A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

Abstract

ObjectiveTo develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI).MethodsEighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation. Radiomic features based on the ADC map were generated automatically by Artificial Intelligence Kit software (A.K. software; GE Healthcare, China). Feature reduction was conducted using the Mann-Whitney test and Spearman correlation after pre-treatment. A prediction model of ADC radiomics was established by logistic linear regression and cross-validation. A nomogram was established based on ADC radiomic features, pharmacokinetics and clinical features, including the morphology and ADC value for breast BI-RADS 4 lesions on MRI.ResultsA total of 396 radiomic features were extracted automatically by the A.K. software. Five features were selected after pre-processing, Mann-Whitney tests and Spearman correlation analysis. The area under the ROC curve of the prediction model comprising ADC radiomic features was 0.79 when the cutoff value was 0.45, and the accuracy, sensitivity and specificity were 80.0%, 0.813 and 0.778, respectively. A visualized differential nomogram based on the radiomic score, pharmacokinetics and clinical features was established. The decision curve showed good consistency.ConclusionsADC radiomic features could provide an important reference for differential diagnosis between benign and malignant lesions in suspicious BI-RADS 4 lesions. The visualized nomogram based on ADC radiomic features, pharmacokinetics and clinical features may have good prospects for clinical application.

Machine learning in breast MRI

“…Other than DCE‐MRI, MR sequences such as nonenhanced T 1 ‐weighted images, diffusion‐weighted images, and T 2 ‐weighted images have been used to improve lesion characterization. Investigators have also developed multiparametric models combining diffusion‐weighted imaging with DCE‐MR for lesion discrimination, with accuracies up to 0.93. Specific features that were significantly different between benign and malignant lesions included entropy and signal enhancement ratio .…”

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

“…Since BI-RADS 5 lesions have typical signs of malignancy, and almost all lesions are malignant (>95% risk of malignancy), the inclusion of BI-RADS 5 lesions may improve the prediction performance of the radiomic nomogram to some extent. Similarly, Hu et al [ 15 ] developed and validated an MRI radiomic nomogram for differentiating benign and malignant BI-RADS 4 lesions with an AUC of 0.79 in the testing set, which was significantly lower than that of our ABVS radiomic nomogram (AUC, 0.925), and the possible reasons are as follow. First, we included a larger sample size of BI-RADS 4 lesions (223 vs. 86), which may be the main reason.…”

“…It can extract high-throughput quantitative features that may not be observed directly by the naked eye from single or multiple medical images, and subsequently combine these features with clinical information to improve disease diagnosis and prognostic evaluation [ 11 ]. A large number of studies have reported the application of radiomic nomograms based on various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and US, which have showed great potential in the classification and prediction of breast cancer [ 12 , 13 , 14 , 15 , 16 ]. However, only a few studies have focused on BI-RADS 4 lesions [ 15 , 16 ], and no studies have been based on automatic breast volume scanner (ABVS).…”

“…A large number of studies have reported the application of radiomic nomograms based on various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and US, which have showed great potential in the classification and prediction of breast cancer [ 12 , 13 , 14 , 15 , 16 ]. However, only a few studies have focused on BI-RADS 4 lesions [ 15 , 16 ], and no studies have been based on automatic breast volume scanner (ABVS).…”

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

“…Radiomics can be combined with the imaging appearance to further improve the differential diagnosis ability of the lesion (30,31). The AK, an imaging analytic software used in this study, has been used in many research reports (32,33). A previous study showed that radiomic feature-based CT imaging signatures allow the prediction of lymph node metastasis in cancer and could facilitate the preoperative individualized prediction of lymph node status (20).…”