Clinical assessment of image quality, usability and patient comfort in dedicated spiral breast computed tomography

OBJECTIVE To investigate aspects of image quality, feasibility and patient comfort in dedicated spiral breast computed tomography (B-CT) in a large patient cohort. METHODS This retrospective study was approved by the institutional review board. 2418 B-CT scans from 1222 women examined between 04/16/2019 and 04/13/2022 were analyzed. Patients evaluated their comfort during the examination, radiographers carrying out the scans evaluated the patient's mobility and usability of the B-CT device, whereas radiologists assessed lesion contrast, detectability of calcifications, breast coverage and overall image quality. For semi-quantitative assessment, a Likert-Scale was used and statistical significance and correlations were calculated using ANOVAs and Spearman tests. RESULTS Comfort, mobility and usability of the B-CT were rated each with either "no" or "negligible" complaints in >99%. Image quality was rated with "no" or "negligible complaints" in 96.7%. Lesion contrast and detectability of calcifications were rated either "optimal" or "good" in 92.6% and 98.4%. "Complete" and "almost complete" breast coverage were reported in 41.9%, while the pectoral muscle was found not to be covered in 56.0%. Major parts of the breast were not covered in 2.1%. Some variables were significantly correlated, such as age with comfort (ρ = -0.168, p < .001) and mobility (ρ = -0.172, p < .001) as well as patient weight with lesion contrast (ρ = 0.172, p < .001) and breast coverage (ρ = -0.109, p < .001). CONCLUSIONS B-CT provides high image quality and contrast of soft tissue lesions as well as calcifications, while covering the pre-pectoral areas of the breast remains challenging. B-CT is easy to operate for the radiographer and comfortable for the majority of women

Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle

OBJECTIVE In this study, we investigate the feasibility of a deep Convolutional Neural Network (dCNN), trained with mammographic images, to detect and classify microcalcifications (MC) in breast-CT (BCT) images. METHODS This retrospective single-center study was approved by the local ethics committee. 3518 icons generated from 319 mammograms were classified into three classes: "no MC" (1121), "probably benign MC" (1332), and "suspicious MC" (1065). A dCNN was trained (70% of data), validated (20%), and tested on a "real-world" dataset (10%). The diagnostic performance of the dCNN was tested on a subset of 60 icons, generated from 30 mammograms and 30 breast-CT images, and compared to human reading. ROC analysis was used to calculate diagnostic performance. Moreover, colored probability maps for representative BCT images were calculated using a sliding-window approach. RESULTS The dCNN reached an accuracy of 98.8% on the "real-world" dataset. The accuracy on the subset of 60 icons was 100% for mammographic images, 60% for "no MC", 80% for "probably benign MC" and 100% for "suspicious MC". Intra-class correlation between the dCNN and the readers was almost perfect (0.85). Kappa values between the two readers (0.93) and the dCNN were almost perfect (reader 1: 0.85 and reader 2: 0.82). The sliding-window approach successfully detected suspicious MC with high image quality. The diagnostic performance of the dCNN to classify benign and suspicious MC was excellent with an AUC of 93.8% (95% CI 87, 4%-100%). CONCLUSION Deep convolutional networks can be used to detect and classify benign and suspicious MC in breast-CT images

Systematic analysis of changes in radiomics features during dynamic breast-MRI: Evaluation of specific biomarkers

OBJECTIVES In this retrospective, single-center study we investigate the changes of radiomics features during dynamic breast-MRI for healthy tissue compared to benign and malignant lesions. METHODS 60 patients underwent breast-MRI using a dynamic 3D gradient-echo sequence. Changes of 34 texture features (TF) in 30 benign and 30 malignant lesions were calculated for 5 dynamic datasets and corresponding 4 subtraction datasets. Statistical analysis was performed with ANOVA, and systematic changes in features were described by linear and polynomial regression models. RESULTS ANOVA revealed significant differences (p < 0.05) between normal tissue and lesions in 13 TF, compared to 9 TF between benign and malignant lesions. Most TF showed significant differences in early dynamic and subtraction datasets. TF associated with homogeneity were suitable to discriminate between healthy parenchyma and lesions, whereas run-length features were more suitable to discriminate between benign and malignant lesions. Run length nonuniformity (RLN) was the only feature able to distinguish between all three classes with an AUC of 88.3%. Characteristic changes were observed with a systematic increase or decrease for most TF with mostly polynomial behavior. Slopes showed earlier peaks in malignant lesions, compared to benign lesions. Mean values for the coefficient of determination were higher during subtraction sequences, compared to dynamic sequences (benign: 0.98 vs 0. 72; malignant: 0.94 vs 0.74). CONCLUSIONS TF of breast lesions follow characteristic patterns during dynamic breast-MRI, distinguishing benign from malignant lesions. Early dynamic and subtraction datasets are particularly suitable for texture analysis in breast-MRI. Features associated with tissue homogeneity seem to be indicative of benign lesions

Dedicated breast computed-tomography in women with a personal history of breast cancer: A proof-of-concept study.

PURPOSE To compare the subjective image quality assessment using B-CT and digital mammography in women with personal history of breast cancer (PHBC). METHOD In this retrospective study 32 patients with PHBC were included. Each patient had undergone a B-CT examination and a previous mammogram in a time interval of less than 18 months between the two examinations. Two radiologists evaluated the two examinations independently with regard to the presence of lesions, BI-RADS classification, level of confidence for the overall exam interpretation, scar evaluation and image quality including image degradation due to clip artifacts. Level of confidence and image quality were assessed using a 5-point Likert scale. A p-value of less than 0.01 was considered statistically significant. RESULTS Thirty-seven operated and 27 non-operated breasts were included. Confidence for the overall interpretation with B-CT was equal or superior to mammography in 63 cases (98.4 %) for reader 1 and in 58 cases (90.6 %) for reader 2 (p <.001). Confidence for scar evaluation with B-CT was equal or superior to mammography in all cases for reader 1 and in 34 cases (91.9 %) for readers 2 (p <.001). One case with local recurrence in B-CT was identified by both readers and no false positive findings were reported. A moderate to high image degradation due to beam-hardening artifacts has been reported by both readers in 29.4 % of cases due to surgical clips in the B-CT volume. CONCLUSIONS B-CT in patients with PHBC provides high quality images that can be evaluated with confidence equal or superior to mammography

Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification

BACKGROUND We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT). METHODS In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a-d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set. RESULTS Of the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected. CONCLUSION TA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool

Breast density in dedicated breast computed tomography: Proposal of a classification system and interreader reliability

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

Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

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

Discrepancies between radiological and histological findings in preoperative core needle (CNB) and vacuum-assisted (VAB) breast biopsies

BACKGROUND: Ultrasound (US)-guided breast biopsy is a routine diagnostic method used to correlate imaging finding to a histological diagnosis which is still the gold standard in preoperative diagnostics. The accuracy of US-guided breast biopsies relies on a precise radiologic-histopathologic correlation, which is discussed amongst an interdisciplinary team of gynecologists, radiologists and pathologists. However, false-negative or non-diagnostic biopsy results occur. Hence, a thorough and honest discussion to clarify the reason for discrepancies and to decide the next diagnostic step between specialists of the different disciplines is warranted. In this retrospective study, we analyzed discrepant findings between imaging and pathology results on preoperative breast biopsies. METHODS: Core and vacuum-assisted breast biopsies from 232 patients were included in this study. Inclusion criteria were (1) non-diagnostic (B1) category on histology independent from imaging category and (2) histological benign (B2) category with a BIRADS 5 (Breast Imaging Reporting and Data System) rating on imaging. Histological diagnoses were retrieved from all cases. Follow-up data were available in most cases. RESULTS: 138 biopsies were classified as B1, 94 biopsies as B2 category. 51 of 138 B1 cases (37%) underwent re-biopsy. Re-biopsy found malignancy (B5) in 19 of 51 cases, and B3/4 (premalignant) lesions in 3 of 51 cases. All B2 cases underwent second-look imaging-diagnosis, in 57 of 94 cases (66%) consecutive direct surgery or re-biopsy. Of these, malignancy was diagnosed histologically in 26 of 57 cases (45.6%). CONCLUSION: Determining imaging-pathology concordance after US-guided breast biopsy is essential. Discrepant cases and further diagnostic steps need to be discussed with an interdisciplinary approach

Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

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

Breast Computed Tomography: Diagnostic Performance of the Maximum Intensity Projection Reformations as a Stand-Alone Method for the Detection and Characterization of Breast Findings

Objectives: This study aimed to evaluate the diagnostic performance of the maximum intensity projection (MIP) reformations of breast computed tomography (B-CT) images as a stand-alone method for the detection and characterization of breast imaging findings. Materials and methods: A total of 160 women undergoing B-CT between August 2018 and December 2020 were retrospectively included; 80 patients with known breast imaging findings were matched with 80 patients without imaging findings according to age and amount of fibroglandular tissue (FGT). A total of 71 benign and 9 malignant lesions were included. Images were evaluated using 15-mm MIP in 3 planes by 2 radiologists with experience in B-CT. The presence of lesions and FGT were evaluated, using the BI-RADS classification. Interreader agreement and descriptive statistics were calculated. Results: The interreader agreement of the 2 readers for finding a lesion (benign or malignant) was 0.86 and for rating according to BI-RADS classification was 0.82. One of 9 cancers (11.1%) was missed by both readers due to dense breast tissue. BI-RADS 1 was correctly applied to 73 of 80 patients (91.3%) by reader 1 and to 74 of 80 patients (92.5%) by reader 2 without recognizable lesions. BI-RADS 2 or higher with a lesion in at least one of the breasts was correctly applied in 69 of 80 patients (86.3%) by both readers. For finding a malignant lesion, sensitivity was 88.9% (95% confidence interval [CI], 51.75%-99.72%) for both readers, and specificity was 99.3% (95% CI, 96.4%-100%) for reader 1 and 100% (95% CI, 97.20%-100.00%) for reader 2. Conclusions: Evaluation of B-CT images using the MIP reformations may help to reduce the reading time with high diagnostic performance and confidence