ObjectivesThe aim of this study was to investigate the feasibility, the image quality, and the correlation with histology of dedicated spiral breast computed tomography (B-CT) equipped with a photon-counting detector in patients with suspicious breast lesions after application of iodinated contrast media.Materials and MethodsThe local ethics committee approved this prospective study. Twelve women with suspicious breast lesions found in mammography or B-CT underwent contrast-enhanced spiral B-CT and supplementary ultrasound. For all lesions, biopsy-proven diagnosis and histological workup after surgical resection were obtained including the size of cancer/ductal carcinoma in situ, which were correlated to sizes measured in B-CT. Signal-to-noise ratio and contrast-to-noise ratio were evaluated for tumor, glandular tissue, and fatty tissue.ResultsOf the 12 patients, 15 suspicious lesions were found, 14 were malignant, and 1 benign lesion corresponded to a chronic inflammation. All lesions showed strong contrast media uptake with a signal-to-noise ratio of 119.7 ± 52.5 with a contrast-to-noise ratio between glandular tissue and breast cancer lesion of 12.6 ± 5.9. The correlation of the size of invasive tumors measured in B-CT compared with histological size was significant and strong R = 0.77 (P < 0.05), whereas the correlation with the size of the peritumoral ductal carcinoma in situ was not significant R = 0.80 (P = 0.11).ConclusionsContrast-enhanced B-CT shows high contrast between breast cancer and surrounding glandular tissue; therefore, it is a promising technique for cancer detection and staging depicting both soft tissue lesions and microcalcifications, which might be a substantial advantage over breast MRI.

The aim of this study was to develop a new breast density classification system for dedicated breast computed tomography (BCT) based on lesion detectability analogous to the ACR BI-RADS breast density scale for mammography, and to evaluate its interrater reliability. In this retrospective study, 1454 BCT examinations without contrast media were screened for suitability. Excluding datasets without additional ultrasound and exams without any detected lesions resulted in 114 BCT examinations. Based on lesion detectability, an atlas-based BCT density (BCTD) classification system of breast parenchyma was defined using 4 categories. Interrater reliability was examined in 40 BCT datasets between 3 experienced radiologists. Among the included lesions were 63 cysts (55%), 18 fibroadenomas (16%), 7 lesions of fatty necrosis (6%), and 6 breast cancers (5%) with a median diameter of 11 mm. X-ray absorption was identical between lesions and breast tissue; therefore, the lack of fatty septae was identified as the most important criteria for the presence of lesions in glandular tissue. Applying a lesion diameter of 10 mm as desired cut-off for the recommendation of an additional ultrasound, an atlas of 4 BCTD categories was defined resulting in a distribution of 17.5% for density A, 39.5% (B), 31.6% (C), and 11.4% (D) with an intraclass correlation coefficient (ICC) among 3 readers of 0.85 to 0.87. We propose a dedicated atlas-based BCTD classification system, which is calibrated to lesion detectability. The new classification system exhibits a high interrater reliability and may be used for the decision whether additional ultrasound is recommended.

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria. Subsequently four different dCNN models (differences in optimizer and spatial resolution) were trained (70% of data), validated (20%) and tested on a “real-world” dataset (10%). Moreover, dCNN accuracy was compared to a human readout. The overall performance of the model with lowest resolution of input data was highest, reaching an accuracy on the “real-world” dataset of 85.8%. The intra-class correlation of the dCNN and the two readers was almost perfect (0.92) and kappa values between both readers and the dCNN were substantial (0.71–0.76). Moreover, the diagnostic performance between the readers and the dCNN showed very good correspondence with an AUC of 0.89. Artificial Intelligence in the form of a dCNN can be used for standardized, observer-independent and reliable classification of parenchymal density in a BCT examination.

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