Faculty Bibliography

Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.

OBJECTIVES/OBJECTIVE:To assess whether no enhancement on pre-treatment MRI can rule out malignancy of additional US mass(es) initially assessed as BI-RADS 3 or 4 in women with newly diagnosed breast cancer. METHODS:This retrospective study included consecutive women from 2010-2018 with newly diagnosed breast cancer; at least one additional breast mass (distinct from index cancer) assigned a BI-RADS 3 or 4 on US; and a bilateral contrast-enhanced breast MRI performed within 90 days of US. All malignant masses were pathologically proven; benign masses were pathologically proven or defined as showing at least 2 years of imaging stability. Incidence of malignant masses and NPV were calculated on a per-patient level using proportions and exact 95% CIs. RESULTS:In 230 patients with 309 additional masses, 140/309 (45%) masses did not enhance while 169/309 (55%) enhanced on MRI. Of the 140 masses seen in 105 women (mean age, 54 years; range 28-82) with no enhancement on MRI, all had adequate follow-up and 140/140 (100%) were benign, of which 89/140 (63.6%) were pathologically proven and 51/140 (36.4%) demonstrated at least 2 years of imaging stability. Pre-treatment MRI demonstrating no enhancement of US mass correlate(s) had an NPV of 100% (95% CI 96.7-100.0). CONCLUSIONS:All BI-RADS 3 and 4 US masses with a non-enhancing correlate on pre-treatment MRI were benign. The incorporation of MRI, when ordered by the referring physician, may decrease unnecessary follow-up imaging and/or biopsy if the initial US BI-RADS assessment and management recommendation were to be retrospectively updated. KEY POINTS/CONCLUSIONS:• Of 309 BI-RADS 3 or 4 US masses with a corresponding mass on MRI, 140/309 (45%) demonstrated no enhancement whereas 169/309 (55%) demonstrated enhancement • All masses classified as BI-RADS 3 or 4 on US without enhancement on MRI were benign • MRI can rule out malignancy in non-enhancing US masses with an NPV of 100.

OBJECTIVES/OBJECTIVE:To compare dynamic magnetic resonance imaging (MRI) with videofluoroscopy (VFS) regarding image quality and assessment of gap size between soft palate (SP) and posterior pharyngeal wall (PPW) in children and adolescents following surgical correction of velopharyngeal dysfunction (VPD). METHODS:Twenty-one patients undergoing unenhanced 3-T MRI and contrast-enhanced VFS were included in this IRB-approved prospective study. The MRI scan protocol comprised refocused gradient-echo sequences in transverse and sagittal planes during speech, with TE 1.97 ms, TR 3.95 ms, flip angle 8°, matrix size 128 × 128, and 5-mm slice thickness. Radial k-space sampling and sliding window reconstruction were used to achieve an image acquisition rate of 28 frames per second (fps). VFS with 30 fps was similarly performed in both planes. Closure of the velopharyngeal port during phonation was evaluated by two experienced radiologists. RESULTS:Eleven (52.4%) patients displayed a complete closure, whereas ten (47.6%) patients showed a post-operative gap during speech. VFS and MRI equally identified the cases with persistent or recurrent VPD. Differences in SP-PPW distance between VFS (3.9 ± 1.6 mm) and MRI (4.1 ± 1.5 mm) were not statistically significant (p = 0.5). The subjective overall image quality of MRI was rated inferior (p < 0.001) compared with VFS, with almost perfect inter-rater agreement (κ = 0.90). The presence of susceptibility artifacts did not limit anatomical measurements. CONCLUSION/CONCLUSIONS:Dynamic MRI is equally reliable as VFS to assess persistent or recurrent inadequate velum closure in patients following surgical treatment of VPD. KEY POINTS/CONCLUSIONS:• Unenhanced 3-T dynamic MRI and contrast-enhanced videofluoroscopy are equally useful for the identification of patients with incomplete velopharyngeal closure during speech. • MRI using refocused gradient-echo acquisition with radial k-space sampling and sliding window reconstruction generates diagnostic images with 28 frames per second. • MRI can offer a radiation-free alternative to currently established videofluoroscopy for young patients.

PURPOSE/OBJECTIVE:The aim of this study was to evaluate the feasibility and the accuracy of a secondary, metachronous ultrasound (US)-guided marking of the stereotactic vacuum-assisted breast biopsy (ST-VABB) area. MATERIALS AND METHODS/METHODS:The institutional ethics committee approved the study. The retrospective study included 98 patients. In ST-VABB of 45 women, no tissue markers were deployed at the biopsy site, even if no residual calcifications remained. After histology proved the necessity for a subsequent operation, the biopsy site was marked under US guidance using a coil marker. All interventions were technically successful. No complications occurred. Mammography was done to visualize the coil deployment. The distances from the center of the lesion and the biopsy cavity to the coil location were measured in both planes to evaluate the accuracy of the marking procedure. RESULTS:In 24 of the 46 cases, the whole lesion was biopsied without residual elements. The mean time between ST-VABB and sonographic marking of the lesion was 9.7 days (median 6.5). The biopsy cavity could be detected in 40 (87%) cases and thus marked exactly. The mean time of US-guided marking was 12.5 min. The mean distance between the coil and the target lesion was 0.6 ± 1.5 cm in the craniocaudal (cc) view and 0.5 ± 1.5 cm in the mediolateral (ml) view for all markings. The mean delta value from the distance nipple-original lesion and from the distance nipple-coil was 0.85 ± 1.2 cm (median 0.5) in the cc view and 0.88 ± 1.2 cm (median 0.6) in the ml view for all cases. Clip migration was not observed. CONCLUSION/CONCLUSIONS:Our study demonstrates the feasibility and the technical success of secondary metachronous coil marking of the biopsy site under US guidance after receipt of histology. This approach seems to be a cost-effective alternative to the standard procedure of the primary coil marking especially in all completely removed lesions. It may offer advantages for allergic patients.

AIM/OBJECTIVE:To assess the prevalence and prognostic significance of NI-LGE in patients undergoing stress-CMR. METHODS:Stress-CMR with either dipyridamole or adenosine was performed in 283 patients (228 men, 81%) including perfusion imaging, wall motion evaluation and LGE. Follow-up was completed in all enrolled patients (median time: 1850 days; interquartile range: 1225-2705 days). Composite endpoint included cardiac death, ventricular tachycardia, myocardial infarction, stroke, hospitalization for cardiac cause and coronary revascularization performed beyond 90 days from stress-CMR scans. RESULTS:One hundred and twelve patients (40%) had negative LGE (no-LGE), 140 patients (49%) I-LGE and 31 patients (11%) NI-LGE. Twenty-five events occurred in the no-LGE group, 68 in I-LGE and 11 in the NI-LGE group. On survival curves, patients with NI-LGE had worse prognosis than patients with no-LGE regardless of the presence of inducible perfusion defects. No significant prognostic differences were found between I-LGE and NI-LGE. CONCLUSION/CONCLUSIONS:NI-LGE can be detected in 11% of patients during stress-CMR providing a diagnosis of nonischemic cardiac disease. Patients with NI-LGE have worse prognosis than those with no-LGE.

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.