Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer
- Authors
- Song, Sung Eun; Cho, Kyu Ran; Cho, Yongwon; Kim, Kwangsoo; Jung, Seung Pil; Seo, Bo Kyoung; Woo, Ok Hee
- Issue Date
- 2월-2022
- Publisher
- SPRINGER
- Keywords
- Breast cancer; Magnetic resonance imaging; Ki-67 antigen; diagnosis; computer-assisted; Machine learning
- Citation
- EUROPEAN RADIOLOGY, v.32, no.2, pp.853 - 863
- Indexed
- SCIE
SCOPUS
- Journal Title
- EUROPEAN RADIOLOGY
- Volume
- 32
- Number
- 2
- Start Page
- 853
- End Page
- 863
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/135363
- DOI
- 10.1007/s00330-021-08127-x
- ISSN
- 0938-7994
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Medicine > Department of Medical Science > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.