Pilot study: A simple CAD-based tool to detect breast cancer on MRI of the breast

PURPOSE

This pilot-study aims to assess, whether quantitatively assessed enhancing breast tissue as a percentage of the entire breast volume can serve as an indicator of breast cancer at breast MRI and whether the contrast-agent employed affects diagnostic efficacy.

MATERIALS

This retrospective IRB-approved study, included 39 consecutive patients, that underwent two subsequent breast MRI exams for suspicious findings at conventional imaging with 0.1 mmol/kg gadobenic and gadoteric acid. Two independent readers, blinded to the histopathological outcome, assessed unenhanced and early post-contrast images using computer-assisted software (Brevis, Siemens Healthcare). Diagnostic performance was statistically determined for percentage of ipsilateral voxel volume enhancement and for percentage of contralateral enhancing voxel volume subtracted from ipsilateral enhancing voxel volume after crosstabulation with the dichotomized histological outcome (benign/malignant).

RESULTS

Ipsilateral enhancing voxel volume versus histopathological outcome resulted in an AUC of 0.707 and 0.687 for gadobenic acid, reader 1 and 2, respectively and in an AUC of 0.778 and 0.773 for gadoteric acid, reader 1 and 2, respectively. Accounting for background parenchymal enhancement by subtracting contralateral enhancing volume from ipsilateral enhancing voxel volume versus histolopathological outcome resulted in an AUC of 0.793 and 0.843 for gadobenic acid, reader 1 and 2, respectively and in an AUC of 0.692 and 0.662 for gadoteric acid, reader 1 and 2, respectively. Pairwise testing yielded no statistically significant difference both between readers and between contrast agents employed (p > 0.05).

CONCLUSION

Our proposed CAD algorithm, which quantitatively assesses enhancing breast tissue as a percentage of the entire breast volume, allows indicating the presence of breast cancer.

Assessment of Hypoxia in Breast Cancer: Emerging Functional MR Imaging and Spectroscopy Techniques and Clinical Applications

Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Hypoxia, a condition characterized by insufficient oxygen supply in tumor tissues, is closely associated with tumor aggressiveness, resistance to therapy, and poor clinical outcomes. Accurate assessment of tumor hypoxia can guide treatment decisions, predict therapy response, and contribute to the development of targeted therapeutic interventions. Over the years, functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) techniques have emerged as promising noninvasive imaging options for evaluating hypoxia in cancer. Such techniques include blood oxygen level-dependent (BOLD) MRI, oxygen-enhanced MRI (OE) MRI, chemical exchange saturation transfer (CEST) MRI, and proton MRS (1H-MRS). These may help overcome the limitations of the routinely used dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) techniques, contributing to better diagnosis and understanding of the biological features of breast cancer. This review aims to provide a comprehensive overview of the emerging functional MRI and MRS techniques for assessing hypoxia in breast cancer, along with their evolving clinical applications. The integration of these techniques in clinical practice holds promising implications for breast cancer management. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

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.

Spatiotemporal analysis of contrast-enhanced ultrasound for differentiating between malignant and benign breast lesions

OBJECTIVES

The aim of this study was to apply spatiotemporal analysis of contrast-enhanced ultrasound (CEUS) loops to quantify the enhancement heterogeneity for improving the differentiation between benign and malignant breast lesions.

MATERIALS AND METHODS

This retrospective study included 120 women (age range, 18-82 years; mean, 52 years) scheduled for ultrasound-guided biopsy. With the aid of brightness-mode images, the border of each breast lesion was delineated in the CEUS images. Based on visual evaluation and quantitative metrics, the breast lesions were categorized into four grades of different levels of contrast enhancement. Grade-1 (hyper-enhanced) and grade-2 (partly-enhanced) breast lesions were included in the analysis. Four parameters reflecting enhancement heterogeneity were estimated by spatiotemporal analysis of neighboring time-intensity curves (TICs). By setting the threshold on mean parameter, the diagnostic performance of the four parameters for differentiating benign and malignant lesions was evaluated.

RESULTS

Sixty-four of the 120 patients were categorized as grade 1 or 2 and used for estimating the four parameters. At the pixel level, mutual information and conditional entropy present significantly different values between the benign and malignant lesions (p < 0.001 in patients of grade 1, p = 0.002 in patients of grade 1 or 2). For the classification of breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893 in patients of grade 1, AUC = 0.848 in patients of grade 1 or 2).

CONCLUSIONS

The proposed spatiotemporal analysis for assessing the enhancement heterogeneity shows promising results to aid in the diagnosis of breast cancer by CEUS.

CLINICAL RELEVANCE STATEMENT

The proposed spatiotemporal method can be developed as a standardized software to automatically quantify the enhancement heterogeneity of breast cancer on CEUS, possibly leading to the improved diagnostic accuracy of differentiation between benign and malignant lesions.

KEY POINTS

• Advanced spatiotemporal analysis of ultrasound contrast-enhanced loops for aiding the differentiation of malignant or benign breast lesions. • Four parameters reflecting the enhancement heterogeneity were estimated in the hyper- and partly-enhanced breast lesions by analyzing the neighboring pixel-level time-intensity curves. • For the classification of hyper-enhanced breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893).

Added value of clinical decision rules for the management of enhancing breast MRI lesions: A systematic comparison of the Kaiser score and the Göttingen score

PURPOSE

We investigated the added value of two internationally used clinical decision rules in the management of enhancing lesions on breast MRI.

METHODS

This retrospective, institutional review board approved study included consecutive patients from two different populations. Patients received breast MRI according to the recommendations of the European Society of Breast Imaging (EUSOBI). Initially, all examinations were assessed by expert readers without using clinical decision rules. All lesions rated as category 4 or 5 according to the Breast Imaging Reporting and Data System were histologically confirmed. These lesions were re-evaluated by an expert reader blinded to the histology. He assigned each lesion a Göttingen score (GS) and a Kaiser score (KS) on different occasions. To provide an estimate on inter-reader agreement, a second fellowship-trained reader assessed a subset of these lesions. Subgroup analyses based on lesion type (mass vs. non-mass), size (>1 cm vs. ≤ 1 cm), menopausal status, and significant background parenchymal enhancement were conducted. The areas under the ROC curves (AUCs) for the GS and KS were compared, and the potential to avoid unnecessary biopsies was determined according to previously established cutoffs (KS > 4, GS > 3) RESULTS: 527 lesions in 506 patients were included (mean age: 51.8 years, inter-quartile-range: 43.0-61.0 years). 131/527 lesions were malignant (24.9 %; 95 %-confidence-interval: 21.3-28.8). In all subgroups, the AUCs of the KS (median = 0.91) were higher than those of the GS (median = 0.83). Except for "premenopausal patients" (p = 0.057), these differences were statistically significant (p ≤ 0.01). Kappa agreement was higher for the KS (0.922) than for the GS (0.358).

CONCLUSION

Both the KS and the GS provided added value for the management of enhancing lesions on breast MRI. The KS was superior to the GS in terms of avoiding unnecessary biopsies and showed superior inter-reader agreement; therefore, it may be regarded as the clinical decision rule of choice.

Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer

In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced (DCE) imaging (tumor Mean Transit Time, Volume Distribution, Plasma Flow), diffusion-weighted imaging (DWI) (tumor ADCmean), and PET (tumor SUVmax, mean and minimum, SUVmean of ipsilateral breast parenchyma). Manual whole-lesion segmentation was also performed on DCE, T2-weighted, DWI, and PET images, and radiomic features were extracted. The dataset was divided into a training (70%) and a test set (30%). Multi-step feature selection was performed, and a support vector machine classifier was trained and tested for predicting axillary LN status. 13 radiomic features from DCE, DWI, T2-weighted, and PET images were selected for model building. The classifier obtained an accuracy of 79.8 (AUC = 0.798) in the training set and 78.6% (AUC = 0.839), with sensitivity and specificity of 67.9% and 100%, respectively, in the test set. A machine learning-based radiomics model comprising 18F-FDG PET/MRI radiomic features extracted from the primary breast cancer lesions allows high accuracy in non-invasive identification of axillary LN metastasis.

Prospective validation of 18F-Fluoroethylcholine as a tracer in PET/MRI for the evaluation of breast lesions and prediction of lymph node status

PURPOSE

To assess 18F-Fluoroethylcholine (18F-FEC) as a PET/MRI tracer in the evaluation of breast lesions, breast cancer aggressiveness, and prediction of lymph node status.

MATERIALS AND METHODS

This prospective, monocentric study was approved by the ethics committee and patients gave written, informed consent. This clinical trial was registered in the EudraCT database (Number 2017-003089-29). Women who presented with suspicious breast lesions were included. Histopathology was used as reference standard. Simultaneous 18F-FEC PET/MRI of the breast was performed in a prone position with a dedicated breast coil. MRI was performed using a standard protocol before and after contrast agent administration. A simultaneous read by nuclear medicine physicians and radiologists collected the imaging data of MRI-detected lesions, including the maximum standardized 18F-FEC-uptake value of breast lesions (SUV_maxT) and axillary lymph nodes (SUV_maxLN). Differences in SUV_max were evaluated with the Mann-Whitney U test. To calculate diagnostic performance, the area under the receiver operating characteristics curve (ROC) was used.

RESULTS

There were 101 patients (mean age 52.3 years, standard deviation 12.0) with 117 breast lesions included (30 benign, 7 ductal carcinomas in situ, 80 invasive carcinomas). 18F-FEC was well tolerated by all patients. The ROC to distinguish benign from malignant breast lesions was 0.846. SUV_maxT was higher if lesions were malignant (p < 0.001), had a higher proliferation rate (p = 0.011), and were HER2-positive (p = 0.041). SUV_maxLN was higher in metastatic lymph nodes, with an ROC of 0.761 for SUV_maxT and of 0.793 for SUV_maxLN. CONCLUSION: Simultaneous 18F-FEC PET/MRI is safe and has the potential to be used for the evaluation of breast cancer aggressiveness, and prediction of lymph node status.

The Potential of Shear Wave Elastography to Reduce Unnecessary Biopsies in Breast Cancer Diagnosis: An International, Diagnostic, Multicenter Trial

PURPOSE

 In this prospective, multicenter trial we evaluated whether additional shear wave elastography (SWE) for patients with BI-RADS 3 or 4 lesions on breast ultrasound could further refine the assessment with B-mode breast ultrasound for breast cancer diagnosis.

MATERIALS AND METHODS

 We analyzed prospective, multicenter, international data from 1288 women with breast lesions rated by conventional 2 D B-mode ultrasound as BI-RADS 3 to 4c and undergoing 2D-SWE. After reclassification with SWE the proportion of undetected malignancies should be < 2 %. All patients underwent histopathologic evaluation (reference standard).

RESULTS

 Histopathologic evaluation showed malignancy in 368 of 1288 lesions (28.6 %). The assessment with B-mode breast ultrasound resulted in 1.39 % (6 of 431) undetected malignancies (malignant lesions in BI-RADS 3) and 53.80 % (495 of 920) unnecessary biopsies (biopsies in benign lesions). Re-classifying BI-RADS 4a patients with a SWE cutoff of 2.55 m/s resulted in 1.98 % (11 of 556) undetected malignancies and a reduction of 24.24 % (375 vs. 495) of unnecessary biopsies.

CONCLUSION

 A SWE value below 2.55 m/s for BI-RADS 4a lesions could be used to downstage these lesions to follow-up, and therefore reduce the number of unnecessary biopsies by 24.24 %. However, this would come at the expense of some additionally missed cancers compared to B-mode breast ultrasound (rate of undetected malignancies 1.98 %, 11 of 556, versus 1.39 %, 6 of 431) which would, however, still be in line with the ACR BI-RADS 3 definition (< 2 % of undetected malignancies).

Potential of Lesion-to-Fat Elasticity Ratio Measured by Shear Wave Elastography to Reduce Benign Biopsies in BI-RADS 4 Breast Lesions

OBJECTIVES

We evaluated whether lesion-to-fat ratio measured by shear wave elastography in patients with Breast Imaging Reporting and Data System (BI-RADS) 3 or 4 lesions has the potential to further refine the assessment of B-mode ultrasound alone in breast cancer diagnostics.

METHODS

This was a secondary analysis of an international diagnostic multicenter trial (NCT02638935). Data from 1288 women with breast lesions categorized as BI-RADS 3 and 4a-c by conventional B-mode ultrasound were analyzed, whereby the focus was placed on differentiating lesions categorized as BI-RADS 3 and BI-RADS 4a. All women underwent shear wave elastography and histopathologic evaluation functioning as reference standard. Reduction of benign biopsies as well as the number of missed malignancies after reclassification using lesion-to-fat ratio measured by shear wave elastography were evaluated.

RESULTS

Breast cancer was diagnosed in 368 (28.6%) of 1288 lesions. The assessment with conventional B-mode ultrasound resulted in 53.8% (495 of 1288) pathologically benign lesions categorized as BI-RADS 4 and therefore false positives as well as in 1.39% (6 of 431) undetected malignancies categorized as BI-RADS 3. Additional lesion-to-fat ratio in BI-RADS 4a lesions with a cutoff value of 1.85 resulted in 30.11% biopsies of benign lesions which correspond to a reduction of 44.04% of false positives.

CONCLUSIONS

Adding lesion-to-fat ratio measured by shear wave elastography to conventional B-mode ultrasound in BI-RADS 4a breast lesions could help reduce the number of benign biopsies by 44.04%. At the same time, however, 1.98% of malignancies were missed, which would still be in line with American College of Radiology BI-RADS 3 definition of <2% of undetected malignancies.

Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on 18F-FDG PET/MRI and Machine Learning

In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models. Methods: Between August 2017 and September 2020, 303 participants from 3 centers prospectively underwent dedicated whole-body ¹⁸F-FDG PET/MRI. Imaging datasets were evaluated for axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data from 2 centers and were tested on data from the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for the machine-learning algorithm. For PET/MRI, the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine-learning algorithm, with no significant differences in diagnostic performance between radiologists and the machine-learning algorithm for MRI (P = 0.671) or PET/MRI (P = 0.683). The most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value, and accuracy were 68.2%, 78.1%, 93.8%, and 83.3%, respectively. A decision tree based on 3 simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high-sensitivity threshold to the random forest results might potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.

Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis

BACKGROUND

Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.

METHODS

We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE.

RESULTS

In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound.

CONCLUSION

The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-09015-8.

The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis

OBJECTIVES

AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms.

METHODS

Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC).

RESULTS

Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05).

CONCLUSIONS

The performance of humans and AI-based algorithms improves with multi-modal information.

KEY POINTS

• The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses.

The potential of combined shear wave and strain elastography to reduce unnecessary biopsies in breast cancer diagnostics - An international, multicentre trial

BACKGROUND

Shear wave elastography (SWE) and strain elastography (SE) have shown promising potential in breast cancer diagnostics by evaluating the stiffness of a lesion. Combining these two techniques could further improve the diagnostic performance. We aimed to exploratorily define the cut-offs at which adding combined SWE and SE to B-mode breast ultrasound could help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3-4 lesions to reduce the number of unnecessary breast biopsies.

METHODS

We report the secondary results of a prospective, multicentre, international trial (NCT02638935). The trial enrolled 1288 women with BI-RADS 3 to 4c breast masses on conventional B-mode breast ultrasound. All patients underwent SWE and SE (index test) and histopathologic evaluation (reference standard). Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after recategorising with SWE and SE were the outcome measures.

RESULTS

On performing histopathologic evaluation, 368 of 1288 breast masses were malignant. Following the routine B-mode breast ultrasound assessment, 53.80% (495 of 920 patients) underwent an unnecessary biopsy. After recategorising BI-RADS 4a lesions (SWE cut-off ≥3.70 m/s, SE cut-off ≥1.0), 34.78% (320 of 920 patients) underwent an unnecessary biopsy corresponding to a 35.35% (320 versus 495) reduction of unnecessary biopsies. Malignancies in the new BI-RADS 3 cohort were missed in 1.96% (12 of 612 patients).

CONCLUSION

Adding combined SWE and SE to routine B-mode breast ultrasound to recategorise BI-RADS 4a patients could help reduce the number of unnecessary biopsies in breast diagnostics by about 35% while keeping the rate of undetected malignancies below the 2% ACR BI-RADS 3 definition.

Microstructural breast tissue characterization: A head-to-head comparison of Diffusion Weighted Imaging and Acoustic Radiation Force Impulse elastography with clinical implications

PURPOSE

Head-to-head comparison of Diffusion Weighted Imaging (DWI) and Acoustic Radiation Force Impulse (ARFI) elastography regarding the characterization of breast lesions in an assessment setting.

METHOD

Patients undergoing an ultrasound examination including ARFI and an MRI protocol including DWI for the characterization of a BI-RADS 3-5 breast lesion between 06/2013 and 10/2016 were eligible for inclusion in this retrospective, IRB-approved study. 60 patients (30-84 years, median 50) with a median lesion size of 16 mm (range 5-55 mm) were included. The maximum shear wave velocity (SWV_max) and mean apparent diffusion coefficient (ADC_mean) for each lesion were retrospectively evaluated by a radiologist experienced in the technique. Histology was the reference standard. Diagnostic performances of ARFI and DWI were assessed using ROC curve analysis. Spearman's rank correlation coefficient and multivariate logistic regression were used to investigate the independence of both tests regarding their diagnostic information to distinguish benign from malignant lesions.

RESULTS

Corresponding areas under the ROC curve for differentiation of benign (n = 16) and malignant (n = 49) lesions were 0.822 (ARFI) and 0.871 (DWI, p-value = 0.48). SWVmax and ADCmean values showed a significant negative correlation (ρ = -0.501, p-value < 0.001). In multivariate analysis, combination of ARFI and DWI did not improve the results of each single modality, thus no significant independent diagnostic information was present.

CONCLUSION

Significant correlation between quantitative findings of ARFI and DWI in breast lesions exists. Thus, ARFI provides similar diagnostic information as a DWI-including protocol of an additional "problem-solving" MRI for the characterization of a sonographically evident breast lesion, improving the immediate patient management in the assessment setting.

One view or two views for wide-angle tomosynthesis with synthetic mammography in the assessment setting?

Objectives

To evaluate the diagnostic performance in the assessment setting of three protocols: one-view wide-angle digital breast tomosynthesis (WA-DBT) with synthetic mammography (SM), two-view WA-DBT/SM, and two-view digital mammography (DM).

Methods

Included in this retrospective study were patients who underwent bilateral two-view DM and WA-DBT. SM were reconstructed from the WA-DBT data. The standard of reference was histology and/or 2 years follow-up. Included were 205 women with 179 lesions (89 malignant, 90 benign). Four blinded readers randomly evaluated images to assess density, lesion type, and level of suspicion according to BI-RADS. Three protocols were evaluated: two-view DM, one-view (mediolateral oblique) WA-DBT/SM, and two-view WA-DBT/SM. Detection rate, sensitivity, specificity, and accuracy were calculated and compared using multivariate analysis. Reading time was assessed.

Results

The detection rate was higher with two-view WA-DBT/SM (p = 0.063). Sensitivity was higher for two-view WA-DBT/SM compared to two-view DM (p = 0.001) and one-view WA-DBT/SM (p = 0.058). No significant differences in specificity were found. Accuracy was higher with both one-view WA-DBT/SM and two-view WA-DBT/SM compared to DM (p = 0.003 and > 0.001, respectively). Accuracy did not differ between one- and two-view WA-DBT/SM. Two-view WA-DBT/SM performed better for masses and asymmetries. Reading times were significantly longer when WA-DBT was evaluated.

Conclusions

One-view and two-view WA-DBT/SM can achieve a higher diagnostic performance compared to two-view DM. The detection rate and sensitivity were highest with two-view WA-DBT/SM. Two-view WA-DBT/SM appears to be the most appropriate tool for the assessment of breast lesions.

Key Points

• Detection rate with two-view wide-angle digital breast tomosynthesis (WA-DBT) is significantly higher than with two-view digital mammography in the assessment setting.

• Diagnostic accuracy of one-view and two-view WA-DBT with synthetic mammography (SM) in the assessment setting is higher than that of two-view digital mammography.

• Compared to one-view WA-DBT with SM, two-view WA-DBT with SM seems to be the most appropriate tool for the assessment of breast lesions.

Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI

OBJECTIVES

the Kaiser score is increasingly recognized as a valuable tool to improve breast MRI interpretation. Contrast enhancement kinetics are the second most important diagnostic criterion, thus defining the curve type plays a crucial role in Kaiser score assessment. We investigate whether the timepoint used to determine the initial enhancement (earlyor peak) for the signal-intensity time curve analysis affects the diagnostic performance of the Kaiser score.

METHODS

This IRB-approved, retrospective, single-center study included 70 consecutives histologically verified breast MRI cases. Two off-site breast radiologists independently read all examinations using the Kaiser score, assessing the initial enhancement using three approaches: -first (1 st), second (2nd) and peak (maximum) of either 1 st or 2nd post-contrast timepoints. The initial enhancement was then compared to the last timepoint (delayed enhancement) to determine the curve type. Visual assessment of curve types was used for this study. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis.

RESULTS

Kaiser score reading results using the peak enhancement of either the first or second timepoint performed significantly better than the other approaches (P < 0.05, respectively) and specifically achieved higher sensitivity. Diagnostic accuracy (AUC area under the curve) ranged between 85.4 % and 91.6 %, without significant differences between the two readers (P < 0.5).

CONCLUSIONS

Diagnostic performance of the Kaiser score is significantly influenced by how the initial enhancement timepoint is determined. Peak enhancement should be used as initial timepoint to avoid pitfalls due to timing or physiological differences.

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

BACKGROUND

 Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information.

METHODS AND RESULTS

 This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed.

CONCLUSION

 Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine.

KEY POINTS

  · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review..

CITATION FORMAT

· Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.

A risk stratification algorithm for lesions of uncertain malignant potential diagnosed by vacuum-assisted breast biopsy (VABB) of mammographic microcalcifications

PURPOSE

To investigate a risk stratification strategy for lesions of uncertain malignant potential (B3) diagnosed by vacuum-assisted breast biopsy (VABB) of mammographic microcalcifications.

METHODS

Patients who underwent VABB for microcalcification-only lesions with a diagnosis of B3 and subsequent surgery were included in this retrospective, IRB-approved study. Seventy-six B3-lesions (final histology: 66 benign, 10 malignant) were included (Tr). Data on B3 lesion type and presence of atypia, microcalcification characteristics (BI-RADS), removal at biopsy and concomitant lesions were collected. After univariate analysis (Chi-square test), data were combined into a risk stratification algorithm by using a ten-fold, cross-validated Classification and Regression Tree analysis (CRT). The algorithm was tested on a testing dataset (Te) of 23 B3-lesions (six malignant, 17 benign).

RESULTS

Malignancy was more frequent in women with a concomitant cancer (P < 0.001) and highly suspicious microcalcifications (P < 0.001). The CRT algorithm retained three characteristics: morphology; presence of atypia; presence of concomitant cancer. The algorithm identified 25/76 (32.9 %,Tr) and 6/23 (26.1 %,Te) lesions at low risk of malignancy. No malignant cases were identified at surgery (0/31). There were 3/76 (3.9 %,Tr) and 1/23 (4.3 %,Te) lesions assigned as high-risk by the algorithm and confirmed at surgery (4/4). In the remaining lesions (48/76, 63.1 %,Tr; 16/23, 69.6 %,Te), malignancy rates varied between 9% and 88.4 %; thus, surgery could not have been avoided.

CONCLUSION

We constructed and tested a risk stratification algorithm for B3 microcalcifications, including clinical, imaging, and pathological features, to assign probabilities of malignancy, which has the potential to reduce unnecessary surgeries.

Non-Invasive Assessment of Hypoxia and Neovascularization with MRI for Identification of Aggressive Breast Cancer

The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric breast MRI including quantitative blood-oxygen-level-dependent (qBOLD) imaging for hypoxia and vascular architecture mapping for neovascularization. Quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO2), mitochondrial oxygen tension (mitoPO2), microvessel radius (VSI), microvessel density (MVD), and microvessel type indicator (MTI) were calculated. Histopathology was the standard of reference. Histopathological markers (vascular endothelial growth factor receptor 1 (FLT1), podoplanin, hypoxia-inducible factor 1-alpha (HIF-1alpha), carbonic anhydrase 9 (CA IX), vascular endothelial growth factor C (VEGF-C)) were used to confirm imaging biomarker findings. Univariate and multivariate regression analyses were performed to differentiate less aggressive luminal from aggressive non-luminal (HER2-positive, triple negative) malignancies and assess the interplay between hypoxia and neoangiogenesis markers. Aggressive non-luminal cancers (n = 40) presented with significantly higher MRO2 (i.e., oxygen consumption), lower mitoPO2 values (i.e., hypoxia), lower MTI, and higher MVD than less aggressive cancers (n = 13). Data suggest that a model derived from OEF, mitoPO2, and MVD can predict tumor proliferation rate. This novel MRI approach, which can be easily implemented in routine breast MRI exams, aids in the non-invasive identification of aggressive breast cancer.

The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams

OBJECTIVES

MRI is an integral part of breast cancer screening in high-risk patients. We investigated whether the application of the Kaiser score, a clinical decision-support tool, may be used to exclude malignancy in contrast-enhancing lesions classified as BI-RADS 4 on breast MRI screening exams.

METHODS

This retrospective study included 183 consecutive, histologically proven, suspicious (MR BI-RADS 4) lesions detected within our local high-risk screening program. All lesions were evaluated according to the Kaiser score for breast MRI by three readers blinded to the final histopathological diagnosis. The Kaiser score ranges from 1 (lowest, cancer very unlikely) to 11 (highest, cancer very likely) and reflects increasing probabilities of malignancy, with scores greater than 4 requiring biopsy. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy.

RESULTS

There were 142 benign and 41 malignant lesions, diagnosed in 159 patients (mean age, 43.6 years). Median Kaiser scores ranged between 2 and 5 in benign and 7 and 8 in malignant lesions. For all lesions, the Kaiser score's accuracy, represented by the area under the curve (AUC), ranged between 86.5 and 90.2. The sensitivity of the Kaiser score was high, between 95.1 and 97.6% for all lesions, and was best in mass lesions. Application of the Kaiser score threshold for malignancy (≤ 4) could have potentially avoided 64 (45.1%) to 103 (72.5%) unnecessary biopsies in 142 benign lesions previously classified as BI-RADS 4.

CONCLUSIONS

The use of Kaiser score in high-risk MRI screening reliably excludes malignancy in more than 45% of contrast-enhancing lesions classified as BI-RADS 4.

KEY POINTS

• The Kaiser score shows high diagnostic accuracy in identifying malignancy in contrast-enhancing lesions in patients undergoing high-risk screening for breast cancer. • The application of the Kaiser score may avoid > 45% of unnecessary breast biopsies in high-risk patients. • The Kaiser score aids decision-making in high-risk breast cancer MRI screening programs.

Development of a Non-invasive Assessment of Hypoxia and Neovascularization with Magnetic Resonance Imaging in Benign and Malignant Breast Tumors: Initial Results

PURPOSE

To develop a novel magnetic resonance imaging (MRI) approach for the noninvasive assessment of hypoxia and neovascularization in breast tumors.

PROCEDURES

In this IRB-approved prospective study, 20 patients with suspicious breast lesions (BI-RADS 4/5) underwent multiparametric breast MRI including quantitative BOLD (qBOLD) and vascular architecture mapping (VAM). Custom-made in-house MatLab software was used for qBOLD and VAM data postprocessing and calculation of quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO₂), and mitochondrial oxygen tension (mitoPO₂) to measure tissue hypoxia and neovascularization including vascular architecture including microvessel radius (VSI), density (MVD), and type (MTI). Histopathology was used as standard of reference. Appropriate statistics were performed to assess and compare correlations between MRI biomarkers for hypoxia and neovascularization.

RESULTS

qBOLD and VAM data with good quality were obtained from all patients with 13 invasive ductal carcinoma (IDC) and 7 benign breast tumors with a lesion diameter of at least 10 mm in all spatial directions. MRI biomarker maps of oxygen metabolism and neovascularization demonstrated intratumoral spatial heterogeneity with a broad range of biomarker values. Bulk tumor neovasculature consisted of draining venous microvasculature with slow flowing blood. High OEF and low mitoPO₂ were associated with low MVD and vice versa. The heterogeneous pattern of MRO₂ values showed spatial congruence with VSI. IDCs showed significantly higher MRO₂ (P = 0.007), lower mitoPO₂ (P = 0.021), higher MVD (P = 0.005), and lower (i.e., more pathologic) MTI (P = 0.001) compared with benign breast tumors. These results indicate that IDCs consume more oxygen and are more hypoxic and neovascularized than benign tumors.

CONCLUSIONS

We developed a novel MRI approach for the noninvasive assessment of hypoxia and neovascularization in benign and malignant breast tumors that can be easily integrated in a diagnostic MRI protocol and provides insight into intratumoral heterogeneity.

Can second-look ultrasound downgrade MRI-detected lesions? A retrospective study

PURPOSE

To determine whether MRI-detected suspicious (BIRADS 4 & 5) breast lesions can be downgraded using second-look ultrasound (SLU) and thus reduce unnecessarily performed breast biopsies.

MATERIALS METHODS

A retrospective single-center review of consecutive patients, who underwent breast MRI studies during a 12-month time period was performed. 94 patients with 103 lesions undergoing SLU of incidentally detected MRI BI-RADS 4&5 lesions which were not identified on previous ultrasound were included in the study. The SLU detection rate and SLU features of the lesions were assessed. Histology (91/103) or two year follow up (n = 12) were defined as the reference standard for lesion diagnosis.

RESULTS

57 (55.3 %) of the 103 lesions were identified on SLU. 17 of the identified lesions were malignant (29.8 %). Lesions detected on ultrasound presented on MRI as masses in 66.7 % (38/57) and non-mass in 33.3 % (19/57). Our findings showed that it is possible to distinguish between malignant and benign lesions with SLU. The results were significant (p < 0.05) for the following morphological features: shape, orientation, margins, architectural distortion, hyperechoic rim/ edema. All lesions classified as SLU BI-RADS 2 in our study were benign and thus, 30 % of all unnecessary biopsies could potentially have been avoided. Including SLU BI-RADS 3 lesions, this rate increased to 60 %, while yielding one (of 17, 5.8 %) false negative result. All three BI-RADS 5 lesions detected by SLU presented as malignant on ultrasound.

CONCLUSION

SLU can potentially downgrade incidental MRI BIRADS 4 lesions. This may reduce the number of unnecessarily performed biopsies by 30-60 %, thus simplifying patient management.

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.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-06678-z) contains supplementary material, which is available to authorized users.

Low-Dose, Contrast-Enhanced Mammography Compared to Contrast-Enhanced Breast MRI: A Feasibility Study

Contrast‐enhanced MRI (CE‐MRI) is the most sensitive technique for breast cancer detection. Contrast‐enhanced mammography (CEM) is emerging as a possible alternative to CE‐MRI.

Purpose

To evaluate the diagnostic performance of a low radiation dose contrast‐enhanced mammography (L‐CEM) in women with suspicious findings on conventional imaging compared to CE‐MRI of the breast.

Study Type

Prospective, single center.

Population

Women with suspicious findings on mammography, tomosynthesis, or ultrasound, and no contraindications for L‐CEM or CE‐MRI. Eighty women were included.

Field Strength/Sequence

1.5 and 3T CE‐MRI, standard protocol for breast, with dedicated coils, according to international guidelines. L‐CEM was performed using a dedicated prototype.

Assessment

Three, off‐site, blinded readers evaluated the images according to the BI‐RADS lexicon in a randomized order, each in two separate reading sessions. Histology served as a gold standard.

Statistical Test

Lesion detection rate, sensitivity, specificity, and negative and positive predictive values (NPV, PPV) were calculated and compared with multivariate statistics.

Results

Included were 80 women (mean age, 54.3 years ±11.2 standard deviation) with 93 lesions (32 benign, 61 malignant). The detection rate was significantly higher with CE‐MRI (92.5–94.6%; L‐CEM 79.6–91.4%, P = 0.014). Sensitivity (L‐CEM 65.6–90.2%; CE‐MRI 83.6–93.4%, P = 0.086) and NPV (L‐CEM 59.6–71.4%; CE‐MRI 63.0–76.5%, P = 0.780) did not differ between the modalities. Specificity (L‐CEM 46.9–96.9%; CE‐MRI 37.5–53.1%, P = 0.001) and PPV (L‐CEM 76.4–97.6%; CE‐MRI 73.3–77.3%, P = 0.007) were significantly higher with L‐CEM. Variations between readers were significant for sensitivity and NPV. The accuracy of L‐CEM was as good as CE‐MRI (75.3–76.3% vs. 72.0–75.3%, P = 0.514).

Data Conclusion

L‐CEM showed a high sensitivity and accuracy in women with suspicious findings on conventional imaging. Compared to CE‐MRI, L‐CEM has the potential to increase specificity and PPV. L‐CEM might help to reduce false‐positive biopsies while obtaining sensitivity comparable to that of CE‐MRI

Level of Evidence

1

Technical Efficacy Stage

2 J. Magn. Reson. Imaging 2020;52:589–595.

Limited role of DWI with apparent diffusion coefficient mapping in breast lesions presenting as non-mass enhancement on dynamic contrast-enhanced MRI

BACKGROUND

Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI.

METHODS

In this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10^-3 mm²/s) or malignant (≤ 1.3 × 10^-3 mm²/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured.

RESULTS

There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = - 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy.

CONCLUSIONS

Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.

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.

Multiparametric 18F-FDG PET/MRI of the Breast: Are There Differences in Imaging Biomarkers of Contralateral Healthy Tissue Between Patients With and Without Breast Cancer?

The rationale was to assess whether there are differences in multiparametric ¹⁸F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined ¹⁸F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer ¹⁸F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from ¹⁸F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in ¹⁸F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric ¹⁸F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

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

Purpose

To develop a multiparametric [¹⁸F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue.

Methods

In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [¹⁸F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [¹⁸F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC).

Results

There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis.

Conclusion

A multiparametric [¹⁸F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer

BACKGROUND

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization.

PURPOSE

To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32-87) and underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3 T, readout-segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast-enhanced (DCE) T₁ -weighted imaging, T₂ -weighted turbo-spin echo (TSE) with fatsat.

ASSESSMENT

Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes.

STATISTICAL TESTS

Wilcoxon rank sum, Mann-Whitney U-tests for associations between radiologic features and histopathology; histogram and q-q plots, Shapiro-Wilk's test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis.

RESULTS

Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER-positive tumors, while PR-positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers.

DATA CONCLUSION

Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836-846.

Can we predict lesion detection rates in second-look ultrasound of MRI-detected breast lesions? A systematic analysis

PURPOSE

To predict detection rates (DR) in second-look ultrasound of MRI-detected breast lesions by systematically combining clinical and anthropomorphic features.

METHODS

A total of 104 suspicious breast-lesions, that were initially detected on breast MRI and underwent subsequent SLU from January 2013 through December 2013, were evaluated in this retrospective analysis. All images were reviewed by an experienced radiologist for this study. Both anthropomorphic, spatial and BI-RADS lesion features were recorded. Uni- and multivariate Classification and Regression Trees (CRT) statistics were used to predict SLU DR by these features.

RESULTS

Among 104 MRI-detected lesions, 58 (55.8%) showed a correlate on SLU. In univariate analysis, homogeneous fatty or dense fibro-glandular-tissue-composition (FGT) as assessed by ultrasound, segmental non-mass-distribution pattern and small breast size as assessed by MRI were significantly associated with higher DR on SLU. The remaining BI-RADS features did not significantly affect SLU DR according to our data. The predictive model could stratify the likelihood of SLU correlates as high, intermediate and low according to FGT, lesion type, size and position.

CONCLUSIONS

By systematically combining the features FGT, lesion type, size and position, we could predict SLU DR of MRI-detected breast lesions. This may help to decide the preferable method for lesion biopsy or follow-up in clinical practice.

Breast lesion detection and characterization with contrast-enhanced magnetic resonance imaging: Prospective randomized intraindividual comparison of gadoterate meglumine (0.15 mmol/kg) and gadobenate dimeglumine (0.075 mmol/kg) at 3T

Background

Contrast‐enhanced magnetic resonance imaging (CE‐MRI) of the breast is highly sensitive for breast cancer detection. Multichannel coils and 3T scanners can increase signal, spatial, and temporal resolution. In addition, the T₁‐reduction effect of a gadolinium‐based contrast agent (GBCA) is higher at 3T. Thus, it might be possible to reduce the dose of GBCA at 3T without losing diagnostic information.

Purpose

To compare a three‐quarter (0.075 mmol/kg) dose of the high‐relaxivity GBCA gadobenate dimeglumine, with a 1.5‐fold higher than on‐label dose (0.15 mmol/kg) of gadoterate meglumine for breast lesion detection and characterization at 3T CE‐MRI.

Study Type

Prospective, randomized, intraindividual comparative study.

Population

Eligible were patients with imaging abnormalities (BI‐RADS 0, 4, 5) on conventional imaging. Each patient underwent two examinations, 24–72 hours apart, one with 0.075 mmol/kg gadobenate and the other with 0.15 mmol/kg gadoterate administered in a randomized order. In all, 109 patients were prospectively recruited.

Field Strength/Sequence

3T MRI with a standard breast protocol (dynamic‐CE, T₂w‐TSE, STIR‐T₂w, DWI).

Assessment

Histopathology was the standard of reference. Three blinded, off‐site breast radiologists evaluated the examinations using the BI‐RADS lexicon.

Statistical Tests

Lesion detection, sensitivity, specificity, and diagnostic accuracy were calculated per‐lesion and per‐region, and compared by univariate and multivariate analysis (Generalized Estimating Equations, GEE).

Results

Five patients were excluded, leaving 104 women with 142 histologically verified breast lesions (109 malignant, 33 benign) available for evaluation. Lesion detection with gadobenate (84.5‐88.7%) was not inferior to gadoterate (84.5–90.8%) (P ≥ 0.165). At per‐region analysis, gadobenate demonstrated higher specificity (96.4–98.7% vs. 92.6–97.3%, P ≤ 0.007) and accuracy (96.3–97.8% vs. 93.6–96.1%, P ≤ 0.001) compared with gadoterate. Multivariate analysis demonstrated superior, reader‐independent diagnostic accuracy with gadobenate (odds ratio = 1.7, P < 0.001 using GEE).

Data Conclusion

A 0.075 mmol/kg dose of the high‐relaxivity contrast agent gadobenate was not inferior to a 0.15 mmol/kg dose of gadoterate for breast lesion detection. Gadobenate allowed increased specificity and accuracy.

Level of Evidence: 1

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:1157–1165.

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

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

This overview will provide an overview of the available breast density assessment modalities, the current breast density screening recommendations for women at average risk of breast cancer, and supplemental methods for breast cancer screening. In addition, we will provide a look at the possibilities for a risk-adapted breast cancer screening.

MRI-based quantification of residual fibroglandular tissue of the breast after conservative mastectomies

PURPOSE

Skin-sparing and nipple-sparing mastectomies (SSM; NSM) remove the breast's fibroglandular tissue (FGT), thereby reducing breast cancer risk. The postoperative presence of residual FGT (RFGT) is associated with remaining cancer risk. This study evaluated the role of MRI in the quantitative assessment of RFGT and its impact on the estimation of the remaining breast cancer risk.

METHODS

The postoperative MRI scans (following EUSOMA recommendations) of 58 patients who had undergone SSM or NSM between 2003 and 2013, as well as preoperative MRI scans that were available in 25 of these patients, were retrospectively evaluated for the presence and location of RFGT by three radiologists. Two different observers quantitatively assessed the volume and percentage of retromamillary and other RFGT (RFGT_rm and RFGT_other) were assessed. The Fisher's exact test, the Student's t-test, and intraclass coherence were used to compare patient groups and to assess reproducibility.

RESULTS

RFGT was found in 20% of all breasts and significantly more frequently after NSM than SSM (50% vs. 13%, p = .003). RFGT_rm and RFGT_other were more prevalent after NSM (p < 0.001; p = .127). RFGT ranged from 0.5 to 26% of the preoperative FGT, with higher percentages after NSM than SSM (p = .181).

CONCLUSIONS

The prevalence and percentage of RFGT found on MRI indicate a considerable remaining postoperative breast cancer risk in some women.

Virtual Touch IQ elastography reduces unnecessary breast biopsies by applying quantitative "rule-in" and "rule-out" threshold values

Our purpose was to evaluate Virtual Touch IQ (VTIQ) elastography and identify quantitative “rule-in” and “rule-out” thresholds for the probability of malignancy, which can help avoid unnecessary breast biopsies. 189 patients with 196 sonographically evident lesions were included in this retrospective, IRB-approved study. Quantitative VTIQ images of each lesion measuring the respective maximum Shear Wave Velocity (SWV) were obtained. Paired and unpaired, non-parametric statistics were applied for comparisons as appropriate. ROC-curve analysis was used to analyse the diagnostic performance of VTIQ and to specify “rule-in” and “rule-out” thresholds for the probability of malignancy. The standard of reference was either histopathology or follow-up stability for >24 months. 84 lesions were malignant and 112 benign. Median SWV of benign lesions was significantly lower than that of malignant lesions (p < 0.001). The application of a “rule-out” threshold of 1.9 m/s lead to a sensitivity of >98% with a concomitant significant (p = 0.032) reduction in false positive cases of almost 15%, whereas a “rule-in” threshold of 6.5 m/s suggested a probability of malignancy of >95%. In conclusion, VTIQ elastography accurately differentiates malignant from benign breast lesions. The application of quantitative “rule-in” and “rule-out” thresholds is feasible and allows reduction of unnecessary benign breast biopsies by almost 15%.

A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions

OBJECTIVES

To assess whether using the Tree flowchart obviates unnecessary magnetic resonance imaging (MRI)-guided biopsies in breast lesions only visible on MRI.

METHODS

This retrospective IRB-approved study evaluated consecutive suspicious (BI-RADS 4) breast lesions only visible on MRI that were referred to our institution for MRI-guided biopsy. All lesions were evaluated according to the Tree flowchart for breast MRI by experienced readers. The Tree flowchart is a decision rule that assigns levels of suspicion to specific combinations of diagnostic criteria. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. To assess reproducibility by kappa statistics, a second reader rated a subset of 82 patients.

RESULTS

There were 454 patients with 469 histopathologically verified lesions included (98 malignant, 371 benign lesions). The area under the curve (AUC) of the Tree flowchart was 0.873 (95% CI: 0.839-0.901). The inter-reader agreement was almost perfect (kappa: 0.944; 95% CI 0.889-0.998). ROC analysis revealed exclusively benign lesions if the Tree node was ≤2, potentially avoiding unnecessary biopsies in 103 cases (27.8%).

CONCLUSIONS

Using the Tree flowchart in breast lesions only visible on MRI, more than 25% of biopsies could be avoided without missing any breast cancer.

KEY POINTS

• The Tree flowchart may obviate >25% of unnecessary MRI-guided breast biopsies. • This decrease in MRI-guided biopsies does not cause any false-negative cases. • The Tree flowchart predicts 30.6% of malignancies with >98% specificity. • The Tree's high specificity aids in decision-making after benign biopsy results.

New diagnostic tools for breast cancer

Imaging plays a major role in the diagnosis, treatment, and follow-up of breast cancer. Findings that require further assessment will be detected both at screening and curative mammography. Most findings that are further worked up tend to yield benign diagnoses. Consequently, there is an ongoing search for new tools to reduce recalls and unnecessary biopsies while maintaining or improving cancer detection rates. The clinically most promising methods in this respect are described and discussed in this review.

The breast lesion excision system (BLES) under stereotactic guidance cannot be used as a therapeutic tool in the excision of small areas of microcalcifications in the breast

OBJECTIVE

The breast lesion excision system (BLES) is a new, automatic percutaneous breast biopsy device that excises single large specimens using radiofrequency cutting. The aim of this study was to determine whether BLES, under stereotactic guidance, can be used as a therapeutic tool in the assessment of small areas of microcalcifications in the breast by providing samples with clear margins.

MATERIAL AND METHODS

In this retrospective study, 149 patients with suspicious (BIRADS 4 or 5) small areas of microcalcifications underwent stereotactic-guided BLES. Of these, 34 patients (22.8%) with microcalcifications that had a diameter smaller than the basket size (≤15mm) underwent both BLES and subsequent surgery. Histopathology findings from BLES and subsequent surgery were compared. Identical, underestimation and total excision findings were assessed.

RESULTS

BLES revealed fourteen (41.1%) high-risk lesions, ten (29.4%) ductal carcinomas in situ, and ten (29.4%) invasive cancers. Identical results between BLES and surgery were seen in 17/34 (50%) lesions. Surgery confirmed total excision of BLES in 15/34 (44.1%) lesions. Underestimation was seen in 2/34 (5.8%) lesions.

CONCLUSION

BLES allows accurate diagnosis of small areas of microcalcifications, with few underestimates. BLES is a diagnostic, but cannot be considered to be a therapeutic tool in the case of suspicious microcalcifications because total excision was seen in only 44.1% of these lesions. Studies are needed to address the therapeutic benefit of this procedure in solid lesions.

Clinical application of Acoustic Radiation Force Impulse Imaging with Virtual Touch IQ in breast ultrasound: diagnostic performance and reproducibility of a new technique

Background Virtual Touch IQ (VTIQ) is a novel technique of quantitative sonoelastography that applies acoustic radiation force impulse (ARFI). Purpose To evaluate breast ARFI imaging with VTIQ in the clinical setting, with regard to reproducibility and diagnostic performance, and to specify cutoff limits for the differentiation of benign and malignant lesions. Material and Methods This retrospective study included 83 patients with 85 breast lesions (51 benign, 34 malignant) who received ARFI imaging with VTIQ. Two independent ARFI measurements of each lesion were performed and shear wave velocities (SWV) of the lesion and the adjacent tissues were measured. A lesion-to-fat velocity ratio (L/F Ratio) was calculated for each lesion. Diagnostic performance of SWV measurements and L/F Ratios was evaluated with receiver operating curve (ROC) analysis. The intraclass correlation coefficient and Bland-Altman plots were used to evaluate measurement reproducibility. Results All measurements showed equal diagnostic performance, as measured by the area under the ROC curve (0.853 for SWV, 0.882 for the L/F Ratio). At a cutoff value of 3.23 m/s, sensitivity and specificity were 82.4% and 80.4%, respectively. An L/F Ratio cutoff value of 2.23 revealed a sensitivity and specificity of 89.7% and 76.5%. The reproducibility of the SWV measurements was moderate (limits of agreement, 40.3-44.4%) and higher than that of the L/F Ratios (54.5-60.2%). Conclusion ARFI imaging with VTIQ is a novel, moderately reproducible, quantitative elastography technique, which provides useful information for the differentiation of benign and malignant breast lesions in the clinical setting.

Diffusion-weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility, repeatability, and diagnostic accuracy

Diffusion-weighted MRI (DWI) provides insights into tissue microstructure by visualization and quantification of water diffusivity. Quantitative evaluation of the apparent diffusion coefficient (ADC) obtained from DWI has been proven helpful for differentiating between malignant and benign breast lesions, for cancer subtyping in breast cancer patients, and for prediction of response to neoadjuvant chemotherapy. However, to further establish DWI of breast lesions it is important to evaluate the quantitative imaging biomarker (QIB) characteristics of reproducibility, repeatability, and diagnostic accuracy. In this intra-individual prospective clinical study 40 consecutive patients with suspicious findings, scheduled for biopsy, underwent an identical 3T breast MRI protocol of the breast on two consecutive days (>24 h). Mean ADC of target lesions was assessed (two independent readers) in four separate sessions. Reproducibility, repeatability, and diagnostic accuracy between examinations (E1, E2), readers (R1, R2), and measurements (M1, M2) were assessed with intraclass correlation coefficients (ICCs), coefficients of variation (CVs), Bland-Altman plots, and receiver operating characteristic (ROC) analysis with calculation of the area under the ROC curve (AUC). The standard of reference was either histopathology (n = 38) or imaging follow-up of up to 24 months (n = 2). Eighty breast MRI examinations (median E1-E2, 2 ± 1.7 days, 95% confidence interval (CI) 1-2 days, range 1-11 days) in 40 patients (mean age 56, standard deviation (SD) ±14) were evaluated. In 55 target lesions (mean size 25.2 ± 20.8 (SD) mm, range 6-106 mm), mean ADC values were significantly (P < 0.0001) higher in benign (1.38, 95% CI 1.27-1.49 × 10(-3)  mm(2) /s) compared with malignant (0.86, 95% CI 0.81-0.91 × 10(-) (3)  mm(2) /s) lesions. Reproducibility and repeatability showed high agreement for repeated examinations, readers, and measurements (all ICCs >0.9, CVs 3.2-8%), indicating little variation. Bland-Altman plots demonstrated no systematic differences, and diagnostic accuracy was not significantly different in the two repeated examinations (all ROC curves >0.91, P > 0.05). High reproducibility, repeatability, and diagnostic accuracy of DWI provide reliable characteristics for its use as a potential QIB, to further improve breast lesion detection, characterization, and treatment monitoring of breast lesions.

A simple scoring system for breast MRI interpretation: does it compensate for reader experience?

PURPOSE

To investigate the impact of a scoring system (Tree) on inter-reader agreement and diagnostic performance in breast MRI reading.

MATERIALS AND METHODS

This IRB-approved, single-centre study included 100 patients with 121 consecutive histopathologically verified lesions (52 malignant, 68 benign). Four breast radiologists with different levels of MRI experience and blinded to histopathology retrospectively evaluated all examinations. Readers independently applied two methods to classify breast lesions: BI-RADS and Tree. BI-RADS provides a reporting lexicon that is empirically translated into likelihoods of malignancy; Tree is a scoring system that results in a diagnostic category. Readings were compared by ROC analysis and kappa statistics.

RESULTS

Inter-reader agreement was substantial to almost perfect (kappa: 0.643-0.896) for Tree and moderate (kappa: 0.455-0.657) for BI-RADS. Diagnostic performance using Tree (AUC: 0.889-0.943) was similar to BI-RADS (AUC: 0.872-0.953). Less experienced radiologists achieved AUC: improvements up to 4.7 % using Tree (P-values: 0.042-0.698); an expert's performance did not change (P = 0.526). The least experienced reader improved in specificity using Tree (16 %, P = 0.001). No further sensitivity and specificity differences were found (P > 0.1).

CONCLUSION

The Tree scoring system improves inter-reader agreement and achieves a diagnostic performance similar to that of BI-RADS. Less experienced radiologists, in particular, benefit from Tree.

KEY POINTS

• The Tree scoring system shows high diagnostic accuracy in mass and non-mass lesions. • The Tree scoring system reduces inter-reader variability related to reader experience. • The Tree scoring system improves diagnostic accuracy in non-expert readers.