Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer.

Tertiary lymphoid structure (TLS) is associated with prognosis in copy-number-driven tumors, including high-grade serous ovarian cancer (HGSOC), although the function of TLS and its interaction with copy-number alterations in HGSOC are not fully understood. In the current study, we confirm that TLS-high HGSOC patients show significantly better progression-free survival (PFS). We show that the presence of TLS in HGSOC tumors is associated with B cell maturation and cytotoxic tumor-specific T cell activation and proliferation. In addition, the copy-number loss of IL15 and CXCL10 may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed. Last, a radiomics-based signature is developed to predict the presence of TLS, which independently predicts PFS in both HGSOC patients and immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients. Overall, we reveal that TLS coordinates intratumoral B cell and T cell response to HGSOC tumor, while the cancer genome evolves to counteract TLS formation and function.

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study.

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support.

O-RADS MRI Classification of Indeterminate Adnexal Lesions: Time-Intensity Curve Analysis Is Better Than Visual Assessment.

Background The MRI Ovarian-Adnexal Reporting and Data System (O-RADS) enables risk stratification of sonographically indeterminate adnexal lesions, partly based on time-intensity curve (TIC) analysis, which may not be universally available. Purpose To compare the diagnostic accuracy of visual assessment with that of TIC assessment of dynamic contrast-enhanced MRI scans to categorize adnexal lesions as benign or malignant and to evaluate the influence on the O-RADS MRI score. Materials and Methods The European Adnex MR Study Group, or EURAD, database, a prospective multicenter study of women undergoing MRI for indeterminate adnexal lesions between March 2013 and March 2018, was queried retrospectively. Women undergoing surgery for an adnexal lesion with solid tissue were included. Solid tissue enhancement relative to outer myometrium was assessed visually and with TIC. Contrast material washout was recorded. Lesions were categorized according to the O-RADS MRI score with visual and TIC assessment. Per-lesion diagnostic accuracy was calculated. Results A total of 320 lesions (207 malignant, 113 benign) in 244 women (mean age, 55.3 years ± 15.8 [standard deviation]) were analyzed. Sensitivity for malignancy was 96% (198 of 207) and 76% (157 of 207) for TIC and visual assessment, respectively. TIC was more accurate than visual assessment (86% [95% CI: 81, 90] vs 78% [95% CI: 73, 82]; P < .001) for benign lesions, predominantly because of higher specificity (95% [95% CI: 92, 98] vs 76% [95% CI: 68, 81]). A total of 21% (38 of 177) of invasive lesions were rated as low risk visually. Contrast material washout and high-risk enhancement (defined as earlier enhancement than in the myometrium) were highly specific for malignancy for both TIC (97% [95% CI: 91, 99] and 94% [95% CI: 90, 97], respectively) and visual assessment (97% [95% CI: 92, 99] and 93% [95% CI: 88, 97], respectively). O-RADS MRI score was more accurate with TIC than with visual assessment (area under the receiver operating characteristic curve, 0.87 [95% CI: 0.83, 0.90] vs 0.73 [95% CI: 0.68, 0.78]; P < .001). Conclusion Time-intensity curve analysis was more accurate than visual assessment for achieving optimal diagnostic accuracy with the Ovarian-Adnexal Reporting and Data System MRI score. Clinical trial registration no. NCT01738789 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Vargas and Woo in this issue. An earlier incorrect version appeared online. This article was corrected on March 7, 2022.

Reliability of high-resolution ultrasound and magnetic resonance arthrography of the shoulder in patients with sports-related shoulder injuries.

INTRODUCTION: The shoulder, a very complex joint, offers a wide range of pathologies. Intraarticular abnormalities and rotator cuff injuries are mainly assessed and diagnosed by magnetic resonance arthrography (MRA). In contrast to this well-established gold standard, high-resolution ultrasound (US) offers an additional easy and excellent modality to assess the shoulder joint. Therefore, the purpose of this study was to evaluate in which anatomic structures and pathologies comparable results of US and MRA could be achieved. MATERIALS AND METHODS: In this IRB-approved prospective study 67 patients with clinically suspected labral lesions, rotator cuff rupture, or injury of the long head of the biceps (LHB) tendon were enrolled. Each participant was examined with high resolution US, and directly followed by MRA at 3 Tesla with a standard sequence protocol. To evaluate the agreement of the diagnostic performance between US and MRA a weighted kappa statistic was used. RESULTS: Both of the investigated modalities yielded a moderate to almost perfect agreement in assessing a wide range of shoulder joint pathologies. For the rotator cuff, consistency was found in 71.64% for the supraspinatus tendon, in 95.52% for the infraspinatus tendon, in 83.58% for the subscapularis tendon, and in 98.51% for the teres minor tendon. The diagnostic accuracy between both modalities was 80.60% for the LHB tendon, 77.61% for the posterior labroligamentous complex, 83.58% for the acromioclavicular joint, and 91.04% for the assessment of osseous irregularities and impaction fractures. CONCLUSIONS: High resolution US is a reliable imaging modality for the rotator cuff, the LHB tendon, and the acromioclavicular joint, so for these structures we recommend a preference for US over MRA based on its diagnostic accuracy, comfortability, cost effectiveness, and availability. If the diagnosis remains elusive, for all other intraarticular structures we recommend MRA for further diagnostic assessment.

Intra- and inter-observer variability in dependence of T1-time correction for common dynamic contrast enhanced MRI parameters in prostate cancer patients.

BACKGROUND: Dynamic contrast enhanced (DCE) MRI parameters are potential biomarkers to characterise tumour vasculature and distinguish it from the non-cancerous blood vessel system within the prostate. However, the inevitable presence of intra- and inter-observer variabilities is challenging in this context. Additionally, pre-contrast T1-time correction is a prerequisite to gain quantitative DCE parameters in the first place. The current study investigated the effect of individualized T1-time correction on intra- and inter-reader variability for quantitative DCE-parameters in prostatic lesions. METHODS: In this IRB-approved retrospective study, two experienced radiologists assessed DCE parameters using individually measured (A) and fixed (B) T1-times twice with a time difference of three weeks. The dataset consisted of 35 MRI-guided biopsy-proven prostate cancer lesions. Limits of agreement (LoA) and coefficients of variability (CoV) were calculated to assess intra- and inter-reader variabilities of the parameters. RESULTS: With exception of kep, for all DCE parameters both intra- and inter-reader CoV were smaller in B compared to A. Absolute kep values were largely insensitive to T1-time correction induced bias. The mean intra-reader CoVs [5%, 95% percentile] (over all four DCE parameters and both readers) were 6.7% [0.5%, 15.1%] in A and 3.9% [0.2%, 11.0%] in B. The inter-reader CoVs were 9.0% [0.6%, 25.8%] (A) and 7.0% [0.3%, 25.4%] (B). CONCLUSIONS: T1-time correction has a significant influence on the intra- and inter-reader variability. By applying individually measured T1-time correction, both intra- and inter-observer variability were found to increase. Out of all investigated DCE parameters, kep is the most robust to this investigated bias.

A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis.

PURPOSE: To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. METHODS: In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). RESULTS: There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5-10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis. CONCLUSION: A multiparametric [18F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.

Diffusion-Weighted MRI of Breast Cancer: Improved Lesion Visibility and Image Quality Using Synthetic b-Values.

BACKGROUND: Diffusion-weighted imaging (DWI) is an MRI technique with the potential to serve as an unenhanced breast cancer detection tool. Synthetic b-values produce images with high diffusion weighting to suppress residual background signal, while avoiding additional measurement times and reducing artifacts. PURPOSE: To compare acquired DWI images (at b = 850 s/mm2 ) and different synthetic b-values (at b = 1000-2000 s/mm2 ) in terms of lesion visibility, image quality, and tumor-to-tissue contrast in patients with malignant breast tumors. STUDY TYPE: Retrospective. POPULATION: Fifty-three females with malignant breast lesions. FIELD STRENGTH/SEQUENCE: T2 w, DWI EPI with STIR fat-suppression, and dynamic contrast-enhanced T1 w at 3T. ASSESSMENT: From acquired images using b-values of 50 and 850 s/mm2 , synthetic images were calculated at b = 1000, 1200, 1400, 1600, 1800, and 2000 s/mm2 . Four readers independently rated image quality, lesion visibility, preferred b-value, as well as the lowest and highest b-value, over the range of b-values tested, to provide a diagnostic image. STATISTICAL TESTS: Medians and mean ranks were calculated and compared using the Friedman test and Wilcoxon signed-rank test. Reproducibility was analyzed by intraclass correlation (ICC), Fleiss, and Cohen's κ. RESULTS: Relative signal-to-noise and contrast-to-noise ratios decreased with increasing b-values, while the signal-intensity ratio between tumor and tissue increased significantly (P < 0.001). Intermediate b-values (1200-1800 s/mm2 ) were rated best concerning image quality and lesion visibility; the preferred b-value mostly lay at 1200-1600 s/mm2 . Lowest and highest acceptable b-values were 850 s/mm2 and 2000 s/mm2 . Interreader agreement was moderate to high concerning image quality (ICC: 0.50-0.67) and lesion visibility (0.70-0.93), but poor concerning preferred and acceptable b-values (κ = 0.032-0.446). DATA CONCLUSION: Synthetically increased b-values may be a way to improve tumor-to-tissue contrast, lesion visibility, and image quality of breast DWI, while avoiding the disadvantages of performing DWI at very high b-values. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1754-1761.

Sequential [18F]FDG-[18F]FMISO PET and Multiparametric MRI at 3T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes: First Clinical Experience.

The aim of this study was to assess whether sequential multiparametric 18[F]fluoro-desoxy-glucose (18[F]FDG)/[18F]fluoromisonidazole ([18F]FMISO) PET-MRI in breast cancer patients is possible, facilitates information on tumor heterogeneity, and correlates with prognostic indicators. In this pilot study, IRB-approved, prospective study, nine patients with ten suspicious breast lesions (BIRADS 5) and subsequent breast cancer diagnosis underwent sequential combined [18F]FDG/[18F]FMISO PET-MRI. [18F]FDG was used to assess increased glycolysis, while [18F]FMISO was used to detect tumor hypoxia. MRI protocol included dynamic breast contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Qualitative and quantitative multiparametric imaging findings were compared with pathological features (grading, proliferation, and receptor status) and clinical endpoints (recurrence/metastases and disease-specific death) using multiple correlation analysis. Histopathology was the standard of reference. There were several intermediate to strong correlations identified between quantitative bioimaging markers, histopathologic tumor characteristics, and clinical endpoints. Based on correlation analysis, multiparametric criteria provided independent information. The prognostic indicators proliferation rate, death, and presence/development of recurrence/metastasis correlated positively, whereas the prognostic indicator estrogen receptor status correlated negatively with PET parameters. The strongest correlations were found between disease-specific death and [18F]FDGmean (R=0.83, p < 0.01) and between the presence/development of metastasis and [18F]FDGmax (R=0.79, p < 0.01), respectively. This pilot study indicates that multiparametric [18F]FDG/[18F]FMISO PET-MRI might provide complementary quantitative prognostic information on breast tumors including clinical endpoints and thus might be used to tailor treatment for precision medicine in breast cancer.

Quantitative Multiparametric Breast Ultrasound: Application of Contrast-Enhanced Ultrasound and Elastography Leads to an Improved Differentiation of Benign and Malignant Lesions.

OBJECTIVES: The aim of this study was to evaluate breast multiparametric ultrasound (mpUS) and its potential to reduce unnecessary breast biopsies with 1, 2, or 3 additional quantitative parameters (Doppler, elastography, and contrast-enhanced ultrasound [CEUS]) to B-mode and investigate possible variations with different reader experience. MATERIALS AND METHODS: This prospective study included 124 women (age range, 18-82 years; mean, 52 years), each with 1 new breast lesion, scheduled for ultrasound-guided biopsy between October 2015 and September 2016. Each lesion was examined with B-mode, elastography (Virtual Touch IQ [VTIQ]), Doppler, and CEUS, and different quantitative parameters were recorded for each modality. Four readers (2 experienced breast radiologists and 2 in-training) independently evaluated B-mode images of each lesion and assigned a BI-RADS (Breast Imaging Reporting and Data System) score. Using the area under the receiver operating characteristic curve (AUC), the most accurate quantitative parameter for each modality was chosen. These were then combined with the BI-RADS scores of all readers. Descriptive statistics and AUC were used to evaluate the diagnostic performance of mpUS. RESULTS: Sixty-five lesions were malignant. MpUS with B-mode and 2 additional quantitative parameters (VTIQ and CEUS or Doppler) showed the highest diagnostic performance for all readers (averaged AUCs, 0.812-0.789 respectively vs 0.683 for B-mode, P = 0.0001). Both combinations significantly reduced the number of false-positive findings up to 46.9% (P < 0.0001). CONCLUSIONS: Quantitative mpUS with 2 different triple assessment modalities (B-mode, VTIQ elastography, CEUS, or Doppler) shows the best diagnostic performance for breast cancer diagnosis and leads to a significant reduction of false-positive biopsy recommendations, for both experienced and inexperienced readers.

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.

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

PURPOSE: The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients. MATERIALS AND METHODS: This institutional review board-approved prospective study included 38 women (median age, 46.5 years; range, 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3 T with dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging before and after 2 cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS (Breast Imaging Reporting and Data System), quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time), and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, 8 classifiers including linear support vector machine, linear discriminant analysis, logistic regression, random forests, stochastic gradient descent, decision tree, adaptive boosting, and extreme gradient boosting (XGBoost) were used to rank the features. Histopathologic residual cancer burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS), and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the extracted qualitative and quantitative features for pCR as defined by RCB class, RFS, and DSS using recursive feature elimination. To overcome overfitting, 4-fold cross-validation was used. RESULTS: Machine learning with mpMRI achieved stable performance as shown by mean classification accuracies for the prediction of RCB class (AUC, 0.86) and DSS (AUC, 0.92) based on XGBoost and the prediction of RFS (AUC, 0.83) with logistic regression. The XGBoost classifier achieved the most stable performance with high accuracies compared with other classifiers. The most relevant features for the prediction of RCB class were as follows: changes in lesion size, complete pattern of shrinkage, and mean transit time on DCE-MRI; minimum ADC on DWI; and peritumoral edema on T2-weighted imaging. The most relevant features for prediction of RFS were as follows: volume distribution, mean plasma flow, and mean transit time; DCE-MRI lesion size; minimum, maximum, and mean ADC with DWI. The most relevant features for prediction of DSS were as follows: lesion size, volume distribution, and mean plasma flow on DCE-MRI, and maximum ADC with DWI. CONCLUSIONS: Machine learning with mpMRI of the breast enables early prediction of pCR to NAC as well as survival outcomes in breast cancer patients with high accuracy and thus may provide valuable predictive information to guide treatment decisions.

Density and tailored breast cancer screening: practice and prediction - an overview.

Mammography, as the primary screening modality, has facilitated a substantial decrease in breast cancer-related mortality in the general population. However, the sensitivity of mammography for breast cancer detection is decreased in women with higher breast densities, which is an independent risk factor for breast cancer. With increasing public awareness of the implications of a high breast density, there is an increasing demand for supplemental screening in these patients. Yet, improvements in breast cancer detection with supplemental screening methods come at the expense of increased false-positives, recall rates, patient anxiety, and costs. Therefore, breast cancer screening practice must change from a general one-size-fits-all approach to a more personalized, risk-based one that is tailored to the individual woman's risk, personal beliefs, and preferences, while accounting for cost, potential harm, and benefits. This overview will provide an overview of the available breast density assessment modalities, the current breast density screening recommendations for women at average risk of breast cancer, and supplemental methods for breast cancer screening. In addition, we will provide a look at the possibilities for a risk-adapted breast cancer screening.

Quantitative Apparent Diffusion Coefficient Derived From Diffusion-Weighted Imaging Has the Potential to Avoid Unnecessary MRI-Guided Biopsies of mpMRI-Detected PI-RADS 4 and 5 Lesions.

OBJECTIVE: The aim of this study was to evaluate the potential of diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) measurements to obviate unnecessary biopsies in multiparametric MRI-detected PI-RADS 4 and 5 lesions. MATERIALS AND METHODS: This retrospective, institutional review board-approved study investigated 101 PI-RADS 4 and 5 prostate lesions (52 malignant, 49 benign) verified by in-bore MRI-guided biopsy in 101 men (mean age, 62.8 years). Two readers, who were not aware of the biopsy results independently and repeatedly measured minimum, mean, and maximum ADC from diffusion-weighted imaging measurements (in line with PI-RADS v2 recommendations) using a 2-dimensional region of interest drawn around the biopsied lesions. Diagnostic performance was evaluated using receiver operating characteristic statistics and reproducibility statistics were calculated. RESULTS: The best diagnostic performance (overall area under the receiver operating characteristic curve [AUC] R1: 0.801; R2: 0.796 peripheral zone AUC R1:0.814, R2: 0.805; transitional zone AUC R1:0.786, R2:0.779) and the tightest limits of interreader agreement (-8.6% to 9.9%) were found in minimum ADC values. Rule-in and rule-out thresholds for diagnosis of prostate cancer were identified, demonstrating a potential to avoid unnecessary biopsies in 32.7% (16/49). CONCLUSIONS: Quantitative ADC measurement in multiparametric MRI-detected PI-RADS 4 and 5 lesions has the potential to avoid unnecessary MRI-guided biopsies in up to 33%.

Potential of Noncontrast Magnetic Resonance Imaging With Diffusion-Weighted Imaging in Characterization of Breast Lesions: Intraindividual Comparison With Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVES: The aim of this study was to assess the potential of noncontrast magnetic resonance imaging (NC-MRI) with diffusion-weighted imaging (DWI) in characterization of breast lesions in comparison to dynamic contrast-enhanced MRI (DCE-MRI) at 3 T. MATERIALS AND METHODS: Consecutive patients with conventional imaging (mammography, ultrasound) BI-RADS 4/5 findings were included in this institutional review board-approved single-center study. All underwent 3 T breast MRI including readout-segmented DWI, DCE, and T2-weighted sequences. Final diagnosis was defined by histopathology or follow-up (>24 months). Two experienced radiologists (R1, R2) independently assigned lesion conspicuity (0 = minimal to 3 = excellent) and BI-RADS scores to NC-MRI (readout-segmented DWI including apparent diffusion coefficient maps) and DCE-MRI (DCE and T2-weighted). Receiver operating characteristics, κ statistics, and visual grading characteristics analysis were applied. RESULTS: Sixty-seven malignant and 56 benign lesions were identified in 113 patients (mean age, 54 ± 14 years). Areas under the receiver operating characteristics curves were similar: DCE-MRI: 0.901 (R1), 0.905 (R2); NC-MRI: 0.882 (R1), 0.854 (R2); P > 0.05, respectively. The κ agreement was 0.968 (DCE-MRI) and 0.893 (NC-MRI). Visual grading characteristics analysis revealed superior lesion conspicuity by DCE-MRI (0.661, P < 0.001). CONCLUSIONS: Diagnostic performance and interreader agreement of both NC-MRI and DCE-MRI is high, indicating a potential use of NC-MRI as an alternative to DCE-MRI. However, inferior lesion conspicuity and lower interreader agreement of NC-MRI need to be considered.

3D T2-weighted imaging to shorten multiparametric prostate MRI protocols.

OBJECTIVES: To determine whether 3D acquisitions provide equivalent image quality, lesion delineation quality and PI-RADS v2 performance compared to 2D acquisitions in T2-weighted imaging of the prostate at 3 T. METHODS: This IRB-approved, prospective study included 150 consecutive patients (mean age 63.7 years, 35-84 years; mean PSA 7.2 ng/ml, 0.4-31.1 ng/ml). Two uroradiologists (R1, R2) independently rated image quality and lesion delineation quality using a five-point ordinal scale and assigned a PI-RADS score for 2D and 3D T2-weighted image data sets. Data were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/area under the curve (AUC) analysis. RESULTS: Image quality was similarly good to excellent for 2D T2w (mean score R1, 4.3 ± 0.81; R2, 4.7 ± 0.83) and 3D T2w (mean score R1, 4.3 ± 0.82; R2, 4.7 ± 0.69), p = 0.269. Lesion delineation was rated good to excellent for 2D (mean score R1, 4.16 ± 0.81; R2, 4.19 ± 0.92) and 3D T2w (R1, 4.19 ± 0.94; R2, 4.27 ± 0.94) without significant differences (p = 0.785). ROC analysis showed an equivalent performance for 2D (AUC 0.580-0.623) and 3D (AUC 0.576-0.629) T2w (p > 0.05, respectively). CONCLUSIONS: Three-dimensional acquisitions demonstrated equivalent image and lesion delineation quality, and PI-RADS v2 performance, compared to 2D in T2-weighted imaging of the prostate. Three-dimensional T2-weighted imaging could be used to considerably shorten prostate MRI protocols in clinical practice. KEY POINTS: • 3D shows equivalent image quality and lesion delineation compared to 2D T2w. • 3D T2w and 2D T2w image acquisition demonstrated comparable diagnostic performance. • Using a single 3D T2w acquisition may shorten the protocol by 40%. • Combined with short DCE, multiparametric protocols of 10 min are feasible.

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.

Differentiation of Intrahepatic Cholangiocellular Carcinoma from Hepatocellular Carcinoma in the Cirrhotic Liver Using Contrast-enhanced MR Imaging.

RATIONALE AND OBJECTIVES: This study aimed to investigate the potential of contrast-enhanced magnetic resonance imaging features to differentiate between mass-forming intrahepatic cholangiocellular carcinoma (ICC) and hepatocellular carcinoma (HCC) in cirrhotic livers. MATERIALS AND METHODS: This study, performed between 2001 and 2013, included 64 baseline magnetic resonance imaging examinations with pathohistologically proven liver cirrhosis, presenting with either ICC (n = 32) or HCC (n = 32) tumors. To distinguish ICC form HCC tumors, 20 qualitative single-lesion descriptors were evaluated by two readers, in consensus, and statistically classified using the chi-square automatic interaction detection (CHAID) methodology. Diagnostic performance was assessed by a receiver operating characteristic analysis. RESULTS: The CHAID algorithm identified three independent categorical lesion descriptors, including (1) liver capsular retraction; (2) progressive or persistent enhancement pattern or wash-out on the T1-weighted delayed phase; and (3) signal intensity appearance on T2-weighted images that could help to reliably differentiate ICC from HCC, which resulted in an AUC of 0.807, and a sensitivity and specificity of 68.8 and 90.6 (95% confidence interval 75.0-98.0), respectively. CONCLUSIONS: The proposed CHAID algorithm provides a simple and robust step-by-step classification tool for a reliable and solid differentiation between ICC and HCC tumors in cirrhotic livers.

Accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue using MRI: correlation with anthropomorphic breast phantoms.

To demonstrate the accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue (FGT), using MRI, and to investigate the impact of different MRI sequences using anthropomorphic breast phantoms as the ground truth. In this study, 10 anthropomorphic breast phantoms that consisted of different known fractions of adipose and protein tissue, which closely resembled normal breast parenchyma, were developed. Anthropomorphic breast phantoms were imaged with a 1.5 T unit (Siemens, Avantofit) using an 18-channel breast coil. The sequence protocol consisted of an isotropic Dixon sequence (Di), an anisotropic Dixon sequence (Da), and T1 3D FLASH sequences with and without fat saturation (T1). Fully automated, quantitative, volumetric measurement of FGT for all anthropomorphic phantoms and sequences was performed and correlated with the amounts of fatty and protein components in the phantoms as the ground truth. Fully automated, quantitative, volumetric measurements of FGT with MRI for all sequences ranged from 5.86 to 61.05% (mean 33.36%). The isotropic Dixon sequence yielded the highest accuracy (median 0.51%-0.78%) and precision (median range 0.19%) compared with anisotropic Dixon (median 1.92%-2.09%; median range 0.55%) and T1 -weighted sequences (median 2.54%-2.46%; median range 0.82%). All sequences yielded good correlation with the FGT content of the anthropomorphic phantoms. The best correlation of FGT measurements was identified for Dixon sequences (Di, R2  = 0.999; Da, R2  = 0.998) compared with conventional T1 -weighted sequences (R2  = 0.971). MRI yields accurate, fully automated, quantitative, volumetric measurements of FGT, an increasingly important and sensitive imaging biomarker for breast cancer risk. Compared with conventional T1 sequences, Dixon-type sequences show the highest correlation and reproducibility for automated, quantitative, volumetric FGT measurements using anthropomorphic breast phantoms as the ground truth.

Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values.

OBJECTIVES: To investigate the influence of region-of-interest (ROI) placement and different apparent diffusion coefficient (ADC) parameters on ADC values, diagnostic performance, reproducibility and measurement time in breast tumours. METHODS: In this IRB-approved, retrospective study, 149 histopathologically proven breast tumours (109 malignant, 40 benign) in 147 women (mean age 53.2) were investigated. Three radiologists independently measured minimum, mean and maximum ADC, each using three ROI placement approaches:1 - small 2D-ROI, 2 - large 2D-ROI and 3 - 3D-ROI covering the whole lesion. One reader performed all measurements twice. Median ADC values, diagnostic performance, reproducibility, and measurement time were calculated and compared between all combinations of ROI placement approaches and ADC parameters. RESULTS: Median ADC values differed significantly between the ROI placement approaches (p < .001). Minimum ADC showed the best diagnostic performance (AUC .928-.956), followed by mean ADC obtained from 2D ROIs (.926-.94). Minimum and mean ADC showed high intra- (ICC .85-.94) and inter-reader reproducibility (ICC .74-.94). Median measurement time was significantly shorter for the 2D ROIs (p < .001). CONCLUSIONS: ROI placement significantly influences ADC values measured in breast tumours. Minimum and mean ADC acquired from 2D-ROIs are useful for the differentiation of benign and malignant breast lesions, and are highly reproducible, with rapid measurement. KEY POINTS: • Region of interest placement significantly influences apparent diffusion coefficient of breast tumours. • Minimum and mean apparent diffusion coefficient perform best and are reproducible. • 2D regions of interest perform best and provide rapid measurement times.