Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database.

OBJECTIVES: To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria. METHODS: This was a multicentric, retrospective analysis of 11 independently conducted institutional review board-approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant. RESULTS: A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10-3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p < .001). The following ADC-B categories were identified: ADC-B0-ADC cannot be assessed; ADC-B1-no contrast-enhancing lesion; ADC-B2-ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3-ADC 1.5 to < 1.9 (0.1-1.7%); ADC-B4-ADC 1.0 to < 1.5 (10-24.5%); and ADC-B5-ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94-0.97) for invasive versus non-invasive breast carcinomas was reached. CONCLUSIONS: The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI. CLINICAL RELEVANCE STATEMENT: The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes. KEY POINTS: • The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.

Breast MRI: does a clinical decision algorithm outweigh reader experience?

OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. METHODS: Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. RESULTS: A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723-0.742) as well as the three residents was equal (AUC 0.842-0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts' ratings using the MR BI-RADS scale (p ≤ 0.05). CONCLUSION: The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical "problem solving MRI" setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. KEY POINTS: • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical "problem solving MRI" setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance.

Economic potential of abbreviated breast MRI for screening women with dense breast tissue for breast cancer.

OBJECTIVES: Abbreviated breast MRI (AB-MRI) was introduced to reduce both examination and image reading times and to improve cost-effectiveness of breast cancer screening. The aim of this model-based economic study was to analyze the cost-effectiveness of full protocol breast MRI (FB-MRI) vs. AB-MRI in screening women with dense breast tissue for breast cancer. METHODS: Decision analysis and a Markov model were designed to model the cumulative costs and effects of biennial screening in terms of quality-adjusted life years (QALYs) from a US healthcare system perspective. Model input parameters for a cohort of women with dense breast tissue were adopted from recent literature. The impact of varying AB-MRI costs per examination as well as specificity on the resulting cost-effectiveness was modeled within deterministic sensitivity analyses. RESULTS: At an assumed cost per examination of $ 263 for AB-MRI (84% of the cost of a FB-MRI examination), the discounted cumulative costs of both MR-based strategies accounted comparably. Reducing the costs of AB-MRI below $ 259 (82% of the cost of a FB-MRI examination, respectively), the incremental cost-effectiveness ratio of FB-MRI exceeded the willingness to pay threshold and the AB-MRI-strategy should be considered preferable in terms of cost-effectiveness. CONCLUSIONS: Our preliminary findings indicate that AB-MRI may be considered cost-effective compared to FB-MRI for screening women with dense breast tissue for breast cancer, as long as the costs per examination do not exceed 82% of the cost of a FB-MRI examination. KEY POINTS: • Cost-effectiveness of abbreviated breast MRI is affected by reductions in specificity and resulting false positive findings and increased recall rates. • Abbreviated breast MRI may be cost-effective up to a cost per examination of 82% of the cost of a full protocol examination. • Abbreviated breast MRI could be an economically preferable alternative to full protocol breast MRI in screening women with dense breast tissue.

A decade of radiomics research: are images really data or just patterns in the noise?

KEY POINTS: • Although radiomics is potentially a promising approach to analyze medical image data, many pitfalls need to be considered to avoid a reproducibility crisis.• There is a translation gap in radiomics research, with many studies being published but so far little to no translation into clinical practice.• Going forward, more studies with higher levels of evidence are needed, ideally also focusing on prospective studies with relevant clinical impact.

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies.

OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies. METHODS: This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network-derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity). RESULTS: Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18-85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8-89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2). CONCLUSION: The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies. KEY POINTS: • Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

Clinical relevance of total choline (tCho) quantification in suspicious lesions on multiparametric breast MRI.

PURPOSE: To assess the additional value of quantitative tCho evaluation to diagnose malignancy and lymph node metastases in suspicious lesions on multiparametric breast MRI (mpMRI, BI-RADS 4, and BI-RADS 5). METHODS: One hundred twenty-one patients that demonstrated suspicious multiparametric breast MRI lesions using DCE, T2w, and diffusion-weighted (DW) images were prospectively enrolled in this IRB-approved study. All underwent single-voxel proton MR spectroscopy (1H-MRS, point-resolved spectroscopy sequence, TR 2000 ms, TE 272 ms) with and without water suppression. The total choline (tCho) amplitude was measured and normalized to millimoles/liter according to established methodology by two independent readers (R1, R2). ROC-analysis was employed to predict malignancy and lymph node status by tCho results. RESULTS: One hundred three patients with 74 malignant and 29 benign lesions had full 1H-MRS data. The area under the ROC curve (AUC) for prediction of malignancy was 0.816 (R1) and 0.809 (R2). A cutoff of 0.8 mmol/l tCho could diagnose malignancy with a sensitivity of > 95%. For prediction of lymph node metastases, tCho measurements achieved an AUC of 0.760 (R1) and 0.788 (R2). At tCho levels < 2.4 mmol/l, no metastatic lymph nodes were found. CONCLUSION: Quantitative tCho evaluation from 1H-MRS allowed diagnose malignancy and lymph node status in breast lesions suspicious on multiparametric breast MRI. tCho therefore demonstrated the potential to downgrade suspicious mpMRI lesions and stratify the risk of lymph node metastases for improved patient management. KEY POINTS: • Quantitative tCho evaluation can distinguish benign from malignant breast lesions suspicious after multiparametric MRI assessment. • Quantitative tCho levels are associated with lymph node status in breast cancer. • Quantitative tCho levels are higher in hormonal receptor positive compared to hormonal receptor negative lesions.

Combination of an ultrafast TWIST-VIBE Dixon sequence protocol and diffusion-weighted imaging into an accurate easily applicable classification tool for masses in breast MRI.

OBJECTIVES: This study aimed to develop a tool for the classification of masses in breast MRI, based on ultrafast TWIST-VIBE Dixon (TVD) dynamic sequences combined with DWI. TVD sequences allow to abbreviate breast MRI protocols, but provide kinetic information only on the contrast wash-in, and because of the lack of the wash-out kinetics, their diagnostic value might be hampered. A special focus of this study was thus to maintain high diagnostic accuracy in lesion classification. MATERIALS AND METHODS: Sixty-one patients who received breast MRI between 02/2014 and 04/2015 were included, with 83 reported lesions (60 malignant). Our institute's standard breast MRI protocol was complemented by an ultrafast TVD sequence. ADC and peak enhancement of the TVD sequences were integrated into a generalised linear model (GLM) for malignancy prediction. For comparison, a second GLM was calculated using ADC and conventional DCE curve type. The resulting GLMs were evaluated for standard diagnostic parameters. For easy application of the GLMs, nomograms were created. RESULTS: The GLM based on peak enhancement of the TVD and ADC was as equally accurate as the GLM based on conventional DCE and ADC, with no significant differences (sensitivity, 93.3%/93.3%; specificity, 91.3%/87.0%; PPV, 96.6%/94.9%; NPV, 84.0%/83.3%; all, p ≥ 0.315). CONCLUSIONS: This study presents a method to integrate ultrafast TVD sequences into a breast MRI protocol, allowing a reduction of the examination time while maintaining diagnostic accuracy. A GLM based on the combination of TVD-derived peak enhancement and ADC provides high diagnostic accuracy, and can be easily applied using a nomogram. KEY POINTS: • Ultrafast TWIST-VIBE Dixon sequence protocols in combination with diffusion-weighted imaging allow to shorten breast MRI examinations, while diagnostic accuracy is maintained. • Integrating peak enhancement from the TWIST-VIBE Dixon sequence and the apparent diffusion coefficient into a generalised linear model provides a comprehensible image evaluation approach. • This approach is further facilitated by nomograms.

Breast MRI in the era of diffusion weighted imaging: do we still need signal-intensity time curves?

OBJECTIVE: Dynamic contrast-enhanced imaging of the initial (IP) and delayed phase (DP) is an integral part of any clinical breast MRI protocol. Furthermore, DWI is increasingly used as an add-on sequence by the breast-imaging community. We investigated whether DWI could be used as a substitute DP. MATERIAL AND METHODS: One hundred thirty-two consecutive patients with equivocal or suspicious findings at ultrasound and/or mammography received a full diagnostic breast MRI according to international recommendations. Histopathological verification served as reference standard. We evaluated three sections of the MRI protocol: IP, DP, and apparent diffusion coefficient (ADC) maps derived from DWI. Circular ROIs (regions of interest, mean size 5-10 mm2) were drawn into the enhancing parts of the lesion (first postcontrast). ROIs were transferred to the corresponding location on ADC maps and IP and DP images. Mean ROI values were investigated signal intensity (SI): (1) Initial-phase enhancement = (SI(IP) - SI(precontrast))/SI(precontrast); (2) Delayed-phase enhancement = (SI(DP) - SI(IP))/SI(IP); (3) ADC. Multiparametric combinations were computed using logistic regression analysis: (1) IP+: Initial-phase enhancement and ADC; (2) Curve: Initial-phase enhancement and delayed-phase enhancement; (3) Curve+: Curve and ADC. The diagnostic performances of these feature combinations to diagnose malignancy were compared by the area under the receiver-operating characteristics curve (AUC). RESULTS: One hundred thirty-two patients (age: mean = 57.1 years, range 23-83 years) with 145 lesions were included (malignant/benign 101/44). IP+ (AUC = 0.877) outperformed Curve (AUC = 0.788, p = 0.03). Curve+ was not superior to IP+ (p = 1). CONCLUSION: DWI could substitute DP. Because DWI is typically used as an add-on to IP and DP, our results might help to abbreviate and to simplify current practice of breast MRI. KEY POINTS: • DWI provides similar but superior diagnostic information for diagnosis of malignancy in enhancing breast lesions compared to DP. • Adding DP to DWI does not provide incremental information to distinguish benign from malignant lesions. • DWI could substitute DP. As DWI is typically used as an add-on to IP and DP, our findings might help to abbreviate and to simplify current breast MRI practice.

Motion artifacts, lesion type, and parenchymal enhancement in breast MRI: what does really influence diagnostic accuracy?

BACKGROUND: Motion artifacts can reduce image quality of breast magnetic resonance imaging (MRI). There is a lack of data regarding their effect on diagnostic estimates. PURPOSE: To evaluate factors that potentially influence readers' diagnostic estimates in breast MRI: motion artifacts; amount of fibroglandular tissue; background parenchymal enhancement; lesion size; and lesion type. MATERIAL AND METHODS: This Institutional Review Board-approved, retrospective, cross-sectional, single-center study included 320 patients (mean age = 55.1 years) with 334 histologically verified breast lesions (139 benign, 195 malignant) who underwent breast MRI. Two expert breast radiologists evaluated the images considering: motion artifacts (1 = minimal to 4 = marked); fibroglandular tissue (BI-RADS FGT); background parenchymal enhancement (BI-RADS BPE); lesion size; lesion type; and BI-RADS score. Univariate (Chi-square) and multivariate (Generalized Estimation Equations [GEE]) statistics were used to identify factors influencing sensitivity, specificity, and accuracy. RESULTS: Lesions were: 230 mass (68.9%) and 59 non-mass (17.7%), no foci. Forty-five lesions (13.5%) did not enhance in MRI but were suspicious or unclear in conventional imaging. Sensitivity, specificity, and accuracy were 93.8%, 83.4%, and 89.8% for Reader 1 and 95.4%, 87.8%, and 91.9% for Reader 2. Lower sensitivity was observed in case of increased motion artifacts ( P = 0.007), non-mass lesions ( P < 0.001), and small lesions ≤ 10 mm ( P < 0.021). No further factors (e.g. BPE, FGT) significantly influenced diagnostic estimates. At multivariate analysis, lesion type and size were retained as independent factors influencing the diagnostic performance ( P < 0.033). CONCLUSION: Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.

A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice.

OBJECTIVES: While magnetic resonance imaging (MRI) is considered a helpful diagnostic tool in breast imaging, discussions are ongoing about appropriate protocols and indications. The European Society of Breast Imaging (EUSOBI) launched a survey to evaluate the utilisation of breast MRI in clinical practice. METHODS: An online survey reviewed by the EUSOBI board and committees was distributed amongst members. The questions encompassed: training and experience; annual breast MRI and MRI-guided-intervention workload; examination protocols; indications; reporting habits and preferences. Data were summarised and subgroups compared using χ2 test. RESULTS: Of 647 EUSOBI members, 177 (27.4%) answered the survey. The majority were radiologists (90.5%), half of them based in academic centres (51.9%). Common indications for MRI included cancer staging, treatment monitoring, high-risk screening and problem-solving, and differed significantly between countries (p≤0.03). Structured reporting and BI-RADS were mostly used. Breast radiologists with ≤10 years of experience preferred inclusion of additional techniques, such as T2/STIR (p=0.03) and DWI (p=0.08) in the scan protocol. MRI-guided interventions were performed by a minority of participants (35.4%). CONCLUSIONS: The utilisation of breast MRI in clinical practice is generally in line with international recommendations. There are substantial differences between countries. MRI-guided interventions and functional MRI parameters are not widely available. KEY POINTS: • MRI is commonly used for the detection and characterisation of breast lesions. • Clinical practice standards are generally in line with current recommendations. • Standardised criteria and diagnostic categories (mainly BI-RADS) are widely adopted. • Younger radiologists value additional techniques, such as T2/STIR and DWI. • MRI-guided breast biopsy is not widely available.

Impact of post-processing methods on apparent diffusion coefficient values.

OBJECTIVE: The apparent diffusion coefficient (ADC) is increasingly used as a quantitative biomarker in oncological imaging. ADC calculation is based on raw diffusion-weighted imaging (DWI) data, and multiple post-processing methods (PPMs) have been proposed for this purpose. We investigated whether PPM has an impact on final ADC values. METHODS: Sixty-five lesions scanned with a standardized whole-body DWI-protocol at 3 T served as input data (EPI-DWI, b-values: 50, 400 and 800 s/mm2). Using exactly the same ROI coordinates, four different PPM (ADC_1-ADC_4) were executed to calculate corresponding ADC values, given as [10-3 mm2/s] of each lesion. Statistical analysis was performed to intra-individually compare ADC values stratified by PPM (Wilcoxon signed-rank tests: α = 1 %; descriptive statistics; relative difference/∆; coefficient of variation/CV). RESULTS: Stratified by PPM, mean ADCs ranged from 1.136-1.206 *10-3 mm2/s (∆ = 7.0 %). Variances between PPM were pronounced in the upper range of ADC values (maximum: 2.540-2.763 10-3 mm2/s, ∆ = 8 %). Pairwise comparisons identified significant differences between all PPM (P ≤ 0.003; mean CV = 7.2 %) and reached 0.137 *10-3 mm2/s within the 25th-75th percentile. CONCLUSION: Altering the PPM had a significant impact on the ADC value. This should be considered if ADC values from different post-processing methods are compared in patient studies. KEY POINTS: • Post-processing methods significantly influenced ADC values. • The mean coefficient of ADC variation due to PPM was 7.2 %. • To achieve reproducible ADC values, standardization of post-processing is recommended.

Differentiation of ductal carcinoma in situ versus fibrocystic changes by magnetic resonance imaging: are there pathognomonic imaging features?

Background In breast magnetic resonance imaging (MRI), the diagnosis of ductal carcinoma in situ (DCIS) remains controversial; the most challenging cause of false-positive DCIS diagnosis is fibrocystic changes (FC). Purpose To search for typical and pathognomonic patterns of DCIS and FC using a standard clinical MRI protocol. Material and Methods Consecutive patients scheduled for breast MRI (standardized protocols @ 1.5T: dynamic-T1-GRE before/after Gd-DTPA [0.1 mmol/kg body weight (BW)]; T1-TSE), with subsequent pathological sampling, were investigated. Sixteen MRI descriptors were prospectively assessed by two experienced radiologists in consensus (blinded to pathology) and explored in patients with DCIS (n = 77) or FC (n = 219). Univariate and multivariate statistics were performed to identify the accuracy of descriptors (alone, combined). Furthermore, pathognomonic descriptor-combinations with an accuracy of 100% were explored (χ2 statistics; decision trees). Results Six breast MRI descriptors significantly differentiated DCIS from FC ( Pcorrected < 0.05; odds ratio < 7.9). Pathognomonic imaging features were present in 33.8% (n = 100) of all cases allowing the identification of 42.9% of FC (n = 94). Conclusion Pathognomonic patterns of DCIS and FC were frequently observed in a standard clinical MRI protocol. Such imaging patterns could decrease the false-positive rate of breast MRI and hence might help to decrease the number of unnecessary biopsies in this clinically challenging subgroup.

Combined reading of Contrast Enhanced and Diffusion Weighted Magnetic Resonance Imaging by using a simple sum score.

OBJECTIVE: To improve specificity of breast MRI by integrating Apparent Diffusion Coefficient (ADC) values with contrast enhanced MRI (CE-MRI) using a simple sum score. METHODS: Retrospective analysis of a consecutive series of patients referred to breast MRI at 1.5 T for further workup of breast lesions. Reading results of CE-MRI were dichotomized into score 1 (suspicious) or 0 (benign). Lesion's ADC-values (in *10-3 mm2/s) were assigned two different scores: ADC2: likely malignant (score +1, ADC ≤ 1), indeterminate (score 0, ADC >1- ≤ 1.4) and likely benign (score -1, ADC > 1.4) and ADC1: indeterminate (score 0, ADC ≤ 1.4) and likely benign (score -1, ADC > 1.4). Final added CE-MRI and ADC scores >0 were considered suspicious. Reference standard was histology and imaging follow-up of >24 months. Diagnostic parameters were compared using McNemar tests. RESULTS: A total of 150 lesions (73 malignant) were investigated. Reading of CE-MRI showed a sensitivity of 100 % (73/73) and a specificity of 81.8 % (63/77). Additional integration of ADC scores increased specificity (ADC2/ADC1, P = 0.008/0.001) without causing false negative results. CONCLUSION: Using a simple sum score, ADC-values can be integrated with CE-MRI of the breast, improving specificity. The best approach is using one threshold to exclude cancer. KEY POINTS: ADC is used to assign levels of suspicion to breast lesions. ADC values >1.4 *10 (-3) mm (2) /s are likely benign and effectively rule out malignancy. ADC values below ≤1*10 (-3) mm (2) /s) are likely malignant but may be false positive. CE-MRI (+1: suspicious, 0: benign) and ADC (0: indeterminate, -1: benign) scores are added. Sum scores >0 should be biopsied.

Magnetic resonance imaging of intraductal papillomas: typical findings and differential diagnosis.

OBJECTIVE: Even upon core biopsy, accurate classification of benign intraductal papillomas (IPs) can be difficult. Accordingly, IPs are still frequently surgically resected. Therefore, accurate classification of IP by magnetic resonance mammography (MRM) would potentially optimize patient management. However, the few investigations assessing MRM of IP included small patient collectives, and overall accuracy is still unknown. We performed this investigation to analyze the morphologic and dynamic MRM profiles of IP in more detail and to identify the overall accuracy of MRM for differential diagnosis of IP versus malignant breast lesions. METHODS: Consecutive patients scheduled for MRM (standardized scanning protocols: dynamic T1-weighted gradient echo before/after Gd-DTPA [gadolinium diethylenetriamine pentaacetate; 0.1 mmol/kg body weight]; T2-weighted turbo spin echo) with subsequent surgicopathologic verification were enrolled. For the detailed assessment of morphologic and dynamic profiles, 2 experienced radiologists (>500 MRM examinations; blinded to surgicopathologic verification) performed prospective evaluation of MRM, in consensus, applying 17 predefined MRM descriptors. From this database, all patients showing IP (n = 83) or malignant breast lesions (n = 648) were further evaluated statistically: univariate analyses (association of single descriptors with IP/breast cancer: contingency table statistics) and multivariate analyses were performed to identify accurate descriptor combinations (CHAID [CHi-squared Automatic Interaction Detection]) and overall accuracy of MRM for differential diagnosis of IP versus malignant breast lesions (logistic regression; receiver operating characteristics [ROC], area under the ROC curve). RESULTS: There were 82.4% of MRM descriptors significantly associated with IP (n = 14; P < 0.05). The accuracy of single descriptors (odds ratio [OR], ≤10.6) could be further increased by descriptor combinations (double combination: OR ≤12.7; triple combination: OR ≤15.0). With area under the ROC curve = 0.90, there was a high overall accuracy of MRM for the differential diagnosis of IP versus malignant breast lesions. CONCLUSIONS: A detailed assessment of MRM allows precise characterization of benign IPs and accurate differentiation from malignant breast lesions.

Initial experience with a next-generation low-field MRI scanner: Potential for breast imaging?

PURPOSE: Broader clinical adoption of breast magnetic resonance imaging (MRI) faces challenges such as limited availability and high procedural costs. Low-field technology has shown promise in addressing these challenges. We report our initial experience using a next-generation scanner for low-field breast MRI at 0.55T. METHODS: This initial cases series was part of an institutional review board-approved prospective study using a 0.55T scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen/Germany: height < 2 m, weight < 3.2 tons, no quench pipe) equipped with a seven-channel breast coil (Noras, Höchberg/Germany). A multiparametric breast MRI protocol consisting of dynamic T1-weighted, T2-weighted, and diffusion-weighted sequences was optimized for 0.55T. Two radiologists with 12 and 20 years of experience in breast MRI evaluated the examinations. RESULTS: Twelve participants (mean age: 55.3 years, range: 36-78 years) were examined. The image quality was diagnostic in all examinations and not impaired by relevant artifacts. Typical imaging phenotypes were visualized. The scan time for a complete, non-abbreviated breast MRI protocol ranged from 10:30 to 18:40 min. CONCLUSION: This initial case series suggests that low-field breast MRI is feasible at diagnostic image quality within an acceptable examination time.

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. / Ermittlung aussagekräftiger Bildgebungsbiomarker: Klinische Anwendungen bei Mamma- und Prostatakarzinomen.

BACKGROUND: Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). METHOD: This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. RESULTS AND CONCLUSION: Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. KEY POINTS: · Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas.. CITATION FORMAT: · Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

Motivation and success factors in radiological research in Germany - results of a survey by the Methodology and Research Working Group of the German Radiological Society. / Motivation und Erfolgsfaktoren in der radiologischen Forschung in Deutschland ­ Ergebnisse einer Umfrage der AG Methodik und Forschung.

Investigation of motivation and identification of success factors in radiology research in Germany.Using a German online survey (54 questions, period: 3.5 months), demographic aspects, intrinsic and extrinsic success characteristics, as well as personal and organizational success factors were surveyed based on a career success model. The survey results were reported descriptively. The correlations between success factors and success characteristics were examined using linear, binary-logistic, and multinomial regression models.176 people (164 academically active, 10 not academically active) answered the survey. Most participants (80%, 139/174) worked at a university hospital. 32% had privatdozent or professor as their highest academic title (56/173). The researchers' main motivation was intrinsic interest in research (55%, 89/163), followed by a desire to increase their own career opportunities (25%, 41/163). The following were identified as factors for intrinsic success: i) support from department management (estimate=ß=0.26, p<0.001), ii) good work-life balance (ß=0.37, p<0.001), and iii) the willingness to pursue science even after reaching the career goal (ß=0.16, p<0.016). Relevant factors for extrinsic scientific success were mentoring, protected research time, and activities in professional societies.Researchers in German radiology are mainly intrinsically motivated. Factors known from the literature that determine intrinsic and extrinsic scientific success were confirmed in this study. Knowledge of these factors allows targeted systematic support and could thus increase scientific success in German radiology. · Main motivation for German radiology research is intrinsic interest, followed by career opportunities.. · Factors for intrinsic scientific success are good work-life balance and support by department management.. · Factors for extrinsic scientific success are mentoring, activities in professional societies, and protected research time.. · Wegner F, Heinrichs H, Stahlmann K et al. Motivation and success factors in radiological research in Germany - results of a survey by the Methodology and Research Working Group of the German Radiological Society. Fortschr Röntgenstr 2024; DOI 10.1055/a-2350-0023.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Breast lesions: diagnosis by using proton MR spectroscopy at 1.5 and 3.0 T--systematic review and meta-analysis.

PURPOSE: To perform a systematic review and meta-analysis to estimate the diagnostic performance of breast proton magnetic resonance (MR) spectroscopy in differentiating benign from malignant lesions and to identify variables that influence the accuracy of MR spectroscopy. MATERIALS AND METHODS: A comprehensive search of the PubMed database was performed on articles listed until January 6, 2012. The Medical Subject Headings and text words for the terms "breast," "spectroscopy," and "magnetic resonance" were used. Investigations including more than 10 patients at 1.5 T or 3.0 T applying one-dimensional single-voxel MR spectroscopy or spatially resolved MR spectroscopy for differentiation between benign and malignant breast lesions were eligible. A reference standard had to be established either by means of histopathologic examination or imaging follow-up of 12 or more months. Statistical analysis included pooling of diagnostic accuracy, control for data inhomogeneity, and identification of publication bias. RESULTS: Nineteen studies were used for general data pooling. The studies included a total of 1183 patients and 1198 lesions (773 malignant, 452 benign). Pooled sensitivity and specificity were 73% (556 of 761; 95% confidence interval [CI]: 64%, 82%) and 88% (386 of 439; 95% CI: 85%, 91%), respectively. The pooled diagnostic odds ratio (DOR) was 34.30 (95% CI: 16.71, 70.43). For breast cancers versus benign lesions, the area under the symmetric summary receiver operating characteristic curve of MR spectroscopy was 0.88, and the Q* index was 0.81. There was evidence of between-studies heterogeneity regarding sensitivity and DOR (P < .0001). No significant influences of higher field strength, postcontrast acquisition, or qualitative versus quantitative MR spectroscopy measurements were identified. Egger testing confirmed significant publication bias in studies including small numbers of patients (P < .0001). CONCLUSION: Breast MR spectroscopy shows variable sensitivity and high specificity in the diagnosis of breast lesions, independent from the technical MR spectroscopy approach. Because of significant publication bias, pooled diagnostic measures might be overestimated.