Influence of fat-water separation and spatial resolution on automated volumetric MRI measurements of fibroglandular breast tissue.

The aim of this study was to investigate the influence of fat-water separation and spatial resolution in MRI on the results of automated quantitative measurements of fibroglandular breast tissue (FGT). Ten healthy volunteers (age range, 28-71 years; mean, 39.9 years) were included in this Institutional Review Board-approved prospective study. All measurements were performed on a 1.5-T scanner (Siemens, AvantoFit) using an 18-channel breast coil. The protocols included isotropic (Di) [TR/TE1 /TE2 = 6.00 ms/2.45 ms/2.67 ms; flip angle, 6.0°; 256 slices; matrix, 360 × 360; 1 mm isotropic; field of view, 360°; acquisition time (TA) = 3 min 38 s] and anisotropic (Da) (TR/TE1 /TE2 = 10.00 ms/2.39 ms/4.77 ms; flip angle, 24.9°; 80 slices; matrix 360 × 360; voxel size, 0.7 × 0.7 × 2.0 mm(3) ; field of view, 360°; TA = 1 min 25 s) T1 three-dimensional (3D) fast low-angle shot (FLASH) Dixon sequences, and a T1 3D FLASH sequence with the same resolution (T1 ) without (TR/TE = 11.00 ms/4.76 ms; flip angle, 25.0°; 80 slices; matrix, 360 × 360; voxel size, 0.7 × 0.7 × 2.0 mm(3) ; field of view, 360°; TA = 50 s) and with (TR/TE = 29.00 ms/4.76 ms; flip angle, 25.0°; 80 slices; matrix, 360 × 360; voxel size, 0.7 × 0.7 × 2.0 mm(3) ; field of view, 360°; TA = 2 min 35 s) fat saturation. Repeating volunteer measurements after 20 min and repositioning were used to assess reproducibility. An automated and quantitative volumetric breast density measurement system was used for FGT calculation. FGT with Di, Da and T1 measured 4.6-63.0% (mean, 30.6%), 3.2-65.3% (mean, 32.5%) and 1.7-66.5% (mean, 33.7%), respectively. The highest correlation between different MRI sequences was found with the Di and Da sequences (R(2) = 0.976). Coefficients of variation (CVs) for FGT calculation were higher in T1 (CV = 21.5%) compared with Dixon (Di, CV = 5.1%; Da, CV = 4.2%) sequences. Dixon-type sequences worked well for FGT measurements, even at lower resolution, whereas the conventional T1 -weighted sequence was more sensitive to decreasing resolution. The Dixon fat-water separation technique showed superior repeatability of FGT measurements compared with conventional sequences. A standard dynamic protocol using Dixon fat-water separation is best suited for combined diagnostic purposes and prognostic measurements of FGT. Copyright © 2016 John Wiley & Sons, Ltd.

Diagnostic accuracy of (18)F-FDG PET/CT compared with that of contrast-enhanced MRI of the breast at 3 T.

PURPOSE: To compare the diagnostic accuracy of prone (18)F-FDG PET/CT with that of contrast-enhanced MRI (CE-MRI) at 3 T in suspicious breast lesions. To evaluate the influence of tumour size on diagnostic accuracy and the use of maximum standardized uptake value (SUVMAX) thresholds to differentiate malignant from benign breast lesions. METHODS: A total of 172 consecutive patients with an imaging abnormality were included in this IRB-approved prospective study. All patients underwent (18)F-FDG PET/CT and CE-MRI of the breast at 3 T in the prone position. Two reader teams independently evaluated the likelihood of malignancy as determined by (18)F-FDG PET/CT and CE-MRI independently. (18)F-FDG PET/CT data were qualitatively evaluated by visual interpretation. Quantitative assessment was performed by calculation of SUVMAX. Sensitivity, specificity, diagnostic accuracy, area under the curve and interreader agreement were calculated for all lesions and for lesions <10 mm. Histopathology was used as the standard of reference. RESULTS: There were 132 malignant and 40 benign lesions; 23 lesions (13.4%) were <10 mm. Both (18)F-FDG PET/CT and CE-MRI achieved an overall diagnostic accuracy of 93%. There were no significant differences in sensitivity (p = 0.125), specificity (p = 0.344) or diagnostic accuracy (p = 1). For lesions <10 mm, diagnostic accuracy deteriorated to 91% with both (18)F-FDG PET/CT and CE-MRI. Although no significant difference was found for lesions <10 mm, CE-MRI at 3 T seemed to be more sensitive but less specific than (18)F-FDG PET/CT. Interreader agreement was excellent (κ = 0.85 and κ = 0.92). SUVMAX threshold was not helpful in differentiating benign from malignant lesions. CONCLUSION: (18)F-FDG PET/CT and CE-MRI at 3 T showed equal diagnostic accuracies in breast cancer diagnosis. For lesions <10 mm, diagnostic accuracy deteriorated, but was equal for (18)F-FDG PET/CT and CE-MRI at 3 T. For lesions <10 mm, CE-MRI at 3 T seemed to be more sensitive but less specific than (18)F-FDG PET/CT. Quantitative assessment using an SUVMAX threshold for differentiating benign from malignant lesions was not helpful in breast cancer diagnosis.

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics.

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46-0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.

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.