Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer
DC Field | Value | Language |
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dc.contributor.author | Song, Sung Eun | - |
dc.contributor.author | Cho, Kyu Ran | - |
dc.contributor.author | Cho, Yongwon | - |
dc.contributor.author | Kim, Kwangsoo | - |
dc.contributor.author | Jung, Seung Pil | - |
dc.contributor.author | Seo, Bo Kyoung | - |
dc.contributor.author | Woo, Ok Hee | - |
dc.date.accessioned | 2022-02-11T17:41:03Z | - |
dc.date.available | 2022-02-11T17:41:03Z | - |
dc.date.created | 2022-02-07 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135363 | - |
dc.description.abstract | Objectives To investigate whether machine learning-based prediction models using 3-T multiparametric MRI (mpMRI) can predict Ki-67 and histologic grade in stage I-II luminal cancer. Methods Between 2013 and 2019, consecutive women with luminal cancers who underwent preoperative MRI with diffusion-weighted imaging (DWI) and surgery were included. For prediction models, morphology, kinetic features using computer-aided diagnosis (CAD), and apparent diffusion coefficient (ADC) at DWI were evaluated by two radiologists. Logistic regression analysis was used to identify mpMRI features for predicting Ki-67 and grade. Diagnostic performance was assessed using eight machine learning algorithms incorporating mpMRI features and compared using the DeLong method. Results Of 300 women, 203 (67.7%) had low Ki-67 and 97 (32.3%) had high Ki-67; 242 (80.7%) had low grade and 58 (19.3%) had high grade. In multivariate analysis, independent predictors for higher Ki-67 were washout component > 13.5% (odds ratio [OR] = 4.16; p < 0.001) and intratumoral high SI on T2-weighted image (OR = 1.89; p = 0.022). Those for higher grade were washout component > 15.5% (OR = 7.22; p < 0.001), rim enhancement (OR = 2.59; p = 0.022), and ADC value < 0.945 x 10(-3) mm(2)/s (OR = 2.47; p = 0.015). Among eight models using these predictors, six models showed the equivalent performance for Ki-67 (area under the receiver operating characteristic curve [AUC]: 0.70) and Naive Bayes classifier showed the highest performance for grade (AUC: 0.79). Conclusions A prediction model incorporating mpMRI features shows good diagnostic performance for predicting Ki-67 and histologic grade in patients with luminal breast cancers. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | INTERNATIONAL EXPERT CONSENSUS | - |
dc.subject | APPARENT DIFFUSION-COEFFICIENT | - |
dc.subject | PROGNOSTIC-FACTOR | - |
dc.subject | IMAGING FEATURES | - |
dc.subject | PRIMARY THERAPY | - |
dc.subject | SUBTYPES | - |
dc.subject | ASSOCIATION | - |
dc.subject | CLASSIFICATION | - |
dc.subject | HIGHLIGHTS | - |
dc.subject | SURVIVAL | - |
dc.title | Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cho, Kyu Ran | - |
dc.contributor.affiliatedAuthor | Cho, Yongwon | - |
dc.identifier.doi | 10.1007/s00330-021-08127-x | - |
dc.identifier.scopusid | 2-s2.0-85112373880 | - |
dc.identifier.wosid | 000684531800004 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, v.32, no.2, pp.853 - 863 | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.citation.title | EUROPEAN RADIOLOGY | - |
dc.citation.volume | 32 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 853 | - |
dc.citation.endPage | 863 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | INTERNATIONAL EXPERT CONSENSUS | - |
dc.subject.keywordPlus | APPARENT DIFFUSION-COEFFICIENT | - |
dc.subject.keywordPlus | PROGNOSTIC-FACTOR | - |
dc.subject.keywordPlus | IMAGING FEATURES | - |
dc.subject.keywordPlus | PRIMARY THERAPY | - |
dc.subject.keywordPlus | SUBTYPES | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | HIGHLIGHTS | - |
dc.subject.keywordPlus | SURVIVAL | - |
dc.subject.keywordAuthor | Breast cancer | - |
dc.subject.keywordAuthor | Magnetic resonance imaging | - |
dc.subject.keywordAuthor | Ki-67 antigen | - |
dc.subject.keywordAuthor | diagnosis | - |
dc.subject.keywordAuthor | computer-assisted | - |
dc.subject.keywordAuthor | Machine learning | - |
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