Imaging features of HER2 overexpression in breast cancer: a systematic review and meta-analysis

Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study

OBJECTIVES

To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis.

METHODS

This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC).

RESULTS

The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort.

CONCLUSIONS

Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers.

CLINICAL RELEVANCE STATEMENT

By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios.

KEY POINTS

• The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Breast cancer stage and molecular subtype distribution: real-world insights from a regional oncological center in Hungary

OBJECTIVE

Examining the distribution of breast cancer (BC) stage and molecular subtype among women aged below (< 45 years), within (45-65 years), and above (> 65 years) the recommended screening age range helps to understand the screening program's characteristics and contributes to enhancing the effectiveness of BC screening programs.

METHODS

In this retrospective study, female patients with newly diagnosed BC from 2010 to 2020 were identified. The distribution of cases in terms of TNM stages, severity classes, and subtypes was analysed according to age groups.

RESULTS

A total of 3282 women diagnosed with BC were included in the analysis. Among these cases 51.4% were detected outside the screening age group, and these were characterized by a higher TNM stage compared to those diagnosed within the screening age band. We observed significantly higher relative frequency of advanced BC in the older age group compared to both the screening age population and women younger than 45 years (14.9% vs. 8.7% and 7.7%, P < 0.001). HR-/HER2- and HER+ tumours were relatively more frequent among women under age 45 years (HR-/HER2-: 23.6%, HER2+: 20.5%) compared to those within the screening age range (HR-/HER2-: 13.4%, HER2+: 13.9%) and the older age group (HR-/HER2-: 10.4%, HER2+: 11.5%).

CONCLUSIONS

The findings of our study shed light on potential areas for the improvement of BC screening programs (e.g., extending screening age group, adjusting screening frequency based on molecular subtype risk status) in Hungary and internationally, as well.

Clinicopathological characteristics and prognostic significance of casting-type calcifications in patients with invasive breast cancer presenting with microcalcification

To explore the clinicopathological characteristics and prognostic significance of casting-type calcification (CC) in patients with breast cancer presenting with microcalcification on mammography. Data on patients with invasive breast cancer who had mammographic calcification was retrospectively analyzed. The chi-square test was utilized to assess the clinicopathological characteristics of two forms of CC-related breast cancer. The examination of prognostic variables was conducted using Kaplan-Meier and Cox regression analyses. A total of 427 eligible patients were included in this study. Chi-square analysis indicated that the presence of CC was associated with estrogen receptor (ER) negativity (P = 0.005), progesterone receptor (PR) negativity (P < 0.001), and epidermal growth factor receptor 2 (HER-2) positivity (P < 0.001); among these, the association was stronger with the CC-predominant type. After a median follow-up of 82 months, those with CC had a worse 5-year recurrence-free survival (RFS) (77.1% vs. 86.9%, p = 0.036; hazard ratio [HR], 1.86; 95% confidence interval [CI] 1.04-3.31) and overall survival (OS) (84.0% vs. 94.4%, p = 0.007; HR, 2.99; 95% CI 1.34-6.65) rates. In COX regression analysis, such differences were still observed in HER-2 positive subgroups (RFS: HR: 2.45, 95% CI 1-5.97, P = 0.049; OS: HR: 4.53, 95% CI 1.17-17.52, P = 0.029). In patients with invasive breast cancer exhibiting calcifications on mammography, the presence of CC, especially the CC-predominant type, is linked to a higher frequency of hormone receptor negativity and HER-2 positivity. The presence of CC is associated with an unfavorable 5-year RFS and OS rates.

Association Between 18F-FDG PET Activity and HER2 Status in Breast Cancer Brain Metastases

Purpose

The objective of this study was to evaluate whether uptake on 18F-fluorodeoxyglucose (18F-FDG) PET could help differentiate HER2-positive from HER2-negative breast cancer brain metastases.

Methods

In this retrospective, cross-sectional study of a cohort of 14 histologically proven breast cancer brain metastases, we analyzed both preoperative 18F-FDG PET/CT and HER2 status of the resected/biopsied brain specimens. The maximum standardized uptake values (SUVmax) of the lesions were normalized to contralateral normal white matter and compared using Mann-Whitney U tests.

Results

The study cohort was comprised of 12 women with breast cancer with a mean age of 59 years (range: 43-76 years) with a total of 14 distinct brain metastatic lesions. The SUVmax ratio of HER2-positive breast cancer brain metastases was significantly greater than that of HER2-negative lesions (3.98 vs 1.79, U = 38.00, p = 0.008).

Conclusion

The SUVmax ratio may help to identify the HER2 status of breast cancer brain metastases, if validated prospectively.

Correlation between Histopathological Prognostic Tumor Characteristics and [18F]FDG Uptake in Corresponding Metastases in Newly Diagnosed Metastatic Breast Cancer

BACKGROUND

In metastatic breast cancer (MBC), [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) can be used for staging. We evaluated the correlation between BC histopathological characteristics and [18F]FDG uptake in corresponding metastases.

PATIENTS AND METHODS

Patients with non-rapidly progressive MBC of all subtypes prospectively underwent a baseline histological metastasis biopsy and [18F]FDG-PET. Biopsies were assessed for estrogen, progesterone, and human epidermal growth factor receptor 2 (ER, PR, HER2); Ki-67; and histological subtype. [18F]FDG uptake was expressed as maximum standardized uptake value (SUVmax) and results were expressed as geometric means.

RESULTS

Of 200 patients, 188 had evaluable metastasis biopsies, and 182 of these contained tumor. HER2 positivity and Ki-67 ≥ 20% were correlated with higher [18F]FDG uptake (estimated geometric mean SUVmax 10.0 and 8.8, respectively; p = 0.0064 and p = 0.014). [18F]FDG uptake was lowest in ER-positive/HER2-negative BC and highest in HER2-positive BC (geometric mean SUVmax 6.8 and 10.0, respectively; p = 0.0058). Although [18F]FDG uptake was lower in invasive lobular carcinoma (n = 31) than invasive carcinoma NST (n = 146) (estimated geometric mean SUVmax 5.8 versus 7.8; p = 0.014), the metastasis detection rate was similar.

CONCLUSIONS

[18F]FDG-PET is a powerful tool to detect metastases, including invasive lobular carcinoma. Although BC histopathological characteristics are related to [18F]FDG uptake, [18F]FDG-PET and biopsy remain complementary in MBC staging (NCT01957332).

Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results

BACKGROUND

We aimed to predict human epidermal growth factor receptor 2 (HER2) 2+ status in patients with breast cancer by constructing and validating machine learning models utilizing ultrasound (US) radiomics and clinical features.

METHODS

We analyzed 203 breast cancer cases immunohistochemically determined as HER2 2+ and used fluorescence in situ hybridization (FISH) as the confirmation method. From each case, the study analyzed 840 extracted radiomics features and 11 clinicopathologic features. Cases were randomly split into training (n = 141) and validation sets (n = 62) at a 7:3 ratio. Univariate logistic regression analysis was first performed on the 11 clinicopathologic characteristics. The least absolute shrinkage and selection operator (LASSO) and decision tree (DT) techniques were employed for post-feature selection. Finally, 19 radiomics features were utilized in logistic regression (LR) and Naive Bayesian (NB) classifiers. Model performance was gauged using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

Our models exhibited notable diagnostic efficacy in differentiating HER2-positive from negative breast cancer cases. In the validation sets, the LR model outperformed the NB model with an AUC of 0.860 and accuracy of 83.8% compared to NB's AUC of 0.684 and accuracy of 79.0%. The LR model demonstrated higher sensitivity (92.3% vs. 46.2%) while the NB model had a better specificity (91.8% vs. 63.3%) in the validation set.

CONCLUSIONS

Machine learning models grounded on radiomics efficiently predicted IHC HER2 2+ status in breast cancer patients, suggesting potential enhancements in clinical decision-making for treatment and management.

Clinicopathological and prognostic value of calcification morphology descriptors in ductal carcinoma in situ of the breast: a systematic review and meta-analysis

BACKGROUND

Calcifications on mammography can be indicative of breast cancer, but the prognostic value of their appearance remains unclear. This systematic review and meta-analysis aimed to evaluate the association between mammographic calcification morphology descriptors (CMDs) and clinicopathological factors.

METHODS

A comprehensive literature search in Medline via Ovid, Embase.com, and Web of Science was conducted for articles published between 2000 and January 2022 that assessed the relationship between CMDs and clinicopathological factors, excluding case reports and review articles. The risk of bias and overall quality of evidence were evaluated using the QUIPS tool and GRADE. A random-effects model was used to synthesize the extracted data. This systematic review is reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA).

RESULTS

Among the 4715 articles reviewed, 29 met the inclusion criteria, reporting on 17 different clinicopathological factors in relation to CMDs. Heterogeneity between studies was present and the overall risk of bias was high, primarily due to small, inadequately described study populations. Meta-analysis demonstrated significant associations between fine linear calcifications and high-grade DCIS [pooled odds ratio (pOR), 4.92; 95% confidence interval (CI), 2.64-9.17], (comedo)necrosis (pOR, 3.46; 95% CI, 1.29-9.30), (micro)invasion (pOR, 1.53; 95% CI, 1.03-2.27), and a negative association with estrogen receptor positivity (pOR, 0.33; 95% CI, 0.12-0.89).

CONCLUSIONS

CMDs detected on mammography have prognostic value, but there is a high level of bias and variability between current studies. In order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.

CRITICAL RELEVANCE STATEMENT

Mammographic calcification morphology descriptors (CMDs) have prognostic value, but in order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.

SYSTEMATIC REVIEW REGISTRATION

CRD42022341599 KEY POINTS: • Mammographic calcifications can be indicative of breast cancer. • The prognostic value of mammographic calcifications is still unclear. • Specific mammographic calcification morphologies are related to lesion aggressiveness. • Variability between studies necessitates standardization in calcification evaluation to achieve clinical utility.

Correlation between the CEM imaging characteristics and different molecular subtypes of breast cancer

PURPOSE

To investigate the correlation between the contrast-enhanced mammography (CEM) imaging characteristics and different molecular subtypes of breast cancer (BC).

METHODS

We retrospectively included 313 eligible female patients who underwent CEM examination and surgery in our hospital from July 2017 to July 2021. Their lesions were confirmed on histopathological examination and immunohistochemical analysis. BC was divided into luminal A, luminal B, HER2-enriched, and triple-negative BC (TNBC) subtypes according to immunohistochemical markers. Nine features were extracted from CEM images, including tumor shape, margins, spiculated mass, lobulated mass, malignant calcification, lesion conspicuity, internal enhancement pattern, multifocal mass, and swollen axillary lymph nodes. Statistical analysis was performed using SPSS 25.0. Univariate analysis and binomial regression were used to analyze the correlation between CEM imaging features and BC molecular subtypes.

RESULTS

There were 184 (58.8 %) Luminal A, 44 (14.1 %) Luminal B, 47 (15.0 %) HER-2-enriched and 38 (12.1 %) TNBC, respectively. Molecular subtypes were significantly related to the tumor shape, margins, spiculated mass, internal enhancement pattern, malignant calcification and swollen axillary lymph nodes. Spiculated and calcified tumors were associated with Luminal subtypes, especially Luminal B (P < 0.05). Irregular tumor shape and malignant calcification were associated with HER-2-enriched subtype (P < 0.05). Oval or round tumor shape, rim enhancement, and swollen axillary lymph nodes were associated with TNBC (P < 0.05).

CONCLUSION

CEM imaging features could distinguish BC molecular subtypes. In particular, TNBC showed oval or round tumor shape, rim enhancement, and swollen axillary lymph nodes, providing insights into the diagnosis and prognosis of TNBC.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

Association of Relative Cerebral Blood Volume from Dynamic Susceptibility Contrast-Enhanced Perfusion MR with HER2 Status in Breast Cancer Brain Metastases

RATIONALE AND OBJECTIVES

With the development of HER2-directed therapies, identifying non-invasive imaging biomarkers of HER2 expression in breast cancer brain metastases has become increasingly important. The purpose of this study was to investigate whether relative cerebral blood volume (rCBV) from dynamic susceptibility contrast-enhanced (DSC) perfusion MR could help identify the HER2 status of breast cancer brain metastases.

MATERIALS AND METHODS

With IRB approval for this HIPAA-compliant cross-sectional study and a waiver of informed consent, we queried our institution's electronic medical record to derive a cohort of 14 histologically proven breast cancer brain metastases with preoperative DSC perfusion MR and HER2 analyses of the resected/biopsied brain specimens from 2011-2021. The rCBV of the lesions was measured and compared using Mann-Whitney tests. Receiver operating characteristic analyses were performed to evaluate the performance of rCBV in identifying HER2 status.

RESULTS

The study cohort was comprised of 14 women with a mean age of 56 years (range: 32-81 years) with a total of 14 distinct lesions. The rCBV of HER2-positive breast cancer brain metastases was significantly greater than the rCBV of HER2-negative lesions (8.02 vs 3.97, U=48.00, p=0.001). rCBV differentiated HER2-positive lesions from HER2-negative lesions with an area under the curve of 0.98 (standard error=0.032, p<0.001). The accuracy-maximizing rCBV threshold (4.8) was associated with an accuracy of 93% (13/14), a sensitivity of 100% (7/7), and a specificity of 86% (6/7).

CONCLUSION

rCBV may assist in identifying the HER2 status of breast cancer brain metastases, if validated in a large prospective trial.

Multicellular spheroids containing synthetic mineral particles: an advanced 3D tumor model system to investigate breast precancer malignancy potential according to the mineral type

Mineral particles that form in soft tissues in association with disease conditions are heterogeneous in their composition and physiochemical properties. Hence, it is challenging to study the effect of mineral type on disease progression in a high-throughput and realistic manner. For example, most early breast precancer lesions, termed ductal carcinoma in situ (DCIS), contain microcalcifications (MCs), calcium-containing pathological minerals. The most common type of MCs is calcium phosphate crystals, mainly carbonated apatite; it is associated with either benign or malignant lesions. In vitro studies indicate that the crystal properties of apatite MCs can affect breast cancer progression. A less common type of MCs is calcium oxalate dihydrate (COD), which is almost always found in benign lesions. We developed a 3D tumor model of multicellular spheroids of human precancer cells containing synthetic MC analogs that link the crystal properties of MCs with the progression of breast precancer to invasive cancer. Using this 3D model, we show that apatite crystals induce Her2 overexpression in DCIS cells. This tumor-triggering effect is increased when the carbonate fraction in the MCs decreases. COD crystals, in contrast, decrease Her2 expression in the spheroids, even compared with a control group with no added MC analogs. Furthermore, COD decreases cell proliferation and migration in DCIS monolayers compared to untreated cells and cells incubated with apatite crystals. This finding suggests that COD is not randomly located only in benign lesions-it may actively contribute to suppressing precancer progression in its surroundings. Our model provides an easy-to-manipulate platform to better understand the interactions between mineral particles and their biological microenvironment. A better understanding of the effect of the crystal properties of MCs on precancer progression will potentially provide new directions for better precancer prognosis and treatment.

Combining conventional ultrasound and ultrasound elastography to predict HER2 status in patients with breast cancer

Introduction: Identifying the HER2 status of breast cancer patients is important for treatment options. Previous studies have shown that ultrasound features are closely related to the subtype of breast cancer. Methods: In this study, we used features of conventional ultrasound and ultrasound elastography to predict HER2 status. Results and Discussion: The performance of model (AUROC) with features of conventional ultrasound and ultrasound elastography is higher than that of the model with features of conventional ultrasound (0.82 vs. 0.53). The SHAP method was used to explore the interpretability of the models. Compared with HER2- tumors, HER2+ tumors usually have greater elastic modulus parameters and microcalcifications. Therefore, we concluded that the features of conventional ultrasound combined with ultrasound elastography could improve the accuracy for predicting HER2 status.

Correlation of androgen receptor with ultrasound, clinicopathological features and clinical outcomes in breast cancer

BACKGROUND

This study aimed to explore whether there is an association between androgen receptor (AR) expression and ultrasound, clinicopathological features and prognosis of breast cancer.

METHODS

A total of 141 breast cancer patients were included in this retrospective study. AR expression was analyzed by immunohistochemistry. The images of B-mode, color Doppler and strain elastography from 104 patients were collected continuously, and the corresponding ultrasound characteristics were obtained. The differences in ultrasound and clinicopathological features in different AR status were analyzed. Progression-free survival (PFS) of patients was obtained through up to 90 months of follow-up; then, the effect of AR on PFS was analyzed. Subsequently, a nomogram was constructed to predict the AR status. The predictive accuracy was calculated using C-index.

RESULTS

The positive expression of AR (AR +) was associated with lower histological grade (p = 0.034) and lower Ki-67 level (p = 0.029). Triple-negative breast cancer (TNBC) had the lowest probability of AR + (p < 0.001). The AR + group mostly showed unsmooth margin (p < 0.001), posterior acoustic shadowing (p = 0.002) and higher elasticity score (p = 0.022) on ultrasound. The echo pattern of most tumors with AR + was heterogeneous (p = 0.024) in Luminal A subtype. AR + could be a sign of a better prognosis in overall breast cancer (p < 0.001), as well as in human epidermal growth factor receptor 2 (HER2) overexpression and Luminal B subtypes (p = 0.001 and 0.025). The nomogram showed relatively reliable performance with a C-index of 0.799.

CONCLUSION

Our research demonstrated that AR expression was closely related to ultrasound, clinicopathological features and prognosis of breast cancer.

Association of lesion contour and lesion composition on MR with HER2 status in breast cancer brain metastases

BACKGROUND AND PURPOSE

With the development of HER2-directed therapies, identifying non-invasive imaging biomarkers of HER2 status in breast cancer brain metastases has become increasingly important, particularly given the risks of tissue sampling within the brain and the possibility of a change in receptor expression from the primary tumor to the brain metastasis. The purpose of this study was to evaluate whether lesion contour and composition on MR could help identify the HER2 status of breast cancer brain metastases.

MATERIALS AND METHODS

We derived a cohort of 34 women with a mean age of 55 years (range: 31-81 years) with a total of 47 distinct histologically proven breast cancer brain metastases with preoperative contrast-enhanced brain MR and HER2 immunohistochemistry and/or fluorescent in-situ hybridization (FISH) of the resected/biopsied brain specimens from 2018 to 2021. Two fellowship-trained neuroradiologists evaluated the lesion contour and lesion composition of each lesion. Logistic regression analyses were performed.

RESULTS

In a logistic regression model, an irregular contour had an odds ratio of 170 (p = 0.007) in differentiating HER2-positive from HER2-negative lesions. In a logistic regression model, when compared to a predominantly cystic lesion composition, a solid lesion composition had an odds ratio of 17 (p = 0.016) in differentiating HER2-positive from HER2-negative lesions.

CONCLUSION

Lesion contour and lesion composition on MR were significantly associated with the HER2 status of breast cancer brain metastases. Current assessment of HER2 status requires tissue sampling and immunochemical and/or FISH analyses. A non-invasive imaging biomarker that may help predict HER2 status may be of great clinical value.

Multimodality Imaging Review of HER2-positive Breast Cancer and Response to Neoadjuvant Chemotherapy

Human epidermal growth factor receptor 2 (HER2/neu or ErbB2)-positive breast cancers comprise 15%-20% of all breast cancers. The most common manifestation of HER2-positive breast cancer at mammography or US is an irregular mass with spiculated margins that often contains calcifications; at MRI, HER2-positive breast cancer may appear as a mass or as nonmass enhancement. HER2-positive breast cancers are often of intermediate to high nuclear grade at histopathologic analysis, with increased risk of local recurrence and metastases and poorer overall prognosis. However, treatment with targeted monoclonal antibody therapies such as trastuzumab and pertuzumab provides better local-regional control and leads to improved survival outcome. With neoadjuvant treatments, including monoclonal antibodies, taxanes, and anthracyclines, women are now potentially able to undergo breast conservation therapy and sentinel lymph node biopsy versus mastectomy and axillary lymph node dissection. Thus, the radiologist's role in assessing the extent of local-regional disease and response to neoadjuvant treatment at imaging is important to inform surgical planning and adjuvant treatment. However, assessment of treatment response remains difficult, with the potential for different imaging modalities to result in underestimation or overestimation of disease to varying degrees when compared with surgical pathologic analysis. In particular, the presence of calcifications at mammography is especially difficult to correlate with the results of pathologic analysis after chemotherapy. Breast MRI findings remain the best predictor of pathologic response. The authors review the initial manifestations of HER2-positive tumors, the varied responses to neoadjuvant chemotherapy, and the challenges in assessing residual cancer burden through a multimodality imaging review with pathologic correlation. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Classifying Breast Cancer Metastasis Based on Imaging of Tumor Primary and Tumor Biology

The molecular classification of breast cancer has allowed for a better understanding of both prognosis and treatment of breast cancer. Imaging of the different molecular subtypes has revealed that biologically different tumors often exhibit typical features in mammography, ultrasound, and MRI. Here, we introduce the molecular classification of breast cancer and review the typical imaging features of each subtype, examining the predictive value of imaging with respect to distant metastases.

Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions

BACKGROUND

Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer.

METHODS

This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer.

RESULTS

Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI).

CONCLUSION

We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer.

Multiparametric MRI Features of Breast Cancer Molecular Subtypes

Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.

Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

BACKGROUND

To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes.

METHODS

A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T₁ -weighted imaging (T₁ C), Apparent diffusion coefficient (ADC), and T₂ -weighted imaging (T₂ W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance.

RESULTS

For the separation of each subtype from other subtypes on the testing set, the T₁ C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T₂ W-based models achieved AUCs from 0.639 to 0.697.

CONCLUSION

T₁ C-based models performed better than ADC-based models and T₂ W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.

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.

Breast microcalcifications: Past, present and future (Review)

Mammary microcalcifications (MCs) are calcium deposits that are considered as robust markers of breast cancer when identified on mammography. MCs are frequently associated with premalignant and malignant lesions. The aim of the present review was to describe the MC types and associated radiological and pathological aspects in detail, provide insights and approaches to the topic, and describe specific clinical scenarios. The primary MC types are composed of calcium oxalate, hydroxyapatite and hydroxyapatite associated with magnesium. The first type is usually associated with benign conditions, while the others remain primarily associated with malignancy. Radiologically, MCs are classified as benign or suspicious. MCs may represent an active pathological mineralization process rather than a passive process, such as degeneration or necrosis. Practical management of breast specimens requires finely calibrated radiological pathological procedures. Understanding the molecular and structural development of MCs may contribute to breast lesion detection and treatment.

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.

Advances in surface plasmon resonance-based biosensor technologies for cancer biomarker detection

Surface plasmon resonance approach is a highly useful option to offer optical and label-free detection of target bioanalytes with numerous advantages (e.g., low-cost fabrication, appreciable sensitivity, label-free detection, and outstanding accuracy). As such, it allows early diagnosis of cancer biomarkers to monitor tumor progression and to prevent the recurrence of oncogenic tumors. This work highlights the recent progress in SPR biosensing technology for the diagnosis of various cancer types (e.g., lung, breast, prostate, and ovarian). Further, the performance of various SPR biosensors is also evaluated in terms of the basic quality assurance criteria (e.g., limit of detection (LOD), selectivity, sensor response time, and reusability). Finally, the limitations and future challenges associated with SPR biosensors are also discussed with respect to cancer biomarker detection.

Ultrasonographic morphological characteristics determined using a deep learning-based computer-aided diagnostic system of breast cancer

To investigate the correlations between ultrasonographic morphological characteristics quantitatively assessed using a deep learning-based computer-aided diagnostic system (DL-CAD) and histopathologic features of breast cancer.This retrospective study included 282 women with invasive breast cancer (<5 cm; mean age, 54.4 [range, 29-85] years) who underwent surgery between February 2016 and April 2017. The morphological characteristics of breast cancer on B-mode ultrasonography were analyzed using DL-CAD, and quantitative scores (0-1) were obtained. Associations between quantitative scores and tumor histologic type, grade, size, subtype, and lymph node status were compared.Two-hundred and thirty-six (83.7%) tumors were invasive ductal carcinoma, 18 (6.4%) invasive lobular carcinoma, and 28 (9.9%) micropapillary, apocrine, and mucinous. The mean size was 1.8 ± 1.0 (standard deviation) cm, and 108 (38.3%) cases were node positive. Irregular shape score was associated with tumor size (P < .001), lymph nodes status (P = .001), and estrogen receptor status (P = .016). Not-circumscribed margin (P < .001) and hypoechogenicity (P = .003) scores correlated with tumor size, and non-parallel orientation score correlated with histologic grade (P = .024). Luminal A tumors exhibited more irregular features (P = .048) with no parallel orientation (P = .002), whereas triple-negative breast cancer showed a rounder/more oval and parallel orientation.Quantitative morphological characteristics of breast cancers determined using DL-CAD correlated with histopathologic features and could provide useful information about breast cancer phenotypes.

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review

The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.

Is There a Correlation between Multiparametric Assessment in Ultrasound and Intrinsic Subtype of Breast Cancer?

Molecular profile of breast cancer provides information about its biological activity, prognosis and treatment strategies. The purpose of our study was to investigate the correlation between ultrasound features and molecular subtypes of breast cancer. From June 2019 to December 2019, 86 patients (median age 57 years; range 32–88) with 102 breast cancer tumors were included in the study. The molecular subtypes were classified into five types: luminal A (LA), luminal B without HER2 overexpression (LB HER2−), luminal B with HER2 overexpression (LB HER2+), human epidermal growth factor receptor 2 positive (HER2+) and triple negative breast cancer (TNBC). Histopathological verification was obtained in core biopsy or/and post-surgery specimens in all cases. Univariate logistic regression analysis was performed to assess the association between the subtypes and ultrasound imaging features. Experienced radiologists assessed lesions according to the BIRADS-US lexicon. The ultrasound scans were performed with a Supersonic Aixplorer and Supersonix. Based on histopathological verification, the rates of LA, LB HER2−, LB HER2+, HER2+, and TNBC were 33, 17, 17, 16, 19, respectively. Both LB HER2+ and HER2+ subtypes presented higher incidence of calcification (OR = 3.125, p = 0.02, CI 0.0917–5.87) and HER2+ subtype presented a higher incidence of posterior enhancement (OR = 5.75, p = 0.03, CI 1.2257–32.8005), compared to other subtypes. The calcifications were less common in TNBC (OR = 0.176, p = 0.0041, CI 0.0469–0.5335) compared to other subtypes. There were no differences with regard to margin, shape, orientation, elasticity values and vascularity among five molecular subtypes. Our results suggest that there is a correlation between ultrasonographic features assessed according to BIRADS-US lexicon and BC subtypes with HER2 overexpression (both LB HER2+ and HER2+). It may be useful for identification of these aggressive subtypes of breast cancer.

Clinico-radio-pathological Features and Biological Behavior of Breast Cancer in Young Indian Women: A Prospective Study

Aims The aim of the study is to evaluate the characteristic imaging features of breast cancer on mammogram, ultrasound, and magnetic resonance imaging (MRI) in women less than 40 years of age and to assess the degree of correlation between clinico-radio-pathological features and biological behavior. Methods and Materials A prospective observational study on consecutive women under 40 years of age evaluated with ultrasound of breast, digital mammogram, or contrast-enhanced breast MRI, diagnosed with breast cancer on histopathology and molecular analysis done at our center between January and December 2019 were included. Patient demographics, clinical presentation, family history, BRCA mutation status, imaging, pathological findings, and molecular status were determined. Results Out of 2,470 women diagnosed with breast cancer, 354 (14.3%) were less than 40 years of age who were included in this study. Mammography showed positive findings in 85%, ultrasonography in 94.3%, and MRI in 96.4% of women. Majority of the women (69.6%) presented in the late stage (Stage III and IV) with high-grade carcinoma in 39.5% and triple-negative breast cancer (TNBC) in 45.7%. Tumors with human epidermal growth factor-2neu expression were associated with the presence of microcalcifications ( p -value = 0.006), and TNBC with circumscribed margins or BI-RADS 3/4a category on imaging ( p -value = 0.007) and high-grade invasive carcinoma compared with others ( p -value <0.0001). Conclusion The incidence of breast cancer in Indian women less than 40 years of age is relatively high as compared with the West. The detection of breast cancer in young women remains challenging due to dense breast tissue, lower incidence rate, and lack of regular breast screening. While ultrasound is the recommended imaging method for evaluation of breast under the age of 40 years, we found a better characterization of lesions and higher cancer detection rates when they were also evaluated with mammography and MRI.

Performance of enhancement on brain MRI for identifying HER2 overexpression in breast cancer brain metastases

PURPOSE

To investigate whether enhancement on MRI could help identify HER2 overexpression in breast cancer brain metastases.

METHODS

We derived a cohort of 38 histologically proven breast cancer brain metastases with preoperative contrast-enhanced brain MRI and HER2 fluorescent in-situ hybridization of the resected/biopsied brain specimens from 2018 to 2021. Enhancement of the lesions was measured and compared using t-tests. Receiver operating characteristic and logistic regression analyses were performed to evaluate the performance of MRI enhancement in identifying HER2 overexpression.

RESULTS

The study cohort was comprised of 29 women with a mean age of 55 years (range: 31-81 years) with a total of 38 distinct lesions. The HER2-positive subcohort was comprised of 17 patients, while the HER2-negative subcohort was comprised of 13 patients. The percent signal intensity change (PSIC) of HER2-positive breast cancer brain metastases was significantly greater than that of HER2-negative lesions (310 v. 153, P = 0.002). The PSIC differentiated HER2-positive lesions from HER2-negative lesions with an area under the curve of 0.88 (P < 0.001). In a model controlling for lesion size, lesion location, tumor grade, patient age, scanner magnetic field strength, and contrast agent, the PSIC had an accuracy of 92% (35/38), sensitivity of 96% (23/24), and specificity of 86% (12/14) in differentiating HER2-positive lesions from HER2-negative lesions.

CONCLUSION

Enhancement on MRI may assist in identifying HER2 overexpression in breast cancer brain metastases, if validated prospectively.

Association between Imaging Features using the BI-RADS and Tumor Subtype in Patients with Invasive Breast Cancer

BACKGROUND

Different molecular breast cancer subtypes present different biologic features, treatment options, and clinical prognoses. The breast cancer imaging phenotype may help precisely classify breast cancer in a non-invasive manner.

OBJECTIVE

To identify the association between the imaging and clinicopathologic features of invasive breast cancer according to the molecular subtype.

METHODS

We retrospectively reviewed the electronic medical records of 313 consecutive women with breast cancer who underwent surgery between March 2018 and February 2019. Preoperative imaging studies were also reviewed and the association between the clinicopathologic and imaging features was evaluated according to the molecular subtype.

RESULTS

On mammography, the presence of microcalcifications was correlated with the human epidermal factor receptor 2-positive subtype (67%, 14/21). Luminal A and B tumors were more likely to have a spiculated margin (57% [63/110] and 41% [34/81]), while human epidermal factor receptor 2-positive and triple-negative breast cancers were more likely to have an indistinct margin (56% [10/18] and 35% [17/48]). On ultrasonography, luminal A tumors were likely to be depicted as masses with an irregular shape (85%, 115/136) and spiculated margin (49%, 66/136). On magnetic resonance imaging, triple-negative breast cancer appeared as a mass (n=13) that frequently had an irregular shape (62%, 8/13) but was more likely to be oval or round (39%, 5/13) than other subtypes.

CONCLUSION

Some imaging features on mammography, ultrasonography, and magnetic resonance imaging could be useful predictors of the molecular subtype of breast cancer and may aid precision medicine development for patients with breast cancer according to the subtype.

Targeting the purinergic pathway in breast cancer and its therapeutic applications

Breast cancer (BC) is the most frequent cause of death among women, representing a global public health problem. Here, we aimed to discuss the correlation between the purinergic system and BC, recognizing therapeutic targets. For this, we analyzed the interaction of extracellular nucleotides and nucleosides with the purinergic receptors P1 and P2, as well as the influence of ectonucleotidase enzymes (CD39 and CD73) on tumor progression. A comprehensive bibliographic search was carried out. The relevant articles for this review were found in the PubMed, Scielo, Lilacs, and ScienceDirect databases. It was observed that among the P1 receptors, the A1, A2A, and A2B receptors are involved in the proliferation and invasion of BC, while the A3 receptor is related to the inhibition of tumor growth. Among the P2 receptors, the P2X7 has a dual function. When activated for a short time, it promotes metastasis, but when activated for long periods, it is related to BC cell death. P2Y2 and P2Y6 receptors are related to BC proliferation and invasiveness. Also, the high expression of CD39 and CD73 in BC is strongly related to a worse prognosis. The receptors and ectonucleotidases involved with BC become possible therapeutic targets. Several purinergic pathways have been found to be involved in BC cell survival and progression. In this review, in addition to analyzing the pathways involved, we reviewed the therapeutic interventions already studied for BC related to the purinergic system, as well as to other possible therapeutic targets.

Predicting Molecular Subtypes of Breast Cancer with Mammography and Ultrasound Findings: Introduction of Sono-Mammometry Score

We studied the correlation of sonographic and digital mammographic features with molecular classification of breast cancer. Imaging features from 313 patients with preliminary ultrasound and digital mammogram between November 2017 and May 2020 were compared with histopathology and immunohistochemical analysis for the prediction of molecular classification of breast cancer. We also devised a score called “sono-mammometry” score consisting of few simple imaging features which can easily be performed in outpatient settings. We studied that non-triple-negative breast cancers are predominantly hypoechoic and strongly correlate with the presence of irregular spiculated margins along with peripheral echogenic halo, posterior shadowing, and microcalcifications, while there is considerable variation in imaging features of TNBC as some of its imaging features overlap with those of typical benign tumors. Although imaging characteristics are helpful in the prediction of molecular classification, the prognostication value of these imaging features is still weak. There is considerable variation in imaging features which warrants vigilance towards improved diagnostic performance. To help better understand these features, our sono-mammometry score can serve as straightforward test which is assumed to be functional and productive in resource-limited settings.

[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.

Mammographic tumour appearance is related to clinicopathological factors and surrogate molecular breast cancer subtype

Mammographic tumour appearance may provide prognostic useful information. For example, spiculation indicates invasiveness, but also better survival compared to tumours with other appearances. We aimed to study the relationship between mammographic tumour appearance and established clinicopathological factors, including surrogate molecular breast cancer subtypes, in the large Malmö Diet and Cancer Study. A total of 1116 women with invasive breast cancer, diagnosed between 1991 and 2014, were included. Mammographic tumour appearance in relation to status for oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2, histological grade, Ki67 and molecular subtype was analysed using various regression models. All models were adjusted for relevant confounders, including breast density, which can affect mammographic appearance. The results consistently showed that spiculated tumours are indicative of favourable characteristics, as they are more likely to be ER and PR positive, and more often exhibit lower histological grade and lower Ki67 expression. Furthermore, spiculated tumours tend to be of luminal A-like subtype, which is associated with a good prognosis. The establishment of associations between mammographic tumour appearance and clinico­pathological factors may aid in characterizing breast cancer at an earlier stage. This could contribute to more individualized breast cancer treatment in the future.

Imaging features of breast cancer molecular subtypes: state of the art

Characterization of breast cancer molecular subtypes has been the standard of care for breast cancer management. We aimed to provide a review of imaging features of breast cancer molecular subtypes for the field of precision medicine. We also provide an update on the recent progress in precision medicine for breast cancer, implications for imaging, and recent observations in longitudinal functional imaging with radiomics.

Mammography features for early markers of aggressive breast cancer subtypes and tumor characteristics: A population-based cohort study

Current breast cancer risk models identify mostly less aggressive tumors, although only women developing fatal breast cancer will greatly benefit from early identification. Here, we evaluated the use of mammography features (microcalcification clusters, computer-generated Breast Imaging Reporting and Data System [cBIRADS] density and lack of breast density reduction) as early markers of aggressive subtypes and tumor characteristics. Mammograms were retrieved from a population-based cohort of women that were diagnosed with breast cancer from 2001 to 2008 in Stockholm-Gotland County, Sweden. Tumor and patient characteristics were obtained from Stockholm Breast Cancer Quality Register and the Swedish Cancer Registry. Multinomial logistic regression was used to individually model each mammographic feature as a function of molecular subtypes, tumor characteristics and detection mode. A total of 4546 women with invasive breast cancer were included in the study. Women with microcalcification clusters in the affected breast were more likely to have human epidermal growth factor receptor 2 subtype (odds ratio [OR] 1.78; 95% confidence interval [CI] 1.24-2.54) and potentially less likely to have basal subtype (OR 0.54; 0.30-0.96) compared to Luminal A subtype. High mammographic cBIRADS showed association with larger tumor size and interval vs screen-detected cancers. Lack of density reduction was associated with interval vs screen-detected cancers (OR 1.43; 1.11-1.83) and potentially of Luminal B subtype vs Luminal A subtype (OR 1.76; 1.04-2.99). In conclusion, microcalcification clusters, cBIRADS density and lack of breast density reduction could serve as early markers of particular subtypes and tumor characteristics of breast cancer. This information has the potential to be integrated into risk models to identify women at risk for developing aggressive breast cancer in need of supplemental screening.

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.

Cone-beam breast CT features associated with HER2/neu overexpression in patients with primary breast cancer

OBJECTIVES

To identify the relationship between human epidermal growth factor receptor 2 (HER2) status and cone-beam breast CT (CBBCT) characteristics in surgically resected breast cancer.

METHODS

Preoperative CBBCT of patients with BI-RADS 4 or 5 lesions identified on mammography or ultrasound and dense or very dense breast tissue were retrospectively evaluated in 181 surgically resected breast cancer (triple-negative excluded) between May 2012 and November 2014. A set of CBBCT descriptors was semiquantitatively assessed by consensus double reading. Reader reproducibility was analyzed. Multivariable logistic regression analysis using backward elimination (BEA) with the Wald criterion was performed to identify independent predictive factors of harboring HER2/neu. Principle component analysis (PCA) was used to determine characteristics that might differentiate HER2 status. Receiver operating characteristic (ROC) curve analyses were conducted to determine the predictive capability.

RESULTS

HER2 positive was found in 101 (55.8%) of 181 patients. Inter-observer agreement was high for characteristics' assessment. Based on BEA, pathologic grade, maximum dimension, lobulation, ΔCT, and calcification morphology were confirmed as independent predictive factors of HER2/neu overexpression. PCA showed that calcification- and border-related characteristics were the most important for differentiation. ROC curve analyses showed that CBBCT features (AUC = 0.853) were superior to clinicopathologic features (AUC = 0.613, p < 0.001) and comparable with combination (AUC = 0.856, p = 0.866).

CONCLUSIONS

CBBCT features could be used to prognosticate HER2 status independently, which are potentially complementary to histopathologic result and helpful in guiding biopsy.

KEY POINTS

• Dmax, lobulation, ΔCT, and calcification morphology are independent predictors of HER2 status. • CBBCT features are superior to clinicopathologic features in HER2+/- discrimination. • CBBCT features are comparable with combination with clinicopathologic features in HER2+/- discrimination.

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.

Correlation between magnetic resonance imaging and the level of tumor-infiltrating lymphocytes in patients with estrogen receptor-negative HER2-positive breast cancer

Background

High levels of tumor-infiltrating lymphocytes (TILs) are associated with improved prognosis and response to therapy in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.

Purpose

This study investigated the associations between TIL levels and magnetic resonance imaging (MRI) findings in patients with estrogen receptor (ER)-negative HER-2 positive breast cancer.

Material and Methods

This study included 110 consecutive patients with surgically confirmed ER-negative HER2-positive breast cancers who underwent preoperative MRI from January to December 2015. Images of all lesions were reviewed in accordance with the BI-RADS lexicon by radiologists blinded to clinicopathologic findings. Tumor kinetic features were acquired by computer-aided diagnosis (CAD). Patients were divided into three TIL groups: low (<10%); intermediate (10–50%); and high (>50%). Associations between TIL levels and clinicopathologic and imaging features were evaluated; independent predictors of high and low TIL were identified by multiple logistic regression analysis.

Results

The 110 patients included 29 (26.4%) with low, 45 (40.9%) with intermediate, and 36 (32.7%) with high TIL levels. Multiple logistic regression analysis showed that older age (odds ratio [OR] = 1.08; P = 0.017), high peak enhancement (OR = 1.01; P = 0.019), positive CK5/6 (OR = 4.36; P = 0.024), and low Ki-67 (OR = 14.29; P = 0.037) were significantly associated with low TILs; low peak enhancement (OR = 1.01; P = 0.020) was significantly associated with high TILs.

Conclusion

MRI features may predict TIL levels in patients with ER-negative HER-2 positive breast cancer, enhancing the ability to diagnose and treat these patients.

Evaluating the HER-2 status of breast cancer using mammography radiomics features

PURPOSE

The aim of our study was to evaluate the HER-2 status in breast cancer patients using mammography (MG) radiomics features.

METHODS

A total of 306 Chinese female patients with invasive ductal carcinoma of no special type (IDC-NST) enrolled from January 2013 to July 2018 were divided into a training set (n = 244) and a testing set (n = 62). One hundred and eighty-six radiomics features were extracted from digital MG images based on the training set. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal predictive features for HER-2 status from the training set. Both support vector machine (SVM) and logistic regression models were employed based on the selected features. The area under the receiver operating characteristic (ROC) curves (AUCs) of the training set and testing set were used to evaluate the predictive performance of the models.

RESULTS

Compared with the SVM model, the performance of the logistic regression model using a combination of cranial caudal (CC) and mediolateral oblique (MLO) MG views was optimal. In the training set, the sensitivity, specificity, accuracy and area under the curve (AUC) values of the logistic regression model for evaluating HER-2 status based on quantitative radiomics features were 87.29%, 58.73%, 80.00% and 0.846 (95% confidence interval (CI), 0.800-0.887), respectively, and in the testing set, the values were 73.91%, 68.75%, 77.00% and 0.787 (95% CI, 0.673-0.885), respectively.

CONCLUSIONS

Radiomics features could be an efficient tool for the preoperative evaluation of HER-2 status in patients with breast cancer.

Outcomes of Ductal Carcinoma In Situ According to Detection Modality: A Multicenter Study Comparing Recurrence Between Mammography and Breast US

The purpose of this study was to determine whether disease recurrence and intrinsic characteristics of ductal carcinoma in situ (DCIS) are associated with the imaging method of detection in asymptomatic women. This multicenter, retrospective study included 844 women treated for asymptomatic DCIS who had pre-operative mammography and breast ultrasonography (US) studies available. Of the 844 women, 25 (3.0%) developed recurrences. Patients in the US group had significantly lower 5- and 10-y recurrence-free survival (RFS) rates compared with patients in the mammography group (p = 0.011). US-detected DCIS showed significantly lower 5-and 10-y RFS rates compared with mammography-detected DCIS in patients <50 y or with mammographically dense breasts (p = 0.002 and 0.002, respectively). US as the detection modality (hazard ratio [HR]: 4.451; 95% confidence interval [CI]: 1.530, 12.950; p = 0.006) and HER2 positivity (HR: 4.036; 95% CI: 1.438; 11.330; p = 0.008) were significantly associated with recurrence. We concluded that US as the detection modality and HER2 positivity were significantly associated with recurrence in patients treated for asymptomatic DCIS.

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.

Correlation of digital mammography and digital breast tomosynthesis features of self-detected breast cancers with human epidermal growth factor receptor type 2/neu status

Context:

Breast cancer is a heterogeneous disease with several histological subtypes. Its prognosis and management are influenced by human epidermal growth factor receptor type 2 (HER2/neu) expression. Varying grades of HER2/neu overexpression are likely to have different morphological features. Digital breast tomosynthesis (DBT) enhances lesion visibility and hence that it may reveal features closer to histomorphological findings.

Aims:

The aim of this study is to correlate digital mammography (DM) and DBT findings of self-detected tumors with HER2/neu status, to determine whether differences in imaging features can help predict the degrees of HER2/neu overexpression.

Settings and Design:

Prospective study conducted in a tertiary care hospital.

Methods:

For 100 consecutive patients with self-detected lumps, DM and DBT data were reviewed by two radiologists who were blinded to histopathology. Of these, 63 patients with histologically proven breast cancer were recruited and their DM and DBT findings compared and correlated with HER2neu status (scores 0–3+).

Statistical Analysis:

Pearson's Chi-squared test and Fisher's exact test were used (SPSS version 22.0, IBM).

Results:

Morphology of lesions at both DM and DBT varied with HER2/neu status (P = 0.04 and 0.015, respectively). HER2-0 tumors mostly presented as masses without microcalcifications (88.8%), while most of HER2-3+ tumors as masses or asymmetries with microcalcifications (61.9%). The presence or absence of calcifications varied significantly with HER2/neu status. Breast imaging-reporting and data system (BI-RADS) scoring varied significantly (P < 0.001) with higher HER2 signal, more frequently associated with BI-RADS 5 score.

Conclusion:

DM and DBT features vary with the intensity of HER2 immunostaining. Higher BI-RADS scores, microcalcifications, and spiculated margins are frequently associated with HER2/neu 3+ lesions.

Imaging and Clinicopathologic Features Associated With Pathologic Complete Response in HER2-positive Breast Cancer Receiving Neoadjuvant Chemotherapy With Dual HER2 Blockade

BACKGROUND

In human epidermal growth factor receptor 2-positive (HER2⁺) breast cancer, the incorporation of a dual HER2 blockade into neoadjuvant chemotherapy (NAC) has been shown to induce a higher rate of pathologic complete response (pCR). The purpose of this study was to investigate whether pretreatment imaging and clinicopathologic features show any association with pCR in HER2⁺ breast cancer receiving NAC plus dual blockade.

MATERIALS AND METHODS

This retrospective study evaluated 94 consecutive patients (mean age, 49.8 ± 9.9 years) with HER2⁺ breast cancer who underwent NAC plus dual blockade with trastuzumab and pertuzumab between April 2016 and June 2018. All patients underwent mammography, ultrasound, and magnetic resonance imaging prior to NAC. Clinicopathologic and imaging features acquired before NAC were evaluated for their ability to predict the pathologic response after surgery. Multivariate analysis was used to identify independent predictors of pCR.

RESULTS

Fifty patients (53.2%) showed pCR and 44 (46.8%) did not. According to a univariate analysis, fine pleomorphic/fine linear or linear-branching calcification morphology on mammography, parallel orientation on ultrasound, intratumoral high signal intensity on T2-weighted magnetic resonance imaging, progesterone receptor negativity, and high levels of tumor-infiltrating lymphocytes were associated with pCR. On multivariate analysis, fine pleomorphic/fine linear or linear-branching calcification morphology on mammography (odds ratio [OR], 7.23), progesterone receptor negativity (OR, 6.76), and a high tumor-infiltrating lymphocyte level (OR, 5.92) remained significant independent factors associated with pCR.

CONCLUSION

Several pretreatment imaging and clinicopathologic features were shown to be independent variables predicting pCR in patients with HER2⁺ breast cancer receiving NAC with dual blockade.

Imaging and the completion of the omics paradigm in breast cancer

Within the field of oncology, “omics” strategies—genomics, transcriptomics, proteomics, metabolomics—have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with “omics” data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology–pathology correlation from the anatomical–histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.

Mammographic casting-type calcification is an independent prognostic factor in invasive breast cancer

This study aimed to determine whether there is an association between mammographic casting-type calcification and other prognostic factors for invasive breast cancer. We also assessed whether casting-type calcification could be an independent prognostic factor. Invasive breast cancer patient information from January 2010 and January 2013 was retrospectively reviewed. The associations between mammographic casting-type calcification and other clinicopathological factors, including tumor size, node status, grade, progesterone receptor (PR) status, estrogen receptor (ER) status, and human epidermal growth factor receptor 2 (HER2) status, were analyzed. The Kaplan–Meier method and a Cox proportional hazards model were used for survival analyses of disease-free survival (DFS) and overall survival (OS). A total of 1155 invasive breast cancer patients who underwent definitive surgery were included, and 136 cases (11.8%) had casting-type calcification on mammography. In multivariate logistic regression, casting-type calcification was significantly associated with axillary node metastasis, ER-negativity, and HER2 overexpression. Casting-type calcification significantly decreased OS and DFS after a median follow-up of 60 months. This result remained after adjusting other prognostic factors in the multivariate analysis. Casting-type calcification is significantly linked to axillary node metastasis, ER-negativity and HER2 overexpression. Casting-type calcification is therefore an independent prognostic factor for breast cancer patients.

Invasive Colloid Carcinoma and the role of Ki-67 and HER2 - Two case reports

Mucinous carcinoma (also termed colloid carcinoma) of the breast accounts for 1%-6% of all breast cancer and is considered to have a good relative prognosis. The most common mammographic appearance of pure mucinous carcinoma is a high-density mass with circumscribed margins and on sonographic examination an isoechoic round mass with circumscribed margins. We report 2 cases of invasive mucinous carcinoma, in which one patient showed an intermediate recurrence risk based on Ki-67 and human epidermal growth factor receptor 2 negativity, while the other showed a low Ki-67 recurrence risk and human epidermal growth factor receptor 2 positive. We also review the literature on Ki-67 and human epidermal growth factor receptor 2 and explore the roles of these molecular markers in mucinous carcinomas.

Deposition of calcium in an in vitro model of human breast tumour calcification reveals functional role for ALP activity, altered expression of osteogenic genes and dysregulation of the TRPM7 ion channel

Microcalcifications are vital mammographic indicators contributing to the early detection of up to 50% of non-palpable tumours and may also be valuable as prognostic markers. However, the precise mechanism by which they form remains incompletely understood. Following development of an in vitro model using human breast cancer cells lines cultured with a combination of mineralisation-promoting reagents, analysis of calcium deposition, alkaline phosphatase (ALP) activity and changes in expression of key genes was used to monitor the calcification process. Two cell lines were identified as successfully mineralising in vitro, MDA-MB-231 and SKBR3. Mineralising cell lines displayed higher levels of ALP activity that was further increased by addition of mineralisation promoting media. qPCR analysis revealed changes in expression of both pro- (RUNX2) and anti- (MGP, ENPP1) mineralisation genes. Mineralisation was suppressed by chelation of intracellular Ca2+ and inhibition of TRPM7, demonstrating a functional role for the channel in formation of microcalcifications. Increased Mg2+ was also found to effectively reduce calcium deposition. These results expand the number of human breast cancer cell lines with a demonstrated in vitro mineralisation capability, provide further evidence for the role of an active, cellular process of microcalcification formation and demonstrate for the first time a role for TRPM7 mediated Ca2+ transport.

Accuracy of Ultrasound for Preoperative Assessment of Tumor Size in Patients With Newly Diagnosed Breast Cancer: Is It Affected by the Background Parenchymal Echotexture?

OBJECTIVES

To assess the impact of the background parenchymal echotexture on the accuracy of tumor size estimation using breast ultrasound (US).

METHODS

A total of 140 women with newly diagnosed invasive breast cancer from January 2014 to December 2015 were enrolled in this study. Two radiologists retrospectively reviewed US images in consensus for background parenchymal echotexture interpretation. The maximum tumor diameter from static images was recorded. Tumor size measurements were considered as having agreement with histologic results if they were within ±5 mm compared to the pathologic size. The relationship between the accuracy of tumor size measurement by the background parenchymal echotexture and clinicopathologic characteristics was evaluated.

RESULTS

Of these 140 patients, 77 (55.0%) showed a homogeneous background parenchymal echotexture, whereas 63 (45.0%) showed a heterogeneous echotexture. The mean tumor size was 1.9 cm (range, 0.5-4.9 cm). The overall accuracy of tumor size measurement was 76.4% (104 of 140). Tumors of women with a homogeneous background parenchymal echotexture were more accurately measured than those of women with a heterogeneous echotexture (87.0% versus 63.5%; P = .001). Tumors with a small size (<2 cm; P = .018) and ductal carcinoma in situ-negative (P = .031), human epidermal growth factor receptor 2 (HER2)-negative (P = .053), and triple-negative (P = .016) types were also more accurately measured. The independent factors associated with inaccurate tumor size measurement were a heterogeneous background parenchymal echotexture, a large tumor size, and the HER2-enriched type (P < .05).

CONCLUSIONS

The background parenchymal echotexture affected the accuracy of tumor size estimation using breast US. Invasive breast cancers with large (≥2 cm) tumors and the HER2-enriched type showed significantly lower breast US accuracy compared to others.

Sonographic Features of Estrogen-Negative Breast Cancers: A Correlation Study With Human Epidermal Growth Factor Type II Overexpression

The purpose of this study was to retrospectively review sonographic imaging features of estrogen-negative breast cancers and compare tumors with and without human epidermal growth factor type II (HER2) overexpression. Breast sonography findings from a sample of 54 patients with estrogen-negative breast cancer as well as pathological data and HER2 status were reviewed. Sonographic features including size, depth shapes, margin, location, patterns of internal echoes, posterior echoes, orientation, and presence of halo and clinicopathologic data and imaging features were correlated with tumor HER2 status. Based on these 54 patients with estrogen-negative breast cancers, 21 patients were positive for HER2 receptor, and 33 patients were negative for HER2 receptor. Among HER2 positive cancers, irregular shape, microlobulation, indistinct margins, posterior shadowing, a thin halo, heterogeneous internal echoes, and parallel orientation were the most frequent sonographic features. No associations were found between HER-2 status and tumor size, shape, margins, posterior feature, halo, internal echoes, or orientation on sonography.