Cancer detection rate of breast-MR in supplemental screening after negative mammography in women with dense breasts. Preliminary results of the MA-DETECT-Study after 200 participants.

BACKGROUND: Due to increased cancer detection rates (CDR), breast MR (breast MRI) can reduce underdiagnosis of breast cancer compared to conventional imaging techniques, particularly in women with dense breasts. The purpose of this study is to report the additional breast cancer yield by breast MRI in women with dense breasts after receiving a negative screening mammogram. METHODS: For this study we invited consecutive participants of the national German breast cancer Screening program with breast density categories ACR C & D and a negative mammogram to undergo additional screening by breast MRI. Endpoints were CDR and recall rates. This study reports interim results in the first 200 patients. At a power of 80% and considering an alpha error of 5%, this preliminary population size is sufficient to demonstrate a 4/1000 improvement in CDR. RESULTS: In 200 screening participants, 8 women (40/1000, 17.4-77.3/1000) were recalled due to positive breast MRI findings. Image-guided biopsy revealed 5 cancers in 4 patients (one bilateral), comprising four invasive cancers and one case of DCIS. 3 patients revealed 4 invasive cancers presenting with ACR C breast density and one patient non-calcifying DCIS in a woman with ACR D breast density, resulting in a CDR of 20/1000 (95%-CI 5.5-50.4/1000) and a PPV of 50% (95%-CI 15.7-84.3%). CONCLUSION: Our initial results demonstrate that supplemental screening using breast MRI in women with heterogeneously dense and very dense breasts yields an additional cancer detection rate in line with a prior randomized trial on breast MRI screening of women with extremely dense breasts. These findings are highly important as the population investigated constitutes a much higher proportion of women and yielded cancers particularly in women with heterogeneously dense breasts.

Contrast-enhanced breast imaging: Current status and future challenges.

BACKGROUND: Contrast-enhanced breast MRI and recently also contrast-enhanced mammography (CEM) are available for breast imaging. The aim of the current overview is to explore existing evidence and ongoing challenges of contrast-enhanced breast imaging. METHODS: This narrative provides an introduction to the contrast-enhanced breast imaging modalities breast MRI and CEM. Underlying principle, techniques and BI-RADS reporting of both techniques are described and compared, and the following indications and ongoing challenges are discussed: problem-solving, high-risk screening, supplemental screening in women with extremely dense breast tissue, breast implants, neoadjuvant systemic therapy (NST) response monitoring, MRI-guided and CEM- guided biopsy. RESULTS: Technique and reporting for breast MRI are standardised, for the newer CEM standardisation is in progress. Similarly, compared to other modalities, breast MRI is well established as superior for problem-solving, screening women at high risk, screening women with extremely dense breast tissue or with implants; and for monitoring response to NST. Furthermore, MRI-guided biopsy is a reliable technique with low long-term false negative rates. For CEM, data is as yet either absent or limited, but existing results in these settings are promising. CONCLUSION: Contrast-enhanced breast imaging achieves highest diagnostic performance and should be considered essential. Of the two contrast-enhanced modalities, evidence of breast MRI superiority is ample, and preliminary results on CEM are promising, yet CEM warrants further study.

External validation of nomograms including MRI features for the prediction of side-specific extraprostatic extension.

BACKGROUND: Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). Multiple nomograms, which include magnetic resonance imaging (MRI) information, are available predict side-specific EPE. It is crucial that the accuracy of these nomograms is assessed with external validation to ensure they can be used in clinical practice to support medical decision-making. METHODS: Data of prostate cancer (PCa) patients that underwent robot-assisted RP (RARP) from 2017 to 2021 at four European tertiary referral centers were collected retrospectively. Four previously developed nomograms for the prediction of side-specific EPE were identified and externally validated. Discrimination (area under the curve [AUC]), calibration and net benefit of four nomograms were assessed. To assess the strongest predictor among the MRI features included in all nomograms, we evaluated their association with side-specific EPE using multivariate regression analysis and Akaike Information Criterion (AIC). RESULTS: This study involved 773 patients with a total of 1546 prostate lobes. EPE was found in 338 (22%) lobes. The AUCs of the models predicting EPE ranged from 72.2% (95% CI 69.1-72.3%) (Wibmer) to 75.5% (95% CI 72.5-78.5%) (Nyarangi-Dix). The nomogram with the highest AUC varied across the cohorts. The Soeterik, Nyarangi-Dix, and Martini nomograms demonstrated fair to good calibration for clinically most relevant thresholds between 5 and 30%. In contrast, the Wibmer nomogram showed substantial overestimation of EPE risk for thresholds above 25%. The Nyarangi-Dix nomogram demonstrated a higher net benefit for risk thresholds between 20 and 30% when compared to the other three nomograms. Of all MRI features, the European Society of Urogenital Radiology score and tumor capsule contact length showed the highest AUCs and lowest AIC. CONCLUSION: The Nyarangi-Dix, Martini and Soeterik nomograms resulted in accurate EPE prediction and are therefore suitable to support medical decision-making.

Estimation of the biomechanical mammographic deformation of the breast using machine learning models.

BACKGROUND: A typical problem in the registration of MRI and X-ray mammography is the nonlinear deformation applied to the breast during mammography. We have developed a method for virtual deformation of the breast using a biomechanical model automatically constructed from MRI. The virtual deformation is applied in two steps: unloaded state estimation and compression simulation. The finite element method is used to solve the deformation process. However, the extensive computational cost prevents its usage in clinical routine. METHODS: We propose three machine learning models to overcome this problem: an extremely randomized tree (first model), extreme gradient boosting (second model), and deep learning-based bidirectional long short-term memory with an attention layer (third model) to predict the deformation of a biomechanical model. We evaluated our methods with 516 breasts with realistic compression ratios up to 76%. FINDINGS: We first applied one-fold validation, in which the second and third models performed better than the first model. We then applied ten-fold validation. For the unloaded state estimation, the median RMSE for the second and third models is 0.8 mm and 1.2 mm, respectively. For the compression, the median RMSE is 3.4 mm for both models. We evaluated correlations between model accuracy and characteristics of the clinical datasets such as compression ratio, breast volume, and tissue types. INTERPRETATION: Using the proposed models, we achieved accurate results comparable to the finite element model, with a speedup of factor 240 using the extreme gradient boosting model. These proposed models can replace the finite element model simulation, enabling clinically relevant real-time application.

Impact of PI-QUAL on PI-RADS and cancer yield in an MRI-TRUS fusion biopsy population.

PURPOSE: To test the inter-reader agreement of the Prostate Imaging Quality (PI-QUAL) score for multiparametric prostate MRI and its impact on diagnostic performance in an MRI-ultrasound fusion biopsy population. PATIENTS AND METHODS: Pre-biopsy multiparametric (T2-weighted, DWI, and DCE) prostate MRIs (mpMRI) of 50 patients undergoing transrectal ultrasound-guided MRI-fusion (MRI-TRUS) biopsy were included. Two radiologists independently assigned a PI-QUAL score to each patient and assessed the diagnostic quality of individual sequences. PI-RADS categories were assigned to six regions per prostate (left and right: base/mid-glandular/apex). Inter-reader agreement was calculated using Cohen's kappa and diagnostic performance was compared by the area under the receiver operating characteristics curve (AUC). RESULTS: In 274 diagnostic areas, the malignancy rate was 62.7% (22.5% clinically significant prostate cancer, ISUP ≥ 2). Inter-reader agreement for the diagnostic quality was poor for T2w (kappa 0.19), fair for DWI and DCE (kappa 0.23 and 0.29) and moderate for PI-QUAL (kappa 0.51). For PI-RADS category assignments, inter-reader agreement was very good (kappa 0.86). Overall diagnostic performance did not differ between studies with a PI-QUAL score > 3 compared to a score ≤ 3 (p = 0.552; AUC 0.805 and 0.839). However, the prevalence of prostate cancer was significantly lower when the PI-QUAL score was ≤ 3 (16.7% vs. 30.2%, p = 0.008). CONCLUSION: PI-QUAL has only a limited impact on PI-RADS diagnostic performance in patients scheduled for MRI-TRUS fusion biopsy. However, the lower cancer prevalence in the lower PI-QUAL categories points out a risk of false-positive referrals and unnecessary biopsies if prostate imaging quality is low.

AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.

PURPOSE: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. METHODS: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. RESULTS: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). CONCLUSION: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions.

Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy.

To investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.

Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies.

OBJECTIVES: To investigate whether combined texture analysis and machine learning can distinguish malignant from benign suspicious mammographic calcifications, to find an exploratory rule-out criterion to potentially avoid unnecessary benign biopsies. METHODS: Magnification views of 235 patients which underwent vacuum-assisted biopsy of suspicious calcifications (BI-RADS 4) during a two-year period were retrospectively analyzed using the texture analysis tool MaZda (Version 4.6). Microcalcifications were manually segmented and analyzed by two readers, resulting in 249 image features from gray-value histogram, gray-level co-occurrence and run-length matrices. After feature reduction with principal component analysis (PCA), a multilayer perceptron (MLP) artificial neural network was trained using histological results as the reference standard. For training and testing of this model, the dataset was split into 70 % and 30 %. ROC analysis was used to calculate diagnostic performance indices. RESULTS: 226 patients (150 benign, 76 malignant) were included in the final analysis due to missing data in 9 cases. Feature selection yielded nine image features for MLP training. Area under the ROC-curve in the testing dataset (n = 54) was 0.82 (95 %-CI: 0.70-0.94) and 0.832 (95 %-CI 0.72-0.94) for both readers, respectively. A high sensitivity threshold criterion was identified in the training dataset and successfully applied to the testing dataset, demonstrating the potential to avoid 37.1-45.7 % of unnecessary biopsies at the cost of one false-negative for each reader. CONCLUSION: Combined texture analysis and machine learning could be used for risk stratification in suspicious mammographic calcifications. At low costs in terms of false-negatives, unnecessary biopsies could be avoided.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis.

BACKGROUND: Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC). METHODS AND FINDINGS: In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95). CONCLUSIONS: None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI.

OBJECTIVES: To investigate whether the application of the Kaiser score for breast magnetic resonance imaging (MRI) might downgrade breast lesions that present as mammographic calcifications and avoid unnecessary breast biopsies METHODS: This IRB-approved, retrospective, cross-sectional, single-center study included 167 consecutive patients with suspicious mammographic calcifications and histopathologically verified results. These patients underwent a pre-interventional breast MRI exam for further diagnostic assessment before vacuum-assisted stereotactic-guided biopsy (95 malignant and 72 benign lesions). Two breast radiologists with different levels of experience independently read all examinations using the Kaiser score, a machine learning-derived clinical decision-making tool that provides probabilities of malignancy by a formalized combination of diagnostic criteria. Diagnostic performance was assessed by receiver operating characteristics (ROC) analysis and inter-reader agreement by the calculation of Cohen's kappa coefficients. RESULTS: Application of the Kaiser score revealed a large area under the ROC curve (0.859-0.889). Rule-out criteria, with high sensitivity, were applied to mass and non-mass lesions alike. The rate of potentially avoidable breast biopsies ranged between 58.3 and 65.3%, with the lowest rate observed with the least experienced reader. CONCLUSIONS: Applying the Kaiser score to breast MRI allows stratifying the risk of breast cancer in lesions that present as suspicious calcifications on mammography and may thus avoid unnecessary breast biopsies. KEY POINTS: • The Kaiser score is a helpful clinical decision tool for distinguishing malignant from benign breast lesions that present as calcifications on mammography. • Application of the Kaiser score may obviate 58.3-65.3% of unnecessary stereotactic biopsies of suspicious calcifications. • High Kaiser scores predict breast cancer with high specificity, aiding clinical decision-making with regard to re-biopsy in case of negative results.

[Artificial intelligence in the diagnosis of breast cancer : Yesterday, today and tomorrow]. / Künstliche Intelligenz in der Mammadiagnostik : Gestern, heute und morgen.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in the field of breast imaging. OBJECTIVES: What are the main areas where AI is applied in breast imaging and what AI and computer-aided diagnosis (CAD) systems are already available? MATERIALS AND METHODS: Basic literature and vendor-supplied information are screened for relevant information, which is then pooled, structured and discussed from the perspective of breast imaging. RESULTS: Original CAD systems in mammography date almost 25 years back. They are much more widely applied in the United States than in Europe. The initial CAD systems exhibited limited diagnostic abilities and disproportionally high rates of false positive results. Since 2012, deep learning mechanisms have been applied and expand the application possibilities of AI. CONCLUSION: To date there is no algorithm that has beyond doubt been proven to outperform double reporting by two certified breast radiologists. AI could, however, in the foreseeable future, take over the following tasks: preselection of abnormal examinations to substantially reduce workload of the radiologists by either excluding normal findings from human review or by replacing the double reader in screening. Furthermore, the establishment of radio-patho-genomic correlations and their translation into clinical practice is hardly conceivable without AI.

Can the addition of clinical information improve the accuracy of PI-RADS version 2 for the diagnosis of clinically significant prostate cancer in positive MRI?

AIM: To report prostate cancer (PCa) prevalence in Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) categories and investigate the potential to avoid unnecessary, magnetic resonance imaging (MRI)-guided in-bore biopsies by adding clinical and biochemical patient characteristics. MATERIALS AND METHODS: The present institutional review board-approved, prospective study on 137 consecutive men with 178 suspicious lesions on 3 T MRI was performed. Routine data collected for each patient included patient characteristics (age, prostate volume), clinical background information (prostate-specific antigen [PSA] levels, PSA density), and PI-RADS v2 scores assigned in a double-reading approach. RESULTS: Histopathological evaluation revealed a total of 93/178 PCa (52.2%). The mean age was 66.3 years and PSA density was 0.24 ng/ml2 (range, 0.04-0.89 ng/ml). Clinically significant PCa (csPCa, Gleason score >6) was confirmed in 50/93 (53.8%) lesions and was significantly associated with higher PI-RADS v2 scores (p=0.0044). On logistic regression analyses, age, PSA density, and PI-RADS v2 scores contributed independently to the diagnosis of csPCa (p=7.9×10-7, p=0.097, and p=0.024, respectively). The resulting area under the receiver operating characteristic curve (AUC) to predict csPCa was 0.76 for PI-RADS v2, 0.59 for age, and 0.67 for PSA density. The combined regression model yielded an AUC of 0.84 for the diagnosis of csPCa and was significantly superior to each single parameter (p≤0.0009, respectively). Unnecessary biopsies could have been avoided in 50% (64/128) while only 4% (2/50) of csPCa lesions would have been missed. CONCLUSIONS: Adding age and PSA density to PI-RADS v2 scores improves the diagnostic accuracy for csPCa. A combination of these variables with PI-RADS v2 can help to avoid unnecessary in-bore biopsies while still detecting the majority of csPCa.

[Contrast-agent free imaging of the prostate : Ready for clinical practice?] / Kontrastmittelfreie Bildgebung der Prostata : Reif für die Praxis?

BACKGROUND: Multiparametric MRI (mpMRI) is currently the most accurate imaging modality for detection and local staging of prostate cancer (PCa). Disadvantages of this modality are high costs, time consumption and the need for a contrast medium. AIMS: The aim of the work was to provide an overview of the current state of fast and contrast-free MRI imaging of the prostate. RESULTS: Biparametric examination protocols and the use of three-dimensional T2-weighted sequences are readily available methods that can be used to shorten the examination time without sacrificing diagnostic accuracy.

[Contrast agent-free breast MRI : Advantages and potential disadvantages]. / Kontrastmittelfreie Mamma-MRT : Vorteile und potenzielle Nachteile.

BACKGROUND: Contrast-enhanced breast magnetic resonance imaging (MRI) is the most sensitive method for detection of breast cancer. The further spread of breast MRI is limited by the complicated examination procedure and the need for intravenously administered contrast media. OBJECTIVES: Can diffusion-weighted imaging (DWI) replace contrast-enhanced sequences to achieve an unenhanced breast MRI examination? MATERIALS AND METHODS: Narrative review and meta-analytic assessment of previously published studies. RESULTS: DWI can visualize breast lesions and distinguish benign from malignant findings. It is thus a valid alternative to contrast-enhanced sequences. As an additional technique, the use of DWI can reduce the numbers of unnecessary breast biopsies. The lack of robustness leading to variable sensitivity that is currently lower than that of contrast-enhanced breast MRI is a disadvantage of DWI. CONCLUSIONS: Presently, DWI can be recommended as an integral part of clinical breast MRI protocols. The application as a stand-alone technique within unenhanced protocols is still under evaluation.

Apparent diffusion coefficient values obtained by unenhanced MRI predicts disease-specific survival in bladder cancer.

AIM: To assess the ability of apparent diffusion coefficient (ADC) measurements obtained by MRI to predict disease-specific survival (DSS) in patients with bladder cancer and compare it with established clinico-pathological prognostic factors. MATERIAL AND METHODS: The ethical review board approved this cross-sectional study. Patients with suspected bladder cancer receiving diagnostic 3 T diffusion-weighted imaging (DWI) of the bladder before transurethral resection of the bladder (TUR-B) or radical cystectomy were evaluated prospectively. Two independent radiologists measured ADC values in bladder cancer lesions in regions of interest. Associations between ADC values and pathological features with DSS were tested statistically. A combined model was established using artificial neuronal network (ANN) methodology. RESULTS: A total of 51 patients (median age 69 years, range 41-89 years) were included. Three patients were lost to follow-up, leaving 48 patients for survival analysis. Seven patients died during the 795 months studied. ADC showed significant potential to predict DSS (p<0.05). Except for grading, all pathological features as assessed by TUR-B could predict DSS (p<0.05, respectively). The combined ANN classifier showed the highest accuracy to predict DSS (0.889, 95% confidence interval: 0.732-1, p=0.001) compared to all single parameters. ADC was the second important predictor of the ANN. CONCLUSIONS: ADC measurements obtained by unenhanced MRI predicts DSS in bladder cancer patients. A combined classifier including ADC and clinico-pathological information showed high accuracy to identify patients at high risk for disease-related death.

DCE-MRI of the breast in a stand-alone setting outside a complementary strategy - results of the TK-study.

OBJECTIVES: To evaluate the accuracy of MRI of the breast (DCE-MRI) in a stand-alone setting with extended indications. MATERIALS AND METHODS: According to the inclusion criteria, breast specialists were invited to refer patients to our institution for DCE-MRI. Depending on the MR findings, patients received either a follow-up or biopsy. Between 04/2006 and 12/2011 a consecutive total of 1,488 women were prospectively examined. RESULTS: Of 1,488 included patients, 393 patients were lost to follow-up, 1,095 patients were evaluated. 124 patients were diagnosed with malignancy by DCE-MRI (76 TP, 48 FP, 971 TN, 0 FN cases). Positive cases were confirmed by histology, negative cases by MR follow-ups or patient questionnaires over the next 5 years in 1,737 cases (sensitivity 100 %; specificity 95.2 %; PPV 61.3 %; NPV 100 %; accuracy 95.5 %). For invasive cancers only (DCIS excluded), the results were 63 TP; 27 FP; 971 TP and 0 FN (sensitivity 100 %; specificity 97.2 %; PPV 70 %; NPV 100 %; accuracy 97.5 %). CONCLUSION: The DCE-MRI indications tested imply that negative results in DCE-MRI reliably exclude cancer. The results were achieved in a stand-alone setting (single modality diagnosis). However, these results are strongly dependent on reader experience and adequate technical standards as prerequisites for optimal diagnoses. KEY POINTS: • DCE-MRI of the breast has a high accuracy in finding breast cancer. • The set of indications for DCE-MRI of the breast is still very limited. • DCE-MRI can achieve a high accuracy in a 'screening-like' setting. • Accuracy of breast DCE-MRI is strongly dependent on technique and reader experience. • A negative DCE-MRI effectively excludes cancer.

Fat saturation in dynamic breast MRI at 3 Tesla: is the Dixon technique superior to spectral fat saturation? A visual grading characteristics study.

PURPOSE: To intra-individually compare the diagnostic image quality of Dixon and spectral fat suppression at 3 T. METHODS: Fifty consecutive patients (mean age 55.1 years) undergoing 3 T breast MRI were recruited for this prospective study. The image protocol included pre-contrast and delayed post-contrast spectral and Dixon fat-suppressed T1w series. Two independent blinded readers compared spectral and Dixon fat-suppressed series by evaluating six ordinal (1 worst to 5 best) image quality criteria (image quality, delineation of anatomical structures, fat suppression in the breast and axilla, lesion delineation and internal enhancement). Breast density and size were assessed. Data analysis included Spearman's rank correlation coefficient and visual grading characteristics (VGC) analysis. RESULTS: Four examinations were excluded; 48 examinations in 46 patients were evaluated. In VGC analysis, the Dixon technique was superior regarding image quality criteria analysed (P < 0.01). Smaller breast size and lower breast density were significantly (P < 0.01) correlated with impaired spectral fat suppression quality. No such correlation was identified for the Dixon technique, which showed reconstruction-based water-fat mixups leading to insufficient image quality in 20.8%. CONCLUSIONS: The Dixon technique outperformed spectral fat suppression in all evaluated criteria (P < 0.01). Non-diagnostic examinations can be avoided by fat and water image reconstruction. The superior image quality of the Dixon technique can improve breast MRI interpretation. KEY POINTS: Optimal fat suppression quality is necessary for optimal image interpretation. Superior fat suppression quality is achieved using the Dixon technique. Lesion margin and internal enhancement evaluation improves using the Dixon technique. Superior image quality of the Dixon technique improves breast MRI interpretation.

Combined staging at one stop using MR mammography: evaluation of an extended protocol to screen for distant metastasis in primary breast cancer - initial results and diagnostic accuracy in a prospective study.

PURPOSE: Accurate staging of primary breast cancer is essential for the therapeutic approach. Modern whole-body MR scanners would allow local and distant staging during a single examination. Accordingly, we designed a dedicated protocol for this purpose and prospectively evaluated the diagnostic accuracy. MATERIALS AND METHODS: 65 consecutive breast cancer patients underwent pre-therapeutic MRI (1.5 T). A bilateral breast protocol (axial: T1w/GRE dynamic contrast-enhanced, T2w/TSE; TA: 10 min) was extended to screen for distant metastasis at one stop without repositioning (coronal: T2w/HASTE, T1w/VIBE; FOV: thorax, abdomen and spine; TA: 90 sec; multichannel surface coils). The standard of reference was S3 guideline-compliant staging examinations. Global assessment regarding the presence of distant metastasis was performed independently by two experienced and blinded radiologists (five-level confidence score). Inter-rater agreement (weighted kappa) and observer scoring were analyzed (contingency tables). RESULTS: The prevalence of synchronous metastases was 7.7 % (n = 5). The protocol enabled global assessment regarding the presence of distant metastasis with high accuracy (sensitivity: 100 %; specificity: 98.3 %) and inter-rater agreement (kappa: 0.92). CONCLUSION: Applying the extended MRI protocol, accurate screening for distant metastasis was possible in combination with a dedicated breast examination.

Application of classification trees for the qualitative differentiation of focal liver lesions suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging.

PURPOSE: To evaluate the diagnostic accuracy of qualitative descriptors alone and in combination for the classification of focal liver lesions (FLLs) suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging. MATERIALS AND METHODS: Consecutive patients with clinically suspected liver metastases were eligible for this retrospective investigation. 50 patients met the inclusion criteria. All underwent Gd-EOB-DTPA-enhanced liver MRI (T2w, chemical shift T1w, dynamic T1w). Primary liver malignancies or treated lesions were excluded. All investigations were read by two blinded observers (O1, O2). Both independently identified the presence of lesions and evaluated predefined qualitative lesion descriptors (signal intensities, enhancement pattern and morphology). A reference standard was determined under consideration of all clinical and follow-up information. Statistical analysis besides contingency tables (chi square, kappa statistics) included descriptor combinations using classification trees (CHAID methodology) as well as ROC analysis. RESULTS: In 38 patients, 120 FLLs (52 benign, 68 malignant) were present. 115 (48 benign, 67 malignant) were identified by the observers. The enhancement pattern, relative SI upon T2w and late enhanced T1w images contributed significantly to the differentiation of FLLs. The overall classification accuracy was 91.3 % (O1) and 88.7 % (O2), kappa = 0.902. CONCLUSION: The combination of qualitative lesion descriptors proposed in this work revealed high diagnostic accuracy and interobserver agreement in the differentiation of focal liver lesions suspicious for metastases using Gd-EOB-DTPA-enhanced liver MRI.