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

External validation of multiparametric magnetic resonance imaging-based decision rules for characterizing breast lesions and comparison to Kaiser score and breast imaging reporting and data system (BI-RADS) category

Background

Breast imaging reporting and data system (BI-RADS) provides standard descriptors but not detailed decision rules for characterizing breast lesions. Diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) are also not incorporated in the BI-RADS. Several multiparametric magnetic resonance imaging (mpMRI)-based decision rules have been developed to differentiate breast lesions, but lack external validation. This study aims to externally validate several mpMRI-based decision rules for characterizing breast lesions and compare them with Kaiser score and BI-RADS category.

Methods

There were 206 patients with 218 pathology-proven breast lesions (99 malignancies) included in this retrospective study from January 2018 to May 2018. Two radiologists blinded to pathology evaluated breast lesions according to the three mpMRI-based decision rules (Kim, Istomin, Zhong) and Kaiser score. BI-RADS category was extracted from radiology reports and also analysed. The diagnostic performances of the four decision rules and BI-RADS category were calculated and compared for different lesion types [mass and non-mass enhancement (NME)] and size (≤10 and >10 mm). The unnecessary biopsy rates for BI-RADS 4 lesions were calculated by the four decision rules.

Results

The three mpMRI-based decision rules showed area under the curve (AUC) of 0.81-0.87 for all lesions, 0.86-0.92 for mass lesions, 0.68-0.82 for NME, and 0.68-0.87 for lesion size ≤10 mm, 0.82-0.87 for lesion size >10 mm. Kaiser score showed the highest diagnostic performance for all subgroups except for lesion size ≤10 mm. No significant differences were found in AUC between Kaiser score and BI-RADS category. The mpMRI-based decision rules showed high sensitivity of 100% in all subgroups at the expense of low specificity (range, 2.9-41.2%). In contrast, Kaiser score demonstrated a significantly higher specificity of 73.5-92.9% than the three mpMRI-based decision rules at the cost of a decreased sensitivity (range, 60.0-93.6%) in different subgroups. The unnecessary biopsy rates for BI-RADS 4 lesions were 9.8% (Istomin), 12.2% (Zhong), 14.6% (Kim) and 70.7% (Kaiser score), respectively.

Conclusions

The mpMRI-based decision rules showed high sensitivity but low specificity for characterizing breast lesions, and their diagnostic efficiencies were inferior to Kaiser score and BI-RADS category.

Extremely dense breasts: A comprehensive review of increased cancer risk and supplementary screening methods

Women with extremely dense breasts account for approximately 10% of the screening population and face an increased lifetime risk of developing breast cancer. At the same time, the sensitivity of mammography, the first-line screening modality, is significantly reduced in this breast density group, owing to the masking effect of the abundant fibroglandular tissue. Consequently, this population has garnered increasing scientific attention due to the unique diagnostic challenge they present. Several research initiatives have attempted to address this diagnostic challenge by incorporating supplemental imaging modalities such as ultrasound, MRI, and contrast-enhanced mammography. Each of these modalities offers different benefits as well as limitations, both clinically and practically, including considerations of availability and costs. The purpose of this article is to critically review the background, latest scientific evidence, and future directions for the use of the various supplemental screening techniques for women with extremely dense breasts.

Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience

OBJECTIVES

To compare the diagnostic performance of three readers using BI-RADS and Kaiser score (KS) based on mass and non-mass enhancement (NME) lesions.

METHODS

A total of 630 lesions, 393 malignant and 237 benign, 458 mass and 172 NME, were analyzed. Three radiologists with 3 years, 6 years, and 13 years of experience made diagnoses. 596 cases had diffusion-weighted imaging, and the apparent diffusion coefficient (ADC) was measured. For lesions with ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 as the modified KS +, and the benefit was assessed.

RESULTS

When using BI-RADS, AUC was 0.878, 0.915, and 0.941 for mass, and 0.771, 0.838, 0.902 for NME for Reader-1, 2, and 3, respectively, better for mass than for NME. The diagnostic accuracy of KS was improved compared to BI-RADS for less experienced readers. For Reader-1, AUC was increased from 0.878 to 0.916 for mass (p = 0.005) and from 0.771 to 0.822 for NME (p = 0.124). Based on the cut-off value of BI-RADS ≥ 4B and KS ≥ 5 as malignant, the sensitivity of KS by Readers-1 and -2 was significantly higher for both Mass and NME. When ADC was considered to change to modified KS +, the AUC and the accuracy for all three readers were improved, showing higher specificity with slightly degraded sensitivity.

CONCLUSION

The benefit of KS compared to BI-RADS was most noticeable for the less experienced readers in improving sensitivity. Compared to KS, KS + can improve specificity for all three readers. For NME, the KS and KS + criteria need to be further improved.

CLINICAL RELEVANCE STATEMENT

KS provides an intuitive method for diagnosing lesions on breast MRI. BI-RADS and KS face greater difficulties in evaluating NME compared to mass lesions. Adding ADC to the KS can improve specificity with slightly degraded sensitivity.

KEY POINTS

KS provides an intuitive method for interpreting breast lesions on MRI, most helpful for novice readers. KS, compared to BI-RADS, improved sensitivity in both mass and NME groups for less experienced readers. NME lesions were considered during the development of the KS flowchart, but may need to be better defined.

DWI in the Differentiation of Malignant and Benign Breast Lesions Presenting with Non-Mass Enhancement on CE-MRI

OBJECTIVES

This study aimed to investigate whether the apparent diffusion coefficient (ADC) maps values of breast lesions presenting as non-mass enhancement (NME) on MRI could predict benign or malignant pathohistological findings.

MATERIALS AND METHODS

This retrospective single-center study included 136 female patients with NME and corresponding ultrasound correlate and a subsequent ultrasound-guided core needle biopsy. The patients were subdivided into benign or malignant subgroups based on pathology reports, which served as the gold standard. Blinded to the pathological results, two radiologists independently measured the ADC values of the depicted NME using punctate, 10 mm and whole tumor regions of interest (ROIs) wherever applicable. The mean of all measurements was also analyzed and compared with the pathologic subdivision.

RESULTS

The sensitivity of whole tumor ROI in detecting benign NME is 91% compared to 74% for 10 mm ROI and 78% for punctate ROI. No significant differences in ADC values were observed when comparing fatty breast tissue and dense breast tissue.

CONCLUSIONS

There were differences in ADC values between benign and malignant findings using all types of measurements, where the whole tumor ROI was the most sensitive.

Diffusion weighted imaging for improving the diagnostic performance of screening breast MRI: impact of apparent diffusion coefficient quantitation methods and cutoffs

Introduction

Diffusion weighted MRI (DWI) has emerged as a promising adjunct to reduce unnecessary biopsies prompted by breast MRI through use of apparent diffusion coefficient (ADC) measures. The purpose of this study was to investigate the effects of different lesion ADC measurement approaches and ADC cutoffs on the diagnostic performance of breast DWI in a high-risk MRI screening cohort to identify the optimal approach for clinical incorporation.

Methods

Consecutive screening breast MRI examinations (August 2014-Dec 2018) that prompted a biopsy for a suspicious breast lesion (BI-RADS 4 or 5) were retrospectively evaluated. On DWI, ADC (b=0/100/600/800s/mm2) measures were calculated with three different techniques for defining lesion region-of-interest (ROI; single slice('2D'), whole volume('3D') and lowest ADC region('hotspot')). An optimal data-derived ADC cutoff for each technique was retrospectively identified to reduce benign biopsies while avoiding any false negatives, inherently producing cutoffs with 100% sensitivity in this particular cohort. Further, diagnostic performance of these measures was validated using two prespecified ADC cutoffs: 1.53x10-3mm2/s from the ECOG-ACRIN A6702 trial and 1.30x10-3mm2/s from the international EUSOBI group. Diagnostic performance was compared between ADC maps generated with 2(0/800s/mm2) and 4(0/100/600/800s/mm2) b-values. Benign biopsy reduction rate was calculated (number of benign lesions with ADC >cutoff)/(total number of benign lesions).

Results

137 suspicious lesions (in 121 women, median age 44 years [range, 20-75yrs]) were detected on contrast-enhanced screening breast MRI and recommended for biopsy. Of those, 30(21.9%) were malignant and 107(78.1%) were benign. Hotspot ADC measures were significantly lower (p<0.001) than ADCs from both 2D and 3D ROI techniques. Applying the optimal data-derived ADC cutoffs resulted in comparable reduction in benign biopsies across ROI techniques (range:16.8% -17.8%). Applying the prespecified A6702 and EUSOBI cutoffs resulted in benign biopsy reduction rates of 11.2-19.6%(with 90.0-100% sensitivity) and 36.4-51.4%(with 70.0-83.3% sensitivity), respectively, across ROI techniques. ADC measures and benign biopsy reduction rates were similar when calculated with only 2 b-values (0,800 s/mm2) versus all 4 b-values.

Discussion

Our findings demonstrate that with appropriate ADC thresholds, comparable reduction in benign biopsies can be achieved using lesion ADC measurements computed from a variety of approaches. Choice of ADC cutoff depends on ROI approach and preferred performance tradeoffs (biopsy reduction vs sensitivity).

Performance of high-resolution diffusion-weighted magnetic resonance imaging for detecting clinically occult early breast cancers: a multi-reader study

PURPOSE

To compare mammography, breast ultrasound (US), high-resolution diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced breast MRI (DCE-MRI), and their combinations for detecting clinically occult early breast cancers (EBCs), including ductal carcinoma in situ (DCIS).

METHODS

Three hundred and three consecutive women with screening imaging-detected early breast cancers (60 pure DCIS, 36 DCIS with microinvasion, and 207 invasive carcinoma less than 20 mm) who underwent breast MRI at 3 T including DW-MRI (b-values of 0, 800 and 1200 s/mm2; in-plane resolution, 1.1 × 1.1 mm2 or 1.3 × 1.3 mm2; section thickness, 3 mm) were retrospectively reviewed. Three radiologists independently reviewed each examination. Statistical analysis included Chi-square test, McNemar test for comparison of cancer detection rates, and Fleiss' Kappa for interreader agreement. Mixed-effect logistic regression analysis was employed to evaluate factors associated with cancer detection on DW-MRI.

RESULTS

The overall cancer detection rates were 54.8% on mammography, 71.0% on breast US, 81.5% on DW-MRI, and 87.1% on DCE-MRI. On McNemar test, DW-MRI detected more cancers than mammography (adjusted p < 0.001), and its combination with mammography showed a similar cancer detection rate to DCE-MRI combined with mammography (adjusted p = 0.808). On multivariable analysis, histologic type, lesion size, ADC and CNR on DW-MRI were independent factors for cancer detection on DW-MRI. The interreader agreement for cancer detection was moderate to substantial (Fleiss' kappa: 0.52-0.65) across each modality.

CONCLUSION

High-resolution DW-MRI plus mammography showed comparable cancer detection rate to DCE-MRI plus mammography for detecting clinically occult EBCs including DCIS.

A risk prediction stratification for non-mass breast lesions, combining clinical characteristics and imaging features on ultrasound, mammography, and MRI

Objectives

Given the inevitable trend of domestic imaging center mergers and the current lack of comprehensive imaging evaluation guidelines for non-mass breast lesions, we have developed a novel BI-RADS risk prediction and stratification system for non-mass breast lesions that integrates clinical characteristics with imaging features from ultrasound, mammography, and MRI, with the aim of assisting clinicians in interpreting imaging reports.

Methods

This study enrolled 350 patients with non-mass breast lesions (NMLs), randomly assigning them to a training set of 245 cases (70%) and a test set of 105 cases (30%). Radiologists conducted comprehensive evaluations of the lesions using ultrasound, mammography, and MRI. Independent predictors were identified using LASSO logistic regression, and a predictive risk model was constructed using a nomogram generated with R software, with subsequent validation in both sets.

Results

LASSO logistic regression identified a set of independent predictors, encompassing age, clinical palpation hardness, distribution and morphology of calcifications, peripheral blood supply as depicted by color Doppler imaging, maximum lesion diameter, patterns of internal enhancement, distribution of non-mass lesions, time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values. The predictive model achieved area under the curve (AUC) values of 0.873 for the training group and 0.877 for the testing group. The model's positive predictive values were as follows: BI-RADS 2 = 0%, BI-RADS 3 = 0%, BI-RADS 4A = 6.25%, BI-RADS 4B = 26.13%, BI-RADS 4C = 80.84%, and BI-RADS 5 = 97.33%.

Conclusion

The creation of a risk-predictive BI-RADS stratification, specifically designed for non-mass breast lesions and integrating clinical and imaging data from multiple modalities, significantly enhances the precision of diagnostic categorization for these lesions.

Evaluation of Features in Probably Benign and Malignant Nonmass Enhancement in Breast MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive breast imaging modality in detecting breast carcinoma. Nonmass enhancement (NME) is uniquely seen on MRI of the breast. The correlation between NME features and pathologic results has not been extensively explored. Our goal was to evaluate the characteristics of probably benign and suspicious NME lesions in MRI and determine which features are more associated with malignancy. We performed a retrospective research after approval by the hospital ethics committee on women who underwent breast MRI from March 2017 to March 2020 and identified 63 lesions of all 400 NME that were categorized as probably benign or suspicious according to the BI-RADS classification (version 2013). MRI features of NME findings including the location, size, distribution and enhancement pattern, kinetic curve, diffusion restriction, and also pathology result or 6-12-month follow-up MRI were evaluated and analyzed in each group (probably benign or suspicious NME). Vacuum-guided biopsies (VAB) were performed under mammographic or sonographic guidance and confirmed with MRI by visualization of the inserted clips. Segmental distribution and clustered ring internal enhancement were significantly associated with malignancy (p value<0.05), while linear distribution or homogeneous enhancement patterns were associated with benignity (p value <0.05). Additionally, the plateau and washout types in the dynamic curve were only seen in malignant lesions (p value <0.05). The presence of DWI restriction in NME lesions was also found to be a statistically important factor. Understanding the imaging findings of malignant NME is helpful to determine when biopsy is indicated. The correlation between NME features and pathologic results is critical in making appropriate management.

Unenhanced Breast MRI With Diffusion-Weighted Imaging for Breast Cancer Detection: Effects of Training on Performance and Agreement of Subspecialty Radiologists

OBJECTIVE

To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm² was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive. The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC).

RESULTS

Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9), 90.8% (95% CI: 85.6-94.2), and 83.5% (95% CI: 78.6-87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52-0.63) before training and 0.68 (95% CI: 0.62-0.74) after training, with a difference of 0.11 (95% CI: 0.02-0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69-0.74) before training and 0.79 (95% CI: 0.76-0.80) after training (P = 0.002).

CONCLUSION

Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring

DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.

Correlation between dynamic contrast-enhanced MRI characteristics and apparent diffusion coefficient with Ki-67-positive expression in non-mass enhancement of breast cancer

As a remarkably specific characteristic of breast cancer observed on magnetic resonance imaging (MRI), the association between the NME type breast cancer and prognosis, including Ki-67, necessitates comprehensive exploration. To investigate the correlation between dynamic contrast-enhanced MRI (DCE-MRI) characteristics and apparent diffusion coefficient (ADC) values with Ki-67-positive expression in NME type breast cancer. A total of 63 NME type breast cancer patients were retrospectively reviewed. Malignancies were confirmed by surgical pathology. All patients underwent DCE and diffusion-weighted imaging (DWI) before surgery. DCE-MRI characteristics, including tumor distribution, internal enhancement pattern, axillary adenopathy, and time-intensity curve types were observed. ADC values and lesion sizes were also measured. The correlation between these features and Ki-67 expression were assessed using Chi-square test, Fisher's exact test, and Spearman rank analysis. The receiver operating characteristic curve and area under the curve (AUC) was used to evaluate the diagnostic performance of Ki-67-positive expression. Regional distribution, TIC type, and ipsilateral axillary lymph node enlargement were correlated with Ki-67-positive expression (χ2 = 0.397, 0.357, and 0.357, respectively; P < 0.01). ADC value and lesion size were positively correlated with Ki-67-positive expression (rs = 0.295, 0.392; P < 0.05). The optimal threshold values for lesion size and ADC value to assess Ki-67 expression were determined to be 5.05 (AUC = 0.759) cm and 0.403 × 10-3 s/mm2 (AUC = 0.695), respectively. The best diagnosis performance was the ADC combined with lesion size (AUC = 0.791). The ADC value, lesion size, regional distribution, and TIC type in NME type breast cancer were correlated with Ki-67-positive expression. These features will aid diagnosis and treatment of NME type breast cancer.

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.

Comparison of the diagnostic value of contrast-enhanced ultrasound combined with conventional ultrasound versus magnetic resonance imaging in malignant non-mass breast lesions

OBJECTIVE

To compare the diagnostic value of contrast-enhanced ultrasound (CEUS)+conventional ultrasound vs MRI for malignant non-mass breast lesions (NMLs).

METHODS

A total of 109 NMLs detected by conventional ultrasound and examined by both CEUS and MRI were retrospectively analysed. The characteristics of NMLs in CEUS and MRI were noted, and agreement between the two modalities was analysed. Sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and area under the curve (AUC) of the two methods for diagnosing malignant NMLs were calculated in the overall sample and subgroups of different sizes(<10 mm, 10-20 mm, >20 mm).

RESULTS

A total of 66 NMLs detected by conventional ultrasound showed non-mass enhancement in MRI. Agreement between ultrasound and MRI was 60.6%. Probability of malignancy was higher when there was agreement between the two modalities. In the overall group, the sensitivity, specificity, PPV, and NPV of the two methods were 91.3%, 71.4%, 60%, 93.4% and 100%, 50.4%, 59.7%, 100%, respectively. The diagnostic performance of CEUS+conventional ultrasound was better than that of MRI (AUC: 0.825 vs 0.762, p = 0.043). The specificity of both methods decreased as lesion size increased, but sensitivity did not change. There was no significant difference between the AUCs of the two methods in the size subgroups (p > 0.05).

CONCLUSION

The diagnostic performance of CEUS+conventional ultrasound may be better than that of MRI for NMLs detected by conventional ultrasound. However, the specificity of both methods decrease significantly as lesion size increases.

ADVANCES IN KNOWLEDGE

This is the first study to compare the diagnostic performance of CEUS+conventional ultrasound vs that of MRI for malignant NMLs detected by conventional ultrasound. While CEUS+conventional ultrasound appears to be superior to MRI, subgroup analysis suggests that diagnostic performance is poorer for larger NMLs.

Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence

Background

In our previous study, we developed a combined diagnostic model based on time-intensity curve (TIC) types and radiomics signature on contrast-enhanced magnetic resonance imaging (CE-MRI) for non-mass enhancement (NME). The model had a high diagnostic ability for differentiation without the additional diffusion-weighted imaging (DWI) sequence. In this study, we aimed to compare the diagnostic performance of the combined clinical-radiomics model based on CE-MRI and DWI in discriminating Breast Imaging-Reporting and Data System (BI-RADS) 4 NME breast lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma.

Methods

This retrospective study enrolled 364 NME lesions (343 patients). Of these, 183 malignant and 84 benign breast lesions classified as BI-RADS 4 NMEs by the initial diagnosis were reclassified based on the combined clinical-radiomics model and DWI, respectively. The nomogram score (NS) values for malignancy risk derived from the combined clinical-radiomics model and the minimal apparent diffusion coefficient (ADC) values from DWI were calculated and compared. The percentage of false positives were estimated in comparison with the original classification. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of the NS and minimal ADC values in distinguishing benign and malignant lesions, DCIS, and invasive breast carcinoma. An ablation experiment was used to test the value of the additional DWI sequence.

Results

The diagnostic value of the NS values [area under curve (AUC) =0.843; 95% CI: 0.789-0.896] for discriminating the 267 NME breast lesions categorized as BI-RADS 4 was significantly higher than the minimal ADC values (AUC =0.662; 95% CI: 0.590-0.735). The NS values showed higher sensitivity, specificity, and accuracy compared with the minimal ADC values (sensitivity: 80.3% vs. 65.6%; specificity: 79.8% vs. 65.5%; accuracy: 80.1% vs. 65.5%). The NS values and minimal ADC values did not achieve high diagnostic accuracy in discriminating between DCIS and invasive cancer. However, the diagnostic performance of the combined NS-ADC model (AUC =0.731; 95% CI: 0.655-0.806) was higher than that of the NS values alone (P=0.008) and comparable to that of the minimal ADC values (P=0.440).

Conclusions

The combined clinical-radiomics model based on CE-MRI could improve the diagnostic performance in discriminating the BI-RADS 4 NME lesions without an additional DWI sequence. However, DWI may improve the diagnostic performance in discriminating DCIS from invasive cancer.

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

BACKGROUND

Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable.

PURPOSE

To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results.

FIELD STRENGTH/SEQUENCE

A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence.

ASSESSMENT

In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant.

RESULTS

For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635.

DATA CONCLUSION

Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

A new scoring system for differentiating malignant from benign "second-look" breast lesions detected by MRI in patients with known breast cancer

AIM

To propose a scoring system made of reproducible and objective criteria to aid in differentiating malignant from benign "second-look" breast lesions detected at magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Data were collected retrospectively for "second-look" lesions identified on breast MRI studies performed at the University Hospitals of Leicester NHS Trust breast unit over a 2-year period (from January 2020 to January 2022). Ninety-five "second look" MRI-detected lesions were included in this retrospective study. Lesions were assessed according to margins, T2 signal, internal enhancement patterns, contrast kinetics, and diffusion-weighted imaging (DWI) patterns.

RESULTS

Fifty-two per cent of the included lesions were confirmed at histopathology to be malignant. The most common contrast kinetics identified in malignant lesions was the plateau pattern followed by the washout pattern while the most common pattern in benign lesions was the progressive pattern. The apparent diffusion coefficient (ADC) cut-off value for separating benign and malignant lesions at the unit was found to be 1.1 × 10-3 mm2/s. Based on the MRI features described above, a scoring system is suggested to help differentiate benign from malignant "second-look" lesions. According to the present results, setting a score of 2 or more points as an indication for biopsy was 100% reliable in identifying malignant lesions and avoiding biopsies in >30% of lesions.

CONCLUSION

The suggested scoring system could avoid biopsy of >30% of the "second-look" lesions detected by MRI without missing any malignant lesions.

Magnetic resonance imaging diagnosis of non-mass enhancement of the breast

Breast Imaging Reporting and Data System magnetic resonance imaging (BI-RADS-MRI) classifies lesions as mass, non-mass enhancement (NME), or focus. BI-RADS ultrasound does not currently have the concept of non-mass. Additionally, knowing the concept of NME in MRI is significant. Thus, this study aimed to provide a narrative review of NME diagnosis in breast MRI. Lexicons are defined with distribution (focal, linear, segmental, regional, multiple regions, and diffuse) and internal enhancement patterns (homogenous, heterogeneous, clumped, and clustered ring) in the case of NME. Among these, linear, segmental, clumped, clustered ring, and heterogeneous are the terms that suggest malignancy. Hence, a hand search was conducted for reports of malignancy frequencies. The malignancy frequency in NME is widely distributed, ranging from 25 to 83.6%, and the frequency of each finding varies. Latest techniques, such as diffusion-weighted imaging and ultrafast dynamic MRI, are attempted to differentiate NME. Additionally, attempts are made in the preoperative setting to determine the concordance of lesion spread based on findings and the presence of invasion.

Probing lipids relaxation times in breast cancer using magnetic resonance spectroscopic fingerprinting

OBJECTIVES

To investigate the clinical relevance of the relaxation times of lipids within breast cancer and normal fibroglandular tissue in vivo, using magnetic resonance spectroscopic fingerprinting (MRSF).

METHODS

Twelve patients with biopsy-confirmed breast cancer and 14 healthy controls were prospectively scanned at 3 T using a protocol consisting of diffusion tensor imaging (DTI), MRSF, and dynamic contrast-enhanced (DCE) MRI. Single-voxel MRSF data was recorded from the tumor (patients) - identified using DTI - or normal fibroglandular tissue (controls), in under 20 s. MRSF data was analyzed using in-house software. Linear mixed model analysis was used to compare the relaxation times of lipids in breast cancer VOIs vs. normal fibroglandular tissue.

RESULTS

Seven distinguished lipid metabolite peaks were identified and their relaxation times were recorded. Of them, several exhibited statistically significant changes between controls and patients, with strong significance (p < 10^-3) recorded for several of the lipid resonances at 1.3 ppm (T₁ = 355 ± 17 ms vs. 389 ± 27 ms), 4.1 ppm (T₁ = 255 ± 86 ms vs. 127 ± 33 ms), 5.22 ppm (T₁ = 724 ± 81 ms vs. 516 ± 62 ms), and 5.31 ppm (T₂ = 56 ± 5 ms vs. 44 ± 3.5 ms, respectively).

CONCLUSIONS

The application of MRSF to breast cancer imaging is feasible and achievable in clinically relevant scan time. Further studies are required to verify and comprehend the underling biological mechanism behind the differences in lipid relaxation times in cancer and normal fibroglandular tissue.

KEY POINTS

•The relaxation times of lipids in breast tissue are potential markers for quantitative characterization of the normal fibroglandular tissue and cancer. •Lipid relaxation times can be acquired rapidly in a clinically relevant manner using a single-voxel technique, termed MRSF. •Relaxation times of T₁ at 1.3 ppm, 4.1 ppm, and 5.22 ppm, as well as of T₂ at 5.31 ppm, were significantly different between measurements within breast cancer and the normal fibroglandular tissue.

Pregnancy-Associated Breast Cancer: A Diagnostic and Therapeutic Challenge

Pregnancy-associated breast cancer (PABC) is commonly defined as a breast cancer occurring during pregnancy, throughout 1 year postpartum, or during lactation. Despite being a rare circumstance, PABC is one of the most common types of malignancies occurring during pregnancy and lactation, with growing incidence in developed countries, due both to decreasing age at onset of breast cancer and to increasing maternal age. Diagnosis and management of malignancy in the prenatal and postnatal settings are challenging for practitioners, as the structural and functional changes that the breast undergoes may be misleading for both the radiologist and the clinician. Furthermore, safety concerns for the mother and child, as well as psychological aspects in this unique and delicate condition, need to be constantly considered. In this comprehensive review, clinical, diagnostic, and therapeutic aspects of PABC (including surgery, chemotherapy and other systemic treatments, and radiotherapy) are presented and fully discussed, based on medical literature, current international clinical guidelines, and systematic practice.

Limited value of multiparametric MRI with dynamic contrast-enhanced and diffusion-weighted imaging in non-mass enhancing breast tumors

PURPOSE

To investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors.

METHOD

Patients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (<1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (<1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader.

RESULTS

The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8-100 %), outperforming all mpMRI reading approaches (R1 74.4-87.1 %, R2 71.7-94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %).

CONCLUSION

mpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions

Background

To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging.

Methods

1.5-T DWI data (b = 0, 50, 250, 800 s/mm²) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D₁′ = ADC (50, 800), D₂′ = ADC (250, 800), f₁′ = f (0, 50, 800), f₂′ = f (0, 250, 800) and D*′ = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D₁′ with f₁′ and D₂′ with f₂′ was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm² (n = 191) and (ii) with suspicious contrast-enhancement (n = 135).

Results

All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm². For task (i), best discrimination was reached for the combination of D₁′ and f₁′ using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D₁′ (88.0%) and f₁′ (87.4%). For task (ii), best discrimination was reached for single parameter D₁′ using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D₂′ (88.1%). Adding f₁′ to D₁′ did not improve discrimination.

Conclusions

IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.

Safely reducing unnecessary benign breast biopsies by applying non-mass and DWI directional variance filters to ADC thresholding

Background

Thresholding apparent diffusion coefficient (ADC) maps obtained from Diffusion-Weighted-Imaging (DWI) has been proposed for identifying benign lesions that can safely avoid biopsy. The presence of malignancies with high ADC values leads to high thresholds, limiting numbers of avoidable biopsies.

Purpose

We evaluate two previously reported methods for identifying avoidable biopsies: using case-set dependent ADC thresholds that assure 100% sensitivity and using negative likelihood ratio (LR-) with a fixed ADC threshold of 1.50 × 10^–3 mm²/s. We evaluated improvements in efficacy obtained by excluding non-mass lesions and lesions with anisotropic intra-lesion morphologic characteristics.

Study type

Prospective.

Population

55 adult females with dense breasts with 69 BI-RADS 4 or 5 lesions (38 malignant, 31 benign) identified on ultrasound and mammography and imaged with MRI prior to biopsy.

Field strength/sequence

1.5 T and 3.0 T. DWI.

Assessment

Analysis of DWI, including directional images was done on an ROI basis. ROIs were drawn on DWI images acquired prior to biopsy, referencing all available images including DCE, and mean ADC was measured. Anisotropy was quantified via variation in ADC values in the lesion core across directional DWI images.

Statistical tests

Improvement in specificity at 100% sensitivity was evaluated with exact McNemar test with 1-sided p-value < 0.05 indicating statistical significance.

Results

Using ADC thresholding that assures 100% sensitivity, non-mass and directional variance filtering improved the percent of avoidable biopsies to 42% from baseline of 10% achieved with ADC thresholding alone. Using LR-, filtering improved outcome to 0.06 from baseline 0.25 with ADC thresholding alone. ADC thresholding showed a lower percentage of avoidable biopsies in our cohort than reported in prior studies. When ADC thresholding was supplemented with filtering, the percentage of avoidable biopsies exceeded those of prior studies.

Data conclusion

Supplementing ADC thresholding with filters excluding non-mass lesions and lesions with anisotropic characteristics on DWI can result in an increased number of avoidable biopsies.

Can DWI provide additional value to Kaiser score in evaluation of breast lesions

OBJECTIVES

To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI).

METHODS

Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10^-3mm²/s, 1.4 × 10^-3mm²/s, 1.53 × 10^-3mm²/s, and 1.6 × 10^-3mm²/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis.

RESULTS

The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05).

CONCLUSION

KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions.

KEY POINTS

• KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.

Multimodality imaging in lobular breast cancer: Differences in mammography, ultrasound, and MRI in the assessment of local tumor extent and correlation with molecular characteristics

Introduction

Invasive lobular breast cancer (ILC) is a diagnostic challenge due to the diversity of morphological features. The objective of the study was to investigate the presentation and local extent of ILC using various imaging techniques and to assess the correlation between imaging and molecular profile.

Materials and methods

We reviewed 162 consecutive patients with ILC found on vacuum-assisted biopsy, who underwent evaluation of the lesion morphology and extent using ultrasound (US), mammography (MMG), and magnetic resonance imaging (MRI). Radiographic features were compared with ILC intrinsic subtype based on the expression of Ki-67 and estrogen, progesterone, and HER2 receptors.

Results

A total of 113 mass lesions and 49 non-mass enhancements (NMEs) were found in MRI. Masses were typically irregular and spiculated, showing heterogeneous contrast enhancement, diffusion restriction, and type III enhancement curve. NMEs presented mainly as the area of focal or multiregional distribution with heterogeneous or clumped contrast enhancement, diffusion restriction, and type III enhancement curve. Lesion extent significantly varied between MRI and MMG/ultrasonography (USG) (P < 0.001) but did not differ between MGF and ultrasonography (USG). The larger the ILC, the higher the disproportion when lesion extent in MRI was compared with MMG (P < 0.001) and ultrasonography (USG) (P < 0.001). In the study group, there were 97 cases of luminal A subtype (59.9%), 54 cases of luminal B HER2− (33.3%), nine cases of luminal B HER2+ (5.5%), and two cases of triple negative (1.2%). The HER2 type was not found in the study group. We did not observe any significant correlation between molecular profile and imaging.

Conclusion

MRI is the most effective technique for the assessment of ILC local extent, which is important for optimal treatment planning. Further studies are needed to investigate if the intrinsic subtype of ILC can be predicted by imaging features on MRI.

Performance of abbreviated protocols versus unenhanced MRI in detecting occult breast lesions of mammography in patients with dense breasts

To assess the diagnostic ability of abbreviated protocols of MRI (AP-MRI) compared with unenhanced MRI (UE-MRI) in mammographically occult cancers in patients with dense breast tissue. The retrospective analysis consisted of 102 patients without positive findings on mammography who received preoperative MRI full diagnostic protocols (FDP) between January 2015 and December 2018. Two breast radiologists read the UE, AP, and FDP. The interpretation times were recorded. The comparisons of the sensitivity, specificity and area under the curve of each MRI protocol, and the sensitivity of these protocols in each subgroup of different size tumors used the Chi-square test. The paired sample t-test was used for evaluating the difference of reading time of the three protocols. Among 102 women, there were 68 cancers and two benign lesions in 64 patients and 38 patients had benign or negative findings. Both readers found the sensitivity and specificity of AP and UE-MRI were similar (p > 0.05), whereas compared with FDP, UE had lower sensitivity (Reader 1/Reader 2: p = 0.023, 0.004). For different lesion size groups, one of the readers found that AP and FDP had higher sensitivities than UE-MRI for detecting the lesions ≤ 10 mm in diameter (p = 0.041, p = 0.023). Compared with FDP, the average reading time of UE-MRI and AP was remarkably reduced (p < 0.001). AP-MRI had more advantages than UE-MRI to detect mammographically occult cancers, especially for breast tumors ≤ 10 mm in diameter.

Diagnostic performance of DCE-MRI, multiparametric MRI and multimodality imaging for discrimination of breast non-mass-like enhancement lesions

OBJECTIVE

The aim of this study was to investigate and compare the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI, multiparametric MRI (mpMRI), and multimodality imaging (MMI) combining mpMRI and mammography (MG) for discriminating breast non-mass-like enhancement (NME) lesions.

METHODS

This retrospective study enrolled 193 patients with 199 lesions who underwent 3.0 T MRI and MG from January 2017 to December 2019. The features of DCE-MRI, turbo inversion recovery magnitude (TIRM), and diffusion-weighted imaging (DWI) were assessed by two breast radiologists. Then, all lesions were divided into microcalcification and non-microcalcification groups to assess the features of MG. Comparisons were performed between groups using univariate analyses. Then, multivariate analyses were performed to construct diagnostic models for distinguishing NME lesions. Diagnostic performance was evaluated by using the area under the curve (AUC) and the differences between AUCs were evaluated by using the DeLong test.

RESULTS

Overall (n = 199), mpMRI outperformed DCE-MRI alone (AUCmpMRI = 0.924 vs. AUCDCE-MRI = 0.884; p = 0.007). Furthermore, MMI outperformed both mpMRI and MG (the microcalcification group [n = 140]: AUCMMI = 0.997 vs. AUCmpMRI = 0.978, p = 0.018 and AUCMMI = 0.997 vs. AUCMG = 0.912, p < 0.001; the non-microcalcification group [n = 59]: AUCMMI = 0.857 vs. AUCmpMRI = 0.768, p = 0.044 and AUCMMI = 0.857 vs. AUCMG = 0.759, p = 0.039).

CONCLUSION & ADVANCES IN KNOWLEDGE

DCE-MRI combined with DWI and TIRM information could improve the diagnostic performance for discriminating NME lesions compared with DCE-MRI alone. Furthermore, MMI combining mpMRI and MG showed better discrimination than both mpMRI and MG.

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.

Breast MRI during pregnancy and lactation: clinical challenges and technical advances

The breast experiences substantial changes in morphology and function during pregnancy and lactation which affects its imaging properties and may reduce the visibility of a concurrent pathological process. The high incidence of benign gestational-related entities may further add complexity to the clinical and radiological evaluation of the breast during the period. Consequently, pregnancy-associated breast cancer (PABC) is often a delayed diagnosis and carries a poor prognosis. This state-of-the-art pictorial review illustrates how despite currently being underutilized, technical advances and new clinical evidence support the use of unenhanced breast MRI during pregnancy and both unenhanced and dynamic-contrast enhanced (DCE) during lactation, to serve as effective supplementary modalities in the diagnostic work-up of PABC.

Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance

Simple Summary

Currently, breast contrast-enhanced MRI is the most sensitive imaging technique for breast cancer detection; however, its specificity is low given the common characteristics shared by benign breast lesions and some cancers. This leads to a high number of false-positive cases and, therefore, unnecessary biopsies. Multiparametric MRI including diffusion-weighted imaging assists in this task by increasing the specificity for breast lesion discrimination. Nevertheless, interpretation of breast MRI is still highly dependent on the reader’s level of experience. Our work combines radiomic features extracted from multiparametric MRI to generate predictive models for breast cancer differentiation. Additionally, decision support models were compared with the performance of two breast dedicated radiologists for lesion differentiation. Our work proves the potential of multiparametric radiomics coupled with machine learning to be implemented in clinical practice for lesion differentiation on breast MRI. AI algorithms show value to assist less experienced readers, improving the accuracy for breast lesion discrimination.

Abstract

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions

OBJECTIVES

To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

METHODS

This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001).

CONCLUSION

Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance.

KEY POINTS

• Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

Non-mass enhancement breast lesions: MRI findings and associations with malignancy

Background

Magnetic resonance imaging (MRI) is a multi-sequence imaging technique. Although MRI is the most sensitive method for detecting breast cancer, it is limited in evaluating the malignant possibility of non-mass enhanced (NME) breast lesions. It is also rarely reported whether MRI can further indicate the invasion of the lesions. In this article, we explore the differentiation of MRI characteristics between benign and malignant NME lesions and determine which features are associated with invasion.

Methods

The MRI findings of 118 NME lesions were evaluated retrospectively to explore the characteristics of the benign and malignant NME lesions in different MRI sequences including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI). The difference of MRI findings between benign and malignant NME lesions were determined by Pearson χ2 test or Fisher's exact test, and the diagnostic value of features for malignancy was evaluated by receiver operating characteristic (ROC) curve.

Results

This study included 118 NME lesions (62 benign and 56 malignant) in 118 patients. We found a segmental distribution, clustered-ring enhancement, wash-out dynamic curve, and lower apparent diffusion coefficient (ADC) value (P=0.01, <0.001, 0.02, 0.001) were associated with malignancy. Wash-out dynamic curves, diffusion restriction on DWI, lower ADC values were more advantageous in distinguishing invasive NME cancer from benign lesions than ductal carcinoma in situ (DCIS) (P<0.001, <0.001, 0.027). Further analysis showed that there were statistical differences between invasive carcinoma and carcinoma in situ in terms of wash-out dynamic curves, diffusion restriction on DWI and lower ADC values (P=0.001, 0.014, 0.024).

Conclusions

MRI is a valuable way to identify malignant NME lesions and could predict the invasion of the lesions. Compared with carcinoma in situ, some sequences have more advantages in distinguishing invasive carcinoma from benign lesions.

Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure.

Objectives

This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients.

Method

The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included.

Results

The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07).

Conclusions

ADC values cannot be used to discriminate between molecular subtypes of BC.

Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses

Purpose

We aimed to assess the additional value of a radiomics-based signature for distinguishing between benign and malignant non-mass enhancement lesions (NMEs) on dynamic contrast-enhanced breast magnetic resonance imaging (breast DCE-MRI).

Methods

In this retrospective study, 232 patients with 247 histopathologically confirmed NMEs (malignant: 191; benign: 56) were enrolled from December 2017 to October 2020 as a primary cohort to develop the discriminative models. Radiomic features were extracted from one post-contrast phase (around 90s after contrast injection) of breast DCE-MRI images. The least absolute shrinkage and selection operator (LASSO) regression model was adapted to select features and construct the radiomics-based signature. Based on clinical and routine MR features, radiomics features, and combined information, three discriminative models were built using multivariable logistic regression analyses. In addition, an independent cohort of 72 patients with 72 NMEs (malignant: 50; benign: 22) was collected from November 2020 to April 2021 for the validation of the three discriminative models. Finally, the combined model was assessed using nomogram and decision curve analyses.

Results

The routine MR model with two selected features of the time-intensity curve (TIC) type and MR-reported axillary lymph node (ALN) status showed a high sensitivity of 0.942 (95%CI, 0.906 - 0.974) and low specificity of 0.589 (95%CI, 0.464 - 0.714). The radiomics model with six selected features was significantly correlated with malignancy (P<0.001 for both primary and validation cohorts). Finally, the individual combined model, which contained factors including TIC types and radiomics signatures, showed good discrimination, with an acceptable sensitivity of 0.869 (95%CI, 0.816 to 0.916), improved specificity of 0.839 (95%CI, 0.750 to 0.929). The nomogram was applied to the validation cohort, reaching good discrimination, with a sensitivity of 0.820 (95%CI, 0.700 to 0.920), specificity of 0.864 (95%CI,0.682 to 1.000). The combined model was clinically helpful, as demonstrated by decision curve analysis.

Conclusions

Our study added radiomics signatures into a conventional clinical model and developed a radiomics nomogram including radiomics signatures and TIC types. This radiomics model could be used to differentiate benign from malignant NMEs in patients with suspicious lesions on breast MRI.

The cone-beam breast computed tomography characteristics of breast non-mass enhancement lesions

BACKGROUND

Cone-beam computed tomography (CBBCT) of the breast is emerging as a way of improving breast cancer diagnostic yield.

PURPOSE

To find characteristics of non-mass enhancement (NME) lesions on breast CBBCT and to identify the characteristics that distinguish malignant and benign lesions.

MATERIAL AND METHODS

Breast CBBCT images of 84 NME lesions were analyzed. Internal enhancement distribution and patterns, calcification distribution and suspicious morphology, and ΔHU enhancement values were compared between post-contrast and pre-contrast malignant and benign lesions. Univariate analyses were applied to find the strongest indicators of malignancy, and logistic regression analysis was used to develop a fitting equation for the combined diagnostic model.

RESULTS

In the 84 NME lesions, the indicators of malignancy were as follows: segmental enhancement distribution (P = 0.011, 53.62% sensitivity, 86.67% specificity, 94.87% positive predictive value [PPV], and 28.89% negative predictive value [NPV]), clumped internal enhancement patterns (P = 0.017, 50.72% sensitivity, 86.67% specificity, 94.59% PPV, and 27.66% NPV), ΔHU ≥ 93.57 Hounsfield units (HU) (P = 0.004, 66.67% sensitivity, 73.33% specificity, 92.00% PPV, and 32.35% NPV), and NME lesions with calcification (P = 0.002, 36.23% sensitivity, 20.00% specificity, 82.14% PPV, and 67.57% NPV). The fitting equation for the combined diagnostic model was as follows: Logit (P) = -0.579 +1.318 × enhancement distribution + 1.000 × internal enhancement patterns + 1.539 × ΔHU value + 1.641 ×NME type.

CONCLUSION

Individual diagnostic criteria based on breast CBBCT characteristics (segmental enhancement distribution, clumped internal enhancement patterns, ΔHU values > 93.57 HU, and NME lesions with calcification) had high specificity and PPV; when combined, they had high sensitivity in predicting malignant NME lesions.

Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy

PURPOSE

Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies.

EXPERIMENTAL DESIGN

This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10-3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis.

RESULTS

There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282).

CONCLUSIONS

An ADC cutoff of ≥1.5 × 10-3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.

Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI

PURPOSE

To compare diffusion-weighted imaging of the breast performed with a conventional readout-segmented echo-planar imaging (rs-EPI) sequence to when using a prototype simultaneous multi-slice single-shot EPI (SMS-ss-EPI) acquisition.

METHOD

From September 2017 to December 2018, 26 women with histologically proven breast cancer were scanned with the conventional rs-EPI and the SMS-ss-EPI at 3 T during the same imaging examination. Four breast radiologists (4-13 years of experience) independently scored both acquired series of 25 women (one case was used for training) for overall image quality (1: extremely poor to 9: excellent) and artifacts (1: very disturbing to 5: not present). All lesions (n = 52; 40 malignant, 12 benign) were also evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible). In addition, lesion characteristics were rated, and a BI-RADS score was given. Results were analyzed using visual grading characteristics and the resulting area under the curve (AUC_VGC), weighted kappa, McNemar test, and dependent-samples t-test when appropriate.

RESULTS

Overall, radiologists significantly preferred the image quality in rs-EPI over that of SMS-ss-EPI (AUC_VGC: 0.698, P = 0.002). Infolding and ghosting, and distortion artifacts were significantly less apparent in the rs-EPI (AUC_VGC: 0.660, P = 0.022 and AUC_VGC: 0.700 P = 0.002, respectively). Lesions were, however, significantly better visible on the SMS-ss-EPI images (AUC_VGC: 0.427, P = 0.016). Malignant lesions had significantly higher visibility with SMS-ss-EPI (P = 0.035). Sensitivity and specificity were comparable between both sequences (P = 0.760 and P = 0.549, respectively).

CONCLUSIONS

Despite the perceived lower image quality and the increased presence of artifacts in the SMS-ss-EPI sequence, malignant lesions are better visualized using this sequence.

Qualitative characterization of breast tumors with diffusion-weighted imaging has comparable accuracy to quantitative analysis

PURPOSE

To evaluate the applicability and accuracy of a new qualitative diffusion-weighted imaging (DWI) assessment method in the characterization of breast tumors compared to quantitative ADC measurement and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

After review board approval, MRIs of 216 consecutive women with final diagnoses (131 malignant, 85 benign) were retrospectively analyzed. Two radiologists independently scored DWI and dynamic contrast-enhanced MRI (DCE-MRI) according to malignancy probability. Qualitative assessments were performed by combined analysis of tumor morphology and diffusion signal. Quantitative data was obtained from apparent diffusion coefficient (ADC) measurements. Lastly, descriptive DWI features were evaluated and recorded. Cohen's kappa, receiver operating characteristic and multivariate analyzes were applied.

RESULTS

Of malignant tumors, 97% were visible on DWI. Qualitative and quantitative DWI assessments provided comparable sensitivities of 89-94% and 88-92% and specificities of 51-61% and 59-67%, respectively. There was no statistical difference between the accuracies of qualitative and quantitative DWI (p ≥ 0.105). Best diagnostic values were obtained with DCE-MRI (sensitivity, 99-100%; specificity, 69-71%). Inter-reader agreement was moderate (kappa = 0.597) for qualitative DWI and substantial (kappa = 0.689) for DCE-MRI (p < 0.001). Agreement between qualitative DWI and DCE-MRI scores was moderate (kappa = 0.536 and 0.442). Visual diffusion signal, mass margin and shape were the most predictive features of malignancy on multivariate analysis of qualitative assessment.

CONCLUSION

Qualitative characterization of breast tumors on DWI has comparable accuracy to quantitative ADC analysis. This method might be used to make DWI more widely available with eliminating the need to a predetermined ADC threshold in tumor characterization. However, lower accuracy and inter-reader agreement of it compared to DCE-MRI should be considered.

Diffusion weighted imaging of the breast: Performance of standardized breast tumor tissue selection methods in clinical decision making

Objectives

In breast diffusion weighted imaging (DWI) protocol standardization, it is recently shown that no breast tumor tissue selection (BTTS) method outperformed the others. The purpose of this study is to analyze the feasibility of three fixed-size breast tumor tissue selection (BTTS) methods based on the reproducibility, accuracy and time-measurement in comparison to the largest oval and manual delineation in breast diffusion weighted imaging data.

Methods

This study is performed with a consecutive dataset of 116 breast lesions (98 malignant) of at least 1.0 cm, scanned in accordance with the EUSOBI breast DWI working group recommendations. Reproducibility of the maximum size manual (BTTS1) and of the maximal size round/oval (BTTS2) methods were compared with three smaller fixed-size circular BTTS methods in the middle of each lesion (BTTS3, 0.12 cm³ volume) and at lowest apparent diffusion coefficient (ADC) (BTTS4, 0.12 cm³; BTTS5, 0.24 cm³). Mean ADC values, intraclass-correlation-coefficients (ICCs), area under the curve (AUC) and measurement times (sec) of the 5 BTTS methods were assessed by two observers.

Results

Excellent inter- and intra-observer agreement was found for any BTTS (with ICC 0.88–0.92 and 0.92–0.94, respectively). Significant difference in ADCmean between any pair of BTTS methods was shown (p = <0.001–0.009), except for BTTS2 vs. BTTS3 for observer 1 (p = 0.10). AUCs were comparable between BTTS methods, with highest AUC for BTTS2 (0.89–0.91) and lowest for BTTS4 (0.76–0.85). However, as an indicator of clinical feasibility, BTTS2-3 showed shortest measurement times (10–15 sec) compared to BTTS1, 4–5 (19–39 sec).

Conclusion

The performance of fixed-size BTTS methods, as a potential tool for clinical decision making, shows equal AUC but shorter ADC measurement time compared to manual or oval whole lesion measurements. The advantage of a fixed size BTTS method is the excellent reproducibility. A central fixed breast tumor tissue volume of 0.12 cm³ is the most feasible method for use in clinical practice.

Comparison of Diagnostic Efficacy Between Contrast-Enhanced Ultrasound and DCE-MRI for Mass- and Non-Mass-Like Enhancement Types in Breast Lesions

Background

Contrast-enhanced ultrasound (CEUS) can provide angiogenesis information about breast lesions; however, its diagnostic performance in comparison with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has not been systematically investigated. This study aimed to evaluate the diagnostic efficacy of CEUS and DCE-MRI in mass-like and non-mass-like enhancement types of breast lesions.

Material and Methods

A retrospective study was conducted on 252 patients with breast lesions who underwent CEUS and DCE-MRI before surgery between January 2016 and February 2020. Histopathological results were used as reference standards. All patients were classified into mass-like and non-mass-like enhancement lesion groups. The mass-like lesion group was further divided into three categories according to different sizes (group 1: <10 mm, group 2: 10-20 mm, and group 3: >20 mm). Sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic curve were analyzed to assess the diagnostic performance of these two modalities.

Results

For mass-like breast lesions, DCE-MRI (Az=0.981) manifested better diagnostic performance than CEUS (Az=0.940) in medium-sized (10-20 mm) tumors (Z=2.018, P=0.043), but both had similar diagnostic performance in smaller (<10 mm) and larger (>20 mm) tumors (P=0.717, P=0.394). For non-mass-like enhancement lesions, CEUS and DCE-MRI showed no significant difference (Z=1.590, P=0.119) and revealed good diagnostic performance (Az=0.859, Az=0.947) in differentiating the two groups.

Conclusion

For mass-like breast lesions, DCE-MRI showed better diagnostic performance than CEUS in differentiating benign and malignant tumors of medium-sizes (10-20mm) but not of smaller (<10mm) and larger (>20 mm) sizes. For non-mass-like lesions, both modalities showed similar diagnostic performance.

MRI-guided breast biopsy based on diffusion-weighted imaging: a feasibility study

Objectives

This study evaluated the feasibility of DWI for lesion targeting in MRI-guided breast biopsies. Furthermore, it assessed device positioning on DWI during biopsy procedures.

Methods

A total of 87 biopsy procedures (5/87 bilateral) consecutively performed between March 2019 and June 2020 were retrospectively reviewed: in these procedures, a preliminary DWI sequence (b = 1300 s/mm²) was acquired to assess lesion detectability. We included 64/87 procedures on lesions detectable at DWI; DWI sequences were added to the standard protocol to localize lesion and biopsy device and to assess the site marker correct positioning.

Results

Mass lesions ranged from 5 to 48 mm, with a mean size of 10.7 mm and a median size of 8 mm. Non-mass lesions ranged from 7 to 90 mm, with a mean size of 33.9 mm and a median size of 31 mm. Positioning of the coaxial system was confirmed on both T1-weighted and DWI sequences. At DWI, the biopsy needle was detectable in 62/64 (96.9%) cases; it was not visible in 2/64 (3.1%) cases. The site marker was always identified using T1-weighted imaging; a final DWI sequence was acquired in 44/64 cases (68.8%). In 42/44 cases (95.5%), the marker was recognizable at DWI.

Conclusions

DWI can be used as a cost-effective, highly reliable technique for targeting both mass and non-mass lesions, with a minimum size of 5 mm, detectable at pre-procedural DWI. DWI is also a feasible technique to localize the biopsy device and to confirm the deployment of the site marker.

Key Points

• MRI-guided breast biopsy is performed in referral centers by an expert dedicated staff, based on prior MR imaging; contrast agent administration is usually needed for lesion targeting.

• DWI represents a feasible, highly reliable technique for lesion targeting, avoiding contrast agent administration.

• DWI allows a precise localization of both biopsy needle device and site marker.

Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI

Objectives

To assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values.

Methods

Eighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2–5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm² images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1–3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1–5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI.

Results

Sixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200–1500 s/mm² were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm² values allowed the visualization of 84–90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm². DWI was more specific (86.3% vs 65.7%, p < 0.001) but less sensitive (62.8% vs 90%, p < 0.001) and accurate (71% vs 80.7%, p = 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases.

Conclusion

The addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite.

Key Points

• The addition of synthetic b-values (1200–1500 s/mm²) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts.

• Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection.

• A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07094-z) contains supplementary material, which is available to authorized users.

Diffusion-weighted MRI at 3.0 T for detection of occult disease in the contralateral breast in women with newly diagnosed breast cancer

PURPOSE

Diffusion-weighted magnetic resonance imaging (DW-MRI) offers unenhanced method to detect breast cancer without cost and safety concerns associated with dynamic contrast-enhanced (DCE) MRI. Our purpose was to evaluate the performance of DW-MRI at 3.0T in detection of clinically and mammographically occult contralateral breast cancer in patients with unilateral breast cancer.

METHODS

Between 2017 and 2018, 1130 patients (mean age 53.3 years; range 26-84 years) with newly diagnosed unilateral breast cancer who underwent breast MRI and had no abnormalities on clinical and mammographic examinations of contralateral breast were included. Three experienced radiologists independently reviewed DW-MRI (b = 0 and 1000 s/mm²) and DCE-MRI and assigned a BI-RADS category. Using histopathology or 1-year clinical follow-up, performance measures of DW-MRI were compared with DCE-MRI.

RESULTS

A total of 21 (1.9%, 21/1130) cancers were identified (12 ductal carcinoma in situ and 9 invasive ductal carcinoma; mean invasive tumor size, 8.0 mm) in the contralateral breast. Cancer detection rate of DW-MRI was 13-15 with mean of 14 per 1000 examinations (95% confidence interval [CI] 9-23 per 1000 examinations), which was lower than that of DCE-MRI (18-19 with mean of 18 per 1000 examinations, P = 0.01). A lower abnormal interpretation rate (14.0% versus 17.0%, respectively, P < 0.001) with higher specificity (87.3% versus 84.6%, respectively, P < 0.001) but lower sensitivity (77.8% versus 96.8%, respectively, P < 0.001) was noted for DW-MRI compared to DCE-MRI.

CONCLUSIONS

DW-MRI at 3.0T has the potential as a cost-effective tool for evaluation of contralateral breast in women with newly diagnosed breast cancer.

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

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.