Non-mass Enhancement Lesions Research Articles

Dynamic contrast-enhanced breast magnetic resonance imaging findings that affect the magnetic resonance-directed ultrasound correlation of non-mass enhancement lesions: a single-center retrospective study.

Our single-center retrospective study aimed to evaluate the relationship between magnetic resonance (MR)-directed ultrasound (MDUS) detectability and MRI findings of non-mass enhancement (NME) lesions, regarding the morphologic and enhancement features, the distance from the skin and nipple, and the presence of concomitant landmarks. A total of 350 MRI-detected NME lesions that were determined between January 2015 and May 2019 and subsequently underwent MDUS were analyzed. The MRI findings, biopsy results, and follow-up outcomes of lesions were recorded. The correlation between the MRI findings of the lesions and MDUS detectability was analyzed. 114 (32.6%) of the 350 lesions had a counterpart in the MDUS. Respectively, 66 (37.9%), 38 (43.2%) and 59 (38.3%) of the lesions detected in MDUS were larger than 20 mm in size, with a distance of less than 20 mm to the nipple and 15 mm to the skin. The lesion size and lesion distance to the nipple and skin were significantly associated with a ultrasound correlate (p < 0.05). The MDUS detection rate was significantly higher in NME lesions with MR findings including diffuse distribution (p < 0.001), clustered-ring enhancement pattern (p < 0.001), washout kinetic curve (p = 0.006), and MR-BIRADS category 5 (p < 0.001). Multivariate logistic regression showed that only the clustered-ring enhancement pattern was significantly associated with an MDUS correlation (p < 0.001). Statistically significant correlations were found between the size, distance to the nipple and skin, distribution pattern, enhancement pattern and kinetic curve of the NME lesions on MRI and ultrasound detectability. We found that clustered-ring enhancement patterns were significantly more frequent in MR-directed ultrasound detectable lesions.

Management of Non-Mass Enhancement at Breast Magnetic Resonance in Screening Settings Referred for Magnetic Resonance-Guided Biopsy.

Rationale and Objectives:According to the Breast Imaging and Reporting Data System (BI-RADS), one of the main limitations of MRI is diagnosing the non-mass enhancement (NME). The NME lesion is challenging since it is unique to the MRI lexicon. This study aims to report our experience with NME lesions diagnosed by MRI referred for MRI-guided biopsies and discuss the management and follow-up of these lesions.Materials and Methods:We retrospectively evaluated all MRI-guide breast biopsies. We included all patients referred for NME breast MRI-guided biopsy in screening settings. All patients had a negative second-look mammography or ultrasonography. We correlated the distribution and internal enhancement pattern (IEP) of the NME lesions with histology. Invasive ductal carcinomas (IDC) of no special type and ductal carcinoma in situ (DCIS) were considered malignant lesions.Results:From January-2018 to July-2021, we included 96 women with a total of 96 lesions in the study. There were 90 benign and 6 malignant lesions with DCIS prevalence (5/6 cancers). The most frequent benign lesion type was fibrocystic changes. There were no NME lesions with diffuse or multiple area distribution features referred to MRI-guided biopsy. The positive-predictive values (PPV) were respectively 0.0%, 2.5%, 9.0%, and 11.0% for linear, focal, regional, and segmental distribution describers, and 0.0, 3.0%, 7.9%, and 50% for homogenous, heterogeneous, clumped, and clustered-ring enhancement patterns.Conclusion:We observe the high potential risk for malignancy in the clustered-ring enhancement followed by the clumped pattern. Segmental distribution presented the highest predictive-positive values.

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

PurposeWe 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).MethodsIn 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.ResultsThe 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.ConclusionsOur 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.

Diagnostic value of mammography for accompanying non-mass enhancement on preoperative breast MRI.

Successful surgical treatment for localized breast cancer can depend on accurate diagnosis for accompanying non-mass enhancement (NME) on preoperative breast magnetic resonance imaging (MRI). To evaluate the diagnostic value of mammography for accompanying NME adjacent to index cancer on preoperative breast MRI. Among 569 consecutive patients who underwent preoperative breast MRI from January 2016 to August 2018 for ultrasound-guided biopsy-proven breast cancer, 471 patients who underwent initial mammography and subsequent surgery were finally included. Two radiologists retrospectively reviewed preoperative MRI findings of the 471 patients and detected accompanying NME adjacent to index cancer. MRI, mammography, and histopathology findings of the accompanying NME were evaluated using Pearson's chi-square test, Mann-Whitney U test, and logistic regression analysis. The area under the receiver operating characteristic curve (AUC) of MRI and combined MRI and mammography was calculated in differentiating benign from malignant accompanying NME. The reference standard was surgical pathologic findings. MRI revealed 93 accompanying NME lesions in 92 (19.5%) of the 471 patients, showing 55 (59.1%) malignant and 38 (40.9%) benign lesions. On multivariate analysis, malignant NME lesions were more associated with mammography-positive findings (P = 0.000), clumped or clustered ring internal enhancement (P = 0.015), and extensive intraductal component presence of index tumor (P = 0.007) compared with benign lesions. The AUC increased after correlation with mammography showing 0.649 (95% confidence interval [CI] 0.533-0.765) for MRI and 0.833 (95% CI 0.747-0.919) for combined MRI and mammography. Mammography is valuable in predicting malignancy for accompanying NME on preoperative breast MRI.

Nonmass enhancement lesions of the breast on core needle biopsy: outcomes, frequency of malignancy, and pathologic findings

Nonmass enhancement (NME) on breast magnetic resonance imaging (MRI) is defined as an area whose internal enhancement characteristics can be distinguished from the normal surrounding breast parenchyma, without an associated mass in the Breast Imaging Reporting and Data System lexicon. In this study, we evaluated the pathologic correlates of NME lesions of the breast identified on MRI at our institution, including the frequency of atypical or malignant lesions in the core needle biopsies (CNBs), performed after such a radiologic finding. A retrospective study was performed on all CNBs performed for NME on breast MRI between 2010 and 2019. A total of 443 biopsies from 411 patients were identified, comprising 5.5% of all CNBs over the study period. The pathologic diagnoses were benign in the majority of the biopsies (68.0%), whereas 11.5% and 20.5% of the cases were atypical and malignant lesions, respectively. Of the malignant cases, 69.2% were ductal carcinoma in situ (DCIS) and 30.8% were invasive carcinomas. The most common invasive cancer was invasive ductal carcinoma (50%), followed by invasive lobular carcinoma (39.3%). NME identified on breast MRI carried a significant (32%) risk of atypia and malignancy in our cohort, which confirms that biopsy evaluation of these lesions is warranted. DCIS was the most commonly identified malignancy. Notably, among invasive cancers, invasive lobular carcinoma was identified at a substantially higher frequency that would be expected for that histotype.

Histopathologic Correlates of Nonmass Enhancement Detected by Breast Magnetic Resonance Imaging.

Dynamic, contrast-enhanced magnetic resonance imaging (MRI) is a highly sensitive imaging modality used for screening and diagnostic purposes. Nonmass enhancement (NME) is commonly seen on MRI of the breast. However, the pathologic correlates of NME have not been extensively explored. Consequently, concordance between MRI and pathologic findings in such cases may be uncertain and this uncertainty may cause the need for additional procedures. To examine the histologic alterations that correspond to NME on MRI. We performed a retrospective search for women who underwent breast MRI between March 2014 and December 2016 and identified 130 NME lesions resulting in biopsy. The MRI findings and pathology slides for all cases were reviewed. The follow-up findings on any subsequent excisions were also noted. Among the 130 cases, the core needle biopsy showed 1 or more benign lesions without atypia in 80 cases (62%), atypical lesions in 21 (16%), ductal carcinoma in situ in 22 (17%), and invasive carcinoma in 7 (5%). Review of the imaging features demonstrated some statistically significant differences in lesions that corresponded to malignant lesions as compared with benign alterations, including homogeneous or clumped internal enhancement, type 3 kinetics, and T2 dark signal; however, there was considerable overlap of features between benign and malignant lesions overall. Of 130 cases, 54 (41.5%) underwent subsequent excision with only 6 cases showing a worse lesion on excision. This study illustrates that NME can be associated with benign, atypical, and/or malignant pathology and biopsy remains indicated given the overlap of radiologic features.

Relationship between MRI findings and invasive breast cancer with podoplanin-positive cancer-associated fibroblasts.

Purpose of our study is to assess the relationship between MRI findings and invasive breast cancer (IBC) with cancer-associated fibroblasts (CAFs) that are positive for podoplanin. We retrospectively analyzed the consecutive 109 IBCs. The IBCs were dichotomized as with (+) or without (-) podoplanin-positive CAFs. In MRI analyses, the dichotomized IBCs were compared the lesion to muscle ratio (L/M ratio) in STIR images, the ADC value, the distribution of kinetic parameters, and morphological findings. Of the 109 IBCs, 28 (26%) IBCs had podoplanin(+) CAFs. Compared to the podoplanin(-) group, the podoplanin(+) group tended to have a more malignant pathological status. In the STIR images, the podoplanin(+) group had significantly higher L/M ratio (7.59 vs. 6.55, p = 0.040). In a dynamic study, the podoplanin(+) group had a significantly higher percentage of the washout pattern (42.21% vs. 29.43%, p = 0.045). There were 23 mass lesions and 5 non-mass enhancement (NME) lesions in the podoplanin(+) group, and 69 mass lesions and 12 NME lesions in the podoplanin(-) group. The mass lesions of the podoplanin(-) group had a significantly higher likelihood of showing an irregular shape (n = 47 vs. 8, p = 0.035). The podoplanin(+) group's lesions had a significantly higher likelihood of showing a circumscribed margin (n = 14 vs. 6, p < 0.001) and a rim enhancement (n = 10 vs. 13, p = 0.047). In multivariate analyses, only high nuclear grade was significant predictive value of podoplanin(+) CAFs. Although not significant in multivariate analyses, MRI findings may be used to determine the podoplanin-positive CAF status of invasive breast cancer.

Accurate Estimation of Breast Tumor Size: A Comparison Between Ultrasonography, Mammography, Magnetic Resonance Imaging, and Associated Contributing Factors.

This study aimed to provide further evidence on the accuracy of tumor size estimates and influencing factors. In this cross-sectional study, patients with a biopsy-proven diagnosis of breast cancer referred to our hospital to obtain a preoperative magnetic resonance imaging (MRI) between 2015 and 2016 were included. Data from 76 breast cancer patients with 84 lesions were collected. All participants underwent ultrasonography and MRI, and their mammograms (MGMs) were reevaluated for tumor size estimation. Measurements by the three imaging modalities were compared with the pathologically determined tumor size to assess their accuracy. Influencing factors such as surgical management, molecular and histopathological subtypes, and Breast Imaging Reporting and Data System enhancement types in MRI were also assessed. The rates of concordance with the gold standard were 64.3%, 76.2%, and 82.1% for MGM, ultrasound (US), and MRI measurements, respectively. Therefore, the highest concordance rate was observed in MRI-based estimates. Among the discordant cases, US and MGM underestimation were more prevalent (70%); nevertheless, MRI showed significant overestimation (80%). Tumor size estimates in patients whose MRIs presented with either non-mass enhancement [p=0.030; odds ratio (OR)=17.2; 95% confidence interval (CI): 1.3-225.9] or mass lesion with non-mass enhancement (p=0.001; OR=51.0; 95% CI: 5.0-518.4) were more likely to be discordant with pathological measurements compared with those in cases with only mass lesion on their MRIs. MRI was more accurate than either US or MGM in estimating breast tumor size but had the highest overestimation rate. Therefore, caution should be practiced in interpreting data obtained from subjects whose MRIs present with non-mass enhancement or mass lesion with non-mass enhancement.

Diagnostic accuracy of contrast-enhanced digital mammography in comparison with sonomammography for characterization of focal asymmetries

BackgroundMammographic focal asymmetry represents normal breast tissue, benign, or malignant lesions. Accurate characterization is important for better management. The study evaluates diagnostic accuracy of contrast-enhanced digital mammography (CEDM) for characterization of focal asymmetries seen in 2D mammography.ResultsThe study was done prospectively on 38 females among 360 patients who underwent baseline sonomammographic assessment for diagnostic and screening purposes. Complementary ultrasound was performed only when a finding was detected in cases of screening mammograms. Focal asymmetries were evaluated according to Breast Imaging Reporting and Data System (BI-RADS) lexicon 2013. CEDM was performed and followed by ultrasound (US) guided core biopsy for solid lesions or aspiration for cystic lesions. CEDM processing resulted in recombined image showing enhancing abnormality. Low energy image and recombined image findings were analyzed blindly and classified into focus enhancement, mass enhancement, non-mass enhancement, and non-enhanced lesions. CEDM and sonomammography findings were compared regarding pathological probability and multiplicity. Histopathology was the reference standard.Mass enhancement showed strong correlation with malignant pathology. Non-mass enhancement showed no correlation with particular pathology. All non-enhanced focal asymmetries were benign in pathology or normal tissue. Rim enhancement needed second look ultrasound evaluation. CEDM was superior to sonomammography with higher sensitivity (77.8%, 65.7% respectively), NPV (0.8, 0.6), accuracy (0.6, 0.2) but lower specificity (81.8% vs. 100%). Multiplicity detection by CEDM was 26.3% and by sonomammography was 10.5%.ConclusionCEDM is more accurate than sonomammography in determination of normal tissue, benign, or malignant lesions in cases of mammographic focal asymmetry. CEDM is more accurate in detection of multiplicity. Undesired biopsies were avoidable with proper management of suspicious and malignant lesions.

Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography

IntroductionWe aimed to interpret MR mammography (MRM) using the Kaiser scores for equivocal or inconclusive lesions on mammography (MG). MethodsRetrospective IRB-approved evaluation of 3623 MG for which MRM was deployed as a problem-solving tool, after inclusion-exclusion criteria were met. Three readers with different levels of experience assigned a final score from 1 to 11 based on the previously established tree classification system. Area under the curve (AUC) derived from receiver operating characteristic (ROC) analysis was used to determine the overall diagnostic performance for all lesions and separately for mass and non-mass enhancement. Sensitivity, specificity, and likelihood ratio values were obtained at different cut-off values of >4, > 5, and > 8 to rule in and rule out malignancy. ResultHistopathology of 183 mass and 133 non-mass enhancement (NME) lesions show benign etiology in 95 and malignant in 221. The AUC was 0.796 [0.851 for mass and 0.715 for NME]. Applying the Kaiser score upgraded 202 lesions with correct prediction in 77 %, and downgraded 28 lesions with correct prediction in 60.8 %. Using a score <5 instead of <4 to rule out malignancy improved our diagnostic ability to correctly identify 100 % benign lesions. Applying Kaiser score correctly downgraded 60.8 % (17/28) lesions; thus avoiding biopsies in these. Using a high cut-off value>8 to rule-in malignancy, we correctly identified 59.7 % of lesions with 80 % specificity and positive likelihood ratio of 3. ConclusionThe Kaiser score has clinical translation benefits when used as a problem-solving tool for inconclusive MG findings.

Histologic Correlation of Breast Biopsies Presenting as Non-Mass Enhancing Lesions on Magnetic Resonance Imaging: A Contemporary Single Institutional Series

Abstract Introduction/Objective Non-mass enhancement (NME) in breast tissue is an area of enhancement on MRI that appears distinct from background breast parenchyma, but lacks definitive features of a mass. NME is characterized by its internal enhancement pattern and distribution, and can represent benign, high risk, or malignant pathology. Given this considerable overlap, a core biopsy is often necessary for diagnosis and management. We aimed to elucidate the most frequent histologic findings found on breast biopsies for MRI NME. Methods Using our institutional database we identified 70 female patients with high risk status for breast cancer with MRI screen detected NME (BIRADS-4-suspicious for malignancy) that underwent subsequent biopsy procedure during the period of 01/2016-12/2017. Primary pathologic diagnoses were subcategorized as follows: malignant, atypical, benign mass-like lesions, fibrocystic changes (proliferative, nonproliferative), or “other” primary diagnoses. Results The median age of patients was 48 years (range: 22-76 years). Of the 70 patients, 66 underwent MRI-guided core biopsy, 3 underwent ultrasound-guided core biopsy and 1 underwent excisional biopsy. The primary diagnosis was analyzed. Of these 70 cases, 8 (11.4%) were malignant (7 with ductal carcinoma in situ and 1 with invasive ductal carcinoma), 1 (1.4%) had atypical lobular hyperplasia, and the remaining 61 (87.1%) showed benign findings (36 with fibrocystic changes (FCC), 22 benign mass-like lesions, 3 with other non-specific findings). The FCC were subcategorized as proliferative (usual ductal hyperplasia, columnar cell change, incidental radial scar, incidental intraductal papilloma, sclerosing adenosis, focal pseudoangiomatous stromal hyperplasia (PASH)) or nonprolifeative (stromal fibrosis, duct ectasia, apocrine metaplasia). Majority (61.1%) of FCC were both proliferative and nonproliferative, 22.2% proliferative only and 16.7% nonproliferative only. Benign mass-like lesions included PASH (45.4%), fibroadenomatoid nodule (22.7%), fat necrosis (18.2 %) and remaining had the diagnosis of clustered apocrine cysts, papillomatosis, and radial sclerosing lesion. Conclusion Less than a third of cases showed malignant findings and more than two-third of cases showed benign findings with a high rate of detection of proliferative lesions and PASH.

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

Cone-beam computed tomography (CBBCT) of the breast is emerging as a way of improving breast cancer diagnostic yield. To find characteristics of non-mass enhancement (NME) lesions on breast CBBCT and to identify the characteristics that distinguish malignant and benign lesions. 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. 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. 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.

AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer.

Computer‐aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine‐learning (ML) techniques. In this review article, we describe applications of ML‐based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state‐of‐the‐art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast.Level of Evidence2Technical Efficacy Stage2

MRI-guided core needle biopsy of the breast: Radiology-pathology correlation and impact on clinical management

ObjectiveBreast MRI is used to screen high-risk patients and determine extent of disease in breast cancer (BC) patients. The goal of this study was to determine the pathologic correlates of breast MRI abnormalities biopsied under MRI guidance. MethodsWe retrospectively identified 101 MRI-guided core needle biopsies (CNB) of the breast from 79 women over a 4-year period. MRI-detected lesions biopsied with ultrasound or stereotactic guidance were excluded. MRI studies and pathology were reviewed by breast radiologists and pathologists. ResultsOf the 79 patients, 72 (91%) had a history of prior (n = 13) or concurrent (n = 59) BC. There were 101 MRI abnormalities: 60 (59%) with non-mass enhancement (NME) and 41 (41%) with mass enhancement. Pathology was benign in 83/101 (82%), including in the majority of NME lesions (43/60, 72%). The most common benign findings were: fibrocystic changes (FCC) (49%), sclerosing lesions (13%), and fibroadenoma (FA) (9%). There were 18 (18%) malignant diagnoses: 8 (44%) invasive lobular carcinoma (ILC), 7 (39%) ductal carcinoma in situ (DCIS), and 3 (17%) invasive ductal carcinoma (IDC). Of the 18 malignant diagnoses, 16 (89%) occurred in 14 unique patients with concurrent BC. Based on the malignant MRI-guided CNB, 6 (46%) of these patients had additional (sentinel lymph node biopsy or contralateral breast surgery) or more extensive (wider lumpectomy) surgery. ConclusionIn this series, most MRI-guided CNB of the breast were benign. The vast majority of malignant diagnoses occurred in patients with concurrent BC and frequently resulted in changes in clinical management.

Differentiation between Clinically Noninflammatory Granulomatous Lobular Mastitis and Noncalcified Ductal Carcinoma in situ Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Introduction: Challenges in differentiation between clinically noninflammatory granulomatous lobular mastitis (GLM) and noncalcified ductal carcinoma in situ (DCIS) remain. Objective: To identify the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics contributing to their differential diagnosis. Methods: A total of 33 clinically noninflammatory GLM and 36 noncalcified DCIS were retrospectively analyzed in the study. Internal enhancement of a nonmass enhancement (NME) lesion was divided into clustered enhanced ring (absence/presence), and clustered enhanced ring (presence) was further classified as small and large ring based on the optimal cutoff value. The 5th Breast Imaging and Data System MRI descriptors were used for assessing the other DCE-MRI characteristics. Multivariate analysis including variables with significant differences in univariate analyses was conducted to identify the independent predictors. The discriminative abilities of different predictors and their combination were compared by area under the receiver-operating characteristic curves (AUCs). Results: An NME lesion was seen more commonly in clinically noninflammatory GLM than in noncalcified DCIS (p = 0.003). DCE-MRI characteristics with significant differences in univariate analyses included NME size, clustered enhanced ring (absence/presence), ring size, initial increase and kinetic characteristics for the differentiation between these two entities presenting as NME lesion. Clustered enhanced ring (presence) was further classified as small (≤7 mm) or large ring (>7 mm). Multivariate analysis revealed that internal enhancement and initial increase were identified as significant independent predictors, and the AUC of their combination achieved the highest value of 0.867 (95% CI, 0.748–0.943). Conclusions: An NME lesion with a large ring is more highly suggestive of clinically noninflammatory GLM.

Abstract P6-02-05: Clumped vs non-clumped internal enhancement patterns in linear non-mass enhancement on breast MRI

Abstract Purpose To compare positive predictive values (PPVs) of clumped versus non-clumped (homogenous and heterogenous) internal enhancement on MRI detected linear non-mass enhancement (NME) on MRI-guided vacuum assisted breast biopsy (MRI-VABB). Materials and Methods With IRB approval, radiology reports were retrospectively reviewed of 598 lesions undergoing 9-gauge MRI-VABB from January 2015 to April 2018 for lesions showing linear NME, recording clumped and non-clumped internal enhancement. We reviewed the electronic medical records for MRI-guided VABB pathology, any follow up surgery and imaging and/or clinical follow-up &amp;gt;1 year (mean 1.7 years). The X2 test was performed for univariate analysis. Results There were 120/598 (20%) linear NME MRI-VABB lesions with clumped (52/120, 43%) vs non-clumped (68/120, 57%) internal enhancement, average size 1.8 cm (range 0.3-7.6 cm). On MRI-VABB, cancer was identified in 22/120 (18%) lesions, ductal carcinoma in situ (DCIS) was found in 18/22 (82%) and invasive cancer (3 IDC, 1 ILC) in 4 (18%). 31/120 (26%) were high-risk lesions with 3/31 (10%) high-risk lesions upgraded to DCIS at surgery, which comprising a total of 25/120 (21%) malignancies. Malignancy was found in 12/52 (23%) clumped lesions and in 13/68 (19%) of non-clumped lesions that showed heterogenous (5/13, 38%) or homogenous (8/13, 62%) internal enhancement. The PPV of linear NME with clumped internal enhancement (23.1%; 95% CI, 11.6-34.6) was not significantly different from the PPV of non-clumped linear NME (19.1%; 95% CI, 9.8-28.4) (p=0.597). The PPV of linear NME lesions &amp;lt;1cm (33.3%; 95% CI, 11.5-55.1) was not significantly different from the PPV of lesions ≥1cm (18.6%; 95% CI, 11.1-26.2) (p=0.157). 53/120 (44%) NME lesions were operated upon. Of the remaining 67 NME not operated upon, 2 benign findings were lost to follow up, and 65 (64 benign, 1 high-risk) showed no suspicious imaging and/or clinical features on follow up. Conclusion Linear NME showed malignancy in 21% of our series. Linear NME with clumped or non-clumped internal enhancement patterns, regardless of lesion size, should undergo MRI-VABB in appropriate clinical settings. Citation Format: Shu-Tian Chen, James Covelli, Satoko Okamoto, Bruce Daniel, Wendy DeMartini, Debra Ikeda. Clumped vs non-clumped internal enhancement patterns in linear non-mass enhancement on breast MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-05.

Abstract P4-13-12: Impact of an image fusion technique for preoperative planning in breast conserving surgery in patients with non -mass enhancement on MRI

Abstract [Objective] The potential benefit of preoperative MRI is still being controversial and international guidelines do not recommend MRI for routine preoperative assessment. However, when breast conserving surgery (BCS) is considered for non-mass enhancement (NME), an area of enhancement without an associated space occupying mass or focus, biopsy-proven malignancy, the precise localization in surgical position is required. Although breast ultrasound (US) plays an adjunctive role in evaluation of MRI findings in preoperative planning for BCS, the US morphology least likely to have an US correlation was NME, not a mass enhancement or a focus, and the identification rate of NME in second look US was heterogeneous. Recently, real-time virtual sonography (RVS), which coordinates US with MRI that uses an image fusion technique by using magnet position tracking system, has been introduced in breast imaging. The purpose of this study was to evaluate the effect of preoperative planning using RVS on BCS in patients with NME. [Methods] We enrolled 12 consecutive patients who had lesions with NME that exceeded the US hypoechoic area, in which it was particularly difficult to evaluate the tumor margin. During preoperative planning before BCS, RVS was used to delineate the enhancing area on the breast surface after additional supine breast MRI was performed. We analyzed both the surgical margin positivity rate and the reoperation rate. [Results] The preoperative diagnosis by core needle biopsy were 7 DCIS and 5 invasive ductal carcinoma. NME distribution types were 5 segmental, 4 branching-ductal and 3 regional. The median diameters of the NME and hypo-echoic lesions were 24 mm (range: 12-39 mm) and 8.0 mm (range: 4.9-18 mm), respectively (p = 0.0002). After RVS-derived skin marking was performed on the surface of the affected breast, 7 lumpectomy and 5 quadrantectomy were conducted. The surgical margins were positive in 2 (17%) patients. The patients with positive margins were found to have ductal carcinoma in situ. None of the patients required additional resection. [Conclusion] Although further prospective studies are required, the findings of our preliminary study suggest that preoperative planning using RVS for BCS in patients with NME could improve both a surgical margin positive rate and a reoperation rate. Citation Format: Shogo Nakano, Kimihito Fujii, Yukako Mouri, Junko Kousaka, Takahito Ando, Mirai Ido, Manami Goto, Yukie Ito, Hirona Banno, Kenjiro Suzuki. Impact of an image fusion technique for preoperative planning in breast conserving surgery in patients with non -mass enhancement on MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-13-12.

Utility and Diagnostic Performance of Automated Breast Ultrasound System in Evaluating Pure Non-Mass Enhancement on Breast Magnetic Resonance Imaging.

ObjectiveTo compare the utility and diagnostic performance of automated breast ultrasound system (ABUS) with that of hand-held ultrasound (HHUS) in evaluating pure non-mass enhancement (NME) lesions on breast magnetic resonance imaging (MRI).Materials and MethodsOne hundred twenty-six consecutive MRI-visible pure NME lesions of 122 patients with breast cancer were assessed from April 2016 to March 2017. Two radiologists reviewed the preoperative breast MRI, ABUS, and HHUS images along with mammography (MG) findings. The NME correlation rate and diagnostic performance of ABUS were compared with that of HHUS, and the imaging features associated with ABUS visibility were analyzed.ResultsAmong 126 pure NME lesions, 100 (79.4%) were malignant and 26 (20.6%) were benign. The overall correlation rate was 87.3% (110/126) in ABUS and 92.9% (117/126) in HHUS. The sensitivity and specificity were 87% and 50% for ABUS and 92% and 42.3% for HHUS, respectively, with no significant differences (p = 0.180 and 0.727, respectively). Malignant NME was more frequently visualized than benign NME lesions on ABUS (93% vs. 65.4%, p = 0.001). Significant factors associated with the visibility of ABUS were the size of NME lesions on MRI (p < 0.001), their distribution pattern (p < 0.001), and microcalcifications on MG (p = 0.027).ConclusionABUS evaluation of pure NME lesions on MRI in patients with breast cancer is a useful technique with high visibility, especially in malignant lesions. The diagnostic performance of ABUS was comparable with that of conventional HHUS in evaluating NME lesions.

Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient

PurposeThe aim of this study was to investigate whether whole-lesion histogram and texture analysis using apparent diffusion coefficient can discriminate between idiopathic granulomatous mastitis (IGM) and invasive breast carcinoma (IBC), both of which appeared as non-mass enhancement lesions without rim-enhanced masses. MethodThis retrospective study included 58 pathology-proven female patients at two independent study sites (27 IGM patients and 31 IBC patients). Diffusion-weighted imaging (3b values, 50, 400 or 500, and 800 s/mm2) was performed using 1.5 T or 3 T MR scanners from the same vendor. Whole-lesions were segmented and 11 features were extracted. Univariate analysis and multivariate logistic regression analysis were performed to identify significant variables for differentiating IGM from IBC. Receiver operating characteristic curve was assessed. The interobserver reliability between two observers for the histogram and texture measurement was also reported. ResultsThe 5th percentile, difference entropy and entropy of apparent diffusion coefficient showed significant differences between the two groups. An area under the curve of 0.778 (95 % CI: 0.648, 0.908), accuracy of 79.3 %, and sensitivity of 87.1 % was achieved using these three significant features. No significant feature was found with the multivariate analysis. For the interobserver reliability, all apparent diffusion coefficient parameters except skewness and kurtosis indicated good or excellent agreement, while these two features showed moderate agreement. ConclusionsWhole-lesion histogram and texture analysis using apparent diffusion coefficient provide a non-invasive analytical approach to the differentiation between IGM and IBC, both presenting with non-mass enhancement without rim-enhanced masses.

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

BackgroundAvailable data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI.MethodsIn this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10−3 mm2/s) or malignant (≤ 1.3 × 10−3 mm2/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured.ResultsThere were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = − 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy.ConclusionsDiagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.