MRI for the assessment of malignancy in BI-RADS 4 mammographic microcalcifications

Assessing the malignancy of suspicious breast microcalcifications: the role of contrast enhanced mammography

PURPOSE

To assess the role of contrast-enhanced mammography (CEM) in predicting the malignancy of breast calcifications.

MATERIAL AND METHODS

We retrospectively evaluated patients with suspicious calcifications (BIRADS 4) who underwent CEM and stereotactic vacuum-assisted biopsy (VAB) at our institution. We assessed the sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of CEM in predicting malignancy of microcalcifications with a 95% confidence interval; we performed an overall analysis and a subgroup analysis stratified into group A-low risk (BIRADS 4a) and group B-medium/high risk (BIRADS 4b-4c). We then evaluated the correlation between enhancement and tumour proliferation index (Ki-67) for all malignant lesions.

RESULTS

Data from 182 patients with 184 lesions were collected. Overall the SE of CEM in predicting the malignancy of microcalcifications was 0.70, SP was 0.85, the PPV was 0.82, the NPV was 0.76 and AUC was 0.78. SE in group A was 0.89, SP was 0.89, PPV was 0.57, NPV was 0.98 and AUC was 0.75. SE in group B was 0.68, SP was 0.80, PPV was 0.87, NPV was 0.57 and AUC was 0.75. Among malignant microcalcifications that showed enhancement (N = 52), 61.5% had Ki-67 ≥ 20% and 38.5% had low Ki-67 values. Among the lesions that did not show enhancement (N = 22), 90.9% had Ki-67 < 20% and 9.1% showed high Ki-67 values 20%.

CONCLUSIONS

The absence of enhancement can be used as an indicative parameter for the absence of disease in cases of low-suspicious microcalcifications, but not in intermediate-high suspicious ones for which biopsy remains mandatory and can be used to distinguish indolent lesions from more aggressive neoplasms, with consequent reduction of overdiagnosis and overtreatment.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions

OBJECTIVE

The aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics.

METHODS

A systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated.

RESULTS

This study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83-0.92) and 0.79 (95% CI 0.72-0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1-5.7), 0.15 (95% CI: 0.10-0.22), and 29.0 (95% CI 15-55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87-0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes.

CONCLUSION

MRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients' medical outcomes and quality of life.

BIRADS 4 - Is it possible to downgrade lesions that do not enhance on recombinant contrast-enhanced mammography images?

PURPOSE

Analysis of the morphology of lesions classified into the BI-RADS 4 category and assessment of the possibility of downgrade the BI-RADS category in those that did not show enhancement on recombinant contrast-enhanced mammography (CEM) images.

METHOD

The retrospective, single-center study included 528 patients who underwent a core needle biopsy performed from January 2017 to November 2022 due to a breast lesion classified as BI-RADS 4 on CEM. Patients' electronic records and imaging examinations were reviewed. Individual lesions were classified into the morphological categories of mass, non-mass, and microcalcifications. Sensitivity, specificity, positive as well as negative predictive values were calculated for the whole group and individual morphological categories. The influence of the lesions' diameter on the results was analyzed.

RESULTS

CEM NPV for the whole group was 93.9% (±95% CI: 90.0-96.4), for mass lesions 100% (±95% CI: 94.5-100), for non-mass lesions 97.8% (±95% CI: 87.0-99.9) and 87.9% (±95% CI: 80.3-93.0) for microcalcifications. Given that 230 out of 383 benign lesions were not contrast-enhancing, 60.1% of unnecessary CNBs would have been correctly avoided. CEM sensitivity for lesions < 20 mm was lower than for lesions ≥ 20 mm and was respectively 86.6% (±95% CI: 76.8-92.8) vs 94.6% (±95% CI: 86.0-98.2), respectively.

CONCLUSION

CEM is characterized by high sensitivity in the detection of malignant lesions in the case of lesions with mass and non-mass morphology. The high NPV for recombinant images suggests that in the case of these lesions, the lack of enhancement supports the benign nature of the lesion and may lead to a downgrade of the BI-RADS category.

The value of MRI for downgrading of breast suspicious lesions detected on ultrasound

BACKGROUND

Most of suspicious lesions classified as breast imaging reporting and data system (BI-RADS) 4A and 4B categories on ultrasound (US) were benign, resulting in unnecessary biopsies. MRI has a high sensitivity to detect breast cancer and high negative predictive value (NPV) to exclude malignancy. The purpose of this study was to investigate the value of breast MRI for downgrading of suspicious lesions with BI-RADS 4A and 4B categories on US.

METHODS

Patients who underwent breast MRI for suspicious lesions classified as 4A and 4B categories were included in this retrospective study. Two radiologists were aware of the details of suspicious lesions detected on US and evaluated MR images. MRI BI-RADS categories were given by consensus on the basis on dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Pathological results and imaging follow-up at least 12 months were used as a reference standard. Sensitivity, specificity, positive predictive value (PPV), NPV and their 95% confidence interval (CI) were calculated for MRI findings.

RESULTS

One sixty seven patients with 186 lesions (US 4A category: 145, US 4B category: 41) consisted of the study cohort. The malignancy rate was 34.9% (65/186). On MRI, all malignancies showed true-positive results and 92.6% (112/121) benign lesions were correctly diagnosed. MRI increased PPV from 34.9% (65/186) to 87.8% (65/74) and reduced the false-positive biopsies by 92.6% (112/121). The sensitivity, specificity, PPV and NPV of MRI were 100% (95% CI: 94.5%-100%), 92.6% (95% CI: 86.3%-96.5%), 87.8% (95% CI: 78.2%-94.3%) and 100% (95% CI: 96.8%-100%), respectively. 2.2% (4/186) of suspicious lesions were additionally detected on MRI, 75% (3/4) of which were malignant.

CONCLUSION

MRI could downgrade suspicious lesions classified as BI-RADS 4A and 4B categories on US and avoided unnecessary benign biopsies without missing malignancy. Additional suspicious lesions detected on MRI needed further work-up.

The value of DCE- MRI of the breast as a diagnostic tool in assessing amorphous calcifications in screening mammography

Purpose

To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging in differentiating benign and malignant amorphous calcifications.

Methods

This study included 193 female patients with 197 suspicious amorphous calcifications detected on screening mammography. The patients' demographics, clinical follow-up, imaging, and pathology outcomes were reviewed, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DCE-MRI were calculated.

Results

Of 197 lesions (193 patients) included in the study, 50 (25.4%) were histologically proved to be malignant. DCE-MRI based on breast imaging report and diagnosis system (BI-RADS) had a sensitivity of 94.4%, specificity of 85.7%, PPV of 69.1%, and NPV of 97.7% for the detection of malignant amorphous calcifications. Notably, diagnosis solely based on the presence or absence of DCE-MRI enhancement showed the same sensitivity but significantly decreased specificity (44.8%, p < 0.001) and PPV (44.8%, p < 0.001). In patients with a minimal or mild degree of background parenchymal enhancement (BPE), the sensitivity, specificity, PPV, and NPV increased to 100%, 90.6%, 78.6%, and 100%, respectively. However, in patients with a moderate degree of BPE, MRI resulted in three false negatives of ductal carcinoma in situ (DCIS). Overall, the addition of DCE-MRI detected all invasive lesions and could decrease unnecessary biopsy by 65.5%.

Conclusion

DCE-MRI based on BI-RADS has the potential to improve the diagnosis of suspicious amorphous calcifications and avoid unnecessary biopsy, especially for those with low-degree BPE.

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.

Advanced Diffusion-Weighted Imaging Sequences for Breast MRI: Comprehensive Comparison of Improved Sequences and Ultra-High B-Values to Identify the Optimal Combination

This study investigated the image quality and choice of ultra-high b-value of two DWI breast-MRI research applications. The study cohort comprised 40 patients (20 malignant lesions). In addition to s-DWI with two m-b-values (b50 and b800) and three e-b-values (e-b1500, e-b2000, and e-b2500), z-DWI and IR m-b1500 DWI were applied. z-DWI was acquired with the same measured b-values and e-b-values as the standard sequence. For IR m-b1500 DWI, b50 and b1500 were measured, and e-b2000 and e-b2500 were mathematically extrapolated. Three readers used Likert scales to independently analyze all ultra-high b-values (b1500-b2500) for each DWI with regards to scan preference and image quality. ADC values were measured in all 20 lesions. z-DWI was the most preferred (54%), followed by IR m-b1500 DWI (46%). b1500 was significantly preferred over b2000 for z-DWI and IR m-b1500 DWI (p = 0.001 and p = 0.002, respectively). Lesion detection was not significantly different among sequences or b-values (p = 0.174). There were no significant differences in measured ADC values within lesions between s-DWI (ADC: 0.97 [±0.09] × 10^-3 mm²/s) and z-DWI (ADC: 0.99 [±0.11] × 10^-3 mm²/s; p = 1.000). However, there was a trend toward lower values in IR m-b1500 DWI (ADC: 0.80 [±0.06] × 10^-3 mm²/s) than in s-DWI (p = 0.090) and z-DWI (p = 0.110). Overall, image quality was superior and there were fewer image artifacts when using the advanced sequences (z-DWI + IR m-b1500 DWI) compared with s-DWI. Considering scan preferences, we found that the optimal combination was z-DWI with a calculated b1500, especially regarding examination time.

Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann-Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_{_ADC}, model_{_IVIM}, model_{_DCE}, model_{_DCE+ADC}, and model_{_DCE+IVIM}). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_{_DCE+IVIM} showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828-0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_{_ADC} (p = 0.014) and model_{_IVIM} (p = 0.033). The model_{_ADC} had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672-0.846) but was not significantly different from model_{_IVIM} (p = 0.168). The united quantitative model with DCE-MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.

Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions

BACKGROUND

Lesions of breast imaging reporting and data system (BI-RADS) 4 at mammography vary from benign to malignant, leading to difficulties for clinicians to distinguish between them. The specificity of magnetic resonance imaging (MRI) in detecting breast is relatively low, leading to many false-positive results and high rates of re-examination or biopsy. Diffusion-weighted imaging (DWI), combined with perfusion-weighted imaging (PWI), might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

AIM

To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.

METHODS

This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital. The lesions were divided into benign and malignant groups according to the classification of histopathological results. The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly. The 95 lesions were divided according to histopathological diagnosis, with 46 benign and 49 malignant. The main statistical methods used included the Student t-test, the Mann-Whitney U-test, the chi-square test or Fisher’s exact test.

RESULTS

The mean apparent diffusion coefficient (ADC) values in the parenchyma and lesion area of the normal mammary gland were 1.82 ± 0.22 × 10^-3 mm²/s and 1.24 ± 0.16 × 10^-3 mm²/s, respectively (P = 0.021). The mean ADC value of the malignant group was 1.09 ± 0.23 × 10^-3 mm²/s, which was lower than that of the benign group (1.42 ± 0.68 × 10^-3 mm²/s) (P = 0.016). The volume transfer constant (Ktrans) and rate constant (Kep) values were higher in malignant lesions than in benign ones (all P < 0.001), but there were no significant statistical differences regarding volume fraction (V_e) (P = 0.866). The sensitivity and specificity of PWI combined with DWI (91.7% and 89.3%, respectively) were higher than that of PWI or DWI alone. The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.

CONCLUSION

DWI, combined with PWI, might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions

Objectives

The probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the present study, we developed and tested a radiomics model based on MRI images that can predict the malignancy of BI-RADS 4 breast lesions.

Methods

We retrospective enrolled a total of 216 BI-RADS 4 patients MRI and clinical information. We extracted 3,474 radiomics features from dynamic contrast-enhanced (DCE), T₂-weighted images (T₂WI), and diffusion-weighted imaging (DWI) MRI images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used to select features and build radiomics models based on different sequence combinations. We built eight radiomics models which were based on DCE, DWI, T₂WI, DCE+DWI, DCE+T₂WI, DWI+T₂WI, and DCE+DWI+T₂WI and a clinical predictive model built based on the visual assessment of radiologists. A nomogram was constructed with the best radiomics signature combined with patient characteristics. The calibration curves for the radiomics signature and nomogram were conducted, combined with the Hosmer-Lemeshow test.

Results

Pearson’s correlation was used to eliminate 3,329 irrelevant features, and then LASSO and logistic regression were used to screen the remaining feature coefficients for each model we built. Finally, 12 related features were obtained in the model which had the best performance. These 12 features were used to build a radiomics model in combination with the actual clinical diagnosis of benign or malignant lesion labels we have obtained. The best model built by 12 features from the 3 sequences has an AUC value of 0.939 (95% CI, 0.884-0.994) and an accuracy of 0.931 in the testing cohort. The sensitivity, specificity, precision and Matthews correlation coefficient (MCC) of testing cohort are 0.932, 0.923, 0.982, and 0.791, respectively. The nomogram has also been verified to have calibration curves with good overlap.

Conclusions

Radiomics is beneficial in the malignancy prediction of BI-RADS 4 breast lesions. The radiomics predictive model built by the combination of DCE, DWI, and T₂WI sequences has great application potential.

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Objective

To build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.

Materials and Methods

266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.

Results

In the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.

Conclusion

The radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.

Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions

Objectives

To evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.

Methods

A total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.

Results

ΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.

Conclusions

The mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.

Can Ultrasound Elastography Help Better Manage Mammographic BI-RADS Category 4 Breast Lesions?

BACKGROUND

To assess the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography.

MATERIALS AND METHODS

Women with breast lesions identified as having mammography BI-RADS 4 lesions and underwent US examination were included in China. US features and US BI-RADS assessment were recorded in real-time and prospectively reported. The pathological result was referred to as the gold standard. The performance of US in the mammographic BI-RADS category 4 lesions was evaluated. Diagnostic performances of US BI-RADS, SE and combined both were compared.

RESULTS

A total of 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. For mammographic findings, 530 (70.6%) were true positive and 221 (29.4%) were false positive. Conventional US achieved higher positive predictive value (PPV) than mammography (78.5% vs. 70.6%, P=.001). The specificity increased from 34.4% to 47.1% (P< .001) without any loss in sensitivity and the PPV increased to 81.9% (P = .122) when conventional US was used in combination with SE. For conventional US combined with SE, it led to a correct diagnosis of no breast cancer in 104 of the 221 false-positive findings (47.1%) and achieved higher PPV than mammography regardless of patient age and lesion size.

CONCLUSION

Conventional US combined with SE is a helpful tool for the noninvasive examination of breast lesions classified as BI-RADS category 4 on mammography. It helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography.

Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography

BACKGROUND

Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification.

PURPOSE

To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM.

MATERIAL AND METHODS

A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated.

RESULTS

In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone.

CONCLUSION

We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.

Added value of abbreviated breast magnetic resonance imaging for assessing suspicious microcalcification on screening mammography-a prospective study

OBJECTIVES

To investigate the added diagnostic value of abbreviated breast magnetic resonance imaging (MRI) for suspicious microcalcifications on screening mammography.

METHODS

This prospective study included 80 patients with suspicious calcifications on screening mammography who underwent abbreviated MRI before undergoing breast biopsy between August 2017 and September 2020. The abbreviated protocol included one pre-contrast and the first post-contrast T1-weighted series. MRI examinations were interpreted as either positive or negative based on the visibility of any significant enhancement. The positive predictive value (PPV) was compared before and after the MRI.

RESULTS

Of the 80 suspicious microcalcifications, 33.8% (27/80) were malignant and 66.2% (53/80) were false positives. Abbreviated MRI revealed 33 positive enhancement lesions, and 25 and two lesions showed true-positive and false-negative findings, respectively. Abbreviated MRI increased PPV from 33.8 (27 of 80 cases; 95% CI: 26.2%, 40.8%) to 75.8% (25 of 33 cases; 95% CI: 62.1%, 85.7%). A total of 85% (45 of 53) false-positive diagnoses were reduced after abbreviated MRI assessment.

CONCLUSIONS

Abbreviated MRI added significant diagnostic value in patients with suspicious microcalcifications on screening mammography, as demonstrated by a significant increase in PPV with a potential reduction in unnecessary biopsy.

KEY POINTS

• Abbreviated breast magnetic resonance imaging increased the positive predictive value of suspicious microcalcifications on screening mammography from 33.8 (27/80 cases) to 75.8% (25/33 cases) (p < .01). • Abbreviated magnetic resonance imaging helped avoid unnecessary benign biopsies in 85% (45/53 cases) of lesions without missing invasive cancer.

Can supplementary contrast-enhanced MRI of the breast avoid needle biopsies in suspicious microcalcifications seen on mammography? A systematic review and meta-analysis

Purpose

To analyze the rate of potentially avoidable needle biopsies in mammographically suspicious calcifications if supplementary Contrast-Enhanced MRI (CE-MRI) is negative.

Methods

Using predefined criteria, a systematic review was performed. Studies investigating the use of supplemental CE-MRI in the setting of mammographically suspicious calcifications undergoing stereotactic biopsy and published between 2000 and 2020 were eligible. Two reviewers extracted study characteristics and true positives (TP), false positives, true negatives and false negatives (FN). Specificity, in this setting equaling the number of avoidable biopsies and FN rates were calculated. The maximum pre-test probability at which post-test probabilities of a negative CE-MRI met with BI-RADS benchmarks was determined by a Fagan nomogram. Random-effects models, I²-statistics, Deek’s funnel plot testing and meta-regression were employed. P-values <0.05 were considered significant.

Results

Thirteen studies investigating 1414 lesions with a cancer prevalence of 43.6% (range: 22.7–66.9%) were included. No publication bias was found (P = 0.91). CE-MRI performed better in pure microcalcification studies compared to those also including associate findings (P < 0.001). In the first group, the pooled rate of avoidable biopsies was 80.6% (95%-CI: 64.6–90.5%) while the overall and invasive cancer FN rates were 3.7% (95%-CI: 1.2–6.2%) and 1.6% (95%-CI 0–3.6%), respectively. Up to a pre-test probability of 22%, the post-test probability did not exceed 2%.

Conclusion

A negative supplementary CE-MRI could potentially avoid 80.6% of unnecessary stereotactic biopsies in BI-RADS 4 microcalcifications at a cost of 3.7% missed breast cancers, 1.6% invasive. BI-RADS benchmarks for downgrading mammographic calcifications would be met up to a pretest probability of 22%.

•

A negative breast MRI can downgrade up to 80.6% of suspicious microcalcifications, potentially avoiding vacuum-assisted breast biopsies.

•

Up to a pretest probability of 22% , a negative breast MRI result would not exceed the 2% cancer rate required for a BI-RADS 3 category assignment.

Improving the Diagnostic Accuracy of Breast BI-RADS 4 Microcalcification-Only Lesions Using Contrast-Enhanced Mammography

BACKGROUND

Contrast-enhanced mammography (CEM) is a novel breast imaging technique that can provide additional information of breast tissue blood supply. This study aimed to test the possibility of CEM in improving the diagnostic accuracy of Breast Imaging Reporting and Data System (BI-RADS) 4 calcification-only lesions with consideration of morphology and distribution.

PATIENTS AND METHODS

Data of patients with suspicious malignant calcification-only lesions (BI-RADS 4) on low-energy CEM and proved pathologic diagnoses were retrospectively collected. Two junior radiologists independently reviewed the two sets of CEM images, low-energy images (LE) to describe the calcifications by morphology and distribution type, and recombined images (CE) to record the presence of enhancement. Low-risk and high-risk groups were divided by calcification morphology, distribution, and both, respectively. Positive predictive values and misdiagnosis rates (MDR) were compared between LE-only reading and CE reading. Diagnostic performance was also tested using machine learning method.

RESULTS

The study included 74 lesions (26 malignant and 48 benign). Positive predictive values were significantly higher and MDRs were significantly lower using CE images than using LE alone for both the low-risk morphology type and low-risk distribution type (P < .05). MDRs were significantly lower when using CE images (18.18%-24.00%) than using LE images alone in low-risk group (76.36%-80.00%) (P < .05). Using a machine learning method, significant improvements in the area under the receiver operating characteristic curve were observed in both low-risk and high-risk groups.

CONCLUSION

CEM has the potential to aid in the diagnosis of BI-RADS 4 calcification-only lesions; in particular, those presented as low risk in morphology and/or distribution may benefit more.

Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis

OBJECTIVE

This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast.

METHODS

The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression.

RESULTS

Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively.

CONCLUSION

Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.

The value of MRI contrast enhancement in biopsy decision of suspicious mammographic microcalcifications: a prospective multicenter study

OBJECTIVES

To investigate the inclusion of breast MRI in radiological assessment of suspicious, isolated microcalcifications detected with mammography.

METHODS

In this prospective, multicenter study, cases with isolated microcalcifications in screening mammography were examined with dynamic contrast-enhanced MRI (DCE-MRI) before biopsy, and contrast enhancement of the relevant calcification localization was accepted as a positive finding on MRI. Six experienced breast radiologists evaluated the images and performed the biopsies. Imaging findings and histopathological results were recorded. Sensitivity, specificity, NPV, and PPV of breast MRI were calculated and compared with histopathological findings.

RESULTS

Suspicious microcalcifications, which were detected by screening mammograms of 444 women, were evaluated. Of these, 276 (62.2%) were diagnosed as benign and 168 (37.8%) as malignant. Contrast enhancement was present in microcalcification localization in 325 (73.2%) of the cases. DCE-MRI was positive in all 102 invasive carcinomas and in 58 (87.9%) of 66 DCIS cases. MRI resulted in false negatives in eight DCIS cases; one was high grade and the other seven were low-to-medium grade. The false-negative rate of DCE-MRI was 4.76%. The sensitivity, specificity, PPV, and NPV for DCE-MRI for mammography-detected suspicious microcalcifications were 95.2%, 40.2%, 49.2%, and 93.3%, respectively.

CONCLUSIONS

In this study, all invasive cancers and all DCIS except eight cases (12.1%) were detected with DCE-MRI. DCE-MRI can be used in the decision-making algorithm to decrease the number of biopsies in mammography-detected suspicious calcifications, with a tradeoff for overlooking a small number of DCIS cases that are of low-to-medium grade.

KEY POINTS

• All invasive cancer cases and 87.8% of all in situ cancer cases were detected with MRI, showing a low false-negative rate of 4.7%. • Dynamic contrast-enhanced MRI can be used in the decision-making algorithm to decrease the number of biopsies in mammography-detected suspicious calcifications, with a tradeoff for overlooking a small number of DCIS cases that are predominantly low-to-medium grade. • If a decision for biopsy were made based on MRI findings in mammography-detected microcalcifications in this study, biopsy would not be performed to 119 cases (26.8%).

Consensus Meeting of Breast Imaging: BI-RADS® and Beyond

Organizers of medical educational courses are often confronted with questions that are clinically relevant yet trespassing the frontiers of scientifically proven, evidence-based medicine at the point of care. Therefore, since 2007 organizers of breast teaching courses in German language met biannually to find a consensus in clinically relevant questions that have not been definitely answered by science. The questions were prepared during the 3 months before the meeting according to a structured process and finally agreed upon the day before the consensus meeting. At the consensus meeting, the open questions concerning 2D/3D mammography, breast ultrasound, MR mammography, interventions as well as risk-based imaging of the breast were presented first for electronic anonymized voting, and then the results of the audience were separately displayed from the expert votes. Thereafter, an introductory statement of the moderator was followed by pros/cons of two experts, and subsequently the final voting was performed. With ≥75% of votes of the expert panel, an answer qualified as a consensus statement. Seventeen consensus statements were gained, addressing for instance the use of 2D/3D mammography, breast ultrasound in screening, MR mammography in women with intermediate breast cancer risk, markers for localization of pathologic axillary lymph nodes, and standards in risk-based imaging of the breast. After the evaluation, comments from the experts on each field were gathered supplementarily. Methodology, transparency, and soundness of statements achieve a unique yield for all course organizers and provide solid pathways for decision making in breast imaging.

New advance in breast cancer pathology and imaging

The improvement of knowledge concerning the pathology of breast cancer could provide the rationale for the development of new imaging diagnostic protocols. Indeed, as for the microcalcifications, new histopathological markers can be used as target for in vivo early detection of breast cancer lesions by using molecular imaging techniques such as positron emission tomography. Specifically, the mutual contribution of these medical specialties can ‘nourish’ the dream of a personalized medicine that takes into account the intrinsic variability of breast cancer. In this review, we report the main discoveries concerning breast cancer pathology highlighting the possible cooperation between the departments of anatomic pathology and imaging diagnostics.

Problem-Solving MR Imaging for Equivocal Imaging Findings and Indeterminate Clinical Symptoms of the Breast

Breast MR imaging is commonly used for high-risk screening and for assessing the extent of disease in patients with newly diagnosed breast cancer, but its utility for assessing suspicious symptoms and equivocal imaging findings is less widely accepted. The authors review current literature and guidelines regarding the use of breast MR imaging for these indications. Overall, problem-solving breast MR imaging is best reserved for pathologic nipple discharge and sonographically occult architectural distortion with limited biopsy options. Further study is necessary to define the role of problem-solving MR imaging for calcifications, mammographic asymmetries, and surgical scarring.