Faculty Bibliography

PURPOSE/OBJECTIVE:To analyze the impact of tube potential and iodine concentration on the visibility of calcified plaques in coronary computed tomography angiography (cCTA). METHODS & MATERIALS/METHODS:164 consecutive patients (65.9 % men and a mean age of 57.1 ± 11.3 years) with suspected coronary artery disease underwent calcium scoring (CaSc) scan followed by cCTA with topogram-based automated tube voltage selection (70 kV, 80 kV, 90 kV, 100 kV or 120 kV). In 127 Patients (HC), we injected 50 mL of contrast material (CM) with a concentration of 400 mg iodine per ml and in 37 patients (LC) 50 mL iodine concentration of 280 mg/mL. Sensitivity of cCTA for detecting calcified plaques was calculated with CaSc serving as gold standard. Density of CM enhanced coronary vessels and calcified plaques were quantified by region-of-interest (ROI) measurements in unenhanced and cCTA image series. RESULTS:Overall sensitivity of cCTA to detect calcified plaques was significantly higher using LC compared to HC (79 % vs. 73 %; p = 0.0035). The impact of LC was impressive at 70 kV with an improved sensitivity of 70 % vs. 57.1 % in HC (p = 0.0082). Furthermore, density values of HC enhanced coronary vessels exceeded those of calcified plaques, especially at low kV levels. In LC, except for the 70 kV setting, higher density values were shown for calculi than enhanced vessels. CONCLUSION/CONCLUSIONS:Low kV cCTA in routine using highly concentrated CM leads to reduced calcified plaque perceptibility and hence potentially underestimation of stenosis. Thus, low kV cCTA using CM with lower iodine concentration is necessary. In addition, a dose reduction up to 77.7 % can also be benefited.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77-0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75-0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

PURPOSE:To investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade. PROCEDURES:This retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference. RESULTS:Radiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI). CONCLUSIONS:Radiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.

PURPOSE:To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. PROCEDURES:In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. RESULTS:For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). CONCLUSIONS:Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

One limitation of mechanical thrombectomy (MT) is clot migration during procedure. This might be caused by abruption of the trapped thrombus at the distal access catheter (DAC) tip during stent-retriever retraction due to the cylindrical shaped tip of the DAC. Aiming to solve this problem, this study evaluates the proof-of-concept of a new designed funnel-shaped tip, in an experimental in vitro setting. Two catheter models, one with a funnel-shaped tip and one with a cylindrical-shaped tip, were compared in an experimental setup. For MT a self-made vessel model and thrombi generated from pig's blood were used. MT was performed 20 times for each device using two different stent-retrievers, 10 times respectively. For the funnel-shaped model: for both stent-retrievers (Trevo XP ProVue 3/20 mm; Trevo XP ProVue 4/20 mm) MT was successful at first pass in 9/10 (90%), respectively. For the cylindrical-shaped model: MT was successful at first pass in 5/10 (50%) with the smaller stent-retriever and in 6/10 (60%) with the larger stent-retriever. The experiments show a better recanalization rate for funnel-shaped tips, than for cylindrical-shaped tips. These results are indicating a good feasibility for this new approach, thus the development of a prototype catheter seems reasonable.

PURPOSE/OBJECTIVE:To define optimal kiloelectron volt (keV) settings for virtual monoenergetic imaging (VMI) reconstruction at dual-energy coronary computed tomography angiography (DE-CCTA). METHOD/METHODS:Fifty-one DE-CCTA data sets (33 men; mean age, 63.9 ± 13.2 years) were reconstructed as standard linearly-blended images (F_0.6; 60% of 90 kVp, 40% of 150 kVpSn), and with traditional (VMI) and noise-optimised (VMI+) algorithms from 40 to 100 keV in 10-keV intervals. Objective image quality was assessed with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. Three observers subjectively evaluated vascular contrast, image sharpness, noise and delineation of coronary plaques. RESULTS:Median values for objective image analysis were highest in VMI + series at 40 keV (SNR, 44.5; CNR: 33.5), significantly superior (allp < 0.001) to the best VMI series at 70 keV (SNR, 28.1; CNR, 18.4) and standard F_0.6 images (SNR, 23.2; CNR, 15.6). Overall subjective metrics achieved higher scores at 40-keV VMI+ series in comparison to 70-keV VMI series and F_0.6 images (all p < 0.001), with optimal vascular contrast (5; ICC, 0.90), good image sharpness (4; 0.88), low noise (4; 0.82), and optimal plaque delineation (5; 0.89). CONCLUSIONS:DE-CCTA image reconstruction with 40-keV VMI + allows for significant improvement of both objective and subjective image quality.

The rationale was to assess whether there are differences in multiparametric ¹⁸F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined ¹⁸F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer ¹⁸F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from ¹⁸F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in ¹⁸F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric ¹⁸F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

BACKGROUND:Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI. METHODS:/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured. RESULTS:There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = - 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy. CONCLUSIONS:Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.