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.

Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI.

PURPOSE: Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. METHODS: Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. RESULTS: The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. CONCLUSION: Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

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.

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.

[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.

Impact of androgen deprivation therapy on apparent diffusion coefficient and T2w MRI for histogram and texture analysis with respect to focal radiotherapy of prostate cancer.

PURPOSE: Accurate prostate cancer (PCa) detection is essential for planning focal external beam radiotherapy (EBRT). While biparametric MRI (bpMRI) including T2-weighted (T2w) and diffusion-weighted images (DWI) is an accurate tool to localize PCa, its value is less clear in the case of additional androgen deprivation therapy (ADT). The aim of this study was to investigate the value of a textural feature (TF) approach on bpMRI analysis in prostate cancer patients with and without neoadjuvant ADT with respect to future dose-painting applications. METHODS: 28 PCa patients (54-80 years) with (n = 14) and without (n = 14) ADT who underwent bpMRI with T2w and DWI were analyzed retrospectively. Lesions, central gland (CG), and peripheral zone (PZ) were delineated by an experienced urogenital radiologist based on localized pre-therapeutic histopathology. Histogram parameters and 20 Haralick TF were calculated. Regional differences (i. e., tumor vs. PZ, tumor vs. CG) were analyzed for all imaging parameters. Receiver-operating characteristic (ROC) analysis was performed to measure diagnostic performance to distinguish PCa from benign prostate tissue and to identify the features with best discriminative power in both patient groups. RESULTS: The obtained sensitivities were equivalent or superior when utilizing the TF in the no-ADT group, while specificity was higher for the histogram parameters. However, in the ADT group, TF outperformed the conventional histogram parameters in both specificity and sensitivity. Rule-in and rule-out criteria for ADT patients could exclusively be defined with the aid of TF. CONCLUSIONS: The TF approach has the potential for quantitative image-assisted boost volume delineation in PCa patients even if they are undergoing neoadjuvant ADT.

[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.

68Ga-PSMA 11 ligand PET imaging in patients with biochemical recurrence after radical prostatectomy - diagnostic performance and impact on therapeutic decision-making.

OBJECTIVE: To evaluate the diagnostic performance of [68Ga]Ga-PSMAHBED-CC conjugate 11 positron emission tomography (PSMA-PET) in the early detection of metastases in patients with biochemical recurrence (BCR) after radical prostatectomy (RP) for clinically non-metastatic prostate cancer, to compare it to CT/MRI alone and to assess its impact on further therapeutic decisions. MATERIAL AND METHODS: We retrospectively assessed 117 consecutive hormone-naïve BCR patients who had 68Ga-PSMA 11 PET/CT (n = 46) or PET/MRI (n = 71) between May 2014 and January 2017. BCR was defined as two PSA rises above 0.2 ng/ml. Two dedicated uro-oncological imaging experts (radiology/nuclear medicine) reviewed separately all images. All results were presented in a blinded sequential fashion to a multidisciplinary tumorboard in order to assess the influence of PSMA-PET imaging on decision-making. RESULTS: The median time from RP to BCR was 36 months (IQR 16-72). Overall, 69 (59%) patients received postoperative radiotherapy. Median PSA level at the time of imaging was 1.04 ng/ml (IQR 0.58-1.87). PSMA-positive lesions were detected in 100 (85.5%) patients. Detection rates were 65% for a PSA value of 0.2 to <0.5 ng/ml, 85.7% for 0.5 to <1, 85.7% for 1 to <2 and 100% for ≥2. PSMA-positive lesions could be confirmed by either histology (16%), PSA decrease in metastasis-directed radiotherapy (45%) or additional information in diffusion-weighted imaging when PET/MRI was performed (18%) in 79% of patients. PSMA-PET detected lesions in 67 patients (57.3%) who had no suspicious correlates according to the RECIST 1.1 criteria on MRI or CT. PSMA-PET changed therapeutic decisions in 74.6% of these 67 patients (p < 0.001), with 86% of them being considered for metastases-directed therapies. CONCLUSIONS: We confirm the high performance of PSMA-PET imaging for the detection of disease recurrence sites in patients with BCR after RP, even at relatively low PSA levels. Moreover, it adds significant information to standard CT/MRI, changing treatment strategies in a significant number of patients.

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.

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

PURPOSE: To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. MATERIALS AND METHODS: Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). RESULTS: Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. CONCLUSION: Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. KEY POINTS: • Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers. • Inter-observer agreement can be improved by practice and experience. • Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI.

Diffusion-weighted imaging of breast tumours at 3 Tesla and 7 Tesla: a comparison.

OBJECTIVES: To compare bilateral diffusion-weighted MR imaging (DWI) at 3 T and 7 T in the same breast tumour patients. METHODS: Twenty-eight patients were included in this IRB-approved study (mean age 56 ± 16 years). Before contrast-enhanced imaging, bilateral DWI with b = 0 and 850 s/mm(2) was performed in 2:56 min (3 T) and 3:48 min (7 T), using readout-segmented echo planar imaging (rs-EPI) with a 1.4 × 1.4 mm(2) (3 T)/0.9 × 0.9 mm(2) (7 T) in-plane resolution. Apparent diffusion coefficients (ADC), signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were assessed. RESULTS: Twenty-eight lesions were detected (18 malignant, 10 benign). CNR and SNR were comparable at both field strengths (p > 0.3). Mean ADC values at 7 T were 4-22% lower than at 3 T (p ≤ 0.03). An ADC threshold of 1.275 × 10(-3) mm(2)/s resulted in a diagnostic specificity of 90% at both field strengths. The sensitivity was 94% and 100% at 3 T and 7 T, respectively. CONCLUSION: 7-T DWI of the breast can be performed with 2.4-fold higher spatial resolution than 3 T, without significant differences in SNR if compared to 3 T. KEY POINTS: • 7 T provides a 2.4-fold higher resolution in breast DWI than 3 T • 7 T DWI has a high diagnostic accuracy comparable to that at 3 T • At 7 T malignant lesions had 22 % lower ADC than at 3 T (p < 0.001).

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.

Benign (BI-RADS 2) lesions in breast MRI.

AIM: To assess the frequency of malignancy in lesions characterized as benign [Breast Imaging-Reporting and Data System (BI-RADS) 2] on breast MRI. MATERIALS AND METHODS: In this institutional review board-approved retrospective single-centre study, 1265 consecutive patients (mean age 50 ± 13 years), undergoing dynamic contrast-enhanced MRI (1.5 T) of the breast during a 6 year time period, were eligible. This study investigated the MRI characteristics and frequency of malignancy in 192 of these patients with breast lesions classified as BI-RADS 2. Examinations were read during clinical practice and classified according to the MRI BI-RADS lexicon. Based on the patient's and referring physician's preferences, lesions were either histopathologically verified or were subjected to both clinical and imaging follow-up of at least 2 years (range 2-9 years). Descriptive statistical metrics were calculated. RESULTS: According to the standard of reference, 0 of 192 (0%) lesions classified as BI-RADS 2 were malignant. Histopathology was available in 67 (34.9%) lesions and revealed benign findings exclusively. The remaining 125 (65.1%) lesions did not exhibit changes during the follow-up period and were, therefore, considered negative for malignancy. CONCLUSIONS: The frequency of malignancy in breast lesions classified as BI-RADS 2 is zero. As a consequence, breast biopsies are unnecessary in these cases.

Three-Tesla dynamic contrast-enhanced MRI: a critical assessment of its use for differentiation of renal lesion subtypes.

PURPOSE: To evaluate the ability of dynamic contrast-enhanced (DCE) 3-T MRI for preoperative differentiation between benign and malignant renal tumors and RCC subtypes. METHODS: Sixty consecutive patients undergoing preoperative DCE 3-T MRI of the kidney were evaluated in this retrospective IRB-approved evaluation. Fifty-four malignant tumors and 17 benign tumors upon surgical verification were included. Relative enhancement values of complete lesions and the most enhancing part of the lesions (hotspot) were measured using four repetitions: precontrast, arterial, venous, and delayed. RESULTS: Mean relative enhancement patterns between malignant and benign lesions did not differ significantly during any postcontrast phase (p > 0.05). The highest mean enhancement during all postcontrast phases was identified in clear cell RCC followed by chromophobic RCC. The enhancement pattern in papillary RCC was significantly less than that of non-papillary RCC lesions. Arterial enhancement was an independent predictor for RCC subtypes (papillary vs. non-papillary, p = 0.008). The diagnostic accuracy for differentiation of papillary from non-papillary RCC based on ROC analysis was 76.4% [95% CI 62.2-87.2%]; p < 0.0001. CONCLUSIONS: Dynamic contrast-enhanced MRI at 3 T showed intermediate diagnostic capability for differentiation between papillary and non-papillary RCC subtypes but could not differentiate between benign and malignant renal lesions.

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.

Clinical application of bilateral high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging of the breast at 7 T.

OBJECTIVE: The objective of our study was to evaluate the clinical application of bilateral high spatial and temporal resolution dynamic contrast-enhanced magnetic resonance imaging (HR DCE-MRI) of the breast at 7 T. METHODS: Following institutional review board approval 23 patients with a breast lesion (BIRADS 0, 4-5) were included in our prospective study. All patients underwent bilateral HR DCE-MRI of the breast at 7 T (spatial resolution of 0.7 mm(3) voxel size, temporal resolution of 14 s). Two experienced readers (r1, r2) and one less experienced reader (r3) independently assessed lesions according to BI-RADS®. Image quality, lesion conspicuity and artefacts were graded from 1 to 5. Sensitivity, specificity and diagnostic accuracy were assessed using histopathology as the standard of reference. RESULTS: HR DCE-MRI at 7 T revealed 29 lesions in 23 patients (sensitivity 100 % (19/19); specificity of 90 % (9/10)) resulting in a diagnostic accuracy of 96.6 % (28/29) with an AUC of 0.95. Overall image quality was excellent in the majority of cases (27/29) and examinations were not hampered by artefacts. There was excellent inter-reader agreement for diagnosis and image quality parameters (κ = 0.89-1). CONCLUSION: Bilateral HR DCE-MRI of the breast at 7 T is feasible with excellent image quality in clinical practice and allows accurate breast cancer diagnosis. KEY POINTS: • Dynamic contrast-enhanced 7-T MRI is being developed in several centres. • Bilateral high resolution DCE-MRI of the breast at 7 T is clinically applicable. • 7-T HR DCE-MRI of the breast provides excellent image quality. • 7-T HR DCE-MRI should detect breast cancer with high diagnostic accuracy.

[The importance of risk models for management of pulmonary nodules]. / Die Bedeutung von Risikomodellen für das Management pulmonaler Rundherde.

CLINICAL/METHODICAL ISSUE: Pulmonary nodules are a frequent finding in computed tomography (CT) investigations. STANDARD RADIOLOGICAL METHODS: Further diagnostic work-up of detected nodules mainly depends on the so-called pre-test probability, i.e. the probability that the nodule is malignant or benign. METHODICAL INNOVATIONS: The pre-test probability can be calculated by combining all relevant information, such as the age and the sex of the patient, the smoking history, and history of previous malignancies, as well as the size and CT morphology of the nodule. PERFORMANCE: If additional investigations are performed to further investigate the nodules, all results must be interpreted taking into account the pre-test probability and the test performance of the investigation in order to estimate the post-test probability. ACHIEVEMENTS: In cases with a low pre-test probability, a negative result from an exact test can exclude malignancies but a positive test cannot prove malignancy in such a setting. In cases with a high pre-test probability, a positive test result can be considered as proof of malignancy but a negative test result does not exclude malignancy.

[Molecular breast imaging. An update]. / Molekulare Brustbildgebung. Ein Update.

CLINICAL/METHODICAL ISSUE: The aim of molecular imaging is to visualize and quantify biological, physiological and pathological processes at cellular and molecular levels. Molecular imaging using various techniques has recently become established in breast imaging. STANDARD RADIOLOGICAL METHODS: Currently molecular imaging techniques comprise multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), proton MR spectroscopy ((1)H-MRSI), nuclear imaging by breast-specific gamma imaging (BSGI), positron emission tomography (PET) and positron emission mammography (PEM) and combinations of techniques (e.g. PET-CT and multiparametric PET-MRI). METHODICAL INNOVATIONS: Recently, novel techniques for molecular imaging of breast tumors, such as sodium imaging ((23)Na-MRI), phosphorus spectroscopy ((31)P-MRSI) and hyperpolarized MRI as well as specific radiotracers have been developed and are currently under investigation. PRACTICAL RECOMMENDATIONS: It can be expected that molecular imaging of breast tumors will enable a simultaneous assessment of the multiple metabolic and molecular processes involved in cancer development and thus an improved detection, characterization, staging and monitoring of response to treatment will become possible.

Using the Kaiser Score as a clinical decision rule for breast lesion classification: Does computer-assisted curve type analysis improve diagnosis?

PURPOSE: We aimed to investigate the effect of using visual or automatic enhancement curve type assessment on the diagnostic performance of the Kaiser Score (KS), a clinical decision rule for breast MRI. METHOD: This IRB-approved retrospective study analyzed consecutive conventional BI-RADS 0, 4 or 5 patients who underwent biopsy after 1.5T breast MRI according to EUSOBI recommendations between 2013 and 2015. The KS includes five criteria (spiculations; signal intensity (SI)-time curve type; margins of the lesion; internal enhancement; and presence of edema) resulting in scores from 1 (=lowest) to 11 (=highest risk of breast cancer). Enhancement curve types (Persistent, Plateau or Wash-out) were assessed by two radiologists independently visually and using a pixel-wise color-coded computed parametric map of curve types. KS diagnostic performance differences between readings were compared by ROC analysis. RESULTS: In total 220 lesions (147 benign, 73 malignant) including mass (n = 148) and non-mass lesions (n = 72) were analyzed. KS reading performance in distinguishing benign from malignant lesions did not differ between visual analysis and parametric map (P = 0.119; visual: AUC 0.875, sensitivity 95 %, specificity 63 %; and map: AUC 0.901, sensitivity 97 %, specificity 65 %). Additionally, analyzing mass and non-mass lesions separately, showed no difference between parametric map based and visual curve type-based KS analysis as well (P = 0.130 and P = 0.787). CONCLUSIONS: The performance of the Kaiser Score is largely independent of the curve type assessment methodology, confirming its robustness as a clinical decision rule for breast MRI in any type of breast lesion in clinical routine.