68Ga-Pentixafor PET/MRI for Treatment Response Assessment in Mantle Cell Lymphoma: Comparison Between Changes in Lesion CXCR4 Expression on PET and Lesion Size and Diffusivity on MRI.

PURPOSE: The aim of this study was to compare CXCR4 imaging with 68Ga-pentixafor PET to MRI for treatment response assessment in patients with mantle cell lymphoma (MCL). PATIENTS AND METHODS: Twenty-two posttreatment 68Ga-pentixafor PET/MRI scans of 16 patients (7 women and 9 men; mean age, 69.9 ± 7.9) with a total of 67 target lesions on baseline PET/MRI were analyzed. Rates of complete remission per lesion and per scan, according to MRI (based on lesion size) and 68Ga-pentixafor PET (based on SUV decrease to lower than liver and blood pool uptake), were compared using McNemar tests. The t tests and Pearson correlation coefficients (r) were used to compare rates of change in lesion diameter products (DPs) on MRI, and standardized uptake values (SUVmax, SUVmean) on PET, relative to baseline. RESULTS: At interim PET/MRI, 18/32 (56.3%) target lesions met CR criteria on 68Ga-pentixafor PET, and 16/32 (50.0%) lesions met size-based MRI criteria for CR (P = 0.63). At end-of-treatment PET/MRI, 40/57 (70.2%) target lesions met 68Ga-pentixafor PET criteria for CR, and 27/57 (47.4%) lesions met size-based MRI criteria for CR (P = 0.021). Complete remission after treatment was observed more frequently on 68Ga-pentixafor PET (11/22 scans, 54.5%) than on MRI (6/22 scans, 27.3%) (P = 0.031). Rates of change did not differ significantly between lesion DP (-69.20% ± 34.62%) and SUVmax (-64.59% ± 50.78%, P = 0.22), or DP and SUVmean (-60.15 ± 64.58, P = 0.064). Correlations were strong between DP and SUVmax (r = 0.71, P < 0.001) and DP and SUVmean (r = 0.73, P < 0.001). CONCLUSIONS: In MCL patients, 68Ga-pentixafor PET may be superior for assessment of complete remission status than anatomic MRI using lesion size criteria, especially at the end of treatment.

Can Dual-energy CT-based Virtual Monoenergetic Imaging Improve the Assessment of Hypodense Liver Metastases in Patients With Hepatic Steatosis?

RATIONALE AND OBJECTIVES: To evaluate the impact of noise-optimized virtual monoenergetic imaging (VMI) on lesion demarcation and measuring accuracy of hypoattenuating liver metastases in patients with fatty liver disease compared to standard reconstructions. MATERIALS AND METHODS: Twenty-eight patients (mean age 62.2 ± 7.7 years) with fatty liver disease and hypoattenuating liver metastases who underwent unenhanced and contrast-enhanced portal-venous dual-energy CT (DECT) were enrolled. Standard linearly blended and VMI series were reconstructed in 10-keV intervals. Lesion-to-parenchyma contrast-to-noise ratio (CNR) was calculated and the best VMI series was further investigated in a subjective evaluation of overall image quality and lesion demarcation. Size measurements were performed independently by measuring all hypodense lesions (n = 58) twice in a predefined sequence. Inter- and intra-rater agreement was assessed using intra-class correlation coefficient (ICC) statistics. RESULTS: The calculated CNR was greatest at 40-keV VMI (4.3 ± 2.6), significantly higher compared to standard reconstructions (2.9 ± 1.9; p < 0.001). Subjective ratings for overall image quality showed no significant difference between the 2 reconstruction techniques (both medians 4; p = 0.147), while lesion margin demarcation was found to be superior for 40-keV VMI (median 5; p ≤ 0.001). Inter- (ICC, 0.98 for 40-keV VMI; ICC, 0.93 for standard reconstruction) and intra-rater (ICC, 0.99 for 40-keV VMI; ICC, 0.94 for standard image series) analysis showed an excellent agreement for lesion measurements in both reconstruction techniques. CONCLUSION: Noise-optimized VMI reconstructions significantly improve contrast and lesion demarcation of hypoattenuating liver metastases in patients with the fatty liver disease compared to standard reconstruction.

Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics.

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.

Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes.

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.

Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results.

BACKGROUND: To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. METHODS: One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), 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 used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. RESULTS: In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). CONCLUSIONS: In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Quantitative in vivo proton MR spectroscopic assessment of lipid metabolism: Value for breast cancer diagnosis and prognosis.

BACKGROUND: Breast magnetic resonance spectroscopy (1 H-MRS) has been largely based on choline metabolites; however, other relevant metabolites can be detected and monitored. PURPOSE: To investigate whether lipid metabolite concentrations detected with 1 H-MRS can be used for the noninvasive differentiation of benign and malignant breast tumors, differentiation among molecular breast cancer subtypes, and prediction of long-term survival outcomes. STUDY TYPE: Retrospective. SUBJECTS: In all, 168 women, aged ≥18 years. FIELD STRENGTH/SEQUENCE: Dynamic contrast-enhanced MRI at 1.5 T: sagittal 3D spoiled gradient recalled sequence with fat saturation, flip angle = 10°, repetition time / echo time (TR/TE) = 7.4/4.2 msec, slice thickness = 3.0 mm, field of view (FOV) = 20 cm, and matrix size = 256 × 192. 1 H-MRS: PRESS with TR/TE = 2000/135 msec, water suppression, and 128 scan averages, in addition to 16 reference scans without water suppression. ASSESSMENT: MRS quantitative analysis of lipid resonances using the LCModel was performed. Histopathology was the reference standard. STATISTICAL TESTS: Categorical data were described using absolute numbers and percentages. For metric data, means (plus 95% confidence interval [CI]) and standard deviations as well as median, minimum, and maximum were calculated. Due to skewed data, the latter were more adequate; unpaired Mann-Whitney U-tests were performed to compare groups without and with Bonferroni correction. ROC analyses were also performed. RESULTS: There were 111 malignant and 57 benign lesions. Mean voxel size was 4.4 ± 4.6 cm3 . Six lipid metabolite peaks were quantified: L09, L13 + L16, L21 + L23, L28, L41 + L43, and L52 + L53. Malignant lesions showed lower L09, L21 + L23, and L52 + L53 than benign lesions (P = 0.022, 0.027, and 0.0006). Similar results were observed for Luminal A or Luminal A/B vs. other molecular subtypes. At follow-up, patients were split into two groups based on median values for the six peaks; recurrence-free survival was significantly different between groups for L09, L21 + L23, and L28 (P = 0.0173, 0.0024, and 0.0045). DATA CONCLUSION: Quantitative in vivo 1 H-MRS assessment of lipid metabolism may provide an additional noninvasive imaging biomarker to guide therapeutic decisions in breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:239-249.

Abbreviated MRI of the Breast: Does It Provide Value?

MRI of the breast is the most sensitive test for breast cancer detection and outperforms conventional imaging with mammography, digital breast tomosynthesis, or ultrasound. However, the long scan time and relatively high costs limit its widespread use. Hence, it is currently only routinely implemented in the screening of women at an increased risk of breast cancer. To overcome these limitations, abbreviated dynamic contrast-enhanced (DCE)-MRI protocols have been introduced that substantially shorten image acquisition and interpretation time while maintaining a high diagnostic accuracy. Efforts to develop abbreviated MRI protocols reflect the increasing scrutiny of the disproportionate contribution of radiology to the rising overall healthcare expenditures. Healthcare policy makers are now focusing on curbing the use of advanced imaging examinations such as MRI while continuing to promote the quality and appropriateness of imaging. An important cornerstone of value-based healthcare defines value as the patient's outcome over costs. Therefore, the concept of a fast, abbreviated MRI exam is very appealing, given its high diagnostic accuracy coupled with the possibility of a marked reduction in the cost of an MRI examination. Given recent concerns about gadolinium-based contrast agents, unenhanced MRI techniques such as diffusion-weighted imaging (DWI) are also being investigated for breast cancer diagnosis. Although further larger prospective studies, standardized imaging protocol, and reproducibility studies are necessary, initial results with abbreviated MRI protocols suggest that it seems feasible to offer screening breast DCE-MRI to a broader population. This article aims to give an overview of abbreviated and fast breast MRI protocols, their utility for breast cancer detection, and their emerging role in the new value-based healthcare paradigm that has replaced the fee-for-service model. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:e85-e100.

Evaluation of a Computer-Aided Diagnosis System for Automated Bone Age Assessment in Comparison to the Greulich-Pyle Atlas Method: A Multireader Study.

OBJECTIVE: The aim of this study was to investigate a novel version of a computer-aided diagnosis (CAD) system developed for automated bone age (BA) assessment in comparison to the Greulich and Pyle method, regarding its accuracy and the influence of carpal bones on BA assessment. METHODS: Total BA, BA of the left distal radius, and BA of carpal bones in 305 patients were determined independently by 3 blinded radiologists and assessed by the CAD system. Pearson product-moment correlation, Bland-Altman plot, root-mean-square deviation, and further agreement analyses were computed. RESULTS: Mean total BA and BA of the distal radius showed high correlation between both approaches (r = 0.985 and r = 0.963). There was significantly higher correlation between values of total BA and BA of the distal radius (r = 0.969) compared with values of total BA and BA of carpal bones (r = 0.923). The assessment of carpal bones showed significantly lower interreader agreement compared with measurements of the distal radius (κ = 0.79 vs κ = 0.98). CONCLUSION: A novel version of a CAD system enables highly accurate automated BA assessment. The assessment of carpal bones revealed lower precision and interreader agreement. Therefore, methods determining BA without analyzing carpal bones may be more precise and accurate.

Multimodality Imaging of Breast Parenchymal Density and Correlation with Risk Assessment.

Purpose of Review: Breast density, or the amount of fibroglandular tissue in the breast, has become a recognized and independent marker for breast cancer risk. Public awareness of breast density as a possible risk factor for breast cancer has resulted in legislation for risk stratification purposes in many US states. This review will provide a comprehensive overview of the currently available imaging modalities for qualitative and quantitative breast density assessment and the current evidence on breast density and breast cancer risk assessment. Recent Findings: To date, breast density assessment is mainly performed with mammography and to some extent with magnetic resonance imaging. Data indicate that computerized, quantitative techniques in comparison with subjective visual estimations are characterized by higher reproducibility and robustness. Summary: Breast density reduces the sensitivity of mammography due to a masking effect and is also a recognized independent risk factor for breast cancer. Standardized breast density assessment using automated volumetric quantitative methods has the potential to be used for risk prediction and stratification and in determining the best screening plan for each woman.

Dual-energy CT in patients with colorectal cancer: Improved assessment of hypoattenuating liver metastases using noise-optimized virtual monoenergetic imaging.

PURPOSE: To assess the value of the noise-optimized virtual monoenergetic imaging (VMI+) technique on quantitative and qualitative image parameters in patients with hypoattenuating liver metastases from colorectal cancer (CRC) at abdominal dual-energy CT (DECT). MATERIALS AND METHODS: Fifty-three consecutive patients (mean age, 70.3 ± 11.4 years; range, 44-86 years) with histologically proven, hypoattenuating liver metastases from CRC were retrospectively included in this IRB-approved study. DECT datasets were reconstructed as standard linearly-blended M_0.6 image series, traditional virtual monoenergetic images (VMI), and noise-optimized VMI+ series. VMI and VMI+ reconstructions were obtained at energy levels ranging from 40 to 100-keV with 10-keV increments. Signal attenuation of liver parenchyma and liver metastases was measured to calculate signal-to-noise (SNR) and contrast-to-noise (CNR) ratios. Each image series was subjectively rated by three blinded radiologists with regard to image quality, lesion delineation, and image noise using a five-point Likert scale. RESULTS: Quantitative image quality parameters peaked at 40-keV VMI+ (SNR, 8.1 ± 3.4; CNR, 6.5 ± 2.6) with statistically significant differences in comparison with standard reconstructions and all traditional VMI series (P ≤ 0.001). Qualitative image analysis revealed best rating scores for 60-keV VMI+ series (median, 5) with significant differences compared to linearly-blended M_0.6 and all traditional VMI series (P ≤ 0.001). Lesion delineation showed significantly superior ratings for 40-keV VMI+ series compared to all other reconstructions (median, 5) (P ≤ 0.001). CONCLUSION: Low-keV VMI+ reconstructions demonstrate significantly increased quantitative and qualitative image quality parameters in patients with hypoattenuating liver metastases from CRC in comparison with standard reconstructions and traditional VMI series at abdominal DECT. Best lesion delineation can be achieved at 40-keV VMI+.

MRI in the Assessment of BI-RADS® 4 lesions.

The American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) lexicon, which is used ubiquitously to standardize reporting of breast magnetic resonance imaging (MRI), provides 7 BI-RADS assessment categories to indicate the level of suspicion of malignancy and guide further management. A BI-RADS category 4 assessment is assigned when an imaging abnormality does not fulfill the typical criteria for malignancy, but is suspicious enough to warrant a recommendation for biopsy. The BI-RADS category 4 assessment covers a wide range of probability of malignancy, from >2 to <95%. MRI is an essential noninvasive technique in breast imaging and the role of MRI in the assessment of ACR BI-RADS 4 lesions is manifold. In lesions classified as suspicious on imaging with mammography, digital breast tomosynthesis, and sonography, MRI can aid in the noninvasive differentiation of benign and malignant lesions and obviate unnecessary breast biopsies. When the suspicion of cancer is confirmed with MRI, concurrent staging of disease for treatment planning can be accomplished. This article will provide a comprehensive overview of the role of breast MRI in the assessment of ACR BI-RADS 4 lesions. In addition, we will discuss strategies to decrease false positives and avoid false negative results when reporting MRI of the breast.

Quantitative Assessment of Breast Parenchymal Uptake on 18F-FDG PET/CT: Correlation with Age, Background Parenchymal Enhancement, and Amount of Fibroglandular Tissue on MRI.

Background parenchymal enhancement (BPE), and the amount of fibroglandular tissue (FGT) assessed with MRI have been implicated as sensitive imaging biomarkers for breast cancer. The purpose of this study was to quantitatively assess breast parenchymal uptake (BPU) on 18F-FDG PET/CT as another valuable imaging biomarker and examine its correlation with BPE, FGT, and age. METHODS: This study included 129 patients with suspected breast cancer and normal imaging findings in one breast (BI-RADS 1), whose cases were retrospectively analyzed. All patients underwent prone 18F-FDG PET/CT and 3-T contrast-enhanced MRI of the breast. In all patients, interpreter 1 assessed BPU quantitatively using SUVmax Interpreters 1 and 2 assessed amount of FGT and BPE in the normal contralateral breast by subjective visual estimation, as recommended by BI-RADS. Interpreter 1 reassessed all cases and repeated the BPU measurements. Statistical tests were used to assess correlations between BPU, BPE, FGT, and age, as well as inter- and intrainterpreter agreement. RESULTS: BPU on 18F-FDG PET/CT varied among patients. The mean BPU SUVmax ± SD was 1.57 ± 0.6 for patients with minimal BPE, 1.93 ± 0.6 for mild BPE, 2.42 ± 0.5 for moderate BPE, and 1.45 ± 0.3 for marked BPE. There were significant (P < 0.001) moderate to strong correlations among BPU, BPE, and FGT. BPU directly correlated with both BPE and FGT on MRI. Patient age showed a moderate to strong indirect correlation with all 3 imaging-derived tissue biomarkers. The coefficient of variation for quantitative BPU measurements with SUVmax was 5.6%, indicating a high reproducibility. Interinterpreter and intrainterpreter agreement for BPE and FGT was almost perfect, with a κ-value of 0.860 and 0.822, respectively. CONCLUSION: The results of our study demonstrate that BPU varied among patients. BPU directly correlated with both BPE and FGT on MRI, and BPU measurements were highly reproducible. Patient age showed a strong inverse correlation with all 3 imaging-derived tissue biomarkers. These findings indicate that BPU may serve as a sensitive imaging biomarker for breast cancer prediction, prognosis, and risk assessment.

Multiparametric MR Imaging with High-Resolution Dynamic Contrast-enhanced and Diffusion-weighted Imaging at 7 T Improves the Assessment of Breast Tumors: A Feasibility Study.

PURPOSE: To ascertain whether multiparametric magnetic resonance (MR) imaging of the breast in combination with dynamic contrast material-enhanced (DCE) imaging and diffusion-weighted imaging (DWI) at 7 T is feasible and improves diagnostic accuracy. MATERIALS AND METHODS: From December 2011 to December 2013, 40 patients with suspicious breast lesions were included in this institutional review board-approved prospective study. Before bilateral multiparametric MR imaging of the breast at 7 T, all patients gave written informed consent. Lesions were classified according to Breast Imaging Reporting and Data System (BI-RADS) and assessed for apparent diffusion coefficient (ADC) values by two readers independently. For combined analysis of DCE MR imaging and DWI, the BI-RADS-adapted reading algorithm, which adapted ADC thresholds to the BI-RADS assessment category, was used. Diagnostic values of multiparametric, DCE MR imaging, and DWI were calculated. Receiver operating characteristic curve analysis was performed. Image quality and interreader agreement were assessed. Histopathologic results were used as the highest standard. RESULTS: There were 29 malignant and 17 benign lesions (range, 6-95 mm; mean, 23.3 mm). Multiparametric MR imaging yielded a sensitivity of 100% (29 of 29 lesions), a specificity of 88.2% (16 of 18 lesions), and an area under the curve of 0.941, which was greater than for DCE MR imaging (P = .003), which had a sensitivity of 100% (29 of 29 lesions), a specificity of 53.2% (nine of 17 lesions), and an area under the curve of 0.765. DWI had a sensitivity of 93.1% (27 of 29 lesions), a specificity of 88.2% (15 of 17 lesions), and an area under the curve of 0.907. Multiparametric MR imaging at 7 T of the breast eliminated all false-negative findings and reduced false-positive findings, from eight false-positive findings with DCE MR imaging to two false-positive findings. Thus, if used clinically, 7-T multiparametric MR imaging may have potentially obviated unnecessary breast biopsies in six of eight lesions (P = .031). Multiparametric MR imaging demonstrated either excellent or good image quality and interreader agreement (κ = 0.89-1.00). CONCLUSION: The clinical use of 7-T multiparametric MR imaging is feasible, provides good or excellent image quality, and has the potential to improve diagnostic accuracy.