Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients

Abstract

Purpose: To assess the potential of machine learning with multiparametric MRI (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients. Materials and Methods: This IRB-approved prospective study included 38 women (median age 46.5 years; range 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3T with DCE, DWI and T2-weighted imaging prior to and after two cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS, quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time) and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, eight classifiers including linear support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), stochastic gradient descent (SGD), decision tree, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) were employed to rank the features. Histopathologic Residual Cancer Burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS) and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

“…roughout this work, we developed an automated machine-learning technique for breast cancer diagnosis. e Model Accuracy (%) NN [22] 96.47 KNN [19] 97.51 LR [29] 92 NB [23] 97.36 NB [26] 82 e proposed model 98.24…”

“…Tahmassebi et al [29] applied magnetic resonance methods for detecting breast cancer in women (average age: 46.5 years, range: 25-70 years). Eight classifiers were used to categorize features, including linear SVM, linear discrimination analysis, logistic regression, decision tree, adaptive enhancement, and enhanced gradient extreme (XGBoost).…”

Image-Based Ultrasomics Classifiers: A Potential Method to Noninvasively Predict the Proliferation-Related Biomarker Ki67

“…In our investigation, the relationship between the quantitative sonographic features of breast cancer in a single or combined transverse/sagittal/coronal section image and the proliferation-related biomarker Ki67 was objectively assessed by using ultrasomics method. Compared with traditional statistical methods, which usually consider and evaluate limited assumptions, machine learning methods are superior in generating models to analyze images for prediction by extensively searching models and parameter spaces [32]. Currently, many definitive diagnoses have been made for breast disease by using the computational algorithms of ultrasomics, including identifying benign and malignant breast lesions based on the texture features of ultrasound images [33], predicting axillary lymph node metastasis and associated potential biomarkers in breast cancer [34], and analyzing the biological behavior of infiltrating ductal carcinoma of the breast [3].…”

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

“…Predicting response on pretherapy imaging would allow for better timing between surgery and chemotherapy, and provide individualized prognosis for the patient. This has been explored by multiple groups, using texture, 112,117 enhancement, 113,118 multiparametric features, 119 and lymph node features. 120 In addition, a relatively new area of study is using unsupervised machine-learning techniques to predict pCR.…”