Correlation between imaging and molecular classification of breast cancers

Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic Images

Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images. A retrospective study was performed using the OPTIMAM Mammography Image Database (OMI-DB). Four binary classification tasks were performed: luminal A vs. non-luminal A, luminal B vs. non-luminal B, TNBC vs. non-TNBC, and HER2 vs. non-HER2. Feature selection was carried out by Pearson correlation and LASSO. The support vector machine (SVM) and naive Bayes (NB) ML classifiers were used, and their performance was evaluated with the accuracy and the area under the receiver operating characteristic curve (AUC). A total of 186 patients were included in the study: 58 luminal A, 35 luminal B, 52 TNBC, and 41 HER2. The SVM classifier resulted in AUCs during testing of 0.855 for luminal A, 0.812 for luminal B, 0.789 for TNBC, and 0.755 for HER2, respectively. The NB classifier showed AUCs during testing of 0.714 for luminal A, 0.746 for luminal B, 0.593 for TNBC, and 0.714 for HER2. The SVM classifier outperformed NB with statistical significance for luminal A (p = 0.0268) and TNBC (p = 0.0073). Our study showed the potential of radiomics for non-invasive breast cancer subtype classification.

MRI features of breast cancer immunophenotypes with a focus on luminal estrogen receptor low positive invasive carcinomas

To compare the magnetic resonance imaging (MRI) features of different immunophenotypes of breast carcinoma of no special type (NST), with special attention to estrogen receptor (ER)-low-positive breast cancer. This retrospective, single-centre, Institutional Review Board (IRB)-approved study included 398 patients with invasive breast carcinoma. Breast carcinomas were classified as ER-low-positive when there was ER staining in 1-10% of tumour cells. Pretreatment MRI was reviewed to assess the tumour imaging features according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. Of the 398 cases, 50 (12.6%) were luminal A, 191 (48.0%) were luminal B, 26 (6.5%) were luminal ER-low positive, 64 (16.1%) were HER2-overexpressing, and 67 (16.8%) were triple negative. Correlation analysis between MRI features and tumour immunophenotype showed statistically significant differences in mass shape, margins, internal enhancement and the delayed phase of the kinetic curve. An oval or round shape and rim enhancement were most frequently observed in triple-negative and luminal ER-low-positive tumours. Spiculated margins were most common in luminal A and luminal B tumours. A persistent kinetic curve was more frequent in luminal A tumours, while a washout curve was more common in the triple-negative, HER2-overexpressing and luminal ER-low-positive immunophenotypes. Multinomial regression analysis showed that luminal ER-low-positive tumours had similar results to triple-negative tumours for almost all variables. Luminal ER-low-positive tumours present with similar MRI findings to triple-negative tumours, which suggests that MRI can play a fundamental role in adequate radiopathological correlation and therapeutic planning in these patients.

Assessment of intravoxel incoherent motion MR imaging for differential diagnosis of breast lesions and evaluation of response: a systematic review

Background

The current study aimed to assess the performance for quantitative differentiation and evaluation of response in categorized observations from intravoxel incoherent motion analyses of patients based on breast tumors. To assess the presence of heterogeneity, the Cochran's Q tests for heterogeneity with a significance level of P < 0.1 and I2 statistic with values > 75% were used. A random-effects meta-analysis model was used to estimate pooled sensitivity and specificity. The standardized mean difference (SMD) and 95% confidence intervals of the true diffusivity (D), pseudo-diffusivity (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) were calculated, and publication bias was evaluated using the Begg's and Egger's tests and also funnel plot. Data were analyzed by STATA v 16 (StataCorp, College Station).

Results

The pooled D value demonstrated good measurement performance showed a sensitivity 86%, specificity 86%, and AUC 0.91 (SMD − 1.50, P < 0.001) in the differential diagnosis of breast lesions, which was comparable to that of the ADC that showed a sensitivity of 76%, specificity 79%, and AUC 0.85 (SMD 1.34, P = 0.01), then by the f it showed a sensitivity 80%, specificity 76%, and AUC 0.85 (SMD 0.89, P = 0.001), and D* showed a sensitivity 84%, specificity 59%, and AUC 0.71 (SMD − 0.30, P = 0.20).

Conclusion

The estimated sensitivity and specificity in the current meta-analysis were acceptable. So, this approach can be used as a suitable method in the differentiation and evaluation response of breast tumors.

Correlation Of Bi-Rads 4 Subcategories Breast Lesions On Tomosynthesis And Histopathological Examination With P63 Immunohistochemistry Expression

Mammography is considered to be the gold standard for screening and detection of breast malignancies. Among different biochemical markers used to detect carcinoma of breasts, p63 has been widely popularized for its effectiveness in the detection of myoepithelial cells which are an important indicator of breast benignity. In this study, we plan to statistically analyze and correlate the Breast Imaging Reporting and Data System (BI-RADS) 4 subcategories grading on mammogram imaging with p63 immunostaining. A total of 80 patients were taken into the study within a period of two years (2016-2018) after ensuring the inclusion and exclusion criteria. They were further sorted into different BI-RADS 4 subcategories, i.e., taking into consideration X-ray mammogram and tomosynthesis findings, 57 samples were categorized as low suspicion (BI-RADS 4A), while 12 were classified as intermediate (BI-RADS 4B), and the remaining 11 samples were categorized as highly suspicious (BI-RADS 4C). Although considered to be leaning toward malignancy, a BI-RADS reading of 4 (namely 4A-low suspicion, 4B-moderate suspicion, and 4C-high suspicion for malignancy) needs further evaluation for accurate diagnosis. There have been cases within our own observation where a lesion that is highly suspicious of malignancy has turned out to be a benign finding. Further, evaluating the expression of a p63 marker can help prevent mutilating surgeries for indeterminate lesions. The present study has been conducted to study the correlation of tomosynthesis grading of lesions that has been categorized from low-to-high suspicion, with a p63 immunostaining pattern in these lesions.

Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours

Objective

To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).

Methods

This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour.

Results

In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower.

Conclusion

Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status.

Key Points

• A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography.

• The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors.

• Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08538-4.

Retracted: Microcalcification-associated breast cancer: HER2-enriched molecular subtype is associated with mammographic features

OBJECTIVE

To investigate whether the mammographic features were different between breast cancer HER2-enriched molecular subtype and non-HER2-enriched molecular subtype.

METHODS

283 microcalcification-associated breast cancers were identified (HER2-enriched: n = 57; non-HER2-enriched: n = 226). Mammographic tumor mass and calcification features in relation to HER2 molecular subtype were analyzed.

RESULTS

On univariate analysis, HER2-enriched molecular subtype rates were significantly higher (a) in tumor size ≤2 cm [33 of 57 (57.9%)] than in tumor size >2 cm lesions [22 of 226 (9.7%)] (p = 0.007), (b) in non-spiculated mass [39 of 57 (68.4%)] than in spiculated mass lesions [18 of 226 (7.9%)] (p = 0.034)，(c) in calcifications extent >2 cm [41 of 57 (71.9%)] lesions than in calcifications extent ≤2 cm lesions [16 of 226 (7.1%)] (p < 0.001) and (d) in calcification density ＞20 cm-2 [44 of 57 (71.2%)] lesions than in calcification density ≤20 cm-2 lesions [13 of 226 (5.8%)] (p = 0.034).On multivariate analysis, three mammographic features [tumor size >2 cm vs size ≤2 cm odds ratio (OR): 0.415 95% confidence interval (CI) (0.215 to 0.802), p = 0.009, spiculated mass vs non-spiculated mass OR: 0.226 95% CI (0.114 to 0.446), p < 0.001 and calcifications extent >2 cm vs calcifications extent ≤2 cm OR: 7.754, 95% CI (3.100 to 19.339) p< 0.001] were independent predictors. Our results indicated that small tumor size, non-spiculated mass and calcification extent >2 cm are more likely to be HER2 molecular subtype. The discrimination of this model, as quantified by the area under the curve, was 0.751 [95% CI (0.701 to 0.854)].

CONCLUSION

Our study presents a prediction model that incorporates the mammographic features of tumor size, non-spiculated mass and calcification extent, which can potentially be used to preoperative predict breast cancer HER2-enriched subtype.

ADVANCES IN KNOWLEDGE

Mammographic features can noninvasively visualize breast tumor phenotype characteristics.

Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI

PURPOSE

This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer.

METHODS

A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics.

RESULTS

The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort.

CONCLUSIONS

Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades

PURPOSE

The purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer.

METHODS

Fifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared.

RESULTS

D* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (-) group (D*:36.8 × 10-3 ± 5.3 × 10-3 mm2/s, f:29.5%, D*:29.8 × 10-3 ± 5.6 × 10-3 mm2/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (-) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease.

CONCLUSION

D* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.

Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis

We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.

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

Background

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

Results

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

Conclusion

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

Nutrigenomics and Breast Cancer: State-of-Art, Future Perspectives and Insights for Prevention

Proper nutrition plays a major role in preventing diseases and, therefore, nutritional interventions constitute crucial strategies in the field of Public Health. Nutrigenomics and nutriproteomics are arising from the integration of nutritional, genomics and proteomics specialties in the era of postgenomics medicine. In particular, nutrigenomics and nutriproteomics focus on the interaction between nutrients and the human genome and proteome, respectively, providing insights into the role of diet in carcinogenesis. Further omics disciplines, like metabonomics, interactomics and microbiomics, are expected to provide a better understanding of nutrition and its underlying factors. These fields represent an unprecedented opportunity for the development of personalized diets in women at risk of developing breast cancer.

Can mammogram readers swiftly and effectively learn to interpret first post-contrast acquisition subtracted (FAST) MRI, a type of abbreviated breast MRI?: a single centre data-interpretation study

OBJECTIVES

To assess whether NHS breast screening programme (NHSBSP) mammogram readers could effectively interpret first post-contrast acquisition subtracted (FAST) MRI, for intended use in screening for breast cancer.

METHODS

Eight NHSBSP mammogram readers from a single centre (four who also read breast MRI (Group 1) and four who do not (Group 2)) were given structured FAST MRI reader training (median 4 h: 32 min). They then prospectively interpreted 125 FAST MRIs (250 breasts: 194 normal and 56 cancer) comprising a consecutive series of screening MRIs enriched with additional cancer cases from 2015, providing 2000 interpretations. Readers were blinded to other readers' opinions and to clinical information. Categorisation followed the NHSBSP MRI reporting categorisation, with categories 4 and 5 considered indicative of cancer. Diagnostic accuracy (reference standard: histology or 2 years' follow-up) and agreement between readers were determined.

RESULTS

The accuracy achieved by Group 2 (847/1000 (85%; 95% confidence interval (CI) 82-87%)) was 5% less than that of Group 1 (898/1000 (90%; 95% CI 88-92)). Good inter-reader agreement was seen between both Group 1 readers (κ = 0.66; 95% CI 0.61-0.71) and Group 2 readers (κ = 0.63; 95% CI 0.58-0.68). The median time taken to interpret each FAST MRI was Group 1: 34 s (range 3-351) and Group 2: 77 s (range 11-321).

CONCLUSION

Brief structured training enabled multiprofessional mammogram readers to achieve similar accuracy at FAST MRI interpretation to consultant radiologists experienced at breast MRI interpretation.

ADVANCES IN KNOWLEDGE

FAST MRI could be feasible from a training-the-workforce perspective for screening within NHSBSP.

OCT-elastography-based optical biopsy for breast cancer delineation and express assessment of morphological/molecular subtypes

Application of compressional optical coherence elastography (OCE) for delineation of tumor and peri-tumoral tissue with simultaneous assessment of morphological/molecular subtypes of breast cancer is reported. The approach is based on the ability of OCE to quantitatively visualize stiffness of studied samples and then to perform a kind of OCE-based biopsy by analyzing elastographic B-scans that have sizes ~several millimeters similarly to bioptates used for "gold-standard" histological examinations. The method relies on identification of several main tissue constituents differing in their stiffness in the OCE scans. Initially the specific stiffness ranges for the analyzed tissue components (adipose tissue, fibrous and hyalinized tumor stroma, lymphocytic infiltrate and agglomerates of tumor cells) are determined via comparison of OCE and morphological/molecular data. Then assessment of non-tumor/tumor regions and tumor subtypes is made based on percentage of pixels with different characteristic stiffness ("stiffness spectrum") in the OCE image, also taking into account spatial localization of different-stiffness regions. Examples of high contrast among benign (or non-invasive) and several subtypes of invasive breast tumors in terms of their stiffness spectra are given.

Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI

Clinical practice in radiology and pathology requires professional expertise and many years of training to visually evaluate and interpret abnormal phenotypic features in medical images and tissue sections to generate diagnoses that guide patient management and treatment. Recent advances in digital image analysis methods and machine learning have led to significant interest in extracting additional information from medical and digital whole-slide images in radiology and pathology, respectively. This has led to significant interest and research in radiomics and pathomics to correlate phenotypic features of disease with image analytics in order to identify image-based biomarkers. The expanding role of big data in radiology and pathology parallels the development and role of immunohistochemistry (IHC) in the daily practice of pathology. IHC methods were initially developed to provide additional information to help classify tumors and then transformed into an indispensable tool to guide treatment in many types of cancer. IHC markers are used in daily practice to identify specific types of cells and highlight their distributions in tissues in order to distinguish benign from neoplastic cells, determine tumor origin, subclassify neoplasms, and support and confirm diagnoses. In this regard, radiomics, pathomics, and IHC methods are very similar since they enable the extraction of image-based features to characterize various properties of diseases. Due to the dramatic advancements in recent radiomics research, we provide a brief overview of the role of established and emerging IHC biomarkers in various tumor types that have been correlated with radiologic biomarkers to improve diagnostic accuracy, predict prognosis, guide patient management, and select treatment strategies. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:341-354.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification

The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm². Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification

The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm². Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.

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.

Triple-negative invasive breast carcinoma: the association between the sonographic appearances with clinicopathological feature

In this study, we aimed to evaluate the clinical and pathological factors that associated with sonographic appearances of triple-negative (TN) invasive breast carcinoma. With the ethical approval, 560 patients who were pathologically confirmed as invasive breast carcinoma were reviewed for ultrasound, clinical, and pathological data. Logistic regression analysis was used to identify the typical sonographic features for TN invasive breast carcinomas. The effect of clinical and pathological factors on the sonographic features of TN invasive breast carcinoma was studied. There were 104 cases of TN invasive breast carcinoma. The independent sonographic features for the TN subgroup included regular shape (odds ratio, OR = 2.14, p = 0.007), no spiculated/angular margin (OR = 1.93, p = 0.035), posterior acoustic enhancement (OR = 2.14, p = 0.004), and no calcifications (OR = 2.10, p = 0.008). Higher pathological grade was significantly associated with regular tumor shape of TN breast cancer (p = 0.012). Higher Ki67 level was significantly associated with regular tumor shape (p = 0.023) and absence of angular/spiculated margin (p = 0.005). Higher human epidermal growth factor receptor 2 (HER2) score was significantly associated with the presence of calcifications (p = 0.033). We conclude that four sonographic features are associated with TN invasive breast carcinoma. Heterogeneity of sonographic features was associated with the pathological grade, Ki67 proliferation level and HER2 score of TN breast cancers.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes

OBJECTIVE

To evaluate the associations between intravoxel incoherent motion (IVIM)-derived parameters and histopathological features and subtypes of breast cancer.

METHODS

Pre-operative MRI from 275 patients with unilateral breast cancer was analyzed. The apparent diffusion coefficient (ADC) and IVIM parameters [tissue diffusion coefficient (Dt), perfusion fraction (fp) and pseudodiffusion coefficient] were obtained from cancer and normal tissue using diffusion-weighted imaging with b-values of 0, 30, 70, 100, 150, 200, 300, 400, 500 and 800 s mm(-2). We then compared the IVIM parameters of tumours with different histopathological features and subtypes.

RESULTS

The ADC and Dt were lower and fp was higher in cancers than in normal tissues (p < 0.001). The Dt was lower in high Ki-67 cancer than in low Ki-67 cancer (p = 0.019), whereas ADC showed no significant difference (p = 0.309). Luminal B [human epidermal growth factor receptor 2 (HER2)-negative] cancer showed lower ADC (p = 0.003) and Dt (p = 0.001) than other types.

CONCLUSION

We found low tissue diffusivity in high Ki-67 cancer and luminal B (HER2-negative) cancer using IVIM imaging.

ADVANCES IN KNOWLEDGE

Low tissue diffusivity is more clearly shown in high Ki-67 tumours and luminal B (HER2-negative) tumours with the IVIM model.

Breast cancer molecular subtype classifier that incorporates MRI features

PURPOSE

To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes.

MATERIALS AND METHODS

This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test.

RESULTS

Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN).

CONCLUSION

We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129.

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.

Direct-Conversion Molecular Breast Imaging of Invasive Breast Cancer: Imaging Features, Extent of Invasive Disease, and Comparison Between Invasive Ductal and Lobular Histology

OBJECTIVE

The purposes of this study were to compare the tumor appearance of invasive breast cancer on direct-conversion molecular breast imaging using a standardized lexicon and to determine how often direct-conversion molecular breast imaging identifies all known invasive tumor foci in the breast, and whether this differs for invasive ductal versus lobular histologic profiles.

MATERIALS AND METHODS

Patients with prior invasive breast cancer and concurrent direct-conversion molecular breast imaging examinations were retrospectively reviewed. Blinded review of direct-conversion molecular breast imaging examinations was performed by one of two radiologists, according to a validated lexicon. Direct-conversion molecular breast imaging findings were matched with lesions described on the pathology report to exclude benign reasons for direct-conversion molecular breast imaging findings and to document direct-conversion molecular breast imaging-occult tumor foci. Associations between direct-conversion molecular breast imaging findings and tumor histologic profiles were examined using chi-square tests.

RESULTS

In 286 patients, 390 invasive tumor foci were present in 294 breasts. A corresponding direct-conversion molecular breast imaging finding was present for 341 of 390 (87%) tumor foci described on the pathology report. Invasive ductal carcinoma (IDC) tumor foci were more likely to be a mass (40% IDC vs 15% invasive lobular carcinoma [ILC]; p < 0.001) and to have marked intensity than were ILC foci (63% IDC vs 32% ILC; p < 0.001). Direct-conversion molecular breast imaging correctly revealed all pathology-proven foci of invasive disease in 79.8% of cases and was more likely to do so for IDC than for ILC (86.1% vs 56.7%; p < 0.0001). Overall, direct-conversion molecular breast imaging showed all known invasive foci in 249 of 286 (87%) patients.

CONCLUSION

Direct-conversion molecular breast imaging features of invasive cancer, including lesion type and intensity, differ by histologic subtype. Direct-conversion molecular breast imaging is less likely to show all foci of ILC compared with IDC.

Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay

PURPOSE

To investigate the association between a validated, gene-expression-based, aggressiveness assay, Oncotype Dx RS, and morphological and texture-based image features extracted from magnetic resonance imaging (MRI).

MATERIALS AND METHODS

This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006-2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2- invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray-scale correlation matrix (GLCM)-based texture features computed from tumors contoured on pre- and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P < 0.05 was considered statistically significant.

RESULTS

Ninety-five patients with IDC were included with a median Oncotype Dx RS of 16 (range: 0-45). Using stepwise multiple linear regression modeling, two MR-derived image features, kurtosis in the first and third postcontrast images and histologic nuclear grade, were found to be significantly correlated with the Oncotype Dx RS with P = 0.0056, 0.0005, and 0.0105, respectively. The overall model resulted in statistically significant correlation with Oncotype Dx RS with an R-squared value of 0.23 (adjusted R-squared = 0.20; P = 0.0002) and a Spearman's rank correlation coefficient of 0.49 (P < 0.0001).

CONCLUSION

A model for IDC using imaging and pathology information correlates with Oncotype Dx RS scores, suggesting that image-based features could also predict the likelihood of recurrence and magnitude of chemotherapy benefit.

The differences in ultrasound and clinicopathological features between basal-like and normal-like subtypes of triple negative breast cancer

Purpose

The aim of this study was to identify the ultrasound features and clinicopathological characteristics of basal-like subtype of triple negative breast cancers (TNBCs).

Materials and Methods

This study was approved by the ethical board of the Second Affiliated Hospital of Harbin Medical University. The patients’ clinicopathological information was available. The ultrasound features of 62 tumors from 62 TNBC patients were interpreted. The immunohistochemical results of cytokertain5/6 (CK5/6) and Epidermal Growth Factor Receptor (EGFR) were used to classify the tumor into basal-like and normal-like groups. The association of the ultrasound features interpreted by experienced ultrasound doctors with the immunohistochemical classification was studied.

Results

Of the 62 TNBC cases, 42 (67.7%) exhibited the basal-like phenotype and 20 (32.3%) exhibited the normal-like phenotype based on the immunohistochemical CK5/6 and EGFR markers. Of all the tumors, 90.3% were invasive carcinomas. The basal-like tumors were significantly associated with a maximum diameter on ultrasound of more than 20 mm (36, 85.7%) (P = 0.0014). The normal-like tumors usually exhibited lateral shadows (15, 75%) (P = 0.0115) as well as microlobulated margins (12, 60%) (P = 0.0204) compared to the basal-like subtype. Other ultrasound features showed no significant differences between the two groups.

Conclusions

Although ultrasound cannot yet be used to differentiate between the basal-like subtype and normal-like subtype of TNBC, ultrasound can be used to provide some useful information to the clinicians.

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

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