Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification

BACKGROUND

Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear.

PURPOSE

To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status.

STUDY TYPE

Retrospective.

POPULATION

954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88).

FIELD STRENGTH/SEQUENCE

3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images.

ASSESSMENT

Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance.

STATISTICAL TESTS

Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result.

RESULTS

In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35.

DATA CONCLUSION

Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Characteristics of Suspicious Breast Lesions Visible Only on MR Imaging: Is It Possible to Classify into Immediate Biopsy and Careful Observation Groups?

PURPOSE

To investigate the characteristics of suspicious MRI-only visible lesions and to explore the validity of subcategorizing these lesions into the following two groups: lesions that would require immediate biopsy (4Bi) and lesions for which careful clinical follow-up could be recommended (4Fo).

METHODS

A retrospective review of 108 MRI-only visible lesions in 106 patients who were diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 between June 2018 and June 2022 at our institution was performed by two radiologists. The breast MR images were evaluated according to BI-RADS and additional MRI descriptors (linear ductal, branching, and apparent diffusion coefficient values). The lesions were categorized by previously reported classification systems, and the positive predictive values (PPVs) for the different categories were determined and compared. Subsequently, a new classification system was developed in this study.

RESULTS

The total malignancy rate was 31% (34/108). No significant differences between benign and malignant lesions were identified for focus and mass lesions. For non-mass lesions, linear ductal and heterogeneous internal enhancement suggested a benign lesion (P = 0.0013 and P = 0.023, respectively), and branching internal enhancement suggested malignancy (P = 0.0066). Segmental distribution suggested malignancy (P = 0.0097). However, the PPV of segmental distribution with heterogeneous enhancement was significantly lower than that of category 4 segmental lesions with other enhancement patterns (11% vs. 59%; P = 0.0198).As a new classification, the distribution of focal, linear, and segmental was given a score of 0, 1, or 2, and the internal enhancement of heterogeneous, linear-ductal, clumped, branching, and clustered-ring enhancement was given a score of 0, 1, 2, 3, and 4, respectively. When categorized using a scoring system, a statistically significant difference in PPV was observed between 4Fo (n = 27) and 4Bi (n = 33) (7% vs. 61%, P = 0.000029).

CONCLUSION

The new classification system was found to be highly capable of subcategorizing BI-RADS category 4 MRI-only visible non-mass lesions into 4Fo and 4Bi.

High Peritumoral and Intratumoral T2 Signal Intensity in HER2-Positive Breast Cancers on Preneoadjuvant Breast MRI: Assessment of Associations With Histopathologic Characteristics

BACKGROUND. Intratumoral necrosis and peritumoral edema are features of aggressive breast cancer that may present as high T2 signal intensity (T2 SI). Implications of high T2 SI in HER2-positive cancers are unclear. OBJECTIVE. The purpose of this study was to assess associations with histopathologic characteristics of high peritumoral T2 SI and intratumoral T2 SI of HER2-positive breast cancer on MRI performed before initiation of neoadjuvant therapy. METHODS. This retrospective study included 210 patients (age, 24-82 years) with 211 HER2 breast cancers who, from January 1, 2015, to July 30, 2022, underwent breast MRI before receiving neoadjuvant therapy. Two radiologists independently assessed cancers for high peritumoral T2 SI and high intratumoral T2 SI on fat-suppressed T2-weighted imaging and classified patterns of high peritumoral T2 SI (adjacent to tumor vs prepectoral extension). A third radiologist resolved discrepancies. Multivariable logistic regression analyses were performed to identify associations of high peritumoral and intratumoral T2 SI with histopathologic characteristics (associated ductal carcinoma in situ, hormone receptor status, histologic grade, lymphovascular invasion, and axillary lymph node metastasis). RESULTS. Of 211 HER2-positive cancers, 81 (38.4%) had high peritumoral T2 SI, and 95 (45.0%) had high intratumoral T2 SI. A histologic grade of 3 was independently associated with high peritumoral T2 SI (OR = 1.90; p = .04). Otherwise, none of the five assessed histopathologic characteristics were independently associated with high intratumoral T2 SI or high peritumoral T2 SI (p > .05). Cancers with high T2 SI adjacent to the tumor (n = 29) and cancers with high T2 SI with prepectoral extension (n = 52) showed no significant difference in frequency for any of the histopathologic characteristics (p > .05). Sensitivities and specificities for predicting the histopathologic characteristics ranged from 35.6% to 43.7% and from 59.7% to 70.7%, respectively, for high peritumoral T2 SI, and from 37.3% to 49.6% and from 49.3% to 62.7%, respectively, for high intratumoral T2 SI. Interreader agreement was almost perfect for high peritumoral T2 SI (Gwet agreement coefficient [AC] = 0.93), high intratumoral T2 SI (Gwet AC = 0.89), and a pattern of high peritumoral T2 SI (Gwet AC = 0.95). CONCLUSION. The only independent association between histopathologic characteristics and high T2 SI of HER2-positive breast cancer was observed between a histologic grade of 3 and high peritumoral T2 SI. CLINICAL IMPACT. In contrast with previously reported findings in broader breast cancer subtypes, peritumoral and intratumoral T2 SI had overall limited utility as prognostic markers of HER2-positive breast cancer.

Diagnostic validity of abbreviated breast MRI in the diagnosis of breast cancer: a comparative study to the full breast MRI protocol using BI-RADS

Purpose

This work aimed to determine the diagnostic performance of the magnetic resonance imaging (MRI) breast abbreviated protocol (AP) in diagnosing malignant breast lesions using BI-RADS compared with the diagnostic accuracy of the full diagnostic protocol (FDP).

Material and methods

A prospective single-centre study was conducted. A total of 125 female patients with suspicious breast masses underwent MRI with the AP and the FDP. The images of AP and FDP were independently interpreted by 2 radiologists with 10 years of experience in breast imaging, and any disagreement was resolved with a third one. Using the histopathological examination as a reference test, the diagnostic effectiveness of both FDP and AP in breast cancer screening was calculated. ROC curve was utilised to estimate the optimal BI-RADS cut-off for prediction of malignancy. The difference in image interpretation time between both protocols was estimated using the Mann-Whitney test. Moreover, the inter-test agreement between both protocols was assessed using Cohen's κ test.

Results

The study included 83 malignant and 42 benign lesions. AP indicated a specificity, sensitivity, and accuracy of 90.5%, 96.4%, and 94.4%, while the FDP showed a specificity, sensitivity, and accuracy of 92.9%, 100%, and 97.6%, respectively. BI-RADS 3 category was the best cut-off for prediction of malignancy. There was a significant difference between both protocols concerning the interpretation time (p < 0.001). There was excellent agreement between both protocols, with a κ of 0.915.

Conclusions

Breast MRI AP may be employed instead of FDP to identify breast cancer with similar diagnostic performance. Moreover, it reduces the interpretation time and the scan cost.

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy

BACKGROUND

Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST).

PURPOSE

To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST.

STUDY TYPE

Prospective.

POPULATION

Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR).

FIELD STRENGTH/SEQUENCE

3.0 T/reduced field of view single-shot echo-planar DTI sequence.

ASSESSMENT

Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery.

STATISTICAL TESTS

Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant.

RESULTS

47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s).

DATA CONCLUSION

Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 4.

Outcomes of High-Risk Breast MRI Screening in Women Without Prior History of Breast Cancer: Effectiveness Data from a Tertiary Care Center

OBJECTIVE

To evaluate the diagnostic performance outcomes of a breast MRI screening program in high-risk women without prior history of breast cancer.

METHODS

Retrospective cohort study of 1 405 consecutive screening breast MRI examinations in 681 asymptomatic women with high risk of breast cancer without prior history of breast cancer from January 1, 2015, to December 31, 2019. Outcomes (sensitivity, specificity, positive predictive value, negative predictive value, false-negative rate [FNR], cancer detection rate [CDR]) and characteristics of cancers were determined based on histopathology or 12-month follow-up. MRI examinations performed, BI-RADS assessments, pathology outcomes, and CDRs were analyzed overall and by age decade. Results in incidence screening round (MRI in last 18 months) and nonincidence round were compared.

RESULTS

Breast MRI achieved CDR 20/1000, sensitivity 93.3% (28/30), and specificity 83.4% (1 147/1375). Twenty-eight (28/1 405, CDR 20/1000) screen-detected cancers were identified: 18 (64.3%, 18/28) invasive and 10 (35.7%, 10/28) ductal carcinoma in situ. Overall, 92.9% (26/28) of all cancers were stage 0 or 1 and 89.3% (25/28) were node negative. All 14 incidence screening round malignancies were stage 0 or 1 with N0 disease. Median size for invasive carcinoma was 8.0 mm and for ductal carcinoma in situ was 9.0 mm. There were two false-negative exams for an FNR 0.1% (2/1 405).

CONCLUSION

High-risk screening breast MRI was effective at detecting early breast cancer and associated with favorable outcomes.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning

BACKGROUND

Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated.

PURPOSE

To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences.

STUDY TYPE

Retrospective.

POPULATION

A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female).

FIELD STRENGTH/SEQUENCE

T1-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) with gradient echo sequences, T2-weighted imaging (T2WI) with spin-echo sequences, diffusion-weighted imaging with single-shot echo-planar sequence and at 1.5-T.

ASSESSMENT

A convolutional neural network and long short-term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI-RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE-MRI and non-DCE sequences, respectively.

STATISTICAL TESTS

Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P-value <0.05 was considered statistically significant.

RESULTS

With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE-MRI, the DL-based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE-MRI/T2WI alone, respectively.

DATA CONCLUSION

The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent-free combination is comparable to DCE-MRI alone and the radiologists' reading in AUC and sensitivity.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Economic evaluation of breast MRI in screening - a systematic review and basic approach to cost-effectiveness analyses

Background

Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening.

Purpose

To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening.

Materials and methods

The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed.

Results

Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes.

Conclusion

Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue.

Additional occult cancers identified on staging breast MRI: imaging appearances and pathologic characteristics

INTRODUCTION

Breast magnetic resonance imaging (MRI) is increasingly being used for staging of patients with breast cancer due to its high sensitivity in detecting additional cancers (ACs). However, the clinical impact of diagnosing and treating these cancers remains unclear.

METHODS

A retrospective study was undertaken of patients with newly diagnosed breast cancer who underwent staging MRI at The American University of Beirut Medical Centre (AUBMC) between 2012 and 2020. Pathology reports and breast MRI examinations were reviewed. Eighteen breast cancer patients with 19 pathology-proven index cancers (ICs) and 19 pathology-proven MRI-detected ACs were included. Chi-square and Fisher's exact tests for categorical variables and Wilcoxon signed rank test for numerical variables were used to compare ICs to ACs.

RESULTS

The ICs consisted of four ductal carcinoma in situ (DCIS), 13 invasive ductal carcinomas (IDC), of which five with associated DCIS, and two invasive lobular carcinomas, (ILC) of which one with associated DCIS. ACs comprised 12 DCIS, five IDC, two with associated DCIS and two ILC, one with associated DCIS. Interval cancers were more frequently invasive whereas ACs were more frequently in situ (P = 0.021). ACs were more frequently nuclear grade 2 (P = 0.009). There was no statistically significant difference between ICs and ACs in lesion type (P = 0.062), shape (P = 0.073), initial enhancement (P = 1), delayed enhancement (P = 0.732), hormonal receptor profile (P = 0.68) and Ki67 (P = 0.388). Among ACs, ten (53%) were larger than 10 mm of which five (26%) were invasive cancers, and five (26%) were larger than the ICs.

CONCLUSIONS

ACs detected by breast MRI were more likely to be in situ and to show a nuclear grade 2. Although not reaching statistical significance, some ACs tend to be clinically significant by their type, size or nuclear grade. The impact on clinical management remains to be determined.

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study

BACKGROUND

While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions.

PURPOSE

To assess the value and robustness of deep learning-derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy.

STUDY TYPE

Retrospective.

SUBJECTS

Training cohort: 102 consecutive female patients with LABC scheduled for neoadjuvant chemotherapy (NAC) from a single institution (age: 25-73 years). Independent testing cohort: 55 consecutive female patients with LABC from four institutions (age: 25-72 years).

FIELD STRENGTH/SEQUENCE

Training cohort: single vendor 1.5 T or 3.0 T. Testing cohort: multivendor 3.0 T. Gradient echo dynamic contrast-enhanced sequences.

ASSESSMENT

A convolutional neural network (nnU-Net) was trained to segment LABC. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)-0/I vs. RCB-II/III. An independent model was developed using functional tumor volume (FTV). Models were tested on an independent testing cohort and response assessment performance and robustness across multiple institutions were assessed.

STATISTICAL TESTS

The receiver operating characteristic (ROC) was used to calculate the area under the ROC curve (AUC). DeLong's method was used to compare AUCs. Correlations were calculated using Pearson's method. P values <0.05 were considered significant.

RESULTS

Automated segmentation resulted in a median (interquartile range [IQR]) Dice score of 0.87 (0.62-0.93), with similar volumetric measurements (R = 0.95, P < 0.05). Automated volumetric measurements were significantly correlated with FTV (R = 0.80). Tumor volume-derived from deep learning of DCE-MRI was associated with RCB, yielding an AUC of 0.76 to discriminate between RCB-0/I and RCB-II/III, performing similar to the FTV-based model (AUC = 0.77, P = 0.66). Performance was comparable across institutions (IQR AUC: 0.71-0.84).

DATA CONCLUSION

Deep learning-based segmentation estimates changes in tumor load on DCE-MRI that are associated with RCB after NAC and is robust against variations between institutions.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 4.

The Impact of Preoperative Breast MRI on Timing of Surgical Management in Newly Diagnosed Breast Cancer

Purpose: Preoperative breast MRI has been recommended at our center since 2016 for invasive lobular carcinoma and cancers in dense breasts. This study examined how preoperative breast MRI impacted surgical timing and outcomes for patients with newly diagnosed breast cancer. Methods: Retrospective single-center study of consecutive women diagnosed with new breast cancer between June 1, 2019, and March 1, 2021, in whom preoperative breast MRI was recommended. MRI, tumor histology, breast density, post-MRI biopsy, positive predictive value of biopsy (PPV3), surgery, and margin status were recorded. Time from diagnosis to surgery was compared using t-tests. Results: There were 1054 patients reviewed, and 356 were included (mean age 60.9). Of these, 44.4% (158/356) underwent preoperative breast MRI, and 55.6% (198/356) did not. MRI referral was more likely for invasive lobular carcinoma, multifocal disease, and younger patients. Following preoperative MRI, 29.1% (46/158) patients required additional breast biopsies before surgery, for a PPV3 of 37% (17/46). The time between biopsy and surgery was 55.8 ± 21.4 days for patients with the MRI, compared to 42.8 ± 20.3 days for those without (P < .00001). MRI was not associated with the type of surgery (mastectomy vs breastconserving surgery) (P = .44) or rate of positive surgical margins (P = .52). Conclusion: Among patients who underwent preoperative breast MRI, we observed significant delays to surgery by almost 2 weeks. When preoperative MRI is requested, efforts should be made to mitigate associated delays.

Feasibility of online non-rigid motion correction for high-resolution supine breast MRI

PURPOSE

Conventional breast MRI is performed in the prone position with a dedicated coil. This allows high-resolution images without breast motion, but the patient position is inconsistent with that of other breast imaging modalities or interventions. Supine breast MRI may be an interesting alternative, but respiratory motion becomes an issue. Motion correction methods have typically been performed offline, for instance, the corrected images were not directly accessible from the scanner console. In this work, we seek to show the feasibility of a fast, online, motion-corrected reconstruction integrated into the clinical workflow.

METHODS

Fully sampled T2 -weighted (T2 w) and accelerated T1 -weighted (T1 w) breast supine MR images were acquired during free-breathing and were reconstructed using a non-rigid motion correction technique (generalized reconstruction by inversion of coupled systems). Online reconstruction was implemented using a dedicated system combining the MR raw data and respiratory signals from an external motion sensor. Reconstruction parameters were optimized on a parallel computing platform, and image quality was assessed by objective metrics and by radiologist scoring.

RESULTS

Online reconstruction time was 2 to 2.5 min. The metrics and the scores related to the motion artifacts significantly improved for both T2 w and T1 w sequences. The overall quality of T2 w images was approaching that of the prone images, whereas the quality of T1 w images remained significantly lower.

CONCLUSION

The proposed online algorithm allows a noticeable reduction of motion artifacts and an improvement of the diagnostic quality for supine breast imaging with a clinically acceptable reconstruction time. These findings serve as a starting point for further development aimed at improving the quality of T1 w images.

Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging

Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction. Studies were performed using data collected from phantoms, 20 retrospectively collected SOC patient exams, and 56 prospectively acquired SOC and HR patient exams. Image quality was quantitatively assessed via signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Qualitatively, all in vivo images were scored by either two or four radiologist readers using 5-point Likert scales in the following categories: artifacts, perceived sharpness, perceived SNR, and overall quality. Differences in reader scores were tested for significance. Reader preference and perception of signal intensity changes were also assessed. Application of the DL resulted in higher average SNR (1.2-2.8 times), CNR (1.0-1.8 times), and image sharpness (1.2-1.7 times). Qualitatively, the SOC acquisition with DL resulted in significantly improved image quality scores in all categories compared to non-DL images. HR acquisition with DL significantly increased SNR, sharpness, and overall quality compared to both the non-DL SOC and the non-DL HR images. The acquisition time for the HR data only required a 20% increase compared to the SOC acquisition and readers typically preferred DL images over non-DL counterparts. Overall, the DL reconstruction demonstrated improved T2w image quality in clinical breast MRI.

Reducing False Negatives in Biopsy of Suspicious MRI Findings

Breast MRI is a highly sensitive imaging modality that often detects findings that are occult on mammography and US. Given the overlap in appearance of benign and malignant lesions, an accurate method of tissue sampling for MRI-detected findings is essential. Although MRI-directed US and correlation with mammography can be helpful for some lesions, a correlate is not always found. MRI-guided biopsy is a safe and effective method of tissue sampling for findings seen only on MRI. The unique limitations of this technique, however, contribute to false negatives, which can result in delays in diagnosis and adverse patient outcomes; this is of particular importance as most MRI examinations are performed in the high-risk or preoperative setting. Here, we review strategies to minimize false negatives in biopsy of suspicious MRI findings, including appropriate selection of biopsy modality, use of meticulous MRI-guided biopsy technique, management after target nonvisualization, assessment of adequate lesion sampling, and determination of radiology-pathology concordance. A proposed management algorithm for MRI-guided biopsy results will also be discussed.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer

Purpose

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.

Experimental design

Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.

Results

Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.

Conclusion

The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR

Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.

A Diagnostic Dilemma: New Enhancing Suspicious Findings on Breast MRI Following Neoadjuvant Chemotherapy

OBJECTIVE

Evaluate the incidence and outcome of new enhancing findings on breast MRI after neoadjuvant chemotherapy (NAC).

METHODS

This IRB-approved retrospective review included women with breast cancer undergoing MRI to evaluate NAC response at our institution from January 1, 1998 to March 3, 2021. Post-NAC MRIs given BI-RADS 4 or 5 with new enhancing findings were identified. Patients were excluded if they lacked pretreatment MRI or insufficient follow-up, or if the finding was a satellite of the primary tumor. Medical records and imaging studies were reviewed to identify patients and to find characteristics and outcomes.

RESULTS

Over the study period, 2880 post-NAC breast MRIs were performed. Of 128 post-NAC MRIs given BI-RADS 4 or 5 (4.4%), 35 new suspicious findings were found on 32 MRIs, incidence rate 1.1% (32/2880). Most were characterized as nonmass enhancement (17/35, 49%), followed by mass (11/35, 31%), and then focus (7/35, 20%), with an average maximum dimension of 1.3 cm (range 0.3-7.1 cm). New findings were ipsilateral to the index cancer in 20/35 (57%) of cases. Of the 35 suspicious findings, 22 underwent image-guided biopsy (62%), 1 was surgically excised (3%), 7 underwent mastectomy (20%), 5 were stable or resolved on follow-up (8%), and none were malignant. Thirty-three were benign (94%), and two were benign high-risk lesions (atypical ductal hyperplasia, radial scar) (6%).

CONCLUSION

New suspicious breast MRI findings after NAC are uncommon with a low likelihood of malignancy. Further study is warranted using multi-institutional data for this low incidence finding.

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

Assessment, Complications, and Surveillance of Breast Implants: Making Sense of 2022 FDA Breast Implant Guidance

As more information about the potential risks and complications related to breast implants has become available, the United States Food and Drug Administration (FDA) has responded by implementing changes to improve patient education, recalling certain devices and updating the recommendations for screening for silicone implant rupture. In addition to staying up-to-date with FDA actions and guidance, radiologists need to maintain awareness about the types of implants they may see, breast reconstruction techniques including the use of acellular dermal matrix, and the multimodality imaging of implants and their complications. Radiologists should also be familiar with some key differences between the updated FDA guidelines for implant screening and the imaging recommendations from the American College of Radiology Appropriateness Criteria. The addition of US as an acceptable screening exam for silicone implant rupture by the FDA is one of the most notable changes that has potentially significant implications.

Circumscribed Masses on Breast MRI: Can MRI Features Guide Management?

OBJECTIVE

Management of circumscribed breast masses seen on MRI is largely extrapolated from mammography and US data with limited MRI-specific data available. This study aimed to assess clinical and MRI imaging features of malignant circumscribed breast masses.

METHODS

In this IRB-approved retrospective study, breast MRIs performed between April 1, 2008, and August 30, 2020, containing circumscribed masses, excluding multiple bilateral circumscribed masses, were reviewed. Clinical and imaging features of all eligible masses were recorded, and associations with malignant outcomes were assessed using Fisher's exact test and Wilcoxon rank sum test, with P < 0.05 considered significant.

RESULTS

For the 165 masses that met study criteria in 158 women, the mean age was 48 years (SD 12.0 years). Nine of 165 masses were malignant (5.5%). Round masses were significantly more likely to be malignant (7/37, 18.9%) compared to oval masses (2/128, 1.7%) (P < 0.001). Among masses with available dynamic contrast kinetics data, the malignancy rate was 0/84 (0%) for persistent kinetics, 2/23 (8.7%) for plateau kinetics, and 4/24 (16.7%) for washout kinetics (P = 0.002). The malignancy rate for oval masses without washout kinetics was 0% (0/92). T2 hyperintense masses had a malignancy rate of 7/104 (6.7%), and homogeneously enhancing masses had a malignancy rate of 5/91 (5.5%).

CONCLUSION

These data support the use of mass shape and dynamic contrast enhancement kinetics to guide management of circumscribed breast masses seen by MRI, with oval masses without washout kinetics and any circumscribed mass with persistent kinetics showing no malignancies in this study.

Optimal treatment strategy for hormone receptor-positive human epidermal growth factor receptor 2-negative breast cancer patients with 1-2 suspicious axillary lymph node metastases on breast magnetic resonance imaging: upfront surgery vs. neoadjuvant chemotherapy

Background

It is unclear whether upfront surgery or neoadjuvant chemotherapy is appropriate for first treatment in hormone receptor (HR)-positive human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients with 1-2 suspicious axillary lymph node (ALN) metastases on preoperative breast magnetic resonance imaging (MRI).

Method

We identified 282 patients with HR+HER2- breast cancer and 1-2 suspicious ALN metastases on baseline breast MRI (147 received upfront surgery; 135 received neoadjuvant chemotherapy). We evaluated the predictive clinicopathological factors for pN2-3 in the adjuvant setting and axillary pathologic complete response (pCR) in the neoadjuvant setting.

Results

Lymphovascular invasion (LVI)-positive and clinical tumors >3 cm were significantly associated with pN2-3 in patients who received upfront surgery. The pN2-3 rate was 9.3% in patients with a clinical tumor ≤ 3 cm and LVI-negative versus 34.7% in the others (p < 0.001). The pN2-3 rate in patients with a clinical tumor ≤ 3 cm and LVI-negative and in the others were 9.3% versus 34.7% in all patients (p < 0.001), 10.7% versus 40.0% (p = 0.033) in patients aged < 50 years, and 8.5% versus 31.0% in patients aged ≥ 50 years (p < 0.001), respectively. In the neoadjuvant setting, patients with tumor-infiltrating lymphocytes (TILs) ≥ 20% had a higher axillary pCR than those with TILs < 20% (46.7% vs. 15.3%, p < 0.001). A similar significant finding was also observed in patients < 50 years.

Conclusions

Upfront surgery may be preferable for patients aged ≥ 50 years with a clinical tumor < 3 cm and LVI-negative, while neoadjuvant chemotherapy may be preferable for those aged < 50 years with TILs ≥ 20%.

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was -2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.

Pathologic Tumor Size versus Mammography, Sonography, and MRI in Breast Cancer Based on Pathologic Subtypes

INTRODUCTION

The standard of care for imaging of breast pathology has historically been mammography and sonography. MRI is a modern adjunct in the surgeon's toolkit. We looked to examine the differences in imaging modalities and their ability to predict the size in relation to the pathologic size after excision with focus on pathologic subtypes.

METHODS

We analyzed patient records across a 4-year period from 2017 to 2021 who were treated surgically for breast cancer at our facility. We used a retrospective chart review to collect measurements that were recorded of the tumors by the radiologist for available mammography, ultrasound, and MRI which were compared to pathology report measurements of the final specimens. We subdivided the results by pathologic subtypes including invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and ductal carcinoma in situ (DCIS).

RESULTS

658 total patients met criteria for analysis. Mammography overestimated specimens with DCIS by 1.93 mm (P = .15), US underestimated by .56 (.55), and MRI overestimated by 5.77 mm (P < .01). There was no statistically significant difference in any modalities with IDC. With specimens of ILC, all 3 imaging modalities underestimated tumor size, with only US being significant.

DISCUSSION

Mammography and MRI consistently overestimated tumor size with the exception of ILC while US underestimated tumor size on all pathologic subtypes. MRI significantly overestimated tumor size in DCIS by 5.77 mm. Mammography was the most accurate imaging modality for all pathologic subtypes and never had a statistically significant difference from actual tumor size.

Patterns of Screening Mammography and Breast MRI During the COVID-19 Pandemic: A Retrospective, Chart-Review Study

Objective

This study examined patterns of breast cancer screening during the COVID-19 pandemic.

Methods

This retrospective study was approved by the Georgetown University IRB. Review of electronic medical records identified screening mammograms and breast MRIs between March 13, 2018 and December 31, 2020, for female patients aged 18 to 85 years. Descriptive statistics characterized patterns of breast cancer screening before and during the COVID-19 pandemic. Logistic regression analyses examined whether receipt of breast MRI differed over time and demographic and clinical factors associated with receipt of breast MRI in 2020.

Results

Data included 47 956 mammography visits in 32 778 patients and 407 screening breast MRI visits in 340 patients. After an initial decrease following the declaration of the COVID-19 pandemic, both screening mammograms and screening breast MRI demonstrated early recovery. Although the mammography receipt remained sustained, the receipt of screening breast MRI decreased in late 2020. Odds of having a breast MRI did not differ between 2018 and 2019 (OR = 1.07; 95% CI = 0.92%-1.25%; P = 0.384) but were significantly lower in 2020 versus 2019 (OR = 0.76; 95% CI = 0.61%-0.94%; P = 0.011). No demographic or clinical factors were associated with receipt of breast MRI during the COVID-19 pandemic (all P-values ≥0.225).

Conclusion

Breast cancer screening decreased following the declaration of the COVID-19 pandemic. Although both procedures demonstrated early recovery, the rebound in screening breast MRI was not sustained. Interventions promoting return to screening breast MRI may be needed for high-risk women.

Imaging of COVID-19 Vaccine-Related Axillary Lymphadenopathy: Initial Outcomes Based on US Features of Axillary Lymph Nodes

OBJECTIVE

The purpose of this study is to describe the imaging characteristics and outcomes of COVID-19 vaccine-related axillary adenopathy and subsequent follow-up.

METHODS

This was an IRB-approved, retrospective study of patients with imaging evidence of axillary lymphadenopathy who had received at least one dose of a COVID-19 vaccine and presented between January 1, 2021, and February 28, 2021. Sonographic cortical thickness and morphology was evaluated. A mixed effects model was used to model lymph node cortical thickness decrease over time.

RESULTS

A total of 57 women were identified with lymphadenopathy and a COVID vaccination during the study period with 51 (89.5%) women completing imaging surveillance or undergoing tissue sampling of a lymph node. Three women (5.9%) were diagnosed with metastatic breast cancer to an axillary node. There was a statistically significant correlation with cortical thickness at initial US evaluation and malignancy (7.7 mm [SD ± 0.6 mm] for metastatic nodes and 5 mm [SD ± 2 mm] for benign nodes, P = 0.02). Suspicious morphological features (effacement of fatty hilum, P = 0.02) also correlated with malignancy. Time to resolution of lymphadenopathy can be prolonged with estimated half-life of the rate of decrease in cortical thickness modeled at 77 days (95% CI, 59-112 days). Diffuse, smooth cortical thickening over 3 mm was the most common lymph node morphology.

CONCLUSION

Malignant lymph node morphology and cortical thickness best predicted malignancy. Benign hyperplastic lymph nodes were the most common morphology observed after COVID-19 vaccination. Lymphadenopathy after vaccination is slow to resolve.

Quadrature transceive wireless coil: Design concept and application for bilateral breast MRI at 1.5 T

PURPOSE

Development of a novel quadrature inductively driven transceive wireless coil for breast MRI at 1.5 T.

METHODS

A quadrature wireless coil (HHMM-coil) design has been developed as a combination of two linearly polarized coils: a pair of 'metasolenoid' coils (MM-coil) and a pair of Helmholtz-type coils (HH-coil). The MM-coil consisted of an array of split-loop resonators. The HH-coil design included two electrically connected flat spirals. All the wireless coils were coupled to a whole-body birdcage coil. The HHMM-coil was studied and compared to the linear coils in terms of transmit and SAR efficiencies via numerical simulations. A prototype of HHMM-coil was built and tested on a 1.5 T scanner in a phantom and healthy volunteer. We also proposed an extended design of the HHMM-coil and compared its performance to a dedicated breast array.

RESULTS

Numerical simulations of the HHMM-coil with a female voxel model have shown more than a 2.5-fold increase in transmit efficiency and a 1.7-fold enhancement of SAR efficiency compared to the linearly polarized coils. Phantom and in vivo imaging showed good agreement with the numerical simulations. Moreover, the HHMM-coil provided good image quality, visualizing all areas of interest similar to a multichannel breast array with a 32% reduction in signal-to-noise ratio.

CONCLUSION

The proposed quadrature HHMM-coil allows the B 1 + $$ {\mathrm{B}}_1^{+} $$ -field to be significantly better focused in the region-of-interest compared to the linearly polarized coils. Thus, the HHMM-coil provides high-quality breast imaging on a 1.5 T scanner using a whole-body birdcage coil for transmit and receive.

Apparent Diffusion Coefficient Reproducibility Across 3 T Scanners in a Breast Diffusion Phantom

BACKGROUND

To date, the accuracy and variability of diffusion-weighted MRI (DW-MRI) metrics have been reported in a limited number of scanner/protocol/coil combinations.

PURPOSE

To evaluate the reproducibility of DW-MRI estimates across multiple scanners and DW-MRI protocols and to assess the effects of using an 8-channel vs. 16-channel breast coil in a breast phantom.

STUDY TYPE

Prospective.

PHANTOM

Breast phantom containing tubes of water and differing polyvinylpyrrolidone (PVP) concentrations with apparent diffusion coefficients (ADCs) matching breast tissue.

FIELD STRENGTH/SEQUENCE

3 T (three standard and one wide bore), three DW-MRI single-shot echo planar imaging protocols of varying acquired spatial resolution.

ASSESSMENT

Accuracy of estimated ADCs was assessed using percent differences (PD) relative to nuclear magnetic resonance spectroscopy-derived reference values. Coefficients of variation (CV) were calculated to determine variation across scanners. CVs based on the median standard deviation (CV_M ) were used to evaluate tube-specific dispersion using 8- or 16-channel coils at a single scanner. ADCs of PVP-containing tubes were additionally normalized by the median water tube ADC to account for temperature effects.

STATISTICAL TESTS

Two-way repeated measures analysis of variance and post hoc tests were used to evaluate differences in ADC, CV, and CV_M across scanners and protocols (α = 0.05).

RESULTS

ADCs were within 11% (interquartile range [IQR] 7%) of reference values and significantly improved to 2% (IQR 7%) after normalization to an internal water reference. Normalization significantly reduced interscanner variability of ADC estimates from 7% to 4%. DW-MRI protocol did not affect ADC accuracy; however, the clinical and higher-resolution clinical protocols resulted in the greatest (9%) and least (6%) interscanner variability, respectively. The 8- and 16-channel receive coils yielded similar accuracy (PD: 12% vs. 16%) and precision (CV_M : 2.7% vs. 2.9%).

DATA CONCLUSION

Normalization by an internal reference improved interscanner ADC reproducibility. High-resolution protocols yielded comparably accurate and significantly less variable ADCs compared to a clinical standard protocol.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

A Retrospective Analysis of Sternal Lesions Detected on Breast MRI in Patients Without History of Cancer

OBJECTIVE

To determine the imaging characteristics and stability over time of sternal lesions identified on breast MRI in patients without history of cancer.

METHODS

An IRB-approved retrospective analysis of all breast MRIs performed at our institution from September 1, 2017 to December 1, 2021 that included one of several key words related to the sternum. Studies with history of non-dermatologic malignancy including breast cancer, absence of a true sternal lesion, or presence of symptoms during the examination were excluded. Imaging was reviewed for size, distribution, signal characteristics, and presence of contrast enhancement, perilesional edema, periosteal edema, or intralesional fat. Available comparison imaging, clinical history, and follow-up recommendations were reviewed. Descriptive statistics were used to summarize lesion data.

RESULTS

Of 60 lesions included from 60 patients, 40 lesions with more than two years of comparison imaging were either stable or decreased in size and none demonstrated change in signal characteristics. The majority of these presumed benign lesions demonstrated hypointense signal on T1-weighted sequences (21/40, 52.5%), hyperintense signal on fluid-sensitive sequences (33/40, 82.5%), contrast enhancement (32/40, 80.0%), and absence of clear intralesional fat (29/40, 72.5%). One patient who did not have comparison imaging was diagnosed with malignancy (multiple myeloma) eight months following their MRI. This lesion demonstrated uniquely diffuse and heterogeneous enhancement but did not undergo biopsy.

CONCLUSION

Sternal lesions in women without history of non-dermatologic malignancy have a very low likelihood of malignancy. Common imaging characteristics of the presumed benign lesions can inform imaging recommendations when incidental sternal lesions are discovered.

Effect of an Educational Intervention on Women's Health Care Provider Knowledge Gaps About Breast Cancer Risk Model Use and High-risk Screening Recommendations

OBJECTIVE

To assess effectiveness of a web-based educational intervention on women's health care provider knowledge of breast cancer risk models and high-risk screening recommendations.

METHODS

A web-based pre- and post-test study including 177 U.S.-based women's health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated.

RESULTS

Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced.

CONCLUSION

Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

Utility of MRI-Directed Contrast-Enhanced Mammography for Biopsy Planning for Suspicious MRI-Detected Breast Lesions

BACKGROUND. Suspicious lesions detected on contrast-enhanced breast MRI often undergo targeted ultrasound evaluation to determine whether they are amenable to ultrasound-guided biopsy. OBJECTIVE. The purpose of this study is to assess the utility of MRI-directed contrast-enhanced mammography (CEM) performed for biopsy planning for suspicious MRI-detected breast lesions and to compare its use with that of MRI-directed ultrasound. METHODS. This retrospective study included 120 patients (median age, 50.3 years) who underwent MRI-directed CEM from September 2014 to July 2020 for biopsy planning for a total of 140 suspicious breast MRI lesions; 109 lesions were also evaluated by MRI-directed ultrasound at the same visit. The reference standard was histopathology or at least 2 years of imaging follow-up for benign lesions. Rates of detecting a correlate for the MRI lesion, among all lesions and among malignant lesions, were compared between MRI-directed CEM, MRI-directed ultrasound, and combined MRI-directed CEM and ultrasound (i.e., with the correlate detected on either modality), by use of the McNemar test. The frequencies with which imaging modalities were used for biopsy guidance after MRI-directed imaging were determined. RESULTS. Twenty-three of 109 lesions were malignant. The lesion detection rate was higher for MRI-directed CEM than for MRI-directed ultrasound (69.7% [76/109] vs 45.9% [50/109]; p < .001) and higher for combined MRI-directed CEM and ultrasound (77.1% [84/109]) than for either MRI-directed CEM (p = .008) or MRI-directed ultrasound (p < .001). The rate of detection of malignant lesions was not significantly different between MRI-directed CEM and MRI-directed ultrasound (95.7% [22/23] vs 78.3% [18/23]; p = .13). A total of 31.2% (34/109) of lesions were seen on MRI-directed CEM only, and 7.3% (8/109) were seen on MRI-directed ultrasound only. A total of 17.4% (4/23) of malignant lesions were seen on MRI-directed CEM only, and none were seen on MRI-directed ultrasound only. Among lesions recommended for biopsy, stereotactic- or tomosynthesis-guided biopsy was recommended for 25.2% (26/103), ultrasound-guided biopsy for 35.9% (37/103), and MRI-guided biopsy for 38.8% (40/103). CONCLUSION. MRI-directed CEM detects a higher fraction of suspicious MRI lesions than does MRI-directed ultrasound. Combined MRI-directed CEM and ultrasound detects a higher fraction than either method does individually. CLINICAL IMPACT. MRI-directed CEM may be a useful alternate or complementary tool to MRI-directed ultrasound in biopsy planning for suspicious MRI lesions, facilitating the use of biopsy guidance methods other than MRI guidance.

Factors Influencing Residual Glandular Breast Tissue after Risk-Reducing Mastectomy in Genetically Predisposed Individuals Detected by MRI Mammography

PURPOSE

This study seeks to evaluate MR imaging morphological factors and other covariates that influence the presence of residual glandular tissue after risk-reducing mastectomy in patients with a familial predisposition.

METHODS

We analyzed women of a high-risk collective with pathogenic mutation (BRCA1 (n = 49), BRCA2 (n = 24), or further mutation (n = 9)). A total of 117 breasts were analyzed, 63 left and 54 right, from a cohort of 81 patients, who were on average 40 years old. The mean follow-up was 63 months (range 12-180 months, SD = 39.67). Retrospective analysis of MR imaging data from 2006-2022 of patients of a high-risk collective (all carriers of a pathogenic mutation) with contralateral (RRCM) or bilateral risk-reducing mastectomy (RRBM) was performed. In the image data the remaining skin flap thickness by distance measurements at eight equally distributed, clockwise points and the retromamillary area, as well as by volumetry of each breast, was elected. Residual glandular tissue was also volumetrized. In addition, patient-related covariates were recorded and their influence on postoperative residual glandular tissue and skin flap thickness was analyzed by uni- and multivariate regressions.

RESULTS

A significant association with postoperative residual glandular tissue was shown in multivariate analysis for the independent variables breast density, skin flap mean, and surgical method (all p-values < 0.01). A negatively significant association could be seen for the variables preoperative breast volume (p-values < 0.01) and surgeon experience (most p-values < 0.05-<0.1).

CONCLUSION

Postoperative residual glandular tissue is an important tool for quantifying the risk of developing breast cancer after risk-reducing mastectomy. Different effects on residual glandular tissue were shown for the independent variables breast density, skin flap, surgical method, preoperative breast volume, and surgeon experience, so these should be considered in future surgical procedures preoperatively as well as postoperatively. Breast MRI has proven to be a suitable method to analyze the skin flap as well as the RGT.

Classifying Breast Cancer Metastasis Based on Imaging of Tumor Primary and Tumor Biology

The molecular classification of breast cancer has allowed for a better understanding of both prognosis and treatment of breast cancer. Imaging of the different molecular subtypes has revealed that biologically different tumors often exhibit typical features in mammography, ultrasound, and MRI. Here, we introduce the molecular classification of breast cancer and review the typical imaging features of each subtype, examining the predictive value of imaging with respect to distant metastases.

Pseudoangiomatous stromal hyperplasia of the breast, imaging and clinical perspective: A review

Pseudoangiomatous stromal hyperplasia (PASH) is a benign breast pathology, which most commonly presents incidentally along with other breast pathologies. The etiology and pathogenesis of PASH are still unknown; however, there is some evidence suggesting PASH is hormone dependent. The clinical history, presentation, and imaging appearance of PASH are variable. Clinically, PASH has a wide spectrum of presentations, from being silent to gigantomastia. On imaging, PASH demonstrates various benign to suspicious features. Here we summarize PASH's clinical presentation, histopathology, imaging features, and management.

Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis

Background: To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced breast magnetic resonance imaging (breast MRI). Like breast MRI, CEM enables tumour visualization by contrast accumulation. CEM seems to be a viable substitute for breast MRI. Methods: This systematic search assessed the diagnostic accuracy of these techniques in women with suspicious breast lesions on prior imaging or physical examination, who have undergone both breast MRI and CEM. CEM had to be performed on a commercially available system. The MRI sequence parameters had to be described sufficiently to ensure that standard breast MRI sequence protocols were used. Pooled values of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR), were estimated using bivariate mixed-effects logistic regression modeling. Hierarchical summary receiver operating characteristic curves for CEM and breast MRI were also constructed. Results: Six studies (607 patients with 775 lesions) met the predefined inclusion criteria. Pooled sensitivity was 96% for CEM and 97% for breast MRI. Pooled specificity was 77% for both modalities. DOR was 79.5 for CEM and 122.9 for breast MRI. Between-study heterogeneity expressed as the I2 -index was substantial with values over 80%. Conclusion: Pooled sensitivity was high for both CEM and breast MRI, with moderate specificity. The pooled DOR estimates, however, indicate higher overall diagnostic performance of breast MRI compared to CEM. Nonetheless, current scientific evidence is too limited to prematurely discard CEM as an alternative for breast MRI.

Injection mammoplasty: Normal imaging appearances, complications, and implications for mammographic screening

BACKGROUND

The normal imaging appearances of the common agents used in injection mammoplasty and the challenges of mammography screening will be reviewed.

METHODS

The local database from a tertiary hospital was accessed for imaging cases of injection mammoplasty.

RESULTS

Free silicone is seen as multiple high-density opacities on mammograms. Silicone deposits can often be seen within axillary nodes due to lymphatic migration. Sonographically, a snowstorm appearance is seen when the silicone is diffusely distributed. On MRI, free silicone is hypointense on T1-weighted and hyperintense on T2-weighted images, with no contrast enhancement. Mammograms have a limited role in screening due to the high density of silicone. MRI is often required in these patients.Polyacrylamide gel and hyaluronic acid are seen as multiple collections on mammography. Polyacrylamide gel collections are of the same density as cysts, while hyaluronic acid collections are of higher density but less dense than silicone. On ultrasound, both can appear anechoic or show variable internal echoes. MRI demonstrates fluid signal with hypointense T1-weighted and hyperintense T2-weighted signal. Mammographic screening is possible if the injected material is located predominantly in the retro-glandular space without obscuring the breast parenchyma.On mammograms, autologous fat locules appear as lucent masses. Rim calcification can be seen if fat necrosis had developed. On ultrasound, focal fat collections can demonstrate varying levels of internal echogenicity, depending on the stage of fat necrosis. Mammographic screening is usually possible for patients after autologous fat injection as fat is hypodense compared to breast parenchyma. However, the dystrophic calcification associated with fat necrosis may mimic abnormal breast calcification. In such cases, MRI can be utilized as a problem-solving tool.

CONCLUSION

It is important for the radiologist to recognize the type of injected material on the various imaging modalities and recommend the best modality for screening.

Adenoid Cystic Carcinoma of the Breast: Radiologic-Pathologic Correlation

Adenoid cystic carcinoma (ACC) is an uncommon malignancy occurring most frequently in the salivary glands. Breast ACC is rare. Pain is common at the site of ACC; otherwise, presentation is similar to other primary breast cancers. Adenoid cystic carcinomas classically lack calcifications; the imaging manifestations of ACC are otherwise highly variable, likely related to multiple pathologic growth patterns. While ACC in other regions of the body tends to be more aggressive, ACC involving the breast typically has less aggressive biologic characteristics. Classic-type breast ACC has a lower tendency to recur locally with radiation, metastasize to regional lymph nodes, and spread to other parts of the body. Perineural spread of disease can be seen but is not common. The rarer solid basaloid-type has a higher tendency for local or distant spread and recurrence. Although ACC is usually triple receptor-negative (estrogen receptor, progesterone receptor, human epidermal growth factor-2 receptor), the indolent nature of this tumor dictates its management. With classic-type ACC, the inclusion of axillary surgery has no consensus and the use of chemotherapy or hormonal therapy is rare. Axillary nodal surgery and chemotherapy are often included in management of the more aggressive solid basaloid-type. An understanding of the breast imaging, histopathology, and clinical course is key for appropriate treatment and follow-up of ACC.

Imaging Surveillance Options for Individuals With a Personal History of Breast Cancer: AJR Expert Panel Narrative Review

Annual surveillance mammography is recommended for breast cancer survivors on the basis of observational studies and meta-analyses showing reduced breast cancer mortality and improved quality of life. However, breast cancer survivors are at higher risk of subsequent breast cancer and have a fourfold increased risk of interval breast cancers compared with individuals without a personal history of breast cancer. Supplemental surveillance modalities offer increased cancer detection compared with mammography alone, but utilization is variable, and benefits must be balanced with possible harms of false-positive findings. In this review, we describe the current state of mammographic surveillance, summarize evidence for supplemental surveillance in breast cancer survivors, and explore a risk-based approach to selecting surveillance imaging strategies. Further research identifying predictors associated with increased risk of interval second breast cancers and development of validated risk prediction tools may help physicians and patients weigh the benefits and harms of surveillance breast imaging and decide on a personalized approach to surveillance for improved breast cancer outcomes.

Feasibility Study and Clinical Impact of Incorporating Breast Tissue Density in High-Risk Breast Cancer Screening Assessment

Breast tissue density (BTD) is known to increase the risk of breast cancer but is not routinely used in the risk assessment of the population-based High-Risk Ontario Breast Screening Program (HROBSP). This prospective, IRB-approved study assessed the feasibility and impact of incorporating breast tissue density (BTD) into the risk assessment of women referred to HROBSP who were not genetic mutation carriers. All consecutive women aged 40-69 years who met criteria for HROBSP assessment and referred to Genetics from 1 December 2020 to 31 July 2021 had their lifetime risk calculated with and without BTD using Tyrer-Cuzick model version 8 (IBISv8) to gauge overall impact. McNemar's test was performed to compare eligibility with and without density. 140 women were referred, and 1 was excluded (BRCA gene mutation carrier and automatically eligible). Eight of 139 (5.8%) never had a mammogram, while 17/131 (13%) did not have BTD reported on their mammogram and required radiologist review. Of 131 patients, 22 (16.8%) were clinically impacted by incorporation of BTD: 9/131 (6.9%) became eligible for HROBSP, while 13/131 (9.9%) became ineligible (p = 0.394). It was feasible for the Genetics clinic to incorporate BTD for better risk stratification of eligible women. This did not significantly impact the number of eligible women while optimizing the use of high-risk supplemental MRI screening.

Bone Lesions Detected on Breast MRI: Clinical Outcomes and Features Associated with Metastatic Breast Cancer

Objective

To determine prevalence and frequency of malignancy among bone lesions detected on breast MRI and to identify clinical and imaging features associated with bone metastases from breast cancer (BC), as bone lesions are suboptimally evaluated on breast imaging protocols and can present a diagnostic challenge.

Methods

This IRB-approved retrospective review of breast MRIs performed from June 2009 to June 2018 identified patients with bone lesions. Demographic, clinical, and MRI features were reviewed. Clinical outcome of bone lesions was determined based on pathology and/or additional diagnostic imaging. All benign lesions had ≥ 2 years of imaging follow-up. Statistics were computed with Fisher's exact and Wilcoxon rank sum tests.

Results

Among all patients with breast MRI, 1.2% (340/29 461) had bone lesions. Of these, 224 were confirmed benign or metastatic BC by pathology or imaging follow-up, with 70.1% (157/224) be- nign and 29.9% (67/224) metastatic. Bone metastases were associated with BC history (P < 0.001), with metastases occurring in 58.2% (53/91) of patients with current BC, 17.9% (14/78) patients with prior BC, and 0.0% (0/55) without BC. Bone metastases were associated with invasive and ad- vanced stage BC and, on MRI, with location in sternum, ribs, or clavicles, larger size, multiplicity, andT1 hypointensity (all P < 0.01 in tests of overall association).

Conclusion

Of clinically confirmed breast MRI-detected bone lesions, 30% were bone metastases; all were detected in patients with current or prior BC. Metastases were associated with advanced stage, invasive carcinoma, larger lesion size, multiplicity, low T1 signal, and non-spine location.

Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

BACKGROUND

To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes.

METHODS

A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T₁ -weighted imaging (T₁ C), Apparent diffusion coefficient (ADC), and T₂ -weighted imaging (T₂ W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance.

RESULTS

For the separation of each subtype from other subtypes on the testing set, the T₁ C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T₂ W-based models achieved AUCs from 0.639 to 0.697.

CONCLUSION

T₁ C-based models performed better than ADC-based models and T₂ W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.

Women's Breast Cancer Screening Confidence by Screening Modality and Breast Density: A Breast Cancer Surveillance Consortium Survey Study

Objective: Little is known about women's confidence in their breast cancer screening. We sought to characterize breast cancer screening confidence by imaging modality and clinically assessed breast density. Materials and Methods: We undertook a cross-sectional survey of women ages 40-74 years who received digital mammography (DM), digital breast tomosynthesis (DBT), and/or breast magnetic resonance imaging (MRI) with a normal screening exam in the prior year. The main outcome was women's confidence (Very, Somewhat, A little, Not at all) in their breast cancer screening detecting any cancer. Multivariable logistic regression identified correlates of being very confident in breast cancer screening by screening modality group: Group 1) DM vs. DBT and Group 2) DM or DBT alone vs. with supplemental MRI. Results: Overall, 2329 of 7439 (31.3%) invitees participated, with 30%-61% being very confident in their screening across modality and density subgroups. Having dense versus nondense breasts was associated with lower odds of being very confident (Group 1: odds ratio [OR]: 0.58; 95% confidence interval [CI]: 0.46-0.79; Group 2: OR: 0.56; 95% CI: 0.40-0.79). There were no differences by modality within Group 1, but for Group 2, women undergoing MRI had higher odds of being very confident (OR: 1.69; 95% CI: 1.21-2.37). Other correlates of greater screening confidence were as follows: Group 1-being offered a screening test choice and cost not influencing modality received, and Group 2-decision satisfaction and worry. Conclusions: Women with dense breasts had lower screening confidence regardless of screening modality and those undergoing MRI had higher confidence regardless of density. The importance of informing women about screening options is underscored by observed associations between screening choice, decision satisfaction, and screening confidence. ClinicalTrials.gov: NCT02980848.

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist

BACKGROUND

Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations.

PURPOSE

To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations.

STUDY TYPE

Retrospective.

POPULATION

Consecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal).

FIELD STRENGTH/SEQUENCE

A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging.

ASSESSMENT

Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards.

STATISTICAL TESTS

Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025).

RESULTS

The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs.

DATA CONCLUSION

Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 3.

The Usefulness of Magnetic Resonance Imaging (MRI) for the Detection of Local Recurrence after Mastectomy with Reconstructive Surgery in Breast Cancer Patients

The purpose of this study is to investigate the usefulness of magnetic resonance imaging (MRI) for the detection of local recurrence after nipple-sparing mastectomy (NSM) or skin-sparing mastectomy (SSM) with immediate reconstructive surgery for breast cancer. Two hundred and eighty-six NSM or SSM procedures and immediate reconstruction cases between August 2015 and February 2020 were reviewed. The detectability rates of for local recurrence using MRI and ultrasound were assessed, and the characteristics of recurrent and primary cancers were evaluated. The patients with multifocal or multicentric primary cancer and a dense parenchymal pattern showed a higher recurrence rate (p < 0.001). A total of 22 cases showed recurrence, and due to multifocal recurrence, a total of 27 recurrent lesions were identified in the reconstructed breast, of which 12 were symptomatic and 15 were asymptomatic (p < 0.001). With the exception of skin recurrence (n = 6), MRI showed a significantly higher detectability rate (95.2%, 20 of 21) than ultrasound (38.1%, 8 of 21) for the recurrence of cancer in the reconstructed breast (p < 0.001), especially for small-sized (<1 cm) asymptomatic lesions. In addition, the mean recurrence interval of MRI-detected asymptomatic lesions was 21.7 months (SD ± 17.7), which was significantly longer than that of symptomatic recurrence. In conclusion, postoperative MRI can be useful for identifying small-sized (<1 cm) asymptomatic recurrence lesions in reconstructed breast tissue after NSM or SSM, which can be implemented within two years of surgery.

Shedding Light on T2 Bright Masses on Breast MRI: Benign and Malignant Causes

While T2 hyperintense masses on breast MRI are often benign, there are several malignant etiologies that can also be T2 hyperintense. Delineation between benign and malignant entities is important for the accurate interpretation of breast MRI. Common benign T2 hyperintense masses include cysts, fibroadenomas, and lymph nodes. Malignant processes that are T2 hyperintense include metastatic lymph nodes, mucinous breast carcinomas, papillary breast carcinomas, and breast cancers with central necrosis. Evaluation of the morphology and enhancement pattern of a T2 hyperintense mass can help to differentiate a benign process from a malignant one. This educational review will present both benign and malignant causes of T2 hyperintense masses on breast MRI and review common imaging findings and pertinent imaging characteristics that can be used to help accurately identify benign entities while also recognizing suspicious lesions that require additional evaluation.

Use of Diagnostic Imaging Modalities in Modern Screening, Diagnostics and Management of Breast Tumours 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

Breast radiologists and nuclear medicine specialists updated their previous recommendation/guidance at the 4th Hungarian Breast Cancer Consensus Conference in Kecskemét. A recommendation is hereby made that breast tumours should be screened, diagnosed and treated according to these guidelines. These professional guidelines include the latest technical developments and research findings, including the role of imaging methods in therapy and follow-up. It includes details on domestic development proposals and also addresses related areas (forensic medicine, media, regulations, reimbursement). The entire material has been agreed with the related medical disciplines.

The Challenge of Digital Breast Tomosynthesis-Detected Architectural Distortion of the Breast: Inter-reader Variability and Imaging Characteristics That May Improve Positive Predictive Value

OBJECTIVE

To compare readers' performances when detecting architectural distortion (AD) on digital breast tomosynthesis (DBT). To determine the risk of malignancy of DBT with synthetic mammogram (SM)-detected AD and evaluate imaging features that are associated with malignancy risk.

METHODS

This IRB-approved retrospective review included all cases of DBT-detected AD that were recommended for biopsy from October 2013 to July 2019. Cases were reviewed by three breast radiologists and the overall agreement between radiologists was calculated. Medical records were reviewed for pathological outcomes and imaging findings. Statistical analyses used were Cohen's kappa and its 95% confidence interval, and one-way analysis of variance.

RESULTS

A total of 172 lesions were included. The overall agreement for the presence of AD in our study was fair (0.253). The majority (20/36, 55.5%) of the malignant ADs were associated with asymmetries (13/36, 36.1%), calcifications (4/36, 11.1%), or both (3/36, 8.3%), compared to nonmalignant ADs (40/136, 31.0%; P = 0.038). The positive predictive value (PPV) of DBT with SM-detected AD for malignancy was 21.8% (36/165), 18.8% (18/96) for DBT-detected AD, and 26.0% (18/69) for SM-detected AD, although the difference was not statistically significant (P = 0.258). A breast MRI correlate was identified for all malignant AD lesions (17/17, 100.0%; P = 0.004).

CONCLUSION

The detection of AD remains a challenging task for radiologists, with moderate-to-fair interobserver agreement. With a PPV for malignancy of 21.8%, percutaneous biopsy and subsequent pathology-imaging correlation are necessary for AD to exclude the possibility of malignancy.

Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2- Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy

Objective

To quantitatively evaluate intratumoral habitats on dynamic contrast-enhanced (DCE) breast MRI to predict pathologic breast cancer response to stereotactic ablative body radiotherapy (SABR).

Methods

Participants underwent SABR treatment (28.5 Gy x3), baseline and post-SABR MRI, and breast-conserving surgery for ER/PR+ HER2- breast cancer. MRI analysis was performed on DCE T1-weighted images. MRI voxels were assigned eight habitats based on high (H) or low (L) maximum enhancement and the sequentially numbered dynamic sequence of maximum enhancement (H1-4, L1-4). MRI response was analyzed by percent tumor volume remaining (%VR = volume post-SABR/volume pre-SABR), and percent habitat makeup (%HM of habitat X = habitat X voxels/total voxels in the segmented volume). These were correlated with percent tumor bed cellularity (%TC) for pathologic response.

Results

Sixteen patients completed the trial. The %TC ranged 20%-80%. MRI %VR demonstrated strong correlations with %TC (Pearson R = 0.7-0.89). Pre-SABR tumor %HMs differed significantly from whole breasts (P = 0.005 to <0.00001). Post-SABR %HM of tumor habitat H4 demonstrated the largest change, increasing 13% (P = 0.039). Conversely, combined %HM for H1-3 decreased 17% (P = 0.006). This change correlated with %TC (P < 0.00001) and distinguished pathologic partial responders (≤70 %TC) from nonresponders with 94% accuracy, 93% sensitivity, 100% specificity, 100% positive predictive value, and 67% negative predictive value.

Conclusion

In patients undergoing preoperative SABR treatment for ER/PR+ HER2- breast cancer, quantitative MRI habitat analysis of %VR and %HM change correlates with pathologic response.