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Erschienen in:
12.08.2021 | Breast
Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer
Erschienen in: European Radiology | Ausgabe 2/2022
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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 mm2/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.