MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study.

BACKGROUND: There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes. METHODS: From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value. RESULTS: In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival. CONCLUSION: MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer.

BACKGROUND: There are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram. METHODS: We identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs. RESULTS: The FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively. CONCLUSION: The FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.

Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer.

OBJECTIVES: To investigate whether machine learning-based prediction models using 3-T multiparametric MRI (mpMRI) can predict Ki-67 and histologic grade in stage I-II luminal cancer. METHODS: Between 2013 and 2019, consecutive women with luminal cancers who underwent preoperative MRI with diffusion-weighted imaging (DWI) and surgery were included. For prediction models, morphology, kinetic features using computer-aided diagnosis (CAD), and apparent diffusion coefficient (ADC) at DWI were evaluated by two radiologists. Logistic regression analysis was used to identify mpMRI features for predicting Ki-67 and grade. Diagnostic performance was assessed using eight machine learning algorithms incorporating mpMRI features and compared using the DeLong method. RESULTS: Of 300 women, 203 (67.7%) had low Ki-67 and 97 (32.3%) had high Ki-67; 242 (80.7%) had low grade and 58 (19.3%) had high grade. In multivariate analysis, independent predictors for higher Ki-67 were washout component > 13.5% (odds ratio [OR] = 4.16; p < 0.001) and intratumoral high SI on T2-weighted image (OR = 1.89; p = 0.022). Those for higher grade were washout component > 15.5% (OR = 7.22; p < 0.001), rim enhancement (OR = 2.59; p = 0.022), and ADC value < 0.945 × 10-3 mm2/s (OR = 2.47; p = 0.015). Among eight models using these predictors, six models showed the equivalent performance for Ki-67 (area under the receiver operating characteristic curve [AUC]: 0.70) and Naive Bayes classifier showed the highest performance for grade (AUC: 0.79). CONCLUSIONS: A prediction model incorporating mpMRI features shows good diagnostic performance for predicting Ki-67 and histologic grade in patients with luminal breast cancers. KEY POINTS: • Among multiparametric MRI features, kinetic feature of washout component >13.5% and intratumoral high signal intensity on T2-weighted image were associated with higher Ki-67. • Washout component >15.5%, rim enhancement, and mean apparent diffusion coefficient value < 0.945 × 10-3 mm2/s were associated with higher histologic grade. • Machine learning-based prediction models incorporating multiparametric MRI features showed good diagnostic performance for Ki-67 and histologic grade in luminal breast cancers.

Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.

OBJECTIVES: To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). METHODS: This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. RESULTS: Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (Ve). CONCLUSIONS: Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer. KEY POINTS: • Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer. • The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network). • The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (Ve) among perfusion parameters.

Simultaneous Multislice Readout-Segmented Echo Planar Imaging for Diffusion-Weighted MRI in Patients With Invasive Breast Cancers.

BACKGROUND: In diffusion-weighted imaging (DWI) of breast MRI, simultaneous multislice acceleration techniques can be used for readout-segmented echo planar imaging (rs-EPI) to shorten the scan time. PURPOSE: To compare the image quality, apparent diffusion coefficient (ADC) value, and scan time of rs-EPI and simultaneous multislice rs-EPI (SMS rs-EPI) sequences. STUDY TYPE: Retrospective. SUBJECTS: In all, 134 consecutive women (mean age: 55.3 years) with invasive breast cancer who underwent preoperative MRI. FIELD STRENGTH/ SEQUENCES: 3.0T; rs-EPI sequence, prototypic SMS rs-EPI sequence and dynamic contrast-enhanced MRI (DCE-MRI) sequence ASSESSMENT: For quantitative comparison, two radiologists independently measured the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion contrast, and apparent diffusion coefficient (ADC). For qualitative comparison, image quality, lesion conspicuity, and reader preference were assessed with a reference of DCE-MRI. STATISTICAL TESTS: Paired t-tests and Mann-Whitney tests were used. RESULTS: For SNR and CNR, there were no differences between the sequences (P = 0.342 and 0.665 for reader 1; P = 0.606 and P = 0.116 for reader 2). Lesion contrast of SMS rs-EPI was higher than that of rs-EPI (P < 0.05 for both reader 1 and reader 2). Mean tumor ADC was similar in rs-EPI and SMS rs-EPI sequences (0.98 ± 0.22 vs. 1.00 ± 0.22; P = 0.291 for reader 1, 0.98 ± 0.21 vs. 1.00 ± 0.22; P = 0.418 for reader 2). Regarding qualitative comparison, image quality and lesion conspicuity were higher in SMS rs-EPI than in rs-EPI (both P < 0.05 for both readers). The two readers regarded SMS rs-EPI as superior or equal to rs-EPI in over 90% of cases. The acquisition time was 4:30 minutes for rs-EPI and 2:31 minutes for SMS rs-EPI. DATA CONCLUSION: The SMS rs-EPI sequence resulted in a similar ADC value and better image quality than the rs-EPI sequence in a 44.1% reduced scan time. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: 3.

Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing.

Background Radiogenomic investigations for breast cancer provide an understanding of tumor heterogeneity and discover image phenotypes of genetic variation. However, there is little research on the correlations between US features of breast cancer and whole-transcriptome profiling. Purpose To explore US phenotypes reflecting genetic alteration relevant to breast cancer treatment and prognosis by comparing US images of tumor with their RNA sequencing results. Materials and Methods From January to October 2016, B-mode and vascular US images in 31 women (mean age, 49 years ± 9 [standard deviation]) with breast cancer were prospectively analyzed. B-mode features included size, shape, echo pattern, orientation, margin, and calcifications. Vascular features were evaluated by using microvascular US and contrast agent-enhanced US: vascular index, vessel morphologic features, distribution, penetrating vessels, enhancement degree, order, margin, internal homogeneity, and perfusion defect. RNA sequencing was conducted with total RNA obtained from a surgical specimen by using next-generation sequencing. US features were compared with gene expression profiles, and ingenuity pathway analysis was used to analyze gene networks, enriched functions, and canonical pathways associated with breast cancer. The P value for differential expression was extracted by using a parametric F test comparing nested linear models. Results Thirteen US features were associated with various patterns of 340 genes (P < .05). Nonparallel orientation at B-mode US was associated with upregulation of TFF1 (log twofold change [log2FC] = 4.0; P < .001), TFF3 (log2FC = 2.5; P < .001), AREG (log2FC = 2.6; P = .005), and AGR3 (log2FC = 2.6; P = .003). Complex vessel morphologic structure was associated with upregulation of FZD8 (log2FC = 2.0; P = .01) and downregulation of IGF1R (log2FC = -2.0; P = .006) and CRIPAK (log2FC = -2.4; P = .01). The top networks with regard to orientation or vessel morphologic structure were associated with cell cycle, death, and proliferation. Conclusion Compared with RNA sequencing, B-mode and vascular US features reflected genomic alterations associated with hormone receptor status, angiogenesis, or prognosis in breast cancer. © RSNA, 2020 Online supplemental material is available for this article.

Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis.

BACKGROUND: As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI. PURPOSE: To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CAD). STUDY TYPE: Retrospective. POPULATION: Eighty-five patients (invasive cancers; mean size, 1.8 cm; range, 0.8-4.8 cm) who underwent diffusion-weighted imaging with 12 b-values (0-1000 s/mm2 ). FIELD STRENGTH/SEQUENCE: 3.0T MRI axial, IVIM-DWI epi-sequence, and DCE-MRI. ASSESSMENT: Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD. STATISTICAL TESTS: The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann-Whitney test and Spearman correlation test. RESULTS: There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029). DATA CONCLUSION: This analysis revealed that IVIM-derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118-130.

Diagnostic performances of supplemental breast ultrasound screening in women with personal history of breast cancer.

Background Supplemental breast ultrasonography (US) has been used as a surveillance imaging method in women with personal history of breast cancer (PHBC). However, there have been limited data regarding diagnostic performances. Purpose To evaluate diagnostic performances of supplemental breast US screening for women with PHBC and to compare with those for women without PHBC. Material and Methods Between 2011 and 2012, 12,230 supplemental US exams were performed in 12,230 women with negative mammograms: 6584 women with PHBC and 5646 women without PHBC. Cancer detection rate, interval cancer rate, abnormal interpretation rate, positive predictive values (PPVs), sensitivity, and specificity were calculated and compared. Results Overall cancer detection rate and first-year interval cancer rate were 1.80/1000 exams and 0.91/1000 negative exams, both of which were higher in women with PHBC than in women without PHBC (2.88 vs. 0.53 per 1000, P = 0.003; 1.50 vs. 0.20 per 1000, P = 0.027). Abnormal interpretation rate was lower in the women with PHBC than in women without PHBC (9.1% vs. 12.1%, P < 0.001). Sensitivity was not different (67.9% vs. 75.0%, P = 1.000), whereas specificity and PPV3 were higher in women with PHBC than in women without PHBC (91.2% vs. 88.0%, P < 0.001; 22.6% vs. 3.1%, P < 0.001). The majority of detected cancers in women with PHBC (78.9%, 15/19) were stage 0 or 1. Conclusion Supplemental breast US screening increases early stage second breast cancers with high specificity and PPV3 in women with PHBC, however, high interval cancer rate in younger women with PHBC should be noted.

Association between US features of primary tumor and axillary lymph node metastasis in patients with clinical T1-T2N0 breast cancer.

Background Most patients with early-stage breast cancer have clinically negative lymph nodes (LNs). However, 15-20% of patients have axillary nodal metastasis based on the sentinel LN biopsy. Purpose To assess whether ultrasound (US) features of a primary tumor are associated with axillary LN metastasis in patients with clinical T1-T2N0 breast cancer. Material and Methods This retrospective study included 138 consecutive patients (median age = 51 years; age range = 27-78 years) who underwent breast surgery with axillary LN evaluation for clinically node-negative T1-T2 breast cancer. Three radiologists blinded to the axillary surgery results independently reviewed the US images. Tumor distance from the skin and distance from the nipple were determined based on the US report. Association between US features of a breast tumor and axillary LN metastasis was assessed using a multivariate logistic regression model after controlling for clinicopathologic variables. Results Of the 138 patients, 28 (20.3%) had nodal metastasis. At univariate analysis, tumor distance from the skin ( P = 0.019), tumor size on US ( P = 0.023), calcifications ( P = 0.036), architectural distortion ( P = 0.001), and lymphovascular invasion ( P = 0.049) were associated with axillary LN metastasis. At multivariate analysis, shorter skin-to-tumor distance (odds ratio [OR] = 4.15; 95% confidence interval [CI] = 1.01-16.19; P = 0.040) and masses with associated architectural distortion (OR = 3.80; 95% CI = 1.57-9.19; P = 0.003) were independent predictors of axillary LN metastasis. Conclusion US features of breast cancer can be promising factors associated with axillary LN metastasis in patients with clinically node-negative early-stage breast cancer.

MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case-control study.

PURPOSE: Preoperative breast magnetic resonance (MR) imaging features of primary breast cancers may have the potential to act as prognostic biomarkers by providing morphologic and kinetic features representing inter- or intra-tumor heterogeneity. Recent radiogenomic studies reveal that several radiologist-annotated image features are associated with genes or signal pathways involved in tumor progression, treatment resistance, and distant metastasis (DM). We investigate whether preoperative breast MR imaging features are associated with worse DM-free survival in patients with invasive breast cancer. METHODS: Of the 3536 patients with primary breast cancers who underwent preoperative MR imaging between 2003 and 2009, 147 patients with DM were identified and one-to-one matched with control patients (n = 147) without DM according to clinical-pathologic variables. Three radiologists independently reviewed the MR images of 294 patients, and the association of DM-free survival with MR imaging and clinical-pathologic features was assessed using Cox proportional hazard models. RESULTS: Of MR imaging features, rim enhancement (hazard ratio [HR], 1.83 [95% confidence interval, CI 1.29, 2.51]; p = 0.001) and peritumoral edema (HR, 1.48 [95% CI 1.03, 2.11]; p = 0.032) were the significant features associated with worse DM-free survival. The significant MR imaging features, however, were different between breast cancer subtypes and stages. CONCLUSION: Preoperative breast MR imaging features of rim enhancement and peritumoral edema may be used as prognostic biomarkers that help predict DM risk in patients with breast cancer, thereby potentially enabling improved personalized treatment and monitoring strategies for individual patients.

Diagnostic performance of tomosynthesis and breast ultrasonography in women with dense breasts: a prospective comparison study.

PURPOSE: To compare the diagnostic performances of tomosynthesis and ultrasonography as adjunctives to digital mammography in women with dense breasts. METHODS: A total of 778 women with dense breasts underwent digital mammography with tomosynthesis and ultrasonography for screening and diagnostic purposes. The findings of tomosynthesis and ultrasonography were evaluated independently. The primary endpoint was overall diagnostic accuracy determined by area under the receiver operating characteristic curve (AUC). Secondary endpoints included sensitivity, specificity, and predictive values. RESULTS: Of the 778 participants, 698 women (140 breast cancers) were included in the analysis. Based on the AUC findings, the non-inferiority of tomosynthesis to ultrasonography was established in the overall group as well as in all subgroups except for that comprising women with extremely dense breast composition. There were no significant differences in AUC between tomosynthesis and ultrasonography among asymptomatic participants and participants who underwent imaging for screening (0.912 vs. 0.934 [P = 0.403] and 0.987 vs. 0.950 [P = 0.270], respectively). Tomosynthesis exhibited lower sensitivity (91.4 vs. 96.4%; P = 0.039), and higher specificity (83.9 vs. 70.4%; P < 0.001) and positive predictive value (58.7 vs. 45.0%; P < 0.001) than ultrasonography. CONCLUSIONS: Tomosynthesis exhibits comparable performance to ultrasonography as an adjunct to mammography for diagnosis of breast cancer, except among women with extremely dense breasts.

Post-clip placement MRI following second-look US-guided core biopsy for suspicious lesions identified on breast MRI.

OBJECTIVES: To evaluate whether the post-clip placement MRI following second-look ultrasound (US)-guided core biopsy is useful to confirm the adequate sampling of suspicious lesions identified on breast MRI. METHODS: Between 2014 and 2016, 31 consecutive women with 34 suspicious lesions that had not been identified on previous mammography or US were detected using MRI. Among them, 26 women with 29 lesions (mean size 1.5 cm, range 0.5-5.8 cm) found by second-look US underwent US-guided biopsy, subsequent clip insertion and post-clip placement MRI. Five women with five lesions that were not found by second-look US underwent MRI-guided biopsy. The technical success rate and lesion characteristics were described. RESULTS: The technical success rate was 96.6% (28/29). One failure case was a benign, 1.1-cm non-mass enhancement. Of the 28 success cases, 23 (82.1%) were masses and 5 (17.9%) were non-mass enhancements; 17 (60.7%) were benign, 4 (14.3%) were high-risk and 7 (25.0%) were malignant lesions. The technical success rate was 100% (28/28) for masses and 83.3% (5/6) for non-mass enhancements. CONCLUSIONS: Post-clip placement MRI following US-guided biopsy is useful in confirming the adequate sampling of lesions identified on MRI. This method could be an alternative to MRI-guided biopsy for lesions visible on US. KEY POINTS: • Post-clip MRI is useful for confirming adequate sampling of US-guided biopsy. • Post-clip MRI following US-guided biopsy revealed a 96.6 % technical success rate. • One technical failure case was a benign, 1.1-cm non-mass enhancement. • The technical success rate of US-guided biopsy for non-mass enhancements was 83.3 %.

Additional value of diffusion-weighted imaging to evaluate multifocal and multicentric breast cancer detected using pre-operative breast MRI.

OBJECTIVES: To investigate whether diffusion-weighted imaging (DWI) aids pre-operative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate additional lesions in breast cancer patients. METHODS: DCE-MRI and DWI were performed on 131 lesions, with available histopathological results. The apparent diffusion coefficient (ADC) of each lesion was measured, and the cut-off value for differentiation between malignant and benign lesions was calculated. A protocol combining the ADC cut-off value with DCE-MRI was validated in a cohort of 107 lesions in 77 patients. RESULTS: When an ADC cut-off value of 1.11 × 10-3 mm2/s from the development cohort was applied to the additional lesions in the validation cohort, the specificity increased from 18.9% to 67.6% (P < 0.001), and the diagnostic accuracy increased from 61.7% to 82.2% (P = 0.05), without significant loss of sensitivity (98.6% vs. 90.0%, P = 0.07). The negative predictive values of lesions in the same quadrant had decreased, as had those of lesions ≥1 cm in diameter. The ADC cut-off value in the validation cohort was 1.05 × 10-3 mm2/s. CONCLUSIONS: Additional implementation of DWI for breast lesions in pre-operative MRI can help to obviate unnecessary biopsies by increasing specificity. However, to avoid missing cancers, clinicians should closely monitor lesions located in the same quadrant or lesions ≥1 cm. KEY POINTS: • DWI can be used to further differentiate lesions during pre-operative cancer staging. • ADC cut-off values were similar in the development and validation cohorts. • DWI improves both PPV and NPV in cases of multicentric lesions. • DWI improves both PPV and NPV in lesions <1 in diameter. • NPVs are decreased in multifocal lesions and lesions ≥1 cm in diameter.

MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study.

Background Human epidermal growth factor receptor 2-positive (HER2+) breast cancer has two distinct subtypes according to hormone receptor (HR) status. Survival, pattern of recurrence, and treatment response differ between HR-/HER2+ and HR+/HER2+ cancers. Purpose To investigate imaging and clinicopathologic features of HER2+ cancers and their correlation with HR expression. Material and Methods Between 2011 and 2013, 252 consecutive patients with 252 surgically confirmed HER2+ cancers (125 HR- and 127 HR+) were included. Two experienced breast radiologists blinded to the clinicopathologic findings reviewed the mammograms and magnetic resonance (MR) images using the BI-RADS lexicon. Tumor kinetic features were acquired by computer-aided detection (CAD). The imaging and clinicopathologic features of 125 HR-/HER2+ cancers were compared with those of 127 HR+/HER2+ cancers. Association between the HR status and each feature was assessed. Results Multiple logistic regression analysis showed that circumscribed mass margin (odds ratio [OR], 4.73; P < 0.001), associated non-mass enhancement (NME) on MR images (OR, 3.29; P = 0.001), high histologic grade (OR, 3.89; P = 0.002), high Ki-67 index (OR, 3.06; P = 0.003), and older age (OR, 2.43; P = 0.006) remained independent indicators associated with HR-/HER2+ cancers. Between the two HER2+ subtypes, there were no differences in mammographic imaging presentations and calcification features and MR kinetic features by a CAD. Conclusion HER2+ breast cancers have different MR imaging (MRI) phenotypes and clinicopathologic feature according to HR status. MRI features related to HR and HER2 status have the potential to be used for the diagnosis and treatment decisions in HER2+ breast cancer patients.

Undiagnosed Breast Cancer: Features at Supplemental Screening US.

PURPOSE: To retrospectively investigate the reasons for and features of undiagnosed cancers at previous supplemental screening ultrasonography (US) in women who subsequently received a diagnosis of breast cancer. MATERIALS AND METHODS: The institutional review board approved this retrospective study and waived the requirement to obtain informed patient consent. The study consisted of 230 women (median age, 49 years; age range, 29-81 years) with 230 pairs of US examinations (prior and subsequent examinations) performed between December 2003 and August 2013 who were found to have cancer (median interval, 12 months; range, 2-24 months). The authors compared the clinical-pathologic features of patients with negative findings on prior images with those of patients with visible findings on prior images. Findings visible at prior US were classified as actionable or underthreshold by means of a blinded review by five radiologists. Lesions classified as Breast Imaging Reporting and Data System category 4 or 5 by fewer than three readers were determined to be underthreshold. Reasons for undiagnosed cancers and their imaging features were analyzed. RESULTS: Among the 230 prior US examinations, 72 (31.3%) showed visible findings and 158 (68.7%) showed negative findings. High-nuclear-grade cancers and triple-negative cancers were more common in patients with negative findings than in those with visible findings (P = .023 and P = .006, respectively). Blinded review revealed that 57 of the 72 visible findings (79%) were actionable. Misinterpretation (39% [28 of 72 lesions]) and multiple distracting lesions (17% [12 of 72 lesions]) were the two most common reasons for missing these actionable findings, which showed more noncircumscribed margins than did underthreshold findings (P = .028). CONCLUSION: At supplemental screening breast US, close attention should be paid to the presence of a margin that is not circumscribed, and multiple lesions should be separately assessed to reduce the number of missed breast cancers.

Heterogeneity of triple-negative breast cancer: mammographic, US, and MR imaging features according to androgen receptor expression.

OBJECTIVE: Our aim was to determine whether triple-negative breast cancers (TNBCs) with and without androgen receptor (AR) expression have distinguishing imaging features on mammography, breast ultrasound (US), and magnetic resonance (MR) imaging. METHODS: AR expression was assessed immunohistochemically in 125 patients with TNBC from a consecutive series of 1,086 operable invasive breast cancers. Two experienced radiologists blinded to clinicopathological findings reviewed all imaging studies in consensus using the BI-RADS lexicon. The imaging and pathological features of 33 AR-positive TNBCs were compared with those of 92 AR-negative TNBCs. RESULTS: The presence of mammographic calcifications with or without a mass (p < 0.001), non-mass enhancement on MR imaging (p < 0.001), and masses with irregular shape or spiculated margins on US (p < 0.001 and p = 0.002) and MR imaging (p = 0.001 and p < 0.001) were significantly associated with AR-positive TNBC. Compared with AR-negative TNBC, AR-positive TNBC was more likely to have a ductal carcinoma in situ component (59.8% vs. 90.9%, p = 0.001) and low Ki-67 expression (30.4% vs. 51.5%, p = 0.030). CONCLUSION: AR-positive and AR-negative TNBCs have different imaging features, and certain imaging findings can be useful to predict AR status in TNBC. KEY POINTS: • Triple-negative breast cancers have distinguishing imaging features according to AR expression. • AR-positive TNBC is associated with calcifications, spiculated masses, and non-mass enhancement. • Multimodality imaging can help predict androgen receptor status in TNBC.

Sonographic diagnosis of abscess following breast-conserving surgery with insertion of nonabsorbable mesh.

Immediate mesh insertion has been recently used for breast reconstruction after breast-conserving surgery. We report a case of abscess formation following immediate nonabsorbable mesh insertion with breast-conserving surgery. In this article, we demonstrate multimodal breast imaging features and pathologic correlations of the case. In addition, we illustrate characteristic sonographic findings of nonabsorbable mesh fibers to differentiate them from a gossypiboma caused by a retained surgical sponge or tumor recurrence.

The sonographic "coffee bean" sign helps distinguish an axillary neurofibroma from a lymphadenopathy.

Axillary masses may represent various soft tissue tumors or lymphadenopathy. Neurofibromas are benign peripheral nerve sheath tumors and, while they are very uncommon, it is important to remember that neurogenic tumors arising from brachial plexus can develop in the axilla. We describe an axillary neurofibroma arising from the brachial plexus that presented with a "coffee bean sign" on sonography that distinguished it from axillary lymphadenopathy.

Tumor Heterogeneity of Breast Cancer Assessed with Computed Tomography Texture Analysis: Association with Disease-Free Survival and Clinicopathological Prognostic Factor.

Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and prognostic factors in patients with invasive breast cancer. A total of 256 consecutive women who underwent preoperative chest CT and surgery in our institution were included. The Cox proportional hazards model was used to determine the relationship between textural features and DFS. Logistic regression analysis was used to reveal the relationship between textural features and prognostic factors. Of 256 patients, 21 (8.2%) had disease recurrence over a median follow-up of 60 months. For the prediction of shorter DFS, higher histological grade (hazard ratio [HR], 6.12; p < 0.001) and lymphovascular invasion (HR, 2.93; p = 0.029) showed significance, as well as textural features such as lower mean attenuation (HR, 4.71; p = 0.003) and higher entropy (HR, 2.77; p = 0.036). Lower mean attenuation showed a correlation with higher tumor size, and higher entropy showed correlations with higher tumor size and Ki-67. In conclusion, CTTA-derived textural features can be used as a noninvasive imaging biomarker to predict shorter DFS and prognostic factors in patients with invasive breast cancer.

Corrigendum: Diffusion-Weighted Magnetic Resonance Imaging for Preoperative Evaluation of Patients With Breast Cancer: Protocol of a Prospective, Multicenter, Observational Cohort Study.

This corrects the article on p. 292 in vol. 26, PMID: 37272245.