Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer

• Published in: European radiology Vol. 32; no. 2; pp. 853 - 863
• Main Authors: Song, Sung Eun, Cho, Kyu Ran, Cho, Yongwon, Kim, Kwangsoo, Jung, Seung Pil, Seo, Bo Kyoung, Woo, Ok Hee
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022; Springer Nature B.V
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Breast; Breast cancer; Breast Neoplasms - diagnostic imaging; Cancer; Diagnostic Radiology; Diagnostic systems; Diffusion; Diffusion coefficient; Diffusion Magnetic Resonance Imaging; Female; Humans; Image enhancement; Imaging; Internal Medicine; Interventional Radiology; Ki-67 Antigen; Learning algorithms; Machine Learning; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Multiparametric Magnetic Resonance Imaging; Multivariate analysis; Neuroradiology; Performance prediction; Prediction models; Radiology; Regression analysis; Retrospective Studies; Ultrasound; Breast cancer; Ki-67 antigen, diagnosis, computer-assisted; Magnetic resonance imaging; Machine learning
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-021-08127-x

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 × 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. Key Points • Among multiparametric MRI features, kinetic feature of washout component >13.5% and intratumoral high signal intensity on T2-weighted image were associated with higher Ki-67. • Washout component >15.5%, rim enhancement, and mean apparent diffusion coefficient value < 0.945 × 10 -3 mm 2 /s were associated with higher histologic grade. • Machine learning–based prediction models incorporating multiparametric MRI features showed good diagnostic performance for Ki-67 and histologic grade in luminal breast cancers.