Estrogen receptor and breast MR imaging features: a correlation study

Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research

Breast cancer is a multifactorial heterogeneous disease and the leading cause of cancer-related deaths in women; its diagnosis and treatment require clinical sensitivity and a comprehensive disciplinary research approach. The expression of different receptors on tumor cells not only provides the basis for molecular typing of breast cancer but also has a decisive role in the diagnosis, treatment, and prognosis of breast cancer. To date, immunohistochemistry (IHC), which uses invasive histological sampling, has been extensively used in clinical practice to analyze the status of receptors and to make an accurate diagnosis of breast cancer. As an invasive assay, IHC can provide important biological information on tumors at a single point in time, but cannot predict future changes (due to treatment or tumor mutations) without additional invasive procedures. These issues highlight the need to develop a non-invasive method for predicting receptor status. The emerging field of radiomics may offer a non-invasive approach to identification of receptor status without requiring biopsy. In this paper, we present a review of the latest research results in radiomics for predicting the status of breast cancer receptors, with potential important clinical applications.

AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer

BACKGROUND AND OBJECTIVES

Tumor microenvironment (TME) is a determining factor in decision-making and personalized treatment for breast cancer, which is highly intra-tumor heterogeneous (ITH). However, the noninvasive imaging phenotypes of TME are poorly understood, even invasive genotypes have been largely known in breast cancer.

METHODS

Here, we develop an artificial intelligence (AI)-driven approach for noninvasively characterizing TME by integrating the predictive power of deep learning with the explainability of human-interpretable imaging phenotypes (IMPs) derived from 4D dynamic imaging (DCE-MRI) of 342 breast tumors linked to genomic and clinical data, which connect cancer phenotypes to genotypes. An unsupervised dual-attention deep graph clustering model (DGCLM) is developed to divide bulk tumor into multiple spatially segregated and phenotypically consistent subclusters. The IMPs ranging from spatial heterogeneity to kinetic heterogeneity are leveraged to capture architecture, interaction, and proximity between intratumoral subclusters.

RESULTS

We demonstrate that our IMPs correlate with well-known markers of TME and also can predict distinct molecular signatures, including expression of hormone receptor, epithelial growth factor receptor and immune checkpoint proteins, with the performance of accuracy, reliability and transparency superior to recent state-of-the-art radiomics and 'black-box' deep learning methods. Moreover, prognostic value is confirmed by survival analysis accounting for IMPs.

CONCLUSIONS

Our approach provides an interpretable, quantitative, and comprehensive perspective to characterize TME in a noninvasive and clinically relevant manner.

Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma

Background

The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status.

Methods

We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 (http://keyan.deepwise.com/), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA).

Results

70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827–0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758–0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904–0.987) in the training set and 0.868 (95%CI: 0.789–0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer–Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram.

Conclusion

Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09967-6.

Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study

Objective

To investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).

Methods

A total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established.

Results

Of the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively.

Conclusion

The DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.

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.

Comparison of Ultrasound Features With Maximum Standardized Uptake Value Assessed by 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography for Prognosis of Estrogen Receptor+/Human Epithelial Growth Factor Receptor 2- Breast Cancer

ABSTRACT

estrogen receptor (ER)+/human epithelial growth factor receptor 2 (HER2)- breast cancers have less aggressive traits and a favorable prognosis when treated early. Prediction of prognosis for treatment outcome or survival in ER+/HER2- cancer is important. Ultrasound (US) is an effective and easy technique for breast cancer diagnosis and tumor characterization. Positron emission tomography/computed tomography (PET/CT) is widely used for diagnosis, staging, and therapeutic response in cancer evaluation, and a high maximum standardized uptake value (SUVmax) is associated with poor prognosis. The study aim was to compare the prognostic value of US features with that of the SUVmax assessed by PET/CT in ER+/HER- breast cancer patients. We retrospectively identified breast cancer patients in our institutional database who had undergone preoperative US and PET/CT, and 96 patients with invasive ductal carcinoma and ductal carcinoma in situ were included in this study. The US features of mass shape, margin, echo pattern, orientation, posterior features, boundary, and calcification in the mass were analyzed. We then analyzed the US features to look for correlations with SUVmax and associations with margins, boundaries, posterior features, histological grade, and ki-67 expression. High SUVmax was correlated with irregular shape, not-circumscribed margin, posterior acoustic enhancement, echogenic halo, and calcification in the mass (P < 0.05, all). Posterior acoustic enhancement was correlated with high ki-67 expression. Many US features of ER+/HER- breast cancer showed associations with SUVmax. Some US features of ER+/HER- breast cancer were useful for predicting prognosis.

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

BACKGROUND

Successful surgical treatment for localized breast cancer can depend on accurate diagnosis for accompanying non-mass enhancement (NME) on preoperative breast magnetic resonance imaging (MRI).

PURPOSE

To evaluate the diagnostic value of mammography for accompanying NME adjacent to index cancer on preoperative breast MRI.

MATERIAL AND METHODS

Among 569 consecutive patients who underwent preoperative breast MRI from January 2016 to August 2018 for ultrasound-guided biopsy-proven breast cancer, 471 patients who underwent initial mammography and subsequent surgery were finally included. Two radiologists retrospectively reviewed preoperative MRI findings of the 471 patients and detected accompanying NME adjacent to index cancer. MRI, mammography, and histopathology findings of the accompanying NME were evaluated using Pearson's chi-square test, Mann-Whitney U test, and logistic regression analysis. The area under the receiver operating characteristic curve (AUC) of MRI and combined MRI and mammography was calculated in differentiating benign from malignant accompanying NME. The reference standard was surgical pathologic findings.

RESULTS

MRI revealed 93 accompanying NME lesions in 92 (19.5%) of the 471 patients, showing 55 (59.1%) malignant and 38 (40.9%) benign lesions. On multivariate analysis, malignant NME lesions were more associated with mammography-positive findings (P = 0.000), clumped or clustered ring internal enhancement (P = 0.015), and extensive intraductal component presence of index tumor (P = 0.007) compared with benign lesions. The AUC increased after correlation with mammography showing 0.649 (95% confidence interval [CI] 0.533-0.765) for MRI and 0.833 (95% CI 0.747-0.919) for combined MRI and mammography.

CONCLUSION

Mammography is valuable in predicting malignancy for accompanying NME on preoperative breast MRI.

Ultrafast Dynamic Contrast-Enhanced MRI Using Compressed Sensing: Associations of Early Kinetic Parameters With Prognostic Factors of Breast Cancer

OBJECTIVE. The purpose of this study was to investigate whether early kinetic parameters derived from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) using compressed sensing are associated with prognostic factors for breast cancer. MATERIALS AND METHODS. We evaluated 201 consecutive women (mean age, 54.6 years) with breast cancer (168 invasive, 33 ductal carcinoma in situ) who underwent both ultrafast DCE-MRI using compressed sensing (temporal resolution, 4.7 seconds; spatial resolution, 0.8 × 1.1 × 0.9 mm) and surgery between 2018 and 2019. Early kinetic parameters (time to enhancement [TTE] and maximum slope [MS]) were measured in breast lesions by two radiologists using a software program and were correlated with histopathologic prognostic factors. The Mann-Whitney U test and linear regression analysis were used. RESULTS. The median TTE and MS values for breast cancer were 11.9 seconds and 7.7%/s, respectively. The median MS was significantly larger in invasive cancer lesions than in ductal carcinoma in situ lesions (8.4%/s vs 4.7%/s, p < .001). In women with invasive cancer, multivariate linear regression analyses showed that a larger tumor size (> 2 cm) (p = .048) and estrogen receptor-negative status (p < .001) were significantly associated with a shorter TTE. A higher histologic grade (grade 3) (p = .01) was significantly associated with a larger MS. We observed excellent interobserver agreement between two readers in the measurements of TTE and MS (intraclass correlation coefficients, 0.943 and 0.890, respectively). CONCLUSION. Ultrafast MRI-derived early enhancement parameters, such as TTE and MS, are associated with histopathologic prognostic factors in women with breast cancer.

Histogram analysis of quantitative parameters from synthetic MRI: Correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer

PURPOSE

To evaluate intra-tumoral heterogeneity through a histogram analysis of quantitative parameters obtained from synthetic MRI (magnetic resonance imaging), and determine correlations of these histogram characteristics with prognostic factors and molecular subtypes of invasive ductal carcinoma (IDC).

METHODS

A total of 122 IDC from 122 women who underwent preoperative synthetic MRI and DCE (dynamic contrast enhancement)-MRI were investigated. The synthetic MRI parameters (T1, T2, and PD (proton density)) were obtained. For each parameter, the minimum, 10th percentile, mean, median, 90th percentile, maximum, skewness, and kurtosis values of tumor were calculated, and correlations with prognostic factors and subtypes were assessed. The Mann-Whitney U test or the Student^'s t test were utilized to analyze the association between the histogram features of synthetic MRI parameters and prognostic factors. The Kruskal-Wallis test followed by the post-hoc test was used to analyze differences of synthetic MRI parameters among molecular subtypes.

RESULTS

IDC with high histopathologic grade showed statistically higher PD_maxium, T1_mean and T1_median values than those with low grade (p = 0.003, p = 0.007, p = 0.003). The T1_10th were significantly higher in cancers with PR (progesterone receptor) negativity than those with PR positivity (p = 0.005). ER-negative cancers had significant higher values of T2_10th, T2_mean, and T2_median than ER-positive cancers (p = 0.006, 0.002, and 0.006, respectively). The values of PD_median were significantly higher in IDC with HER2 (human epidermal growth factor receptor 2) positivity than those with HER2 negativity (p = 0.001). When discriminating molecular subtypes of IDC, the T2_mean achieved the highest performance. The T2_mean values of TN (triple-negative), luminal B and luminal A types are arranged in descending order (p <  0.0001).

CONCLUSIONS

Histogram features derived from synthetic MRI quantifies the distributions of tissue relaxation time and proton density, and may serve as a potential biomarker for discriminating histopathological grade, hormone receptor status, HER2 expression status and breast cancer subtypes.

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

BACKGROUND

 Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information.

METHODS AND RESULTS

 This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed.

CONCLUSION

 Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine.

KEY POINTS

  · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review..

CITATION FORMAT

· Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.

MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

Analysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

Breast cancer imaging features as a predictor of the hormonal receptor status, HER2neu expression and molecular subtype

Background

Determination of the hormonal receptor (HR) status, HER2neu expression, and the molecular subtype has valuable diagnostic, therapeutic, and prognostic implications for breast cancer as breast cancer stratification during the last two decades has become dependent upon the underlying biology. The aim of this study is to assess the correlation between imaging features of breast cancer and the HR status, HER2neu expression, and the molecular subtype. Sixty breast cancer patients underwent breast ultrasound, mammography, and MRI evaluation. Pathological evaluation using immunohistochemistry and FISH was used to detect the HR status, HER2/neu expression, and the molecular subtype. Those findings were then correlated with the radiologic data.

Results

HR-positive tumors were associated with posterior acoustic shadowing (34/44, 77.3%; p = 0.004). Hormonal-negative tumors presenting as masses were more likely circumscribed on US and MRI compared to hormonal positive mass tumors (6/14, 42.9% vs 3/36, 7.7%; p = 0.003 on US and 6/13, 46.3% vs 3/36, 8.3%; P = 0.007 on MRI) and had malignant DCE kinetics with washout curves compared to the hormonal positive group (10/16, 62.5% vs 4/44, 9.1%; P < 0.001). HER2neu-positive tumors were significantly associated with calcifications and multifocality on mammography compared to HER2neu-negative group (9/13, 69% vs 12/34, 25.5%; P = 0.007) and (7/13, 53% vs 3/47, 6%; P < 0.001). TNBC and HER2neu-enriched were associated with washout kinetic curve pattern (57.1% and 66.7%, respectively). TNBCs were associated with circumscribed margins on US and MRI (6/9, 66.7%; P < 0.001).

Conclusion

Microcalcifications, margins, posterior acoustic features, and malignant washout kinetics strongly correlate with the hormonal receptor status, HER2neu status, and molecular subtype of breast cancer. These findings may suggest the molecular subtype of breast cancer and further expand the role of imaging.

The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status

In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naïve Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER-, PR+ versus PR-, HER2+ versus HER2-, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.

Agreement between dynamic contrast-enhanced magnetic resonance imaging and pathologic tumour size of breast cancer and analysis of the correlation with BI-RADS descriptors

Purpose

The purpose of this study was to evaluate magnetic resonance imaging (MRI)-pathology concordance of tumour size in patients with invasive breast carcinoma, with an emphasis on Breast Imaging Reporting and Data System (BI-RADS) descriptors of dynamic contrast-enhanced MRI (DCE-MRI).

Material and methods

Of patients who had preoperative DCE-MRI, 94 were enrolled. Concordance between MRI and the pathological findings was defined as a difference in tumour size of 5 mm or less. The greatest dimension was measured by two radiologists, and BI-RADS descriptives were described in accordance. The gold standard was chosen as the pathologic assessment.

Results

Tumour measurements determined by MRI and the pathological reports were not statistically different (2.64 ± 1.16 cm, Wilcaxon Z = –1.853, p = 0.064). Tumour sizes were concordant in 72/94 patients (76.6%). The mean difference between the pathological and MRI tumour sizes was –0.1 cm. MRI overestimated the size of 17/94 tumours (18.1%) and underestimated the size of 5/94 tumours (5.3%). Discordance was associated with larger tumour size. Histologic and molecular type of tumours, patient age, histologic grade, lymphovascular invasion or perineural invasion positivity, fibroglandular volume, background parenchymal enhancement, and being mass or non-mass were not associated with concordance. Irregular margin and heterogenous enhancement in DCE-MRI were associated with discordance in logistic regression analysis (p = 0.035, OR: 4.24; p = 0.021, OR: 4.96).

Conclusions

Two BI-RADS descriptors of irregular contour and heterogeneous contrast uptake were found to be associated with tumour size discrepancy. This might be attributed to the dynamic and morphologic specialities of tumours primarily rather than tumour biology.

Agreement between MRI and pathologic analyses for determination of tumor size and correlation with immunohistochemical factors of invasive breast carcinoma

Background There may be discordance between tumor size determined by magnetic resonance imaging (MRI) and that observed during pathologic analyses. Purpose To evaluate MRI-pathology concordance of tumor size in patients with invasive breast carcinoma. Material and Methods Data from 307 invasive breast carcinomas were analyzed retrospectively. Preoperative breast MRI was reviewed for size, lesion type, morphology, and dynamic contrast-enhanced tumor kinetics. MRI tumor size was compared with tumor size measurements from the pathologic analysis. Concordance was defined as a difference in diameter of ≤ 0.5 cm. MRI-pathology concordance was compared according to clinical and histopathologic features. Results The mean tumor size on MRI was 2.48 ± 1.41 cm. Tumor measurements determined by MRI were not significantly different from those recorded in the pathologic reports (2.56 ± 1.61 cm, P = 0.199). MRI-pathology concordance was found in 229/307 (74.6%) cases; the size was overestimated in 36 (11.7%) tumors and underestimated in 42 (13.7%). On univariate analysis, MRI-pathology discordance was associated with larger tumor size ( P < 0.001), estrogen receptor (ER) negativity ( P = 0.006), and lymphovascular invasion ( P = 0.003). Human epidermal growth factor receptor 2 positive molecular subtype showed worse correlation between the tumor size measured by MRI and pathology compared with luminal A and luminal B subtypes ( P = 0.008 and 0.007). On multivariate analysis, tumor size and ER status significantly influenced MRI-pathology concordance ( P < 0.05). Conclusion ER negativity and larger tumor size were strongly associated with MRI-pathology discordance in invasive breast carcinomas. Awareness of these factors might improve surgical planning.

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI

PURPOSE

Recognizing molecular markers is helpful for guiding treatment plans for breast cancer. This study correlated estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and triple-negative breast cancer (TNBC) statuses to the degree of heterogeneity on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

A total of 102 biopsy-proven cancers from 102 patients between October 2010 and December 2012 were used in this study, including ER (59 positive, 43 negative), HER2 (47 positive, 55 negative), and TNBC (22 TNBC, 80 non-TNBC). At first, the tumor region was segmented by using a region growing method. Then, the region-based features were extracted by the proposed regionalization method to quantify intra-tumoral heterogeneity on breast DCE-MRI. The three-dimensional morphological features (texture features and shape feature) and the pharmacokinetic model were also extracted from the segmented tumor region. After feature extraction, a logistic regression was used to classify ER, HER2, and TNBC statuses respectively. The performances were evaluated by using receiver operating characteristic (ROC) curve analysis.

RESULTS

The proposed region-based features achieved the accuracy of 73.53%, 82.35%, and 77.45% for ER, HER2, and TNBC classifications. The corresponding area under the ROC curves (Az) achieves 0.7320, 0.8458, and 0.8328 that were better than those of texture features, shape features, and Tofts pharmacokinetic model.

CONCLUSION

The intra-tumoral heterogeneity quantified by the region-based features can be used to reflect the vasculature complexity of different molecular markers and to provide prediction information of cell surface receptors on clinical examination.

Role of MRI in the staging of breast cancer patients: does histological type and molecular subtype matter?

OBJECTIVE

To assess the role of MRI in the pre-operative staging of patients with different histological types and molecular subtypes of breast cancer, by the assessment of the dimensions of the main tumour and identification of multifocal and/or multicentric disease.

METHODS

The study included 160 females diagnosed with breast cancer who underwent breast MRI for pre-operative staging. The size of the primary tumour evaluated by MRI was compared with the pathology (gold standard) using the Pearson's correlation coefficient (r). The presence of multifocal and/or multicentric disease was also evaluated.

RESULTS

The mean age of patients was 52.6 years (range 30-81 years). Correlation between the largest dimension of the main tumour measured by MRI and pathology was worse for non-special type/invasive ductal carcinoma than for other histological types and was better for luminal A and triple-negative than for luminal B and Her-2 molecular subtypes. Multifocal and/or multicentric disease was present in 48 patients (30.0%), and it was more common in breast carcinomas classified as Her-2 molecular subtype. There was no statistically significant difference in the frequency of multifocal and/or multicentric tumours identified only by MRI in relation to histological type or molecular subtype.

CONCLUSION

The results of this retrospective study demonstrated that histological types and molecular subtypes might influence the MRI assessment of breast cancers, especially in the evaluation of tumour size.

ADVANCES IN KNOWLEDGE

The real benefit of MRI for treatment planning in patients with breast cancer may be different according to the histological type and molecular subtype.

MRI kinetics with volumetric analysis in correlation with hormonal receptor subtypes and histologic grade of invasive breast cancers

OBJECTIVE. The aim of this study was to assess whether computer-assisted detection-processed MRI kinetics data can provide further information on the biologic aggressiveness of breast tumors. MATERIALS AND METHODS. We identified 194 newly diagnosed invasive breast cancers presenting as masses on contrast-enhanced MRI by a HIPAA-compliant pathology database search. Computer-assisted detection-derived data for the mean and median peak signal intensity percentage increase, most suspicious kinetic curve patterns, and volumetric analysis of the different kinetic patterns by mean percentage tumor volume were compared against the different hormonal receptor (estrogen-receptor [ER], progesterone-receptor [PR], ERRB2 (HER2/neu), and triple-receptor expressivity) and histologic grade subgroups, which were used as indicators of tumor aggressiveness. RESULTS. The means and medians of the peak signal intensity percentage increase were higher in ER-negative, PR-negative, and triple-negative (all p ≤ 0.001), and grade 3 tumors (p = 0.011). Volumetric analysis showed higher mean percentage volume of rapid initial enhancement in biologically more aggressive ER-negative, PR-negative, and triple-negative tumors compared with ER-positive (64% vs 53.6%, p = 0.013), PR-positive (65.4% vs 52.5%, p = 0.001), and nontriple-negative tumors (65.3% vs 54.6%, p = 0.028), respectively. A higher mean percentage volume of rapid washout component was seen in ERRB2-positive tumors compared with ERRB2-negative tumors (27.5% vs 17.9%, p = 0.020). CONCLUSION. Peak signal intensity percentage increase and volume analysis of the different kinetic patterns of breast tumors showed correlation with hormonal receptor and histologic grade indicators of cancer aggressiveness. Computer-assisted detection-derived MRI kinetics data have the potential to further characterize the aggressiveness of an invasive cancer.

Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis

PURPOSE

To evaluate heterogeneity within tumor subregions or "habitats" via textural kinetic analysis on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the classification of two clinical prognostic features; 1) estrogen receptor (ER)-positive from ER-negative tumors, and 2) tumors with four or more viable lymph node metastases after neoadjuvant chemotherapy from tumors without nodal metastases.

MATERIALS AND METHODS

Two separate volumetric DCE-MRI datasets were obtained at 1.5T, comprised of bilateral axial dynamic 3D T1 -weighted fat suppressed gradient recalled echo-pulse sequences obtained before and after gadolinium-based contrast administration. Representative image slices of breast tumors from 38 and 34 patients were used for ER status and lymph node classification, respectively. Four tumor habitats were defined based on their kinetic contrast enhancement characteristics. The heterogeneity within each habitat was quantified using textural kinetic features, which were evaluated using two feature selectors and three classifiers.

RESULTS

Textural kinetic features from the habitat with rapid delayed washout yielded classification accuracies of 84.44% (area under the curve [AUC] 0.83) for ER and 88.89% (AUC 0.88) for lymph node status. The texture feature, information measure of correlation, most often chosen in cross-validations, measures heterogeneity and provides accuracy approximately the same as with the best feature set.

CONCLUSION

Heterogeneity within habitats with rapid washout is highly predictive of molecular tumor characteristics and clinical behavior.

Prevalence of BRCA1 gene mutation in breast cancer patients in Guangxi, China

OBJECTIVE

The prevalence of breast cancer susceptibility gene 1 mutation in breast cancer patients of south China has not been well revealed. This study was to invest the prevalence of BRCA1 gene mutation in breast cancer patients in Guangxi, China, and to try reflecting its relevance in genetic counseling of breast cancer.

METHODS

In this study, 463 breast cancer patients and 30 healthy women (control group) were involved. Entire sequence and splicing sites of BRCA1 genes were detected by PCR-DNA sequencing.

RESULTS

About 8.9% (41/463) patients were with 22 BRCA1 mutations (all in exon 10). The average hospitalized age of BRCA1-associated breast cancer cases was significantly younger (t = -2.965, P = 0.003). The nuclear grade (U = 2321.0, P = 0.030), ER (U = 4343.5, P = 0.041) and CerbB-2 (U = 3894.0, P = 0.038) expression levels, and triple negative breast cancer diagnosing rate (χ(2) = 4.719, P = 0.03) were disclosed more in BRCA1-associated patients.

CONCLUSIONS

The four most frequent BRCA1 mutation (2798 T > C, 3971 G > A, 3971 G > A and 624 C > T) found in female breast cancer cases in Guangxi are all located in exon 10. BRCA1-associated breast cancer cases have earlier onset age, higher nuclear grade and negative ER and CerbB-2 expression.

Correlation between MR imaging - prognosis factors and molecular classification of breast cancers

The molecular classification of breast cancers defines subgroups of cancer with different prognoses and treatments. Each molecular type representing the intrinsic signature of the cancer corresponds to a histological profile incorporating hormone receptors, HER2 status and the proliferation index. This article describes the correlations between this molecular classification obtained in routine clinical practice using histological parameters and MRI. It shows that there is a specific MRI profile for triple-negative cancers: distinct demarcation, regular edges, hyperintensity on T2 weighted signals and, particularly, a crown enhancement. It is important for the radiologist to understand this molecular classification, firstly because of the relatively suggestive appearance of triple-negative basal-like cancers in the molecular classification, secondly, and particularly, as cancers in patients with the BRCA1 mutation are often triple-negative meaning that the criteria for reading the MRI needs to be tailored to this feature of the cancers, and finally because the efficacy of MRI in assessing response to neoadjuvant chemotherapy depends on the molecular class of cancer treated.

Invasive breast carcinoma: influence of prognosis and patient-related factors on kinetic MR imaging characteristics

PURPOSE

To retrospectively compare the kinetic magnetic resonance (MR) imaging characteristics of invasive breast carcinomas with both prognostic tumoral and patient-related parameters.

MATERIALS AND METHODS

This HIPAA-compliant retrospective study was approved by the institutional review board, and informed consent was waived. From January 2008 to January 2011, 273 consecutive women (mean age, 55 years; range, 23-83 years) with invasive breast cancers who had undergone MR imaging were selected. The kinetic curves were retrospectively classified according to the Breast Imaging Reporting and Data System lexicon. Initial enhancement and maximal enhancement percentages, time to peak enhancement, and the signal enhancement ratio were calculated for each lesion. Kinetic characteristics were compared according to tumoral parameters (size, pathologic type, grade, hormone receptor status, and c-erbB-2 status) and patient parameters (menopausal status, personal history of breast carcinoma) by means of univariate and then multivariate analysis by using false-discovery-rate statistics.

RESULTS

Lesions in menopausal patients exhibited less suspicious quantitative and qualitative characteristics than lesions in nonmenopausal patients. There was an independent association between the kinetic characteristics and menopausal status, with an odds ratio of 2.94 for the lack of rapid initial contrast material uptake and of 2.38 for the lack of washout in menopausal patients as compared with nonmenopausal patients. The odds ratio was 4.00 for not having rapid initial contrast material uptake in patients with a personal history of ipsilateral breast cancer.

CONCLUSION

Kinetic data in invasive breast cancer are associated with the patient's menopausal status, with a typical kinetic pattern of malignancy being less common in menopausal patients.

Correlation between imaging and molecular classification of breast cancers

The histological type of tumour according to the WHO: ductal, lobular, rare forms, is correlated with specific aspects of the imaging based on each type. This morphological classification was improved by knowledge of the molecular anomalies of breast cancers, resulting in the definition of cancer sub-groups with distinct prognoses and different responses to treatment: luminal A, luminal B, HER2 positive, basal-like, triple-negative. Studies are beginning to deal with the appearance of each sub-type in the imaging. It is now important for the radiologist to be familiar with them.

Computer-aided diagnosis for 3-d power Doppler breast ultrasound

In recent studies, both tumor morphology and vascularity played an important role in differentiating breast tumors. In this article, a computer-aided diagnosis (CAD) system was proposed to quantify the tumor morphology of vascularity on three-dimensional (3-D) power Doppler breast ultrasound (PDUS) images. We segmented the tumor margin by the level set method and skeletonized vessels by the 3-D thinning algorithm from 3-D PDUS data to capture the B-mode and vascularity features. The B-mode features including texture, shape and ellipsoid fitting and the vascularity features containing volume, complexity, length, radius and tortuosity were used to differentiate breast tumors. In the experiment, 82 biopsy-verified lesions including 41 benign and 41 malignant lesions were used to test the performance of the proposed system. The proposed method performed well, achieving accuracy, sensitivity, specificity and Az values of 85.37% (70/82), 85.37% (35/41), 85.37% (35/41) and 0.9104, respectively.

[TRP calcium channel and breast cancer: expression, role and correlation with clinical parameters]

Breast cancer (BC) has the highest incidence rate in women in industrialized countries. Statistically, it is estimated that one out of 10 women will develop BC during her life. Evidence is accumulating for the role of ion channels in the development of cancer. Most studied ion channels in BC are K(+) channels, which are involved in cell proliferation, cell cycle progression and cell migration, and Na(+) channels, which correlate with invasiveness. Emerging studies demonstrated the role of Ca(2+) signaling in cancer cell proliferation, survival and migration. Recent findings demonstrated that the expression and/or activity of the transient receptor potential (TRP) channels are altered in several cancers. Among the TRP families, TRPC (canonical or classical), TRPM (melastatin) and TRPV (vanilloid) are related to malignant growth and cancer progression. Although these channels are frequently and abundantly expressed in many tumors, their specific expression, activity and roles in BC are still poorly understood. The expression of TRP channels has also been proposed as a tool for diagnosis, prognosis and/or therapeutic issues of several diseases. In cancer, TRPV6 and TRPM8 have been proposed as tumor progression markers of prostate cancer outcome and TRPC6 as a novel therapeutic target for esophageal carcinoma. Interestingly high levels of TRPC3 expression correlate with a favorable prognosis in patients with lung adenocarcinoma. Our team has recently reported the expression and role of TRPC1, TRPC6, TRPM7, TRPM8 and TRPV6 in BC cell lines and primary cultures. We have also investigated TRP expression and their clinical significance in human breast adenocarcinoma and we suggest that TRP channels are new potential BC markers. Indeed TRPC1 and TRPM8 may be considered as good prognosis markers of well-differentiated tumors, TRPM7 as a proliferative marker of poorly differentiated tumors and TRPV6 as a prognosis marker of aggressive cancers. In this review, we summarize the data reported to date regarding the changes in TRP expression associated with BC. We also discuss the importance of TRP channels in BC cells proliferation and migration and their interest as new BC markers.

Evaluation of breast cancer using proton MR spectroscopy: total choline peak integral and signal-to-noise ratio as prognostic indicators

OBJECTIVE

The purpose of this article is to determine whether the peak integral and signal-to-noise ratio (SNR) of total choline-containing compounds obtained by MR spectroscopy (MRS) correlate with histologic biomarkers currently used for predicting prognosis in patients with breast cancer.

MATERIALS AND METHODS

Single-voxel proton MRS using a 1.5-T scanner was performed in 184 patients (mean age, 48 years; range, 28-72 years) with breast cancer. We obtained absolute total choline-containing compound peak integral, total choline-containing compound peak integral normalized for the volume of interest, and SNR after MRI. On surgical pathology, pathologic subtype and prognostic factors such as nuclear grade, histologic grade, estrogen receptor (ER), HER-2≠neu, extensive intraductal component (EIC), lymphovascular invasion, and lymph node metastasis were also evaluated. Statistical analysis was performed using Mann-Whitney U test and Spearman rank correlation.

RESULTS

The total choline-containing compound SNR, absolute total choline-containing compound peak integral, and normalized total choline-containing compound integral were significantly higher for invasive ductal carcinoma, cancer of high nuclear or histologic grade, and EIC-negative cancer (p < 0.001) than for in situ or other invasive carcinomas (p = 0.005), cancer of low nuclear or histologic grade (p = 0.009), and EIC-positive cancer (p = 0.017). There was a significant difference in the total choline-containing compound SNR between ER-positive and -negative groups (p = 0.007) and between triple-negative and non-triple-negative groups (p = 0.002). A positive correlation was found between the volume of interest (p < 0.001), tumor size (p = 0.011), and three MRS parameters (p = 0.003).

CONCLUSION

Our study suggests that proton MRS can play a role in predicting prognostic indicators of tumor aggressiveness in patients with newly diagnosed breast cancer.

Serum calcium, tumor size, and hormone receptor status in women with untreated breast cancer

Elevated serum levels of calcium are frequently observed in advanced breast cancer, but data on serum calcium and breast cancer characteristics at the time of breast cancer diagnosis are limited. We conducted a cross-sectional study of 555 women with newly-diagnosed, untreated breast cancer in North Dakota. We examined the relationship between tumor size, serum calcium and other clinical characteristics of breast tumors, including age and hormone receptor status, using multiple linear regressions. Tumors that were estrogen receptor negative tended to be associated with higher serum calcium levels (p = 0.07). We observed a significant positive correlation between tumor volume and serum calcium levels (adjusted for patient age, body mass index, hormonal receptors, stage at diagnosis, and grade). The association between tumor volume and serum calcium was limited to post-menopausal women. Our finding that postmenopausal women with larger breast tumors had significantly higher serum calcium levels is consistent with a calciotropic effect of early breast cancer in postmenopausal women.

Radiological features of triple-negative breast cancers (73 cases)

OBJECTIVES

Triple-negative breast cancers generally occur in young women and they have the potential to be aggressive. It is important for this subtype of tumour to be detected early. We studied the appearance of 73 tumours on mammography, sonography and MRI in order to determine what specific features they showed on imaging.

PATIENTS AND METHODS

From July 2009 to December 2010, we retrospectively reviewed mammogram and sonogram images of 73 triple-negative cancers. Colour Doppler had been used to depict vascularisation in 34 cases and elastography score calculated in 17 cases. Sixteen patients had undergone MRI. The radiological description of these different modalities draws on the BI-RADS lexicon and categorisation.

RESULTS

On mammography, triple-negative cancers often presented as a round mass (59.3%) or an oval or lobulated mass (65%), with circumscribed (15%), microlobulated (12.5%), indistinct (55%) or occasionally spiculated margins (15%). On sonography, the vast majority of these cancers appeared as masses (92.8%) with occasional posterior acoustic attenuation (22.6%). MRI showed more suspicious images than the standard examinations, notably rim-enhancement (eight out of 12 masses).

CONCLUSION

. Radiological images appear as lobulated masses more readily, while on sonography posterior enhancement is shown more often than attenuation, and MRI finds rim-enhancement.

Molecular Imaging and Contrast Agent Database (MICAD): evolution and progress

The purpose of writing this review is to showcase the Molecular Imaging and Contrast Agent Database (MICAD; www.micad.nlm.nih.gov ) to students, researchers, and clinical investigators interested in the different aspects of molecular imaging. This database provides freely accessible, current, online scientific information regarding molecular imaging (MI) probes and contrast agents (CA) used for positron emission tomography, single-photon emission computed tomography, magnetic resonance imaging, X-ray/computed tomography, optical imaging and ultrasound imaging. Detailed information on >1,000 agents in MICAD is provided in a chapter format and can be accessed through PubMed. Lists containing >4,250 unique MI probes and CAs published in peer-reviewed journals and agents approved by the United States Food and Drug Administration as well as a comma separated values file summarizing all chapters in the database can be downloaded from the MICAD homepage. Users can search for agents in MICAD on the basis of imaging modality, source of signal/contrast, agent or target category, pre-clinical or clinical studies, and text words. Chapters in MICAD describe the chemical characteristics (structures linked to PubChem), the in vitro and in vivo activities, and other relevant information regarding an imaging agent. All references in the chapters have links to PubMed. A Supplemental Information Section in each chapter is available to share unpublished information regarding an agent. A Guest Author Program is available to facilitate rapid expansion of the database. Members of the imaging community registered with MICAD periodically receive an e-mail announcement (eAnnouncement) that lists new chapters uploaded to the database. Users of MICAD are encouraged to provide feedback, comments, or suggestions for further improvement of the database by writing to the editors at micad@nlm.nih.gov.

Association of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 status with total choline concentration and tumor volume in breast cancer patients: an MRI and in vivo proton MRS study

The association of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2) status of breast cancer patients with total choline (tCho) concentration and tumor volume was investigated using in vivo proton magnetic resonance spectroscopy and MRI at 1.5 T. Values for tCho concentration were determined in 120 locally advanced breast cancer patients (stages IIB, IIIA, IIIB, and IIIC), 31 early breast cancer patients (stage IIA), 38 patients with benign lesions, and 37 controls. Significantly higher tCho concentration and lower tumor volume were observed in early breast cancer patients compared to locally advanced breast cancer patients (P<0.05). tCho concentration and tumor volume did not correlate with age and menstruation. tCho cutoff values were obtained for the differentiation of malignant from benign breast tissues (2.54 mmol/kg); malignant versus normal (1.45 mmol/kg) and benign versus normal tissues (0.82 mmol/kg). Estrogen receptor negative patients showed significantly larger tumor volumes, indicating higher angiogenesis with aggressive tumor behavior. Nontriple negative and triple positive patients had a significantly higher tCho concentration compared to triple negative patients (P<0.05), indicating complex molecular mechanism of cell proliferation and the molecular heterogeneity of breast lesions. The results indicate the potential use of integration of breast 1H magnetic resonance spectroscopy in diagnostic workup.

MRI staging after neoadjuvant chemotherapy for breast cancer: does tumor biology affect accuracy?

BACKGROUND

A discrepancy often exists between the post-neoadjuvant chemotherapy (NAC) breast tumor size on magnetic resonance imaging (MRI) and pathologic tumor size. We seek to quantify this MRI/pathology discrepancy and determine if the accuracy of MRI post NAC varies with tumor subtype.

METHODS

The University of Pittsburgh Medical Center (UPMC) Cancer Registry and radiology database were searched for patients with breast cancer who underwent NAC and MRI staging between 2004 and 2009. We compared radiologic to pathologic staging and stratified differences based on tumor biology using univariate, multivariate, and receiver operating characteristic (ROC) analysis.

RESULTS

Two hundred three of 592 patients undergoing surgery after NAC for breast cancer had MRI staging pre and post chemotherapy. All patients had intact tumors prior to the initiation of chemotherapy. Average tumor size by MRI was 4.0 cm pre chemotherapy and 1.2 cm post chemotherapy. The average pathologic tumor size was 1.7 cm (range 0-13 cm). The difference between MRI and pathologic tumor size was greatest in luminal (1.1 cm) and least in triple-negative (TN) and human epidermal growth factor receptor 2 (HER2)-positive tumors (<0.1 cm) (p = 0.015). MRI was a good discriminator for pathologic complete response (pCR) [area under the curve (AUC) 0.777]. Its predictive value for pCR was much greater in TN and estrogen receptor(ER)-/HER2+ than in luminal tumors (73.6 vs. 27.3%).

CONCLUSIONS

MRI is an effective tool for predicting response to NAC. The accuracy of MRI in estimating postchemotherapy tumor size varies with tumor subtype. It is highest in ER-/HER2+ and TN and lowest in luminal tumors. Knowledge of how tumor subtype affects MRI accuracy can guide recommendations for surgery following NAC.

Clinical characteristics and biomarkers of breast cancer associated with choline concentration measured by 1H MRS

This study investigated the association between the total choline (tCho) concentration and the clinical characteristics and biomarker status of breast cancer. Sixty-two patients with breast cancer, 1.5  cm or larger in size on MR images, were studied. The tCho concentration was correlated with the MRI features, contrast enhancement kinetics, clinical variables and biomarkers. Pairwise two-tailed Spearman's nonparametric test was used for statistical analysis. The tCho concentration was higher in high-grade than moderate-/low-grade tumors (p = 0.04) and in tumors with higher K(trans) and k(ep) (p < 0.001 for both). The association of tCho concentration with age (p = 0.05) and triple negative biomarker (p = 0.09) approached significance. tCho was not detected in 17 patients, including 15 with invasive ductal cancer and two with infiltrating lobular cancer. Fifteen of the 17 patients had moderate- to low-grade cancers, and 11 had human epidermal growth factor-2-negative cancer, suggesting that these two factors might lead to false-negative choline. Higher tCho concentration in high-grade tumors and tumors with higher K(trans) and k(ep) indicates that choline is associated with cell proliferation and tumor angiogenesis. The higher choline level in younger women may be caused by their more aggressive tumor type. The results presented here may aid in the better interpretation of (1)H MRS for the diagnosis of breast lesions.

Breast MR imaging: current indications and advanced imaging techniques

Breast cancer is the most common solid tumor diagnosed in women. In the past decades, great strides have been made in breast cancer screening. While multiple screening trials have shown the benefits of screening mammography, there are limitations to x-ray mammography. Given these inherent limitations, efforts have been made to develop adjunctive imaging techniques, including screening ultrasonography, gamma-specific breast imaging, breast tomosynthesis, dedicated breast computed tomography, and breast magnetic resonance (MR) imaging. This article addresses the current indications and advanced imaging applications of breast MR imaging.

MRI-model to guide the surgical treatment in breast cancer patients after neoadjuvant chemotherapy

OBJECTIVE

The aim of this study was to establish an magnetic resonance imaging (MRI)-based interpretation model to facilitate the selection of breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NAC).

SUMMARY OF BACKGROUND DATA

Although MRI is the most reliable method to assess tumor size after NAC, criteria for the correct selection of surgery remain unclear.

METHODS

In 208 patients, dynamic contrast-enhanced MRI was performed before and after NAC. Imaging was correlated with pathology. Differences <20 mm in tumor extent were considered to accurately indicate disease extent. Multivariate analysis with cross-validation was performed to analyze features affecting the potential of MRI to correctly indicate BCS (ie, residual tumor size <30 mm on pathology).

RESULTS

The accuracy of MRI to detect residual disease was 76% (158/208). The positive and negative predictive value of MRI were 90% (130/144) and 44% (28/64), respectively. In 35 patients (17%), MRI underestimated the tumor size by >20 mm and in 27 patients (13%) this would have lead to incorrect indication of BCS. The features most predictive of indicating feasibility of BCS in tumors <30 mm on preoperative MRI were the largest diameter at the baseline MRI, the reduction in diameter and the tumor subtype based on hormone-, and human epidermal growth factor receptor 2-status (area under the curve: 0.78).

CONCLUSIONS

Optimal selection of patients for BCS after NAC based on MRI should take into account (1) the tumor size at baseline (2) the reduction in tumor size, and (3) the subtype based on hormone-, and human epidermal growth factor receptor 2-status.