Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging

• Published in: Frontiers in oncology Vol. 12; p. 1032809
• Main Authors: Lee, Hyo-Jae, Park, Jae Hyeok, Nguyen, Anh-Tien, Do, Luu-Ngoc, Park, Min Ho, Lee, Ji Shin, Park, Ilwoo, Lim, Hyo Soon
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 02-11-2022
• Subjects: ductal carcinoma in situ; histologic upgrade; machine learning; magnetic resonance imaging; Oncology; radiomics analysis; support vector machine; ductal carcinoma in situ; magnetic resonance imaging; support vector machine; machine learning; histologic upgrade; radiomics analysis
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2022.1032809

To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma
(DCIS) diagnosed by core needle biopsy (CNB) after surgical excision.
This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set.
The model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619-0.829) and 0.742 (0.623-0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575-0.892) and 0.7 (0.558-0.842), 0.714 (0.608-0.820) and 0.767 (0.651-0.882), respectively.
Our study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.