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

The association of magnetic resonance imaging features with five molecular subtypes of breast cancer

Objective

To identify the association of magnetic resonance imaging (MRI) features with molecular subtypes of breast cancer (BC).

Materials and methods

A retrospective study was conducted on 112 invasive BC patients with preoperative breast MRI. The confirmed diagnosis and molecular subtypes of BC were based on the postoperative specimens. MRI features were collected by experienced radiologists. The association of MRI features of each subtype was compared to other molecular subtypes in univariate and multivariate logistic regression analyses.

Results

The proportions of luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-enriched, and triple-negative BC were 14.3 %, 52.7 %, 12.5 %, 10.7 %, and 9.8 %, respectively. Luminal A was associated with hypo-isointensityon T2-weighted images (OR=6.214, 95 % CI: 1.163-33.215) and non-restricted diffusion on DWI-ADC (OR=6.694, 95 % CI: 1.172-38.235). Luminal B HER2-negative was related to the presence of mass (OR=7.245, 95 % CI: 1.760-29.889) and slow/medium initial enhancement pattern (OR=3.654, 95 % CI: 1.588-8.407). There were no associations between MRI features and luminal B HER2-positive. HER2-enriched tended to present as non-mass enhancement lesions (OR=20.498, 95 % CI: 3.145-133.584) with fast uptake in the initial postcontrast phase (OR=9.788, 95 % CI: 1.689-56.740), and distortion (OR=11.471, 95 % CI: 2.250-58.493). Triple-negative were associated with unifocal (OR=7.877, 95 % CI: 1.180-52.589), hyperintensityon T2-weighted images (OR=14.496, 95 % CI: 1.303-161.328), rim-enhanced lesions (OR=18.706, 95 % CI: 1.915-182.764), and surrounding tissue edema (OR=5.768, 95 % CI: 1.040-31.987).

Conclusion

Each molecular subtype of BC has distinct features on breast MRI. These characteristics can serve as an adjunct to immunohistochemistry in diagnosing molecular subtypes, particularly in cases, where traditional methods yield equivocal results.

Access to prior screening mammograms affects the specificity but not sensitivity of radiologists' performance

AIMS

To establish the impact that access to prior mammograms has on radiologists' performance and the influence of radiologists' characteristics and breast density on their subsequent performance.

METHODS

Eight participants independently interpreted 72 digital screening mammograms in two reading sessions using the Royal Australian and New Zealand College of Radiologist's classification. In the first reading session, participants were given access to current and prior mammograms. In the second reading session six months later, participants only had access to the current mammograms. Radiologists' specificity, sensitivity, lesion sensitivity, Receiver Operating Characteristic (ROC) curve, and Jacknife Alternative Free-response ROC (JAFROC) were calculated. A Paired T-test was used to compare readings with and without prior mammograms, and to assess if breast density influenced participants performance. Independent Sample T-test was used to compare performance across radiologists' characteristics. A relative risk analysis was conducted to assess the probability of false positives and false negatives when prior mammograms were available.

RESULTS

Access to prior mammograms improved specificity in dense and non-dense breasts (p≤0.01) and reduced false positives (p = 0.01) but had no effect on sensitivity (p = 0.37), lesion sensitivity (p = 0.67), ROC (p = 0.16), and JAFROC (p = 0.24). Prior mammogram also reduced the probability of false positives (RR = 0.38; 95%CI:0.26-0.57, p<0.0001) without affecting the false negative rate (RR = 1.14; 95%CI:0.88-1.49, p = 0.30). The impact of prior mammograms on performance was not influenced by breast density or radiologists' characteristics.

CONCLUSIONS

Access to prior mammograms improves radiologists' specificity and reduces false positives without affecting sensitivity and the false negative rate regardless of radiologists' characteristics and breast density.

A model combining BI-RADS® descriptors from pre-treatment B-mode breast ultrasound with clinicopathological tumor features shows promise in the prediction of residual disease after neoadjuvant chemotherapy

PURPOSE

To create a simple model using standard BI-RADS® descriptors from pre-treatment B-mode ultrasound (US) combined with clinicopathological tumor features, and to assess the potential of the model to predict the presence of residual tumor after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients.

METHOD

245 female BC patients receiving NAC between January 2017 and December 2019 were included in this retrospective study. Two breast imaging fellows independently evaluated representative B-mode tumor images from baseline US. Additional clinicopathological tumor features were retrieved. The dataset was split into 170 training and 83 validation cases. Logistic regression was used in the training set to identify independent predictors of residual disease post NAC and to create a model, whose performance was evaluated by ROC curve analysis in the validation set. The reference standard was postoperative histology to determine the absence (pathological complete response, pCR) or presence (non-pCR) of residual invasive tumor in the breast or axillary lymph nodes.

RESULTS

100 patients (40.8%) achieved pCR. Logistic regression demonstrated that tumor size, microlobulated margin, spiculated margin, the presence of calcifications, the presence of edema, HER2-positive molecular subtype, and triple-negative molecular subtype were independent predictors of residual disease. A model using these parameters demonstrated an area under the ROC curve of 0.873 in the training and 0.720 in the validation set for the prediction of residual tumor post NAC.

CONCLUSIONS

A simple model combining standard BI-RADS® descriptors from pre-treatment B-mode breast US with clinicopathological tumor features predicts the presence of residual disease after NAC.

Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

Pushing the envelope in breast conserving surgery - is multiple-wire localization (3 or more wires) associated with increased risk of compromised margins and long-term recurrence?

INTRODUCTION

In the last two decades there has been a paradigm shift with breast conserving surgery (BCS) being applied to larger and more extensive breast malignancies. The aim of this study is to examine the success of BCS being performed in patients with extensive breast malignancies requiring at least 3 wires for localization, and to assess possible risk factors for failure.

MATERIALS AND METHODS

We performed a retrospective single center review of 232 patients who underwent BCS between 2010 and 2020 requiring at least 3 wires for localization, thus comprising the multi-wire group (MWG). The cohort included a control group of 232 single-wire BCS patients (SWG) chronologically matched with the MWG. Patients with either invasive malignancy or ductal carcinoma in situ (DCIS) were included in the study. Clinical, radiological, and pathological data was collected. Proportions of positive surgical margins, re-lumpectomies and conversion to mastectomy were calculated. Survival analysis for locoregional and distant recurrence was performed.

RESULTS

Women in the MWG were younger (mean age 57 vs. 63.1, P < 0.001), had larger tumor size (mean size 5.1 cm vs. 1.3 cm, p < 0.001), a higher prevalence of calcifications on mammograms (72 % vs. 17 %, P < 0.001), a higher proportion of positive lymph nodes (75 % vs. 45 %, P = 0.019), and an elevated incidence of a ductal carcinoma in situ (DCIS) component (72 % vs. 38 %, P < 0.001). Positive surgical margins were higher in the MWG (13 % vs 7 %, P = 0.03), which lead to higher proportions of re-lumpectomies or conversion to mastectomies (7 % vs 4 %, P = 0.17). On multivariate analysis of the entire cohort, patients with positive margins were more likely to have a DCIS component (77 % vs 53 %, P = 0.001), an infiltrating lobular carcinoma (ILC) component (15 % vs 9 %, P = 0.013), and positive ER hormonal status (94 % vs 85 %, p = 0.05). The number of wires was not an independent predictor of positive margins. On long-term analysis, the locoregional disease-free survival was similar between the SWG and MWG (P = 0.1). However, the MWG showed higher rates of distant metastasis (12 % vs 4 %, P = 0.006).

CONCLUSIONS

BCS requiring 3 or more wires is associated with a slightly higher proportion of positive margins. The increased risk of positive margins appears to be related to the type of tumor (DCIS component, ILC component and ER status) rather than to the number of wires. The number of wires does not significantly impact locoregional disease-free survival.

The effect of data resampling methods in radiomics

Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).

Association of clinicopathologic and molecular factors with the occurrence of positive margins in breast cancer

PURPOSE

To explore the association of clinicopathologic and molecular factors with the occurrence of positive margins after first surgery in breast cancer.

METHODS

The clinical and RNA-Seq data for 951 (75 positive and 876 negative margins) primary breast cancer patients from The Cancer Genome Atlas (TCGA) were used. The role of each clinicopathologic factor for margin prediction and also their impact on survival were evaluated using logistic regression, Fisher's exact test, and Cox proportional hazards regression models. In addition, differential expression analysis on a matched dataset (71 positive and 71 negative margins) was performed using Deseq2 and LASSO regression.

RESULTS

Association studies showed that higher stage, larger tumor size (T), positive lymph nodes (N), and presence of distant metastasis (M) significantly contributed (p ≤ 0.05) to positive surgical margins. In case of surgery, lumpectomy was significantly associated with positive margin compared to mastectomy. Moreover, PAM50 Luminal A subtype had higher chance of positive margin resection compared to Basal-like subtype. Survival models demonstrated that positive margin status along with higher stage, higher TNM, and negative hormone receptor status was significant for disease progression. We also found that margin status might be a surrogate of tumor stage. In addition, 29 genes that could be potential positive margin predictors and 8 pathways were identified from molecular data analysis.

CONCLUSION

The occurrence of positive margins after surgery was associated with various clinical factors, similar to the findings reported in earlier studies. In addition, we found that the PAM50 intrinsic subtype Luminal A has more chance of obtaining positive margins compared to Basal type. As the first effort to pursue molecular understanding of the margin status, a gene panel of 29 genes including 17 protein-coding genes was also identified for potential prediction of the margin status which needs to be validated using a larger sample set.

Treated Primary Cutaneous Malignant Melanoma With Later Metastasis Found in Clinical Presentation of Left Axilla Lymphadenopathy: A Case Report

This case report details the rare instance of metastatic spread of cutaneous malignant melanoma to the breast in a 50-year-old female. The patient presented with a palpable axillary mass confirmed to be metastasis despite excision and closure of the primary malignancy. The mass seen in clinical and radiological presentations presented with features of complicated differentiation from a primary breast tumor. Biopsy and staining with immunohistochemical markers S100 and Sox10 played a critical role in confirming the melanocytic origin of this metastatic lesion. Breast metastases are associated with poor prognosis, and this case emphasizes the importance of in-depth evaluations for patients with a history of malignant melanoma and the need for ongoing clinical awareness in this field.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes

Objective

To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models.

Materials and Methods

A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations.

Results

The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups.

Conclusions

Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of 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.

MRI as a biomarker for breast cancer diagnosis and prognosis

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

Circulating non-coding RNAs as a diagnostic and management biomarker for breast cancer: current insights

Cancer biomarkers can be used to determine the molecular status of a tumor or its metastases, which either release them directly into body fluids or indirectly through disruption of tumor/metastatic tissue. New minimally invasive and repeatable sample collection methods, such as liquid biopsy, have been developed in the last decade to apply cancer knowledge and track its progression. Circulating non-coding RNAs, which include microRNAs, long non-coding RNAs, and PIWI-interacting RNAs, are increasingly being recognized as potential cancer biomarkers. The growing understanding of cancer's molecular pathogenesis, combined with the rapid development of new molecular techniques, encourages the study of early molecular alterations associated with cancer development in body fluids. Specific genetic and epigenetic changes in circulating free RNA (cf-RNA) in plasma, serum, and urine could be used as diagnostic biomarkers for a variety of cancers. Only a subset of these cf-RNAs have been studied in breast cancer, with the most extensive research focusing on cf-miRNA in plasma. These findings pave the way for immediate use of selected cf-RNAs as biomarkers in breast cancer liquid biopsy, as well as additional research into other cf-RNAs to advance.

Circulating tumor cells as prognostic biomarkers in breast cancer: current status and future prospects

Introduction : Despite advances in diagnostic and therapeutic techniques breast cancer is still associated with significant morbidity and mortality. CTCs play a crucial role in the metastatic process, which is the main cause of death in BC patients.Areas covered : This review discusses the prognostic and predictive value of CTCs and their prospective in management of BC patients.Expert opinion : The analysis of CTCs through improved technologies offers a new insight into the metastatic cascade. Assessment of the number and molecular profile of CTCs holds great promises for disease monitoring and therapeutic decisions. However, more research is needed until they can be used in therapeutic decisions in clinical practice.