Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer

c-Myc knockdown restores tamoxifen sensitivity in triple-negative breast cancer by reactivating the expression of ERα: the central role of miR-152 and miR-148a

BACKGROUND

Poor prognosis of triple-negative breast cancer (TNBC) is owing to its intrinsic heterogeneity and lack of targeted therapies. Emerging evidence has characterized that targeting c-Myc might be a promising way to treat TNBC.

METHODS

c-Myc knocked down TNBC cells were generated and the tamoxifen sensitivity was determined. Methylation-specific PCR analysis was used to detect the methylation status of ERα promoter, and c-Myc-mediated miRNA transcription was examined using chromatin immunoprecipitation and dual-luciferase reporter assays. The in vivo tamoxifen sensitivity was determined by mouse xenograft model.

RESULTS

c-Myc knockdown in TNBC cells leads to the reactivation of ERα and consequent acquisition its sensitivity to tamoxifen. c-Myc depletion decreased the methylation in the promoter of ERα and DNMT1 was identified as the main executor. c-Myc knockdown-induced tamoxifen sensitivity was reversed by DNMT1 overexpression. The expression of miR-152-3p and miR-148a-3p was largely induced in c-Myc knockdown TNBC cells, and both miR-152-3p and miR-148a-3p could target DNMT1 to regulate its expression. c-Myc binds to the promoter regions of miR-152-3p and miR-148a-3p to exert transcriptional suppression. By suppressing miR-152-3p or miR-148a-3p expression using inhibitors, enhanced sensitivity to tamoxifen induced by c-Myc knockdown was partially reversed. In vivo xenograft tumor model demonstrated that c-Myc knockdown mildly inhibits the growth of tumor, and a dramatic decline was observed when administrated with tamoxifen combined with c-Myc knockdown.

CONCLUSION

Our study first illustrated that c-Myc knockdown in TNBC cells reactivate ERα expression in a miR-152/miR-148a-DNMT1-dependent manner, and brought new sights into treating TNBC using hormonal therapies.

[Importance of parametric and molecular imaging for therapeutic management of breast cancer]

BACKGROUND

In recent years, various magnetic resonance (MRI) and positron emission tomography (PET) parameters have been investigated in breast cancer. Parametric imaging focuses on the visualization and quantification of biological, physiological, and pathological processes at the cellular and molecular level. It therefore provides important insights into the key processes in carcinogenesis and tumor progression. This article aims to illustrate the importance for the management of breast cancer.

MATERIALS AND METHODS

Based on the current literature, an overview of the current state of parametric breast imaging and its importance in therapy management is given. Moreover, future opportunities and challenges are highlighted.

RESULTS

Parametric imaging in breast cancer includes MRI, nuclear medicine procedures such as PET, the combination of different techniques (PET-CT, PET-MRI) and the use of specific radiotracers. Parametric MRI of the breast mainly uses T2 and diffusion-weighted imaging (DWI) as well as dynamic contrast-enhanced MRI (CM-MRI). Quantitative and qualitative imaging biomarkers provide insights into tumor biology and allow conclusions to be drawn about the molecular subtype or prognosis.

CONCLUSIONS

Recently, parametric imaging has become established in breast diagnostics. It is constantly evolving and will continue to gain in importance in the forthcoming years. It offers the opportunity to improve the diagnosis and treatment management of breast cancer.

Evaluating dynamic contrast-enhanced MRI for differentiating HER2-zero, HER2-low, and HER2-positive breast cancers in patients undergoing neoadjuvant chemotherapy

OBJECTIVES

To quantitatively assess the differences in parameters of dynamic contrast-enhanced MRI (DCE-MRI) in HER2-zero, HER2-low, or HER2-positive tumors, and to build optimal model for early prediction of HER2-low breast cancer (BC).

MATERIALS AND METHODS

Clinical and DCE-MRI data from 220 BC patients receiving neoadjuvant chemotherapy (NACT) were retrospectively analyzed. Quantitative and semi-quantitative DCE-MRI parameters were compared in the HER2-zero, HER2-low, or HER2-positive groups before and after early NACT. Empirical models were developed to predict HER2-low BC using logistic regression analysis and receiver operating characteristic (ROC) analysis.

RESULTS

Patients of HER2-low BC have a lower pCR rate compared with HER2-zero and HER2-positive (17.9% vs. 10.4% vs. 29.5%, p < 0.001), predominantly in the HR (hormone receptor) negative group (22.2% vs. 7.7% vs. 40.5%, p < 0.001). Before NACT, HER2-low BC exhibited higher Kep, Ktrans, Washin, and lower TME intratumoral perfusion characteristics, and higher Kep and lower TME in peritumoral region compared to HER2-zero and HER2-positive BC patients. Notably, after early NACT, changes in intratumoral perfusion (Kep) and in peritumoral perfusion (Ktrans, Washin) were more pronounced in the HER2-low group compared to HER2-zero and HER2-positive group. The ROC curves (AUC) for the pre-NACT intratumoral, peritumoral, and combined perfusion models were 0.675(95% CI 0.600-0.750), 0.661(95% CI 0.585-0.738), 0.731(95% CI 0.660-0.802). The combined pre-and-post-NACT perfusion model further improved predictive performance accordingly, with AUCs of 0.764 (95% 0.637-0.865), 0.795 (95% CI 0.711-0.878), 0.850 (95% CI 0.774-0.926).

CONCLUSIONS

The study revealed perfusion heterogeneity between different HER2 statuses and identified the best imaging model as a non-invasive tool to predict HER2-low BC, which can help pre-treatment clinical decision-making.

Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM)

We sought to develop new quantitative approaches to characterize the spatial distribution of mammographic density and contrast enhancement of suspicious contrast-enhanced mammography (CEM) findings to improve malignant vs. benign classifications of breast lesions. We retrospectively analyzed all breast lesions that underwent CEM imaging and tissue sampling at our institution from 2014–2020 in this IRB-approved study. A penalized linear discriminant analysis was used to classify lesions based on the averaged histograms of radial distributions of mammographic density and contrast enhancement. T-tests were used to compare the classification accuracies of density, contrast, and concatenated density and contrast histograms. Logistic regression and AUC-ROC analyses were used to assess if adding demographic and clinical data improved the model accuracy. A total of 159 suspicious findings were evaluated. Density histograms were more accurate in classifying lesions as malignant or benign than a random classifier (62.37% vs. 48%; p < 0.001), but the concatenated density and contrast histograms demonstrated a higher accuracy (71.25%; p < 0.001) than the density histograms alone. Including the demographic and clinical data in our models led to a higher AUC-ROC than concatenated density and contrast images (0.81 vs. 0.70; p < 0.001). In the classification of invasive vs. non-invasive malignancy, the concatenated density and contrast histograms demonstrated no significant improvement in accuracy over the density histograms alone (77.63% vs. 78.59%; p = 0.504). Our findings suggest that quantitative differences in the radial distribution of mammographic density could be used to discriminate malignant from benign breast findings; however, classification accuracy was significantly improved with the addition of contrast-enhanced imaging data from CEM. Adding patient demographic and clinical information further improved the classification accuracy.

Radiomics nomogram for predicting axillary lymph node metastasis in breast cancer based on DCE-MRI: A multicenter study

OBJECTIVES

This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer.

METHODS

This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwent DCE-MRI examination before surgery in two hospitals. All patients had a defined ALN status based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets.

RESULTS

ALNM rates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone.

CONCLUSIONS

The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner.

Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2-G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45-58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

MRI as a biomarker for breast cancer diagnosis and prognosis

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

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.

Association between quantitative and qualitative image features of contrast-enhanced mammography and molecular subtypes of breast cancer

Background

The molecular subtype of breast cancer is one of the most important factors affecting patient prognosis. The study aimed to analyze the association between quantitative and qualitative features of contrast-enhanced mammography (CEM) images and breast cancer molecular subtypes.

Methods

This retrospective double-center study included women who underwent CEM between November 2017 and April 2020. Each patient had at least 1 malignant lesion confirmed by pathology. The CEM images were evaluated by 2 radiologists to obtain quantitative and qualitative image features. The molecular subtypes were studied as dichotomous outcomes, including luminal versus non-luminal, human epidermal growth factor receptor (HER2)-enriched versus non-HER2-enriched, and triple-negative breast cancer (TNBC) versus non-TNBC subtypes. The association between the image features and molecular subtypes was analyzed by multivariate logistic regression, with odds ratios (ORs) and 95% confidence intervals (CIs) provided.

Results

A total of 151 patients with 160 malignant lesions were included in the study. For quantitative features, a higher standard deviation of lesion density was associated with non-luminal (OR =0.88, 95% CI: 0.81 to 0.96, P=0.004) and HER2-enriched breast cancers (OR =1.16, 95% CI: 1.04 to 1.28, P=0.006). The relative degree of enhancement (RDE) and contrast-to-noise ratio (CNR) were not associated with molecular subtypes. However, a higher CNR/lesion size (OR =1.06, 95% CI: 1.01 to 1.12, P=0.012) was associated with luminal subtype cancers, and a higher RDE/lesion size (OR =0.94, 95% CI: 0.88 to 1.00, P=0.035) or a higher CNR/lesion size (OR =0.94, 95% CI: 0.88-1.00, P=0.038) was associated with non-TNBCs. For qualitative features, the presence of calcification was associated with HER2-enriched breast cancers (OR =2.91, 95% CI: 1.10 to 7.67, P=0.031). The presence of architectural distortion was associated with luminal cancer (OR =14.50, 95% CI: 1.91 to 110.14, P=0.010) and non-TNBC (OR =0.05, 95% CI: 0.00 to 0.43, P=0.022). Non-mass enhancement (OR =2.78, 95% CI: 1.08 to 7.14, P=0.033) was associated with HER2-enriched breast cancers. An association remained after adjustments for age, breast thickness, and breast density (all adjusted P<0.050).

Conclusions

The quantitative and qualitative imaging features of CEM could contribute to distinguishing breast cancer molecular subtypes.

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

OBJECTIVE

Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.

METHODS

We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria.

RESULTS

All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features.

CONCLUSION

The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard.  Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.

Computer-Aided Diagnosis Parameters of Invasive Carcinoma of No Special Type on 3T MRI: Correlation with Pathologic Immunohistochemical Markers

Purpose

To investigate the correlation between computer-aided diagnosis (CAD) parameters in 3-tesla (T) MRI and pathologic immunohistochemical (IHC) markers in invasive carcinoma of no special type (NST).

Materials and Methods

A total of 94 female who were diagnosed with NST carcinoma and underwent 3T MRI using CAD, from January 2018 to April 2019, were included. The relationship between angiovolume, curve peak, and early and late profiles of dynamic enhancement from CAD with pathologic IHC markers and molecular subtypes were retrospectively investigated using Dwass, Steel, Critchlow-Fligner multiple comparison analysis, and univariate binary logistic regression analysis.

Results

In NST carcinoma, a higher angiovolume was observed in tumors of higher nuclear and histologic grades and in lymph node (LN) (+), estrogen receptor (ER) (−), progesterone receptor (PR) (−), human epidermal growth factor 2 (HER2) (+), and Ki−67 (+) tumors. A high rate of delayed washout and a low rate of delayed persistence were observed in Ki−67 (+) tumors. In the binary logistic regression analysis of NST carcinoma, a high angiovolume was significantly associated with a high nuclear and histologic grade, LN (+), ER (−), PR (−), HER2 (+) status, and non-luminal subtypes. A high rate of washout and a low rate of persistence were also significantly correlated with the Ki-67 (+) status.

Conclusion

Angiovolume and delayed washout/persistent rate from CAD parameters in contrast enhanced breast MRI correlated with predictive IHC markers. These results suggest that CAD parameters could be used as clinical prognostic, predictive factors.

A deep matrix factorization framework for identifying underlying tissue-specific patterns of DCE-MRI: applications for molecular subtype classification in breast cancer

Objective.Breast cancer is heterogeneous in that different angiogenesis and blood flow characteristics could be present within a tumor. The pixel kinetics of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assume several distinct signal patterns related to specific tissue characteristics. Identification of the latent, tissue-specific dynamic patterns of intratumor heterogeneity can shed light on the biological mechanisms underlying the heterogeneity of tumors.Approach.To mine this information, we propose a deep matrix factorization-based dynamic decomposition (DMFDE) model specifically designed according to DCE-MRI characteristics. The time-series imaging data were decomposed into tissue-specific dynamic patterns and their corresponding proportion maps. The image pixel matrix and the reference matrix of population-level kinetics obtained by clustering the dynamic signals were used as the inputs. Two multilayer neural network branches were designed to collaboratively project the input matrix into a latent dynamic pattern and a dynamic proportion matrix, which was justified using simulated data. Clinical implications of DMFDE were assessed by radiomics analysis of proportion maps obtained from the tumor/parenchyma region for classifying the luminal A subtype.Main results.The decomposition performance of DMFDE was evaluated by the root mean square error and was shown to be better than that of the conventional convex analysis of mixtures (CAM) method. The predictive model withK = 3, 4, and 5 dynamic proportion maps generated AUC values of 0.780, 0.786 and 0.790, respectively, in distinguishing between luminal A and nonluminal A tumors, which are better than the CAM method (AUC = 0.726). The combination of statistical features from images with different proportion maps has the highest prediction value (AUC = 0.813), which is significantly higher than that based on CAM.Conclusion.This proposed method identified the latent dynamic patterns associated with different molecular subtypes, and radiomics analysis based on the pixel compositions of the uncovered dynamic patterns was able to determine molecular subtypes of breast cancer.

Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer

Objective

To investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.

Materials and Methods

Patients diagnosed with clinical T2–4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed. A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). The performances of binary classification models were assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). And the performances of multiclass classification models were assessed via AUC, overall accuracy, precision, recall rate, and F1-score.

Results

A total of 162 patients (mean age, 46.91 ± 10.08 years) were enrolled in this study; 30 were low-AR expression and 132 were high-AR expression. HR+/HER2− cancers were diagnosed in 56 cases (34.6%), HER2+ cancers in 81 cases (50.0%), and TNBC in 25 patients (15.4%). There was no significant difference in clinicopathologic characteristics between low-AR and high-AR groups (P > 0.05), except the menopausal status, ER, PR, HER2, and Ki-67 index (P = 0.043, <0.001, <0.001, 0.015, and 0.006, respectively). No significant difference in clinicopathologic characteristics was observed among three molecular subtypes except the AR status and Ki-67 (P = <0.001 and 0.012, respectively). The Multilayer Perceptron (MLP) showed the best performance in discriminating AR expression, with an AUC of 0.907 and an accuracy of 85.8% in the testing dataset. The highest performances were obtained for discriminating TNBC vs. non-TNBC (AUC: 0.965, accuracy: 92.6%), HER2+ vs. HER2− (AUC: 0.840, accuracy: 79.0%), and HR+/HER2− vs. others (AUC: 0.860, accuracy: 82.1%) using MLP as well. The micro-AUC of MLP multiclass classification model was 0.896, and the overall accuracy was 0.735.

Conclusions

Multi-parametric MRI-based radiomics combining with machine learning approaches provide a promising method to predict the molecular subtype and AR expression of breast cancer non-invasively.

Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging

OBJECTIVE

To investigate whether intratumoral heterogeneity, assessed via dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI), reflects the molecular subtypes of invasive breast cancers.

MATERIAL AND METHODS

We retrospectively evaluated data from 248 consecutive women (mean age ± standard deviation, 54.6 ± 12.2 years) with invasive breast cancer who underwent preoperative DCE-MRI and DWI between 2019 and 2020. To evaluate intratumoral heterogeneity, kinetic heterogeneity (a measure of heterogeneity in the proportions of tumor pixels with delayed washout, plateau, and persistent components within a tumor) was assessed with DCE-MRI using a commercially available computer-aided diagnosis system. Apparent diffusion coefficients (ADCs) were obtained using a region-of-interest technique, and ADC heterogeneity was calculated using the following formula: (ADC_max-ADC_min)/ADC_mean. Possible associations between imaging-based heterogeneity values and breast cancer subtypes were analyzed.

RESULTS

Of the 248 invasive breast cancers, 61 (24.6%) were classified as luminal A, 130 (52.4%) as luminal B, 25 (10.1%) as HER2-enriched, and 32 (12.9%) as triple-negative breast cancer (TNBC). There were significant differences in the kinetic and ADC heterogeneity values among tumor subtypes (p < 0.001 and p = 0.023, respectively). The TNBC showed higher kinetic and ADC heterogeneity values, whereas the HER2-enriched subtype showed higher kinetic heterogeneity values compared to the luminal subtypes. Multivariate linear analysis showed that the HER2-enriched (p < 0.001) and TNBC subtypes (p < 0.001) were significantly associated with higher kinetic heterogeneity values. The TNBC subtype (p = 0.042) was also significantly associated with higher ADC heterogeneity values.

CONCLUSIONS

Quantitative assessments of heterogeneity in enhancement kinetics and ADC values may provide biological clues regarding the molecular subtypes of breast cancer.

KEY POINTS

• Higher kinetic heterogeneity was associated with HER2-enriched and triple-negative breast cancer. • Higher ADC heterogeneity was associated with triple-negative breast cancer. • Aggressive breast cancer subtypes exhibited higher intratumoral heterogeneity based on MRI.

Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer

Objective

To evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer.

Materials and Methods

This study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis.

Results

Among the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SI_slope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%).

Conclusions

Texture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.

Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results

PURPOSE

To investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade.

PROCEDURES

This retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference.

RESULTS

Radiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI).

CONCLUSIONS

Radiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.

Molecular subtypes of invasive breast cancer: correlation between PET/computed tomography and MRI findings

OBJECTIVE

The aim of the study was to investigate the diagnostic value of fluorodeoxyglucose-18 (FDG)-PET/computed tomography (CT) and MRI parameters in determining the molecular subtypes of invasive breast cancer.

METHODS

Data from 55 primary invasive breast cancer masses in 51 female patients who underwent pre-treatment PET/CT and MRI scans, and histopathological diagnosis at the authors' center were retrospectively reviewed. The relationship between FDG-PET/CT and MRI parameters, including maximum and mean standard uptake values (SUVmax and SUVmean, respectively), mean metabolic index (MImean) and metabolic tumor volume (MTV) values obtained from FDG-PET, and shape, margin, internal contrast-enhancement characteristics, kinetic curve types, functional tumor volume (FTV), apparent diffusion coefficient (ADC) values obtained from MRI was evaluated. Subsequently, differences among molecular subtypes (i.e. luminal A, luminal B, c-erbB-2 positive, and triple-negative) in terms of PET/CT and MRI parameters were evaluated.

RESULTS

The luminal B subtype of invasive breast cancer had higher SUVmax and SUVmean (P = 0.002 and P = 0.017, respectively) values than the luminal A subtype. In addition, the triple-negative subtype had a higher SUVmax (P = 0.028) than the luminal A subtype. There was a statistically significant positive correlation between pathological tumor volume (PTV) and SUVmean (P = 0.019, r = 0.720). SUVmax and ADC were negatively correlated (P = 0.001; r = -0.384). A very strong positive correlation was detected between MTV and FTV (P = 0.000; r = 0.857), and between MTV and PTV (P = 0.006, r = 0.796), and between FTV and PTV (P = 0.006, r = 0.921).

CONCLUSION

Results of the present study suggest that SUVmax was superior to MRI findings in predicting molecular subtypes and that MRI was superior to PET/CT in predicting PTV.

A multicompartment model for intratumor tissue-specific analysis of DCE-MRI using non-negative matrix factorization

PURPOSE

A pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data is subject to inaccuracy and instability partly owing to the partial volume effect (PVE). We proposed a new multicompartment model for a tissue-specific pharmacokinetic analysis in DCE-MRI data to solve the PVE problem and to provide better kinetic parameter maps.

METHODS

We introduced an independent parameter named fractional volumes of tissue compartments in each DCE-MRI pixel to construct a new linear separable multicompartment model, which simultaneously estimates the pixel-wise time-concentration curves and fractional volumes without the need of the pure-pixel assumption. This simplified convex optimization model was solved using a special type of non-negative matrix factorization (NMF) algorithm called the minimum-volume constraint NMF (MVC-NMF).

RESULTS

To test the model, synthetic datasets were established based on the general pharmacokinetic parameters. On well-designed synthetic data, the proposed model reached lower bias and lower root mean square fitting error compared to the state-of-the-art algorithm in different noise levels. In addition, the real dataset from QIN-BREAST-DCE-MRI was analyzed, and we observed an improved pharmacokinetic parameter estimation to distinguish the treatment response to chemotherapy applied to breast cancer.

CONCLUSION

Our model improved the accuracy and stability of the tissue-specific estimation of the fractional volumes and kinetic parameters in DCE-MRI data, and improved the robustness to noise, providing more accurate kinetics for more precise prognosis and therapeutic response evaluation using DCE-MRI.

[Multimodal, multiparametric and genetic breast imaging]

CLINICAL/METHODOLOGICAL ISSUE

Multiparametric magnetic resonance imaging (MRI) aims to visualize and quantify biological, physiological and pathological processes at the cellular and molecular level and provides valuable information about key processes in cancer development and progression. "Omics" strategies (genomics, transcriptomics, proteomics, metabolomics) have many uses in oncology.

STANDARD RADIOLOGICAL METHODS

Multiparametric MRI of the breast currently includes T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI (DCE-MRI) METHODOLOGICAL INNOVATIONS: Additional parameters such as proton magetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), blood oxygen level-dependent (BOLD), hyperpolarized (HP) MRI or lipid MRS are currently being developed and are being evaluated in breast cancer diagnostics.

ACHIEVEMENTS

Radiogenomics is a new direction in medical science that has been made possible by significant advances in imaging and image analysis methods, as well as the development of techniques to extract and correlate various imaging parameters with "omics" data. The aim of radiogenomics is to correlate imaging characteristics (phenotypes) with gene expression patterns, gene mutations and other genome-associated properties and is the evolution of the correlation between radiology and pathology from the anatomical-histological to the molecular level. Quantitative and qualitative imaging biomarkers provide insights into the complex tumor biology. Initial results suggest that radiogemics will play an important role in the diagnosis, prognosis, and treatment of breast cancer.

PRACTICAL RECOMMENDATIONS

This article provides an overview of the current state of radiogenomics of the breast and future applications and challenges.

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

OBJECTIVE

Purpose of our study is to assess the relationship between MRI findings and invasive breast cancer (IBC) with cancer-associated fibroblasts (CAFs) that are positive for podoplanin.

METHODS

We retrospectively analyzed the consecutive 109 IBCs. The IBCs were dichotomized as with (+) or without (-) podoplanin-positive CAFs. In MRI analyses, the dichotomized IBCs were compared the lesion to muscle ratio (L/M ratio) in STIR images, the ADC value, the distribution of kinetic parameters, and morphological findings.

RESULTS

Of the 109 IBCs, 28 (26%) IBCs had podoplanin(+) CAFs. Compared to the podoplanin(-) group, the podoplanin(+) group tended to have a more malignant pathological status. In the STIR images, the podoplanin(+) group had significantly higher L/M ratio (7.59 vs. 6.55, p = 0.040). In a dynamic study, the podoplanin(+) group had a significantly higher percentage of the washout pattern (42.21% vs. 29.43%, p = 0.045). There were 23 mass lesions and 5 non-mass enhancement (NME) lesions in the podoplanin(+) group, and 69 mass lesions and 12 NME lesions in the podoplanin(-) group. The mass lesions of the podoplanin(-) group had a significantly higher likelihood of showing an irregular shape (n = 47 vs. 8, p = 0.035). The podoplanin(+) group's lesions had a significantly higher likelihood of showing a circumscribed margin (n = 14 vs. 6, p < 0.001) and a rim enhancement (n = 10 vs. 13, p = 0.047). In multivariate analyses, only high nuclear grade was significant predictive value of podoplanin(+) CAFs.

CONCLUSION

Although not significant in multivariate analyses, MRI findings may be used to determine the podoplanin-positive CAF status of invasive breast cancer.

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER-, PR+/PR-, HER2+/HER2-, Ki67+/Ki67-, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2- (90.87%), ER+/ER- (83.79%) and Ki67+/Ki67- (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors' molecular subtype.

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm

To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer. A CNN architecture was designed with 14 layers. Residual connections were used in the earlier layers to allow stabilization of gradients during backpropagation. Inception style layers were utilized deeper in the network to allow learned segregation of more complex feature mappings. Extensive regularization was utilized including dropout, L2, feature map dropout, and transition layers. The class imbalance was addressed by doubling the input of underrepresented classes and utilizing a class sensitive cost function. Parameters were tuned based on a 20% validation group. A class balanced holdout set of 40 patients was utilized as the testing set. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. Seventy-four luminal A, 106 luminal B, 13 HER2+, and 23 basal breast tumors were evaluated. Testing set accuracy was measured at 70%. The class normalized macro area under receiver operating curve (ROC) was measured at 0.853. Non-normalized micro-aggregated AUC was measured at 0.871, representing improved discriminatory power for the highly represented Luminal A and Luminal B subtypes. Aggregate sensitivity and specificity was measured at 0.603 and 0.958. MRI analysis of breast cancers utilizing a novel CNN can predict the molecular subtype of breast cancers. Larger data sets will likely improve our model.

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

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.

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.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

Breast Cancer Radiogenomics: Current Status and Future Directions

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.

Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype

PURPOSE

The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project.

MATERIALS AND METHODS

Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers.

RESULTS

ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER- tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema (P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8).

CONCLUSIONS

Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.

Deep learning for identifying radiogenomic associations in breast cancer

RATIONALE AND OBJECTIVES

To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

In this institutional review board-approved single-center study, we analyzed DCE-MR images of 270 patients at our institution. Lesions of interest were identified by radiologists. The task was to automatically determine whether the tumor is of the Luminal A subtype or of another subtype based on the MR image patches representing the tumor. Three different deep learning approaches were used to classify the tumor according to their molecular subtypes: learning from scratch where only tumor patches were used for training, transfer learning where networks pre-trained on natural images were fine-tuned using tumor patches, and off-the-shelf deep features where the features extracted by neural networks trained on natural images were used for classification with a support vector machine. Network architectures utilized in our experiments were GoogleNet, VGG, and CIFAR. We used 10-fold crossvalidation method for validation and area under the receiver operating characteristic (AUC) as the measure of performance.

RESULTS

The best AUC performance for distinguishing molecular subtypes was 0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features approach. The highest AUC performance for training from scratch was 0.58 (95% CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60 (95% CI:[0.52,0.65]) respectively. For the off-the-shelf approach, the features extracted from the fully connected layer performed the best.

CONCLUSION

Deep learning may play a role in discovering radiogenomic associations in breast cancer.

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer

IMPORTANCE

There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer.

OBJECTIVE

To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy.

DESIGN, SETTING, AND PARTICIPANTS

In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019.

MAIN OUTCOMES AND MEASURES

Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting.

RESULTS

In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002).

CONCLUSIONS AND RELEVANCE

A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.

Tumor-stromal ratio (TSR) of invasive breast cancer: correlation with multi-parametric breast MRI findings

OBJECTIVE

To correlate the tumor-stromal ratio (TSR) of invasive breast cancer and MRI findings.

METHODS

This study was approved by our institutional review board. 126 consecutive patients with surgically proven invasive breast cancer were included. All patients underwent MRI exams including short-tau inversion-recovery (STIR) T ₂ weighted imaging, diffusion-weighted imaging (DWI) and post-contrast dynamic imaging. The mean signal intensity (SI) and apparent diffusion coefficient (ADC) value of each lesion were measured. To objectively evaluate the STIR images, the ratio of the SI of the lesion to the muscle (L/M ratio) was also measured. Percentages of MRI kinetic parameters obtained from dynamic images were also measured. The TSR was defined as the percentage of the stromal component, and categorized into high-stroma (> 50%) and low-stroma (< 50%) groups. Intergroup differences in the SI, L/M ratio, ADC value and percentages of kinetic parameters were examined.

RESULTS

The SI and L/M ratio of the high-stroma group were significantly lower than those of the low-stromal group (208.64 vs 331.86 for SI, 5.69 vs 9.31 for L/M ratio) (p < 0.001). The high-stroma group had significantly lower percentages of a washout pattern (25% vs 34.7 %) (p = 0.012) and significantly higher percentages of a persistent pattern (36.92% vs 28.26 %) (p = 0.044). There were no significant correlations between the TSR and ADC value.

CONCLUSION

STIR and dynamic sequence of breast MRI reflects the stromal component of invasive breast cancer.

ADVANCES IN KNOWLEDGE

This is the first study to correlate TSR and MRI findings. STIR and post-contrast dynamic study correlated with the stromal component of breast cancer.

Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer

OBJECTIVES

This study aimed to predict the molecular subtypes of breast cancer via intratumoural and peritumoural radiomic analysis with subregion identification based on the decomposition of contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS

The study included 211 women with histopathologically confirmed breast cancer. We utilised a completely unsupervised convex analysis of mixtures (CAM) method by unmixing dynamic imaging series from heterogeneous tissues. Each tumour and the surrounding parenchyma were thus decomposed into multiple subregions, representing different vascular characterisations, from which radiomic features were extracted. A random forest model was trained and tested using a leave-one-out cross-validation (LOOCV) method to predict breast cancer subtypes. The predictive models from tumoural and peritumoural subregions were fused for classification.

RESULTS

Tumour and peritumour DCE-MR images were decomposed into three compartments, representing plasma input, fast-flow kinetics, and slow-flow kinetics. The tumour subregion related to fast-flow kinetics showed the best performance among the subregions for differentiating between patients with four molecular subtypes (area under the receiver operating characteristic curve (AUC) = 0.832), exhibiting an AUC value significantly (p < 0.0001) higher than that obtained with the entire tumour (AUC = 0.719). When the tumour- and parenchyma-based predictive models were fused, the performance, measured as the AUC, increased to 0.897; this value was significantly higher than that obtained with other tumour partition methods.

CONCLUSIONS

Radiomic analysis of intratumoural and peritumoural heterogeneity based on the decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features and serve as a valuable clinical marker to enhance the prediction of breast cancer subtypes.

KEY POINTS

• Decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features. • Fusion of intratumoural- and peritumoural-based predictive models improves the prediction of breast cancer subtypes.

Preliminary Study on Molecular Subtypes of Breast Cancer Based on Magnetic Resonance Imaging Texture Analysis

OBJECTIVE

The aim of the study was to investigate the molecular subtypes of breast cancer based on the texture features derived from magnetic resonance images (MRIs).

METHODS

One hundred seven patients with preoperative confirmed breast cancer were recruited. One hundred eight breast lesions were divided into 4 subtypes according to the status of estrogen receptor, progesterone receptor, human epidermal growth factor receptor type 2, and Ki67. Fisher discriminant analysis was performed on the texture features that extracted from the enhanced high-resolution T1-weighted images and diffusion weighted images to establish the classification model of molecular subtypes.

RESULTS

The differentiation accuracies of Fisher discriminant analysis on the enhanced high-resolution T1-weighted images were 82.8% and 86.4% for 1.5T and 3.0T imaging. Fisher discriminant analysis on diffusion weighted imaging texture features were achieved with a classification ability of 73.4% and 88.6%. The combined discriminant results for 2 kinds magnetic resonance images were 95.0%, 97.7% in 1.5T and 3.0T imaging, respectively.

CONCLUSIONS

The fine results indicated a promising approach to predict the molecular subtypes of breast cancer.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

BACKGROUND

Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.

METHODS

We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients.

RESULTS

Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647-0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589-0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591-0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569-0.674, p < .0001). Associations between individual features and subtypes we also found.

CONCLUSIONS

There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?

Background Intrinsic molecular profiling of breast cancer provides clinically relevant information that helps tailor therapy directed to the specific tumor subtype. We hypothesized that dynamic contrast-enhanced MRI (DCE-MRI) derived quantitative kinetic parameters (CD-QKPs) may help predict molecular tumor profiles non-invasively. Purpose To determine the association between DCE-MRI (CD-QKPs) and breast cancer clinicopathological prognostic factors. Material and Methods Clinicopathological factors in consecutive women with biopsy-confirmed invasive breast cancer who underwent breast DCE-MRI were retrospectively reviewed. Analysis of variance was used to examine associations between prognostic factors and CD-QKPs. Fisher's exact test was used to investigate the relationship between kinetic curve type and prognostic factors. Results A total of 198 women with invasive breast cancer were included. High-grade and HER2+ tumors were more likely to have a washout type curve while luminal A tumors were less likely. High-grade was significantly associated with increased peak enhancement (PE; P = 0.01), enhancement maximum slope (MS; P = 0.03), and mean enhancement ( ME, P = 0.03), while high clinical lymph node stage (cN3) was significantly associated with increased MS and time to peak (tP; P = 0.01). HER2+ tumors were associated with a higher PE ( P = 0.03) and ME ( P = 0.06) than HER2- counterparts, and ER-/HER2+ tumors showed higher PE and ME values than ER+/HER2- tumors ( P = 0.06). Conclusion DCE-MRI time-intensity CD-QKPs are associated with high tumor grade, advanced nodal stage, and HER2+ status, indicating their utility as imaging biomarkers.

Precision Medicine for Breast Cancer: The Paths to Truly Individualized Diagnosis and Treatment

Precision medicine in oncology seeks to individualize each patient's treatment regimen based on an accurate assessment of the risk of recurrence or progression of that person's cancer. Precision will be achieved at each phase of care, from detection to diagnosis to surgery, systemic therapy, and radiation therapy, to survivorship and follow-up care. The precision arises from detailed knowledge of the inherent biological propensities of each tumor, rather than generalizing treatment approaches based on phenotypic, or even genotypic, categories. Extensive research is being conducted in multiple disciplines, including radiology, pathology, molecular biology, and surgical, medical, and radiation oncology. Clinical trial design is adapting to the new paradigms and moving away from grouping heterogeneous patient populations into limited treatment comparison arms. This review touches on several areas invested in clinical research. This special issue highlights the specific work of a number of groups working on precision medicine for breast cancer.

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

Breast cancer is a disease affecting an increasing number of women worldwide. Several efforts have been made in the last years to identify imaging biomarker and to develop noninvasive diagnostic tools for breast tumor characterization and monitoring, which could help in patients' stratification, outcome prediction, and treatment personalization. In particular, radiomic approaches have paved the way to the study of the cancer imaging phenotypes. In this work, a group of 49 patients with diagnosis of invasive ductal carcinoma was studied. The purpose of this study was to select radiomic features extracted from a DCE-MRI pharmacokinetic protocol, including quantitative maps of ktrans, kep, ve, iAUC, and R1 and to construct predictive models for the discrimination of molecular receptor status (ER+/ER-, PR+/PR-, and HER2+/HER2-), triple negative (TN)/non-triple negative (NTN), ki67 levels, and tumor grade. A total of 163 features were obtained and, after feature set reduction step, followed by feature selection and prediction performance estimations, the predictive model coefficients were computed for each classification task. The AUC values obtained were 0.826 ± 0.006 for ER+/ER-, 0.875 ± 0.009 for PR+/PR-, 0.838 ± 0.006 for HER2+/HER2-, 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67+/ki67-, and 0.895 ± 0.006 for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokinetic-based phenotyping shows promising for discrimination of the histological outcomes.

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers

BACKGROUND

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.

PURPOSE

To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

STUDY TYPE

Retrospective study.

POPULATION

Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.

FIELD STRENGTH/SEQUENCE

T₁ -weighted 3.0T DCE-MR images.

ASSESSMENT

Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.

STATISTICAL TESTING

Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.

RESULTS

In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).

DATA CONCLUSION

Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

[18F]FDG PET/CT features for the molecular characterization of primary breast tumors

PURPOSE

The aim of this study was to evaluate the role of imaging features derived from [¹⁸F]FDG-PET/CT to provide in vivo characterization of breast cancer (BC).

METHODS

Images from 43 patients with a first diagnosis of BC were reviewed. Images were acquired before any treatment. Histological data were derived from pretreatment biopsy or surgical histological specimen; these included tumor type, grade, ER and PgR receptor status, lymphovascular invasion, Ki67 index, HER2 status, and molecular subtype. Standard parameters (SUV_mean, TLG, MTV) and advanced imaging features (histogram-based and shape and size features) were evaluated. Univariate analysis, hierarchical clustering analysis, and exact Fisher's test were used for statistical analysis of data. Imaging-derived metrics were reduced evaluating the mutual correlation within group of features as well as the mutual correlation between groups of features to form a signature.

RESULTS

A significant correlation was found between some advanced imaging features and the histological type. Different molecular subtypes were characterized by different values of two histogram-based features (median and energy). A significant association was observed between the imaging signature and luminal A and luminal B HER2 negative molecular subtype and also when considering luminal A, luminal B HER2-negative and HER2-positive groups. Similar results were found between the signature and all five molecular subtypes and also when considering the histological types of BC.

CONCLUSIONS

Our results suggest a complementary role of standard PET imaging parameters and advanced imaging features for the in vivo biological characterization of BC lesions.

Radiogenomic Analysis of Oncological Data: A Technical Survey

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics

PURPOSE

To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI.

METHODS

Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers. The similarity of bounding boxes was analyzed using Dice coefficients. An automatic tumor segmentation algorithm was used to segment tumors from the readers' annotations. The segmented tumors were then compared between readers using Dice coefficients as the similarity metric. Cases showing high interobserver variability (average Dice coefficient <0.8) after segmentation were analyzed by a panel of radiologists to identify the reasons causing the low level of agreement. Furthermore, an imaging feature, quantifying tumor and breast tissue enhancement dynamics, was extracted from each segmented tumor for a patient. Pearson's correlation coefficients were computed between the features for each pair of readers to assess the effect of the annotation on the feature values. Finally, the authors quantified the extent of variation in feature values caused by each of the individual reasons for low agreement.

RESULTS

The average agreement between readers in terms of the overlap (Dice coefficient) of the bounding box was 0.60. Automatic segmentation of tumor improved the average Dice coefficient for 92% of the cases to the average value of 0.77. The mean agreement between readers expressed by the correlation coefficient for the imaging feature was 0.96.

CONCLUSIONS

There is a moderate variability between readers when identifying the rectangular outline of breast tumors on MRI. This variability is alleviated by the automatic segmentation of the tumors. Furthermore, the moderate interobserver variability in terms of the bounding box does not translate into a considerable variability in terms of assessment of enhancement dynamics. The authors propose some additional ways to further reduce the interobserver variability.

Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer

The purpose of this study was to investigate the role of features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to incorporated clinical information to predict the molecular subtypes of breast cancer. In particular, 60 breast cancers with the following four molecular subtypes were analyzed: luminal A, luminal B, human epidermal growth factor receptor-2 (HER2)-over-expressing and basal-like. The breast region was segmented and the suspicious tumor was depicted on sequentially scanned MR images from each case. In total, 90 features were obtained, including 88 imaging features related to morphology and texture as well as dynamic features from tumor and background parenchymal enhancement (BPE) and 2 clinical information-based parameters, namely, age and menopausal status. An evolutionary algorithm was used to select an optimal subset of features for classification. Using these features, we trained a multi-class logistic regression classifier that calculated the area under the receiver operating characteristic curve (AUC). The results of a prediction model using 24 selected features showed high overall classification performance, with an AUC value of 0.869. The predictive model discriminated among the luminal A, luminal B, HER2 and basal-like subtypes, with AUC values of 0.867, 0.786, 0.888 and 0.923, respectively. An additional independent dataset with 36 patients was utilized to validate the results. A similar classification analysis of the validation dataset showed an AUC of 0.872 using 15 image features, 10 of which were identical to those from the first cohort. We identified clinical information and 3D imaging features from DCE-MRI as candidate biomarkers for discriminating among four molecular subtypes of breast cancer.

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.

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.

Molecular imaging of breast cancer: present and future directions

Medical imaging technologies have undergone explosive growth over the past few decades and now play a central role in clinical oncology. But the truly transformative power of imaging in the clinical management of cancer patients lies ahead. Today, imaging is at a crossroads, with molecularly targeted imaging agents expected to broadly expand the capabilities of conventional anatomical imaging methods. Molecular imaging will allow clinicians to not only see where a tumor is located in the body, but also to visualize the expression and activity of specific molecules (e.g., proteases and protein kinases) and biological processes (e.g., apoptosis, angiogenesis, and metastasis) that influence tumor behavior and/or response to therapy. Breast cancer, the most common cancer among women and a research area where our group is actively involved, is a very heterogeneous disease with diverse patterns of development and response to treatment. Hence, molecular imaging is expected to have a major impact on this type of cancer, leading to important improvements in diagnosis, individualized treatment, and drug development, as well as our understanding of how breast cancer arises.

Magnetic resonance imaging features of breast cancer according to intrinsic subtypes: correlations with neoadjuvant chemotherapy effects

PURPOSE

The purpose of this study was to evaluate the magnetic resonance imaging (MRI) features of breast cancer according to intrinsic subtypes and to investigate whether the MRI and immunohistochemical findings were related to neoadjuvant chemotherapy (NAC) effects.

MATERIALS AND METHODS

The MRI in 116 women with breast cancers who underwent NAC was reviewed. The mass margin, presence of intratumoral necrosis, tumor extension around the mass, relative signal enhancement (RSE), and kinetic curve pattern were analyzed. We investigated the possible correlations between MRI findings and the effects of NAC.

RESULTS

An irregular mass margin was significantly associated with luminal-A cancers, while a smooth mass margin was associated with human epidermal growth factor receptor2 (HER2) cancers. Intratumoral necrosis was significantly associated with triple-negative cancers. Tumor extension around the mass was significantly infrequent in luminal-B cancers and frequent in HER2 cancers. Luminal-B and HER2 cancers showed a significantly higher RSE at 2 min than Luminal-A cancers. Estrogen receptor (ER)-positive cancers, HER2-negative cancers, and presence of intratumoral necrosis were significantly associated with the NAC non-response.

CONCLUSIONS

Several MR features can be used to predict the intrinsic subtype of breast cancers. ER-positivity, HER2-negativity, and presence of intratumoral necrosis were significantly associated with NAC non-response.