Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

Abstract

Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence

“…Because DBT is a relatively new technology, data availability is limited for training neural networks, but work is being done to possibly circumvent this limitation by using FFDM studies to enrich training with some success [72]. Finally, DL-based CAD is also being developed in contrast-enhanced digital mammography as a natural extension of work in mammography, capitalizing on advantages of functional imaging using contrast enhancement, with studies showing new CAD significantly outperforming traditional CAD and improving specificity as compared with human readers [73, 74].…”

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

“…To date, the evidence for radiomics using CEM is scarce, with only few published studies. Patel et al [24] evaluated the use of a computer-aided diagnosis (CAD)-CEM in the diagnostic performance of CEM, compared with that of experienced radiologists. The authors constructed a predictive model by using a support vector machine (SVM) classification method with a set of both morphologic and textural features extracted from the low-energy and recombined images of 50 lesions.…”

“…Several studies have already reported the value of radiomics analyses of DCE-MRI, including the value of DCE-MRI morphologic and functional radiomic features to provide insight into individual genomic signatures, breast cancer molecular subtypes, and clinically used recurrence scores [7,[20][21][22][23]. There are fewer studies on the value of radiomics analyses of CEM; nevertheless, based on these limited studies, the results have been encouraging [23][24][25]. Recently, we reported preliminary results on the potential of CEM radiomics analysis of 100 patients in a larger-scale CEM-only study [25].…”

Developing global image feature analysis models to predict cancer risk and prognosis

“…Second, the most current CAD schemes are tumorbased schemes aiming to detect suspicious tumors, classify malignant and benign tumors, and predict or assess tumor response to chemotherapies. The challenges of using these tumor-based CAD schemes include (1) high false-positive rates, which may impose a negative impact on radiologists and reduce their image reading performance [17,19], and (2) difficulty and error in tumor segmentation, which reduces the accuracy and robustness of the computed image features [18]. The case-based CAD schemes only use global image features without detecting tumor locations and segmenting tumor regions.…”

“…Despite great research enthusiasm and effort, false-positive detection rates of CAD schemes remain high [16], and whether using CAD can add values in clinical practice to help improve radiologists' performance in reading and interpreting mammograms remains controversial [17]. The technical challenges and limitations in developing CAD schemes may include but not limited to (1) difficulty in accurate segmentation of the targeted tumors from the images due to tissue overlap, connection, and fuzzy boundary, which reduce the accuracy and reproducibility of the computed image features to build robust machine learning models [18]; (2) high false-positive cues in the detection schemes, which can mislead radiologists and reduce their performance [19]; (3) use of small or biased training datasets, which causes overfitting and reduces robustness of CAD schemes when applied to new testing cases [20]; (4) higher correlation of the detection results between CAD and radiologists, which reduces the clinical utility of CAD as "the second reader" [21]; and (5) difficulty in developing multi-image-based CAD schemes [22] to fuse and compare variation of the image features in the longitudinal images [23] or different views of images [24]. Thus, exploring new approaches in developing CAD schemes or machine learning models remains an unsolved but important research topic in the field of CAD-related medical imaging informatics.…”