Breast carcinomas with strong high-signal intensity on T2-weighted MR images: Pathological characteristics and differential diagnosis

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions

PURPOSE

This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS).

MATERIALS AND METHODS

In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions.

RESULTS

Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements.

CONCLUSION

This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

MRI-visualized T2 hyperintense breast lesions: identifying clinical and imaging factors linked to malignant biopsy outcomes

PURPOSE

To determine the malignancy rate for MRI-guided breast biopsies performed for T2 hyperintense breast lesions and to assess additional clinical and MRI characteristics that can predict benign and malignant outcomes.

METHODS

A retrospective chart review of consecutive MRI-guided breast biopsies performed in two tertiary hospitals was conducted over two years. Biopsies performed for T2 hyperintense lesions were selected, and further lesion imaging characteristics and patient risk factors were collected. Univariate and multivariate modeling regression were used to determine additional imaging and patient factors associated with malignant outcomes for biopsies of T2 hyperintense lesions.

RESULTS

Out of 369 MRI-guided breast biopsies, 100 (27%) were performed for T2 hyperintense lesions. Two biopsy-proven benign lesions were excluded as the patient was lost on follow-up. With a study cohort of 98 lesions, the final pathology results were benign for 80 (80%) of these lesions, while 18 (18%) were malignant. Using multivariate logistic modeling, patient age > 50 (OR 5.99 (1.49, 24.08 95% CI), p < 0.05) and lesion size > 3 cm (OR 5.54 (1.54-18.7), p < 0.01) were found to be important predictors of malignant outcomes for MRI biopsies performed for T2 hyperintense lesions.

CONCLUSION

Our study observed a high malignancy rate, challenging the assumption that T2 hyperintensity can be considered a benign imaging characteristic for otherwise suspicious MRI-detected lesions. Decision-making regarding tissue sampling should be made based on a thorough evaluation of more reliable additional demographic and imaging factors, including patient age and lesion size.

The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma

AIM

To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma.

MATERIALS AND METHODS

The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values.

RESULTS

Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z = 2.624; p=0.007, z = 2.679).

CONCLUSION

The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.

Whole-tumor histogram analysis of multiparametric breast magnetic resonance imaging to differentiate pure mucinous breast carcinomas from fibroadenomas with high-signal intensity on T2WI

PURPOSE

To investigate the utility of whole-tumor histogram analysis based on multiparametric MRI in distinguishing pure mucinous breast carcinomas (PMBCs) from fibroadenomas (FAs) with strong high-signal intensity on T2-weighted imaging (T2-SHi).

MATERIAL AND METHODS

The study included 20 patients (mean age, 55.80 ± 15.54 years) with single PBMCs and 29 patients (mean age, 42.31 ± 13.91 years) with single FAs exhibiting T2-SHi. A radiologist performed whole-tumor histogram analysis between PBMC and FA groups with T2-SHi using multiparametric MRI, including T2-weighted imaging (T2WI), diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) maps, and the first (DCE_T1) and last (DCE_T4) phases of T1-weighted dynamic contrast-enhanced imaging (DCE) images, to extract 11 whole-tumor histogram parameters. Histogram parameters were compared between the two groups to identify significant variables using univariate analyses, and their diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis and logistic regression analyses. In addition, 15 breast lesions were randomly selected and histogram analysis was repeated by another radiologist to assess the intraclass correlation coefficient for each histogram feature. Pearson's correlation coefficients were used to analyze the correlations between histogram parameters and Ki-67 expression of PMBCs.

RESULTS

For T2WI images, mean, median, maximum, 90th percentile, variance, uniformity, and entropy significantly differed in PBMCs and FAs with T2-SHi (all P < 0.05), yielding a combined area under the curve (AUC) of 0.927. For ADC maps, entropy was significantly lower in FAs with T2-SHi than in PMBCs (P = 0.03). In both DCE_T1 and DCE_T4 sequences, FAs with T2-SHi showed significantly higher minimum values than PBMCs (P = 0.007 and 0.02, respectively). The highest AUC value of 0.956 (sensitivity, 0.862; specificity, 0.944; positive predictive value, 0.962; negative predictive value, 0.810) was obtained when all significant histogram parameters were combined.

CONCLUSIONS

Whole-tumor histogram analysis using multiparametric MRI is valuable for differentiating PBMCs from FAs with T2-SHi.

Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features

PURPOSE

To build an automatic computer-aided diagnosis (CAD) pipeline based on multiparametric magnetic resonance imaging (mpMRI) and explore the role of different imaging features in the classification of breast cancer.

MATERIALS AND METHODS

A total of 222 histopathology-confirmed breast lesions, together with their BI-RADS scores, were included in the analysis. The cohort was randomly split into training (159) and test (63) cohorts, and another 50 lesions were collected as an external cohort. An nnUNet-based lesion segmentation model was trained to automatically segment lesion ROI, from which radiomics features were extracted for diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and contrast-enhanced (DCE) pharmacokinetic parametric maps. Models based on combinations of sequences were built using support vector machine (SVM) and logistic regression (LR). Also, the performance of these sequence combinations and BI-RADS scores were compared. The Dice coefficient and AUC were calculated  to evaluate the segmentation and classification results. Decision curve analysis (DCA) was used to assess clinical utility.

RESULTS

The segmentation model achieved a Dice coefficient of 0.831 in the test cohort. The radiomics model used only three features from diffusion coefficient (ADC) images, T2WI, and DCE-derived kinetic mapping, and achieved an AUC of 0.946 [0.883-0.990], AUC of 0.842 [0.6856-0.998] in the external cohort, which was higher than the BI-RADS score with an AUC of 0.872 [0.752-0.975]. The joint model using both radiomics score and BI-RADS score achieved the highest test AUC of 0.975 [0.935-1.000], with a sensitivity of 0.920 and a specificity of 0.923.

CONCLUSION

Three radiomics features can be used to construct an automatic radiomics-based pipeline to improve the diagnosis of breast lesions and reduce unnecessary biopsies, especially when using jointly with BI-RADS scores.

Free-breathing BLADE fat-suppressed T2 weighted turbo spin echo sequence for distinguishing lung cancer from benign pulmonary nodules or masses: A pilot study

OBJECTIVE

Diffusion Weighted Imaging (DWI) can be used to differentiate benign and malignant pulmonary nodules or masses, while T2WI is also of great value in the differential diagnosis of them. For example, T2WI can be used to differentiate abscess from lung cancer. The study aims to quantitatively evaluate the efficacy of free-breathing BLADE fat-suppressed T2 weighted turbo spin echo sequence (BLADE T2WI) for differentiating lung cancer (LC) and benign pulmonary nodule or mass (BPNM).

METHODS

A total of 291 patients with LC (197 males, 94 females; mean age 63.2 years) and 74 BPNM patients (53 males, 21 females; mean age 62.8 years) who underwent BLADE T2WI at 3-T MRI between November 2016 and May 2022were included in this retrospective study. Two radiologists independently blinded observed the MR images and measured the T2 contrast ratio (T2CR). Mann-Whitney U test was used to compare T2CR values between the two groups, ROC curves were used to evaluate the diagnostic efficacy of BLADE T2WI.

RESULTS

The two radiologists had good inter-observer consistency for T2CR (ICC = 0.958). The T2CR of BPNM was significantly higher than LC (all p < 0.001); the cut-off value of T2CR was 2.135, and the sensitivity, specificity, and accuracy of diagnosis were 75.6%, 63.5%, and 73.2%, respectively. Moreover, T2CR correctly diagnosed 220 LC cases (220/291 = 75.6%) and 47 BPNM cases (47/74 = 63.5%).

CONCLUSION

The T2CR value of MR non-enhanced BLADE T2WI can be easily obtained and can quantitatively distinguish BPNM from LC, thus avoiding misdiagnosis caused by lack of work experience.

Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes

The purpose of this study is to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes. Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67 and were compared between ER-positive and ER-negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis. Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < 0.05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson’s r = 0.75, 0.69, and 0.60 respectively; p < 0.001). Among ER-positive cancers, T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < 0.05). The area under the curve (AUC) of T2 for discriminating ER-negative from ER-positive cancers was 0.87 (95% CI: 0.69−0.97). The AUC of T1 for discriminating Luminal A from Luminal B cancers was 0.83 (95% CI: 0.61−0.95). In conclusion, quantitative values derived from synthetic MRI show potential for subtyping of invasive breast cancers.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

Primary neuroendocrine carcinomas of the breast and neuroendocrine differentiated breast cancers: Relationship between histopathological and radiological features

PURPOSE

The aim of this study wasto investigate whole-breast imaging findings (mammography, ultrasonography (US), magnetic resonance imaging (MRI),clinical, and histopathological findings of primary neuroendocrine carcinomas of the breast (NEC) and neuroendocrine differentiated breast cancers (NEBC).

METHODS

Patients withadiagnosis of breast cancer with histopathological neuroendocrine features between the years 2010 and 2021 were retrospectively screened.The lesions were divided into two main groups depending on staining with neuroendocrine markers (synaptophysin and chromogranin A). Those showing focal staining were categorized as NEBC while those with diffuse staining as NEC.The mammography, US, and MRI of the lesionswere reviewed in consensus by two breast radiologists in order to assess imaging featuresretrospectively according to the Breast Imaging Reporting and Data System (BI-RADS) 5th lexicon.The findings were compared with breast cancers without neuroendocrine features (BC-WNE) which were randomly selected from the same database.

RESULTS

A total of 105 lesions [NEBC (n = 44), NEC(n = 11), BC-WNE (n = 50)] were evaluated.Patients with neuroendocrine tumors were older (p < 0.001) than those with BC-WNE. Compared with BC-WNE tumors, radiological findings typical of malignancy such as irregular shape [NEBC (7/20); NEC(3/7) vs BC-WNE (35/43); p < 0.001], spiculation [NEBC (2/20); NEC(0/7) vs BC-WNE (21/43); p < 0.001], architectural distortion [(NEBC (3/24); NEC(0/9) vs BC-WNE (31/50); p < 0.001)], calcification [(NEBC (6/24), NEC(0/9) vs BC-WNE (n = 27/50); p = 0.001)] on mamography, non-parallel orientation to skin [(NEBC (n = 17/29), NEC(n = 4/9), BC-WNE (n = 35/42); p = 0.008)], acoustic shadowing [(NEBC (n = 12/29), NEC(1/9), BC-WNE (n = 29/42); p = 0.009)], axillary lymphadenopathy [(NEBC(n = 3/30), NEC(n = 1/9), BC-WNE (21/50); p < 0.001)]on US were less common features of the neuroendocrine carcinomas of breast. Aside from shape features, there was no significant difference in contrast pattern (p = 0.866), kinetic curve (p = 0.454) and diffusion restriction (p = 0.242) on MRI.

CONCLUSION

Characteristic malignant imaging features, including irregular shape, spiculated margins, suspicious calcifications, and posterior acoustic shadowing, are uncommon in neuroendocrine carcinomas of breast. These carcinomas tend to show more benign imaging features when compared with BC-WNE.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

Simple Summary

Although diffusion-weighted imaging (DWI) can be valuable for differential diagnosis of lung cancer from benign pulmonary nodules and masses (PNMs), the diagnostic capability may not be perfect. This study’s purpose was to compare the diagnostic efficacy of 18-fluoro-2-deoxy-glucose positron emission tomography–computed tomography (FDG-PET/CT) and magnetic resonance imaging (MRI) of DWI and T2-weighted imaging (T2WI) in PNMs. There were 278 lung cancers and 50 benign PNMs that were examined by FDG-PET/CT and MRI. The sensitivity of the maximum standardized uptake value (SUVmax) was significantly lower than that of the apparent diffusion coefficient (ADC) and the T2 contrast ratio (T2 CR). The accuracy of SUVmax was significantly lower than that of ADC and that of T2 CR. The sensitivity and accuracy of MRI were significantly higher than those of FDG-PET/CT. MRI can replace FDG-PET/CT for differential diagnosis of PNMs.

Abstract

The purpose of this retrospective study was to compare the diagnostic efficacy of FDG-PET/CT and MRI in discriminating malignant from benign pulmonary nodules and masses (PNMs). There were 278 lung cancers and 50 benign PNMs that were examined by FDG-PET/CT and MRI. The T2 contrast ratio (T2 CR) was designated as the ratio of T2 signal intensity of PNM divided by T2 signal intensity of the rhomboid muscle. The optimal cut-off values (OCVs) for differential diagnosis were 3.605 for maximum standardized uptake value (SUVmax), 1.459 × 10⁻³ mm²/s for apparent diffusion coefficient (ADC), and 2.46 for T2 CR. Areas under the receiver operating characteristics curves were 67.5% for SUVmax, 74.3% for ADC, and 72.4% for T2 CR, respectively. The sensitivity (0.658) of SUVmax was significantly lower than that (0.838) of ADC (p < 0.001) and that (0.871) of T2 CR (p < 0.001). The specificity (0.620) of SUVmax was that the same as (0.640) ADC and (0.640) of T2 CR. The accuracy (0.652) of SUVmax was significantly lower than that (0.808) of ADC (p < 0.001) and that (0.835) of T2 CR (p < 0.001). The sensitivity and accuracy of DWI and T2WI in MRI were significantly higher than those of FDG-PET/CT. Ultimately, MRI can replace FDG PET/CT for differential diagnosis of PNMs saving healthcare systems money while not sacrificing the quality of care.

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

Simple Summary

Breast cancer is the most common cancer in women worldwide. Currently the use of MR is mandatory in staging phase. The standard protocol includes T2-weighted sequences for morphology and signal analysis, T1-weighted images for adding information (i.e., ematic or adipous components), diffusion-weighted sequences which provide information on tissue cellularity, and dynamic post-contrast sequences useful for detecting and locating lesions. Although not considered among the main prognostic factors in current guidelines, tumor-associated edema provides useful information on tumor aggressiveness, and has been shown to be associated with the main histological tumor characteristics. With this work, entitled “The Impact of Tumor Edema on T2-weighted 3T-MRI Invasive Breast Cancer Histological Characterization: a Pilot Radiomics Study”, we want to demonstrate that radiomics edema, based on algorithms that allow the extraction of imaging features not visible to the human eye, can further increase the accuracy in the prediction of histological factors compared to the use of traditional information only.

Abstract

Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.

Novel Insights of T2-Weighted Imaging: Significance for Discriminating Lung Cancer from Benign Pulmonary Nodules and Masses

Diffusion-weighted imaging is useful for discriminating lung cancer from benign pulmonary nodules and masses (BPNMs), however the diagnostic capability is not perfect. The aim of this research was to clarify whether T2-weighted imaging (T2WI) is efficient in discriminating lung cancer from BPNMs, especially from pulmonary abscesses. A T2 contrast ratio (T2 CR) for a pulmonary nodule is defined as the ratio of T2 signal intensity of a pulmonary nodule divided by the T2 signal intensity of the rhomboid muscle. There were 52 lung cancers and 40 inflammatory BPNMs (mycobacteria disease 12, pneumonia 13, pulmonary abscess 9, other 6) and seven non-inflammatory BPNMs. The T2 CR (2.14 ± 0.63) of lung cancers was significantly lower than that (2.68 ± 1.04) of BPNMs (p = 0.0021). The T2 CR of lung cancers was significantly lower than that (2.93 ± 0.26) of pulmonary abscesses (p = 0.011). When the optical cutoff value of T2 CR was set as 2.44, the sensitivity was 0.827 (43/52), the specificity 0.596 (28/47), the accuracy 0.717 (71/99), the positive predictive value 0.694 (43/62), and the negative predictive value 0.757 (28/37). T2 CR of T2WI is useful in discriminating lung cancer from BPNMs. Pulmonary abscesses, which show strong restricted diffusion in DWI, can be differentiated from lung cancers using T2WI.

Intratumoral Intensity in T2-weighted MRI and the Association With Histological and Molecular Prognostic Factors in Women With Invasive Breast Cancer

OBJECTIVE

To compare the intratumoral T2 signal intensity on MRI and histopathological and molecular expression of biomarkers of aggressiveness (histological grade, hormonal status, HER2, and Ki-67).

METHODS

This retrospective study included all women with invasive breast cancer undergoing MRI from January 2014 to October 2016. The intratumoral T2 signal as interpreted at consensus by two radiologists was compared to histopathological and molecular prognostic factors from the surgical specimen. Statistical analyses used Pearson χ 2 test with a confidence level of 95% (P ≤ 0.05).

RESULTS

Fifty patients with 50 lesions met study criteria (mean age 65.8 ± 13.5 years). Mean lesion size was 28 mm ± 15.7 mm (range, 15 to 76 mm). Cancer types were invasive ductal (35/50, 70%), invasive lobular (10/50, 20%), and mixed (5/50, 10%). Most lesions were histological grade 1 or 2 (41/50, 82%) and luminal type (45/50, 90%). On T2 images, lesions were hypointense in 62% (31/50), isointense in 20% (10/50), and hyperintense in 18% (9/50) of cases. Among hypointense lesions, 94% (29/31) were low or intermediate grade tumors (P = 0.02), low HER2 overexpression (30/31, 97%) (P = 0.005), and high ER status (30/31, 97%) (P = 0.006), high PR (26/31, 84%) (P = 0.02), and low incidence of necrosis (2/31, 6%). The difference in Ki-67 tumoral expression between groups was not significant.

CONCLUSION

Intratumoral T2 hypointensity in invasive breast cancer is associated with better prognostic tumors, such as histological low-grade high hormone receptor status.

Combination Assessment of Diffusion-Weighted Imaging and T2-Weighted Imaging Is Acceptable for the Differential Diagnosis of Lung Cancer from Benign Pulmonary Nodules and Masses

Simple Summary

The purpose of this study is to determine whether the combination assessment of DWI and T2WI improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). As using the OCV (1.470 × 10⁻³ mm²/s) for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV (2.45) for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Abstract

The purpose of this study is to determine whether the combination assessment of DWI and T2-weighted imaging (T2WI) improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). The optimal cut-off value (OCV) for differential diagnosis was set at 1.470 × 10⁻³ mm²/s for apparent diffusion coefficient (ADC), and at 2.45 for T2 contrast ratio (T2 CR). The ADC (1.24 ± 0.29 × 10⁻³ mm²/s) of lung cancer was significantly lower than that (1.69 ± 0.58 × 10⁻³ mm²/s) of BPNM. The T2 CR (2.01 ± 0.52) of lung cancer was significantly lower than that (2.74 ± 1.02) of BPNM. As using the OCV for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning

RATIONALE AND OBJECTIVES

Ki-67 is one of the most important biomarkers of breast cancer traditionally measured invasively via immunohistochemistry. In this study, deep learning based radiomics models were established for preoperative prediction of Ki-67 status using multiparametric magnetic resonance imaging (mp-MRI).

MATERIALS AND METHODS

Total of 328 eligible patients were retrospectively reviewed [training dataset (n = 230) and a temporal validation dataset (n = 98)]. Deep learning imaging features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (T1+C). Transfer learning techniques constructed four feature sets based on the individual three MR sequences and their combination (i.e., mp-MRI). Multilayer perceptron classifiers were trained for final prediction of Ki-67 status. Mann-Whitney U test compared the predictive performance of individual models.

RESULTS

The area under curve (AUC) of models based on T2WI,T1+C,DWI and mp-MRI were 0.727, 0.873, 0.674, and 0.888 in the training dataset, respectively, and 0.706, 0.829, 0.643, and 0.875 in the validation dataset, respectively. The predictive performance of mp-MRI classification model in the AUC value was significantly better than that of the individual sequence model (all p< 0.01).

CONCLUSION

In clinical practice, a noninvasive approach to improve the performance of radiomics in preoperative prediction of Ki-67 status can be provided by extracting breast cancer specific structural and functional features from mp-MRI images obtained from conventional scanning sequences using the advanced deep learning methods. This could further personalize medicine and computer aided diagnosis.

The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images

PURPOSE

To assess morphology on diffusion-weighted imaging (DWI) and intratumoral signal intensity (SI) on T2-weighted images (T2WI) of breast carcinomas, and to evaluate the association between the combined DWI and T2WI findings and breast cancer subtypes.

METHODS

Two hundred and eighty breast cancer patients who underwent breast MRI prior to therapy were included in this retrospective study. All had invasive carcinomas, which were classified into five subtypes: Luminal A-like (n = 149), Luminal B-like (n = 63), Hormone receptor-positive HER2 (n = 31), Hormone receptor-negative HER2 (n = 13), or Triple-negative (TN) (n = 24). Based on the morphology on DWI, the tumors were classified into two patterns: DWI-homogeneous or DWI-heterogeneous. If DWI-heterogeneous, an assessment of intratumoral SI on T2WI was performed: tumors with intratumoral high/low SI on T2WI were classified as Hete-H/Hete-L, respectively. The associations between (1) the morphological patterns on DWI and the five subtypes, and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous were evaluated.

RESULTS

There was a significant association between (1) the morphological patterns on DWI and the five subtypes (p < 0.0001), and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous (p < 0.0001). DWI-homogeneous was dominant in Luminal A-like (67.1%), and Hete-H was dominant in TN type (75%). Hete-H, suggesting the presence of intratumoral necrosis, included high proliferative and/or aggressive subtypes more frequently (80%) than Hete-L, suggesting the presence of fibrotic focus. Fibrotic focus was seen more commonly in the luminal subtypes.

CONCLUSION

The combined findings on DWI and T2WI revealed breast carcinomas that were associated with particular subtypes.

Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions

BACKGROUND

MRI-based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion kurtosis imaging (DKI) exists.

PURPOSE

To develop and validate a multimodal MRI-based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences.

STUDY TYPE

Retrospective.

POPULATION

In all, 207 female patients with 207 histopathology-confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group.

FIELD STRENGTH/SEQUENCE

T₂ -weighted (T₂ W), T₁ -weighted (T₁ W), diffusion-weighted MR imaging (b-values = 0, 500, 800, and 2000 seconds/mm² ) and quantitative DCE-MRI were performed on a 3.0T MR scanner.

ASSESSMENT

Radiomics features were extracted from T₂ WI, T₁ WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set.

STATISTICAL TESTS

Optimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set.

RESULTS

The area under the ROC curve (AUC) of the optimal radiomics model, including T₂ WI, DKI, and quantitative DCE-MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T₁ WI, T₂ WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively.

DATA CONCLUSION

The model based on radiomics features from T₂ WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:596-607.

Scoring System to Predict Malignancy for MRI-Detected Lesions in Breast Cancer Patients: Diagnostic Performance and Effect on Second-Look Ultrasonography

Purpose

To design a scoring system to predict malignancy of additional MRI-detected lesions in breast cancer patients.

Materials and Methods

Eighty-six lesions (64 benign and 22 malignant) detected on preoperative MRI of 68 breast cancer patients were retrospectively included. The clinico-radiologic features were correlated with the histopathologic results using the Student's t-test, Fisher's exact test, and logistic regression analysis. The scoring system was designed based on the significant predictive features of malignancy, and its diagnostic performance was compared with that of the Breast Imaging-Reporting and Data System (BI-RADS) category.

Results

Lesion size ≥ 8 mm (p < 0.001), location in the same quadrant as the primary cancer (p = 0.005), delayed plateau kinetics (p = 0.010), T2 isointense (p = 0.034) and hypointense (p = 0.024) signals, and irregular mass shape (p = 0.028) were associated with malignancy. In comparison with the BI-RADS category, the scoring system based on these features with suspicious non-mass internal enhancement increased the diagnostic performance (area under the receiver operating characteristic curve: 0.918 vs. 0.727) and detected three false-negative cases. With this scoring system, 22 second-look ultrasound examinations (22/66, 33.3%) could have been avoided.

Conclusion

The scoring system based on the lesion size, location relative to the primary cancer, delayed kinetic features, T2 signal intensity, mass shape, and non-mass internal enhancement can provide a more accurate approach to evaluate MRI-detected lesions in breast cancer patients.

Association among T2 signal intensity, necrosis, ADC and Ki-67 in estrogen receptor-positive and HER2-negative invasive ductal carcinoma

PURPOSE

To determine whether T2 signal intensity, necrosis, and ADC values are associated with Ki-67 in patients with Estrogen Receptor (ER)-positive and Human epidermal growth factor receptor type 2 (HER2)-negative invasive ductal carcinoma (IDC).

MATERIALS AND METHODS

Between March 2012 and February 2013, one hundred eighty seven women with ER-positive and HER2-negative IDC who underwent breast MRI and subsequent surgery were included. Intratumoral signal intensity was evaluated based on a combination of T2-weighted (low or equal, high, or very high) and contrast-enhanced MR images (enhancement or not). Necrosis was defined as very high T2 and no enhancement. Using the analysis of variance and pairwise t-test, a model based on intratumoral signal intensity was developed to assess Ki-67 of the surgical specimen. Inter-observer agreement for the developed model was analyzed. Conventional mean and minimum apparent diffusion coefficient (ADC) measurements were performed and correlated with Ki-67.

RESULTS

As the grade of the developed model increased (Grade I: low or equal T2, Grade II: high T2, or necrosis < 50%, Grade III: necrosis ≥ 50%), mean Ki-67 significantly increased (Grade I to III: 12.5%, 17.6%, 45.0%, respectively; P < 0.001). Good inter-observer agreement was found for the model (κ = 0.846, P < 0.001). ADC did not show significant correlations with Ki-67 (Pearson's correlation coefficient, 0.140 [P = 0.057] for mean ADC; -0.079 [P = 0.284] for minimum ADC).

CONCLUSION

Intratumoral signal intensity but not ADC was associated with Ki-67 in patients with ER-positive and HER2-negative IDC.

Differentiation between Pure Mucinous Breast Carcinomas and Fibroadenomas with Strong High-Signal Intensity on T2-Weighted Images from Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Objective

This study aimed to identify characteristics that can differentiate between pure mucinous breast carcinomas (PMBCs) and fibroadenomas (FAs) with strong high-signal intensity on T2-weighted images (T2-SHi) from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

The DCE-MRI tumor characteristics were compared and analyzed between 35 PMBCs and 70 FAs with T2-SHi.

Results

Multivariate analysis revealed that delayed enhancement pattern was the only significant independent predictor (p = 0.007).

Conclusion

A delayed enhancement pattern is the most reliable characteristic for differentiating PMBCs from FAs with T2-SHi from DCE-MRI.

T2-hypointense rim of breast mass lesions on magnetic resonance images: Radiologic-pathologic correlation

We investigated the radiologic-pathologic correlation of a strong hypointense rim on T2-weighted images (T2-hypo-rim) surrounding breast mass lesions and evaluated its clinical significance. We retrospectively reviewed 3503 consecutive breast magnetic resonance imaging (MRI) examinations. The T2-hypo-rim was defined as a border of strong hypointensity compared with the fat signal on fat-suppressed T2-weighted images. Detected lesions with T2-hypo-rim were classified as a solid or cystic mass with MRI and correlated with histopathologic findings. Sixty-two masses (2%; 34 solid, 28 cystic) with T2-hypo-rim were detected [44 breast cancers, 18 benign lesions, including 15 (24%) papillary tumors]. Patients with cancer were significantly older than those with benign lesions (P = .002). Breast cancers were significantly larger than benign masses (P = .023). In 49 of 62 lesions (24 solid and 16 cystic cancers; three solid and six cystic benign masses), the rims were accurately correlated with the histopathologic findings. All malignant and benign cystic masses exhibited hemosiderin deposits in the cyst walls. However, 22 of 24 solid cancers and no solid benign masses exhibited hemosiderin at the tumor periphery (92% and 0%, respectively, P < .001). In addition, a thick fibrous capsule was present in nine (38%) of 24 solid cancers and none of the solid benign lesions. Strong T2-hypo-rims mostly correlated with hemosiderin deposits and/or sometimes fibrous capsules. Although the rims could not distinguish malignant from benign cystic lesions, they indicated malignancy in solid mass lesions on MRI. Additionally, the rims often indicated papillary tumors.

Diagnostic Usefulness of Combination of Diffusion-weighted Imaging and T2WI, Including Apparent Diffusion Coefficient in Breast Lesions: Assessment of Histologic Grade

PURPOSE

This study aimed to compare the diagnostic values of a combination of diffusion-weighted imaging and T2-weighted imaging (DWI-T2WI) with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and to evaluate the correlation of DWI with the histologic grade in breast cancer.

MATERIALS AND METHODS

This study evaluated a total of 169 breast lesions from 136 patients who underwent both DCE-MRI and DWI (b value, 1000s/mm²). Morphologic and kinetic analyses for DCE-MRI were classified according to the Breast Imaging-Reporting and Data System. For the DWI-T2WI set, a DWI-T2WI score for lesion characterization that compared signal intensity of DWI and T2WI (benign: DWI-T2WI score of 1, 2; malignant: DWI-T2WI score of 3, 4, 5) was used. The diagnostic values of DCE-MRI, DWI-T2WI set, and combined assessment of DCE and DWI-T2WI were calculated.

RESULTS

Of 169 breast lesions, 48 were benign and 121 were malignant (89 invasive ductal carcinoma, 24 ductal carcinoma in situ, 4 invasive lobular carcinoma, 4 mucinous carcinoma). The mean apparent diffusion coefficient (ADC) of invasive ductal carcinoma (0.92 ± 0.19 × 10^-3 mm²/s) and ductal carcinoma in situ (1.11 ± 0.13 × 10^-3 mm²/s) was significantly lower than the value seen in benign lesions (1.36 ± 0.22 × 10^-3 mm²/s). The specificity, positive predictive value (PPV), and accuracy of DWI-T2WI set and combined assessment of DCE and DWI-T2WI (specificity, 87.5% and 91.7%; PPV, 94.3% and 96.2%; accuracy, Az = 0.876 and 0.922) were significantly higher than those of the DCE-MRI (specificity, 45.8%; PPV, 81.7%; accuracy, Az = 0.854; P < .05). A low ADC value and the presence of rim enhancement were associated with a higher histologic grade cancer (P < .05).

CONCLUSION

Combining DWI, T2WI, and ADC values provides increased accuracy for differentiation between benign and malignant lesions, compared with DCE-MRI. A lower ADC value was associated with a higher histologic grade cancer.

BI-RADS 3: Current and Future Use of Probably Benign

PURPOSE OF REVIEW

Probably benign (BI-RADS 3) causes confusion for interpreting physicians and referring physicians and can induce significant patient anxiety. The best uses and evidence for using this assessment category in mammography, breast ultrasound, and breast MRI will be reviewed; the reader will have a better understanding of how and when to use BI-RADS 3.

RECENT FINDINGS

Interobserver variability in the use of BI-RADS 3 has been documented. The 5th edition of the BI-RADS atlas details the appropriate use of BI-RADS 3 for diagnostic mammography, ultrasound, and MRI, and discourages its use in screening mammography. Data mining, elastography, and diffusion weighted MRI have been evaluated to maximize the accuracy of BI-RADS 3.

SUMMARY

BI-RADS 3 is an evolving assessment category. When used properly, it reduces the number of benign biopsies while allowing the breast imager to maintain a high sensitivity for the detection of early stage breast cancer.

MR Imaging Features of Tubular Carcinoma: Preliminary Experience in Twelve Masses

Objective

We retrospectively analyzed the magnetic resonance (MR) imaging features and diffusion-weighted imaging findings of the 12 masses of 10 patients with tubular carcinoma (TC), including mammography and sonography findings.

Materials and Methods

Mammographic, sonographic and magnetic resonance imaging features in 12 histopathologically confirmed masses diagnosed as TC of the breast within 10 patients were evaluated. Morphologic characteristics, enhancement features, apparent diffusion coefficient (ADC) values were reviewed.

Results

On mammography (n=5), TC appeared as high density masses with indistinct, spiculated or obscured margins. Sonographically, TC appeared as a hypoechoic appearance (n=12) with posterior acoustic shadowing in nine. On MR imaging, the margins of ten of twelve masses were irregular. Internal enhancement patterns were heterogeneous in 10 patients. Dynamic enhancement patterns illustrated plateau kinetics (n=8). On the T2-weighted images 4 masses were hypointense, and 8 were hyperintense; hypointense internal septation was found in seven of these. Tubular carcinoma appeared as hyperintense on diffusion-weighted imaging with ADC values of 0.85±0.16×10-3 mm²/s that was lower than the normal parenchyma of 1.25±0.25×10-3 mm²/s.

Conclusion

According to our study with a limited number of cases, tubular carcinomas can be described as hyperintense breast carcinomas with or without dark internal septation like appearance on T2-weighted images. Low ADC values from DW imaging can be used to differentiate TC from hyperintense benign breast lesions.

Differentiation of malignant and benign breast lesions: Added value of the qualitative analysis of breast lesions on diffusion-weighted imaging (DWI) using readout-segmented echo-planar imaging at 3.0 T

Objective

To determine the added value of qualitative analysis as an adjunct to quantitative analysis for the discrimination of benign and malignant lesions in patients with breast cancer using diffusion-weighted imaging (DWI) with readout-segmented echo-planar imaging (rs-EPI).

Methods

A total of 99 patients with 144 lesions were reviewed from our prospectively collected database. DWI data were obtained using rs-EPI acquired at 3.0 T. The diagnostic performances of DWI in the qualitative, quantitative, and combination analyses were compared with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Additionally, the effect of lesion size on the diagnostic performance of the DWI combination analysis was evaluated.

Results

The strongest indicators of malignancy on DWI were a heterogeneous pattern (P = 0.005) and an apparent diffusion coefficient (ADC) value <1.0 × 10–3 mm2/sec (P = 0.002). The area under the curve (AUC) values for the qualitative analysis, quantitative analysis, and combination analysis on DWI were 0.732 (95% CI, 0.651–0.803), 0.780 (95% CI, 0.703–0.846), and 0.826 (95% CI, 0.754–0.885), respectively (P<0.0001). The AUC for the combination analysis on DWI was superior to that for DCE-MRI alone (0.651, P = 0.003) but inferior to that for DCE-MRI plus the ADC value (0.883, P = 0.03). For the DWI combination analysis, the sensitivity was significantly lower in the size ≤1 cm group than in the size >1 cm group (80% vs. 95.6%, P = 0.034).

Conclusions

Qualitative analysis of tumor morphology was diagnostically applicable on DWI using rs-EPI. This qualitative analysis adds value to quantitative analyses for lesion characterization in patients with breast cancer.

Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features

OBJECTIVE

The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer.

MATERIALS AND METHODS

Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's t test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features. In addition, multiple linear regression analysis was performed to find independent predictive factors associated with the T2* values.

RESULTS

Of the 107 breast cancers, 92 were invasive and 15 were ductal carcinoma in situ (DCIS). The mean T2* value of invasive cancers was significantly longer than that of DCIS (p = 0.029). Signal intensity on T2-weighted imaging (T2WI) and histologic grade of invasive breast cancers showed significant correlation with T2* relaxation time in univariate and multivariate analysis. Breast cancer groups with higher signal intensity on T2WI showed longer T2* relaxation time (p = 0.005). Cancer groups with higher histologic grade showed longer T2* relaxation time (p = 0.017).

CONCLUSION

The T2* value is significantly longer in invasive cancer than in DCIS. In invasive cancers, T2* relaxation time is significantly longer in higher histologic grades and high signal intensity on T2WI. Based on these preliminary data, quantitative T2* mapping has the potential to be useful in the characterization of breast cancer.

Magnetic resonance imaging and pathological characteristics of pure mucinous carcinoma in the breast according to echogenicity on ultrasonography

Purpose

The aim of this study was to explore the clinical and pathological characteristics of pure mucinous breast carcinoma (PMBC) according to internal echogenicity on ultrasonography (US).

Methods

Thirty-three patients with PMBC diagnosed at surgery were included in this study. Cases of PMBC were classified according to internal echogenicity on US. The imaging features on magnetic resonance (MR) imaging and clinicohistopathological characteristics were compared between the hypoechogenic and the isoechogenic to hyperechogenic groups.

Results

Eleven cases of PMBC (33.3%) exhibited hypoechogenicity on US, while 22 cases (66.7%) exhibited isoechogenicity or hyperechogenicity. Of the isoechogenic to hyperechogenic PMBCs, 95.5% showed a high signal on T2-weighted images, which was a significantly greater percentage than was observed for the hypoechogenic group (54.5%) (P=0.010). Of the hypoechogenic PMBCs, 63.6% showed a washout pattern in the delayed phase, which was substantially more than the result of 23.8% observed for the isoechogenic to hyperechogenic PMBCs (P=0.053).

Conclusion

PMBCs with isoechogenicity or hyperechogenicity were more likely to show a high signal intensity on T2-weighted images than hypoechogenic PMBCs. However, other MR imaging and clinicohistopathological characteristics were not significantly different between the two groups.

Takeaways from Pre-Contrast T1 and T2 Breast Magnetic Resonance Imaging in Women with Recently Diagnosed Breast Cancer

Background

Dynamic contrast-enhanced magnetic resonance imaging (DCE - MRI) has been widely used in the management of breast cancer, and its diagnostic value in breast imaging has been demonstrated. There have only been a few reports regarding the usefulness of pre-contrast imaging. Knowledge about clinically significant findings of preoperative, pre-contrast T1 and T2 MR images will allow more accurate decisions regarding patient treatment and management.

Objectives

The aim of this study was to evaluate the clinically significant findings of preoperative, pre-contrast T1 and T2 MR images in recently diagnosed breast cancer patients.

Patients and Methods

We analyzed 390 preoperative 3-T MRIs of recently diagnosed breast cancer patients in whom the diagnosis was confirmed by a core needle biopsy.

Results

MRI findings that were correlated with post-core needle-biopsy changes were observed in 27.9% of the pre-contrast T1 and T2 MRIs (n = 109/390). Two of 35 cases that had a subareolar ductal high signal area on the pre-contrast T1 were confirmed by surgery as having nipple-areolar complex involvement.

Conclusion

A subareolar ductal high signal area on a pre-contrast T1 MRI must be carefully assessed in combination with dynamic, contrast-enhanced images for proper surgical management.

Diffusion-weighted imaging reflects variable cellularity and stromal density present in breast fibroadenomas

OBJECTIVE

To determine the underlying histopathologic features influencing apparent diffusion coefficient (ADC) values of breast fibroadenomas.

MATERIALS AND METHODS

Biopsy-proven fibroadenomas (n=26) initially identified as suspicious on breast magnetic resonance imaging (MRI) were retrospectively evaluated. Histopathologic assessments of lesion cellularity and stromal type were compared with ADC measures on diffusion-weighted MRI.

RESULTS

Presence of epithelial hyperplasia (increased cellularity) and dense collagenous stroma were both significantly associated with lower lesion ADC values (P=.02 and .004, respectively).

CONCLUSION

Variations in epithelial cellularity and stromal type influence breast lesion ADC values and may explain the wide range of ADC measures observed in benign fibroadenomas.

Use of BI-RADS-MRI descriptors for differentiation between mucinous carcinoma and fibroadenoma

OBJECTIVE

We evaluated the latest breast imaging reporting and data system (BI-RADS) magnetic resonance imaging (MRI) (5th edition) descriptors and non BI-RADS MRI factors that contribute to differentiation between mucinous carcinomas (MCs) and fibroadenomas (FAs).

MATERIALS AND METHODS

This retrospective study included 27 patients with P-MCs or M-MCs similar to P-MCs and 22 patients with FAs who underwent breast MRI between October 2008 and July 2014 at our institution. Definitive histopathological diagnoses were made for all of the MCs and FAs. The latest BI-RADS MRI descriptors for abnormal enhancement, including maximum diameter, shape (irregular or round/oval), margin (irregular or circumscribed), rim enhancement (present or absent), dark internal septation (absent or present), delayed internal enhancement (heterogeneous or homogeneous), and the time-intensity curve pattern (not persistent or persistent) were evaluated. As additional non BI-RADS MRI factors related to differentiation between MC and FA, age, signal intensity in the T2-weighted image (high or not high), extent of lobulation (strong or weak), enhancing internal septation (present or absent), and the apparent diffusion coefficient value were also evaluated. One radiologist retrospectively evaluated interpreted MR findings and analyzed the findings. Statistically significant findings were identified through univariate and multivariate analyses. Then, three blinded radiologists reviewed the MR images where MR findings had shown a significant association with outcomes during univariate analyses. Independently, the three blinded readers reviewed the MR images for the evaluation of inter-observer variability, and then arrived at a consensus for the evaluation of observer performance. Observer performance and inter-observer variability were determined via a receiver-operating-characteristic curve analysis and weighted k statistics. The sensitivity, specificity, and accuracy of each of the MR findings were calculated.

RESULTS

Univariate analyses showed that irregular margins were observed more frequently in MCs than in FAs (11/27, 41% vs. 1/22, 0.5%, p<0.05). MCs also showed rim enhancement, delayed heterogeneous enhancement, and enhancing internal septation more frequently than FAs (p<0.05). FAs showed circumscribed margins more frequently than MCs (21/22, 95% vs. 16/27, 59%, p<0.05). FAs also showed dark internal septation more frequently than MCs (18/22, 82% vs. 3/27, 11%, p<0.05). In multivariate analyses, the most significant feature in lesion characterization was delayed heterogeneous enhancement. In the blinded reading, a combination of irregular margin and delayed heterogeneous enhancement showed the highest sensitivity (96.3%) and accuracy (87.8%). Enhancing internal septation showed the highest specificity (90.9%). The κ values with confidence ratings for differentiation between MCs and FAs were 0.63-0.67, which showed substantial agreement among the three radiologists.

CONCLUSIONS

The combination of irregular margin and delayed heterogeneous enhancement and enhancing internal septation were significant findings for differentiation between P-MC or M-MC similar to P-MC and FA.

Incidentally detected enhancing lesions found in breast MRI: analysis of apparent diffusion coefficient and T2 signal intensity significantly improves specificity

OBJECTIVES

To evaluate the value of adding T2- and diffusion-weighted imaging (DWI) to the BI-RADS® classification in MRI-detected lesions.

METHODS

This retrospective study included 112 consecutive patients who underwent 3.0T structural breast MRI with T2- and DWI on the basis of EUSOMA recommendations. Morphological and kinetic features, T2 signal intensity (T2 SI) and apparent diffusion coefficient (ADC) findings were assessed.

RESULTS

Thirty-three (29.5 %) patients (mean age 57.0 ± 12.7 years) had 36 primarily MRI-detected incidental lesions of which 16 (44.4 %) proved to be malignant. No single morphological or kinetic feature was associated with malignancy. Both low T2 SI (P = 0.009) and low ADC values (≤0.87 × 10^-3 mm²s^-1, P < 0.001) yielded high specificity (80.0 %/80.0 %). The BI-RADS classification supplemented with information from DWI and T2-WI improved the diagnostic performance of the BI-RADS classification as sensitivity remained 100 % and specificity improved from 30 % to 65.0 %. The numbers of false positive lesions declined from 39 % (N = 14) to 19 % (N = 7).

CONCLUSION

MRI-detected incidental lesions may be challenging to characterize as they have few specific malignancy indicating features. The specificity of MRI can be improved by incorporating T2 SI and ADC values into the BI-RADS assessment.

KEY POINTS

• MRI-detected incidental lesions have few specific malignancy indicating features. • ≥ 1 suspicious morphologic or kinetic feature may warrant biopsy. • T2 signal intensity and DWI assessment are feasible in primarily MRI-detected lesions. • T2 SI and DWI assessment improve the BI-RADS specificity in MRI-detected lesions.

Apparent diffusion coefficient in differentiation between malignant and benign breast masses: does size matter?

AIM

To determine whether lesion size affects the diagnostic performance of apparent diffusion coefficient (ADC) in the evaluation of breast masses.

MATERIALS AND METHODS

Consecutive breast lesions detected at magnetic resonance imaging (MRI) from June 2010 to July 2013 were retrospectively reviewed. Differences in the ADCs of benign and malignant mass lesions were compared. Receiver operating characteristics analysis was performed to evaluate diagnostic performance of ADC regarding lesion size (≤ 1 cm or >1 cm) and their T2W signal intensities.

RESULTS

Seventy-four malignant lesions (77.9%) and 21 (22.1%) benign lesions were included. Twenty-two of the 95 (23.2%) masses measured ≤ 1 cm (mean 0.73 ± 0.4; range 0.51-0.8 cm) and 73/95 (76.9%) masses measured >1 cm (mean 2.11 ± 0.1; range 1.1-3.3 cm). The mean ADC was significantly lower for malignant than for benign lesions (mean for malignant lesion, 0.89 ± 0.29 × 10(-3) mm(2)/s; mean for benign lesions, 1.27 ± 0.42 × 10(-3) mm(2)/s; p<0.01). The optimal ADC cut-off for differentiating benign and malignant lesion was 1.088 × 10(-3) mm(2)/s with a sensitivity of 85.9% and specificity of 77% for lesions >1 cm. The sensitivity and specificity were lowered to 60% and 50%, respectively, for lesions of size ≤ 1. Maximal sensitivity and specificity were reached when the ADC value was used to evaluate T2-dark lesions.

CONCLUSION

Diffusion-weighted MRI is useful for characterizing masses that are hypointense on T2-weighted images. Lower sensitivity and specificity were found for breast lesions ≤ 1 cm.

Sonography of Invasive Apocrine Carcinoma of the Breast in Five Cases

OBJECTIVE

To evaluate the sonographic features of invasive apocrine carcinoma (IAC) of the breast.

MATERIALS AND METHODS

This study included five pathologically proven cases of IAC, and their sonographic features were retrospectively analyzed according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon.

RESULTS

All five lesions involved the left breast and were seen as irregularly shaped masses. All lesions, except one, had a parallel orientation to the chest wall. All five lesions showed noncircumscribed margins and heterogeneous echotexture; however, they showed various posterior features. One lesion had edema as an associated feature. Sonographic assessments were classified as BI-RADS category 4 in all five cases.

CONCLUSION

Invasive apocrine carcinoma sonographic findings are difficult to differentiate from those of invasive ductal carcinoma of no special type.

Breast Imaging Reporting and Data System category 3 for magnetic resonance imaging

The Breast Imaging Reporting and Data System (BI-RADS) category 3 signifies a probably benign finding and should be reserved for those findings that have a less than 2% risk of malignancy. There are limited data defining the use of this category in magnetic resonance imaging (MRI) and the imaging features of probably benign lesions are not well defined. Most recent studies have shown that the frequency of use of BI-RADS 3 is comparable with that reported in mammography and ultrasound and that the rate of malignancy is within the targeted range. Several findings, once assessed as BI-RADS 3, are now known to represent benign entities, such as variations of background parenchymal enhancement and intramammary lymph nodes. A subset of masses, foci, and nonmass enhancement can appropriately be assigned BI-RADS 3 on baseline MRI. The BI-RADS 3 is most appropriately used for round, dot-like foci, which are unique and separate from background parenchymal enhancement and for oval masses with circumscribed margins and homogeneous enhancement, such as those typically seen in incidental fibroadenomas. The BI-RADS 3 may also be appropriate for a focal or regional distribution of nonmass enhancement without suspicious features.

Effectiveness of additional diagnostic parameters in magnetic resonance mammography: a comparative study with the BI-RADS classification and scoring system

OBJECTIVE

This study aims to evaluate the strength of magnetic resonance (MR) lesion descriptors for malignancy and to determine the effectiveness of a scoring system that combines BI-RADS parameters with additional criteria.

MATERIALS AND METHODS

Three hundred thirty-nine histopathologically proven lesions that had undergone MR imaging were analyzed retrospectively. Based on the Fischer scoring system, an optimal cutoff value was calculated for combined parameters. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for all lesions with MR BI-RADS classification without using additional parameters. Finally, the results of the scoring system and MR BI-RADS classification were compared.

RESULTS

The optimal cutoff value according to the total score was calculated as 5. The sensitivity and the specificity of BI-RADS classification were calculated to be 94.20% and 56.12%, respectively. The scoring system using a cutoff value of 5 resulted in a little loss of sensitivity (89.86%) but resulted in a reasonable increase in the specificity (88.49%).

CONCLUSIONS

Additional parameters can improve the specificity of MR imaging. T2-weighted signal features, adjacent vessel sign, unilateral-focal edema, and hook sign were considered as effective parameters.

Rim sign in breast lesions on diffusion-weighted magnetic resonance imaging: diagnostic accuracy and clinical usefulness

PURPOSE

To investigate the diagnostic accuracy and clinical usefulness of the rim sign in breast lesions observed in diffusion-weighted magnetic resonance imaging (DWI).

MATERIALS AND METHODS

The magnetic resonance imaging (MRI) findings of 98 pathologically confirmed lesions (62 malignant and 36 benign) in 84 patients were included. Five breast radiologists were asked to independently review the breast MRI results, to grade the degree of high peripheral signal, the "rim sign," in the DWI, and to confirm the mean apparent diffusion coefficient (ADCmean ) values. We analyzed the diagnostic accuracy and compared the consensus (when ≥ 4 of 5 independent reviewers agreed) results of the rim sign with the ADCmean values. Additionally, we evaluated the correlation between the dynamic contrast-enhanced (DCE)-MRI morphologic appearance and DWI rim sign.

RESULTS

According to the consensus results, the rim sign in DWI was observed on 59.7% of malignant lesions and 19.4% of benign lesions. The sensitivity, specificity, and area under the curve (AUC) value for the rim sign in DWI were 59.7%, 80.6%, and 0.701, respectively. The sensitivity, specificity, and AUC value for the ADCmean value (criteria ≤ 1.46 × 10(-3) mm(2) /sec) were 82.3%, 63.9%, and 0.731, respectively. Based on consensus, no correlation was observed between the DCE-MRI and DWI rim signs.

CONCLUSION

In DWI, a high-signal rim is a valuable morphological feature for improving specificity in DWI.

Sonographic and pathologic image analysis of pure mucinous carcinoma of the breast

The aims of this study were to elucidate sonographic and histologic features of pure mucinous carcinoma (P-MC) of the breast using quantitative analysis and to evaluate the relationship between quantitative analysis and visual qualitative assessment. Eleven P-MCs (nine patients) were evaluated qualitatively and quantitatively. Three experts assessed these sonographic images using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. For assessment of internal echoes and posterior echoes, quantitative measures were determined using ImageJ software. Histologic thin sections were stained for classification into separate parts of the tumor (stroma, mucin and cancer cells) and were digitized. Internal echoes were isoechoic in 7 of 11 (63.6%) tumors and hypoechoic in 4 of 11 (36.4%); all P-MCs were "enhanced" in qualitative evaluation. As internal echoes increased, the proportion of stroma increased and that of mucin decreased. The high level of internal echoes is correlated with reflection and back-scattering, which are caused mainly by the interface between mucin and stroma.

Intratumoral concentration of estrogens and clinicopathological changes in ductal carcinoma in situ following aromatase inhibitor letrozole treatment

Background:

Estrogens have important roles in ductal carcinoma in situ (DCIS) of the breast. However, the significance of presurgical aromatase inhibitor treatment remains unclear. Therefore, we examined intratumoral concentration of estrogens and changes of clinicopathological factors in DCIS after letrozole treatment.

Methods:

Ten cases of postmenopausal oestrogen receptor (ER)-positive DCIS were examined. They received oral letrozole before the surgery, and the tumour size was evaluated by ultrasonography. Surgical specimens and corresponding biopsy samples were used for immunohistochemistry. Snap-frozen specimens were also available in a subset of cases, and used for hormone assays and microarray analysis.

Results:

Intratumoral oestrogen levels were significantly lower in DCIS treated with letrozole compared with that in those without the therapy. A great majority of oestrogen-induced genes showed low expression levels in DCIS treated with letrozole by microarray analysis. Moreover, letrozole treatment reduced the greatest dimension of DCIS, and significantly decreased Ki-67 and progesterone receptor immunoreactivity in DCIS tissues.

Conclusion:

These results suggest that estrogens are mainly produced by aromatase in DCIS tissues, and aromatase inhibitors potently inhibit oestrogen actions in postmenopausal ER-positive DCIS through rapid deprivation of intratumoral estrogens.

Updates and revisions to the BI-RADS magnetic resonance imaging lexicon

This article summarizes the updates and revisions to the second edition of the BI-RADS MRI lexicon. A new feature in the lexicon is background parenchymal enhancement and its descriptors. Another major focus is on revised terminology for masses and non-mass enhancement. A section on breast implants and associated lexicon terms has also been added. Because diagnostic breast imaging increasingly includes multimodality evaluation, the new edition of the lexicon also contains revised recommendations for combined reporting with mammography and ultrasound if these modalities are included as comparison, and clarification on the use of final assessment categories in MR imaging.

3D T2-weighted spin echo imaging in the breast

PURPOSE

To evaluate the performance of 2D versus 3D T2-weighted spin echo imaging in the breast.

MATERIALS AND METHODS

2D and 3D T2-weighted images were acquired in 25 patients as part of a clinically indicated breast magnetic resonance imaging (MRI) exam. Lesion-to-fibroglandular tissue signal ratio was measured in 16 identified lesions. Clarity of lesion morphology was assessed through a blinded review by three radiologists. Instances demonstrating the potential diagnostic contribution of 3D versus 2D T2-weighted imaging in the breast were noted through unblinded review by a fourth radiologist.

RESULTS

The lesion-to-fibroglandular tissue signal ratio was well correlated between 2D and 3D T2-weighted images (R(2)  = 0.93). Clarity of lesion morphology was significantly better with 3D T2-weighted imaging for all observers based on a McNemar test (P ≤ 0.02, P ≤ 0.01, P ≤ 0.03). Instances indicating the potential diagnostic contribution of 3D T2-weighted imaging included improved depiction of signal intensity and improved alignment between DCE and T2-weighted findings.

CONCLUSION

In this pilot study, 3D T2-weighted imaging provided comparable contrast and improved depiction of lesion morphology in the breast in comparison to 2D T2-weighted imaging. Based on these results further investigation to determine the diagnostic impact of 3D T2-weighted imaging in breast MRI is warranted.