae Eun | - |
dc.contributor.googleauthor | Daesung Kang | - |
dc.contributor.googleauthor | Eun Ju Son | - |
dc.contributor.googleauthor | Ji Hyun Youk | - |
dc.contributor.googleauthor | Jeong-Ah Kim | - |
dc.contributor.googleauthor | Hye Mi Gweon | - |
dc.identifier.doi | 10.1007/s00330-021-07816-x | - |
dc.contributor.localId | A00265 | - |
dc.contributor.localId | A00888 | - |
dc.contributor.localId | A01988 | - |
dc.contributor.localId | A02537 | - |
dc.contributor.localId | A04778 | - |
dc.relation.journalcode | J00851 | - |
dc.identifier.eissn | 1432-1084 | - |
dc.identifier.pmid | 33693994 | - |
dc.identifier.url | https://link.springer.com/article/10.1007%2Fs00330-021-07816-x | - |
dc.subject.keyword | Breast neoplasms | - |
dc.subject.keyword | Machine learning | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.subject.keyword | Recurrence | - |
dc.contributor.alternativeName | Gweon, Hye Mi | - |
dc.contributor.affiliatedAuthor | 권혜미 | - |
dc.contributor.affiliatedAuthor | 김정아 | - |
dc.contributor.affiliatedAuthor | 손은주 | - |
dc.contributor.affiliatedAuthor | 육지현 | - |
dc.contributor.affiliatedAuthor | 은나래 | - |
dc.citation.volume | 31 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 6916 | - |
dc.citation.endPage | 6928 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, Vol.31(9) : 6916-6928, 2021-09 | - |
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