Triple-negative breast cancer: correlation between imaging and pathological findings

Assessing the Influence of B-US, CDFI, SE, and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms

OBJECTIVES

Our study aims to investigate the impact of B-mode ultrasound (B-US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions.

METHODS

Totally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet-18 network was employed to predict four molecular subtypes from B-US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1-scores, and confusion matrix.

RESULTS

Based on B-US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1-scores is 0.73. By combining B-US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B-US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40 years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40 years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively.

CONCLUSION

Multimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B-US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40 years old compared with those who are 40 years old and above.

Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules

Background

The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system.

Methods

This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves.

Results

The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies.

Conclusion

The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies.

Clinical and radiological manifestations associated with triple-negative breast cancer in women from northern Peru. A case-control study

Objective

Triple-negative breast cancer (TNBC) has an aggressive clinical behaviour, with advanced stages at initial diagnostic evaluation, early recurrences and poor survival, so the purpose was to determine the clinical and radiological manifestations associated with TNBC.

Materials and methods

A case-control study in women diagnosed with breast cancer from January 2015 to August 2022 at the 'Instituto Regional de Enfermedades Neoplásicas del Norte'. We classified cases (Triple Negative subtype) and controls (Luminal A, Luminal B and HER2) according to immunohistochemistry ical analysis. Bivariate and multivariate logistic regression models were used to calculate the odds ratio (OR) with their respective 95% confidence intervals (CIs).

Results

The medical reports of 88 cases and 236 controls were reviewed. Cases were more likely to report pain (p = 0.001), nodules on ultrasound (p = 0.01) and mammography (p = 0.003), superior median size (p < 0.05), posterior enhancement (p = 0.001) and moderate density (p = 0.003). Multivariate analysis identified that TNBC was more likely to have a nodular type lesion by ultrasound (OR: 9.73, 95% CI: 1.10-86.16; p = 0.04), ultrasound lesion larger than 36 mm (OR: 4.99, 95% CI: 1.75-14.17; p = 0.003) and moderate density (OR: 3.83, 95% CI: 1.44-10.14; p = 0.007).

Conclusion

There are particular clinical and imaging manifestations of TNBC, showing that radiological lesions that presented characteristics in ultrasound as nodular type lesions larger than 36 mm and in mammography moderate grade density, were associated with this subtype of breast tumours in a Peruvian population.

Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?

OBJECTIVES

Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clinical variables to predict pathologic complete response (pCR) prior to the initiation of therapy.

METHODS

We used retrospective data on patients who underwent ultrasound and tomosynthesis before starting NAST. We developed a support vector machine algorithm using pretreatment ultrasound and tomosynthesis radiomics features in addition to patient and tumor variables to predict pCR status (ypT0 and ypN0). Findings were compared to the histopathologic evaluation of the surgical specimen. The main outcome measures were area under the curve (AUC) and false-negative rate (FNR).

RESULTS

We included 720 patients, 504 in the development set and 216 in the validation set. Median age was 51.6 years and 33.6% (242 of 720) achieved pCR. The addition of radiomics features significantly improved the performance of the algorithm (AUC 0.72 to 0.81; p = 0.007). The FNR of the multi-modal radiomics and clinical algorithm was 6.7% (10 of 150 with missed residual cancer). Surface/volume ratio at tomosynthesis and peritumoral entropy characteristics at ultrasound were the most relevant radiomics. Hormonal receptors and HER-2 status were the most important clinical predictors.

CONCLUSION

A multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features may aid in predicting residual cancer after NAST. Pending prospective validation, this may facilitate individually tailored NAST regimens.

CLINICAL RELEVANCE STATEMENT

Multi-modal radiomics using pretreatment ultrasound and tomosynthesis showed significant improvement in assessing response to NAST compared to an algorithm using clinical variables only. Further prospective validation of our findings seems warranted to enable individualized predictions of NAST outcomes.

KEY POINTS

• We proposed a multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features to predict response to neoadjuvant breast cancer treatment. • Compared with the clinical algorithm, the AUC of this integrative algorithm is significantly higher. • Used prior to the initiative of therapy, our algorithm can identify patients who will experience pathologic complete response following neoadjuvant therapy with a high negative predictive value.

Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study

OBJECTIVES

Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.

METHODS

We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC).

RESULTS

We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002).

CONCLUSION

A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.

Artificial Intelligence Decision Support for Triple-Negative Breast Cancers on Ultrasound

OBJECTIVE

To assess performance of an artificial intelligence (AI) decision support software in assessing and recommending biopsy of triple-negative breast cancers (TNBCs) on US.

METHODS

Retrospective institutional review board-approved review identified patients diagnosed with TNBC after US-guided biopsy between 2009 and 2019. Artificial intelligence output for TNBCs on diagnostic US included lesion features (shape, orientation) and likelihood of malignancy category (benign, probably benign, suspicious, and probably malignant). Artificial intelligence true positive was defined as suspicious or probably malignant and AI false negative (FN) as benign or probably benign. Artificial intelligence and radiologist lesion feature agreement, AI and radiologist sensitivity and FN rate (FNR), and features associated with AI FNs were determined using Wilcoxon rank-sum test, Fisher's exact test, chi-square test of independence, and kappa statistics.

RESULTS

The study included 332 patients with 345 TNBCs. Artificial intelligence and radiologists demonstrated moderate agreement for lesion shape and orientation (k = 0.48 and k = 0.47, each P <.001). On the set of examinations using 6 earlier diagnostic US, radiologists recommended biopsy of 339/345 lesions (sensitivity 98.3%, FNR 1.7%), and AI recommended biopsy of 333/345 lesions (sensitivity 96.5%, FNR 3.5%), including 6/6 radiologist FNs. On the set of examinations using immediate prebiopsy diagnostic US, AI recommended biopsy of 331/345 lesions (sensitivity 95.9%, FNR 4.1%). Artificial intelligence FNs were more frequently oval (q < 0.001), parallel (q < 0.001), circumscribed (q = 0.04), and complex cystic and solid (q = 0.006).

CONCLUSION

Artificial intelligence accurately recommended biopsies for 96% to 97% of TNBCs on US and may assist radiologists in classifying these lesions, which often demonstrate benign sonographic features.

Imaging Challenges in Diagnosing Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) refers to a heterogeneous group of carcinomas that have more aggressive biologic features, faster growth, and a propensity for early distant metastasis and recurrence compared with other breast cancer subtypes. Due to the aggressiveness and rapid growth of TNBCs, there are specific imaging challenges associated with their timely and accurate diagnosis. TNBCs commonly manifest initially as circumscribed masses and therefore lack the typical features of a primary breast malignancy, such as irregular shape, spiculated margins, and desmoplastic reaction. Given the potential for misinterpretation, review of the multimodality imaging appearances of TNBCs is important for guiding the radiologist in distinguishing TNBCs from benign conditions. Rather than manifesting as a screening-detected cancer, TNBC typically appears clinically as a palpable area of concern that most commonly corresponds to a discrete mass at mammography, US, and MRI. The combination of circumscribed margins and hypoechoic to anechoic echogenicity may lead to TNBC being misinterpreted as a benign fibroadenoma or cyst. Therefore, careful mammographic and sonographic evaluation with US image optimization can help avoid misinterpretation. Radiologists should recognize the characteristics of TNBCs that can mimic benign entities, as well as the subtle features of TNBCs that should raise concern for malignancy and aid in timely and accurate diagnosis. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment

Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive group of tumors that are defined by the absence of estrogen and progesterone receptors and lack of ERBB2 (formerly HER2 or HER2/neu) overexpression. TNBC accounts for 8%-13% of breast cancers. In addition, it accounts for a higher proportion of breast cancers in younger women compared with those in older women, and it disproportionately affects non-Hispanic Black women. TNBC has high metastatic potential, and the risk of recurrence is highest during the 5 years after it is diagnosed. TNBC exhibits benign morphologic imaging features more frequently than do other breast cancer subtypes. Mammography can be suboptimal for early detection of TNBC owing to factors that include the fast growth of this cancer, increased mammographic density in young women, and lack of the typical features of malignancy at imaging. US is superior to mammography for TNBC detection, but benign-appearing features can lead to misdiagnosis. Breast MRI is the most sensitive modality for TNBC detection. Most cases of TNBC are treated with neoadjuvant chemotherapy, followed by surgery and radiation. MRI is the modality of choice for evaluating the response to neoadjuvant chemotherapy. Survival rates for individuals with TNBC are lower than those for persons with hormone receptor-positive and human epidermal growth factor receptor 2-positive cancers. The 5-year survival rates for patients with localized, regional, and distant disease at diagnosis are 91.3%, 65.8%, and 12.0%, respectively. The early success of immunotherapy has raised hope regarding the development of personalized strategies to treat TNBC. Imaging and tumor biomarkers are likely to play a crucial role in the prediction of TNBC treatment response and TNBC patient survival in the future. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer

Purpose

The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures.

Patients and Methods

Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features.

Results

Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC.

Conclusion

The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.

Calcifications in triple-negative breast cancer: Molecular features and treatment strategies

Despite the high prevalence of mammographic calcifications, our understanding remains limited regarding the clinical and molecular features of calcifications within triple-negative breast cancer (TNBC). To investigate the clinical relevance and biological basis of TNBC with calcifications of high suspicion for malignancy, we established a study cohort (N = 312) by integrating mammographic records with clinical data and genomic, transcriptomic, and metabolomic profiling. Despite similar clinicopathological features, patients with highly suspicious calcifications exhibited a worse overall survival than those without. In addition, TNBC with highly suspicious calcifications was characterized by a higher frequency of PIK3CA mutation, lower infiltration of immune cells, and increased abnormality of lipid metabolism. Overall, our study systematically revealed clinical and molecular heterogeneity between TNBC with or without calcifications of high suspicion for malignancy. These data might help to understand the clinical relevance and biological basis of mammographic calcifications.

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.

Mammographic and ultrasonographic features of triple-negative breast cancer compared with non-triple-negative breast cancer

OBJECTIVE

To evaluate and compare the mammographic and ultrasonographic features of TNBC with non-TNBC.

METHODS

A retrospective review of 193 invasive breast cancer patients (TNBC = 32 and non-TNBC = 161) was collected from January 2014 to June 2019. The imaging features were reviewed according to the 5th edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon. We used the student t-test, Mann-Whitney U test, and Fisher's exact test for statistical analyses.

RESULTS

Mass without calcifications was the most mammographic feature of TNBC (22 of 32, 68.8%) and more commonly found in TNBC than in non-TNBC (p = 0.007). The irregular shape (19 of 28, 67.9%) and indistinct margin (10 of 28, 35.7%) were the most common findings in the TNBC group. However, TNBC lesions appeared as round or oval shape and microlobulated margin more frequently than non-TNBC lesions (p < 0.001). Additionally, the tumor size and histological grade of TNBC were significantly higher than non-TNBC (p < 0.001).

CONCLUSION

TNBC has distinct imaging features compared to non-TNBC. The imaging features on mammography combined with ultrasonography can be used to detect and differentiate this subtype from other breast cancers.

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.

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images

To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 145 patients and 831 images were retrospectively enrolled at Peking Union College Hospital from April 2018 to March 2019. Ultrasound images and clinical information were collected accordingly. Molecular subtypes were determined from immunohistochemical (IHC) results. A CNN with VGG-based architecture was then used to predict TNBC. The model's performance was evaluated using randomized k-fold stratified cross-validation. A t-SNE analysis and saliency maps were used for model visualization. TNBC was identified in 16 of 145 (11.03%) patients. One hundred fifteen (80%) patients, 15 (10%) patients, and 15 (10%) patients formed the train, validation, and test set respectively. The deep learning system exhibits good efficacy, with an AUC of 0.86 (95% CI: 0.64, 0.95), an accuracy of 85%, a sensitivity of 86%, a specificity of 86%, and an F1-score of 0.74. In addition, the internal representation features learned by the model showed clear differentiation across molecular subtype groups. Such a deep learning system can automatically predict triple-negative breast cancer preoperatively and accurately. It may help to get to more precise and comprehensive management.

Calcification, Posterior Acoustic, and Blood Flow: Ultrasonic Characteristics of Triple-Negative Breast Cancer

Previous studies suggest that triple-negative breast cancer (TNBC) may have unique imaging characteristics, however, studies focused on the imaging characteristics of TNBC are still limited. The aim of the present study is to analyze the ultrasonic characteristics of TNBC and to provide more reliable information on imaging diagnosis of TNBC. This retrospective study was performed including 162 TNBC patients with 184 TNBC lesions. 174 non-TNBC cases with 196 lesions were used as the control group. The median size of TNBC lesions and non-TNBC lesions were 23 mm × 16 mm and 21 mm × 15 mm, respectively. The shape of most breast cancer lesions was irregular. However, 15.30% (28/183) TNBC lesions and 16.84% (33/196) non-TNBC lesions were oval-shaped. Most breast cancer lesions (79.78% TNBC & 85.71% non-TNBC) were ill-defined. In comparison to non-TNBC, the distinctive ultrasonic characteristics of TNBC were summarized as three features: calcifications, posterior acoustic, and blood flow. Microcalcifications was less common in non-TNBC. The remarkable posterior acoustic characteristics on TNBC were no posterior acoustic features (136, 73.91%). Avascular pattern (21.74%) was also more common in TNBC. The other feature of TNBC was markedly hypoechoic lesions (23.91%). The above-mentioned differences between TNBC and non-TNBC were significant. 93.48% TBNC and 94.39% non–TNBC lesions were in BI-RADS-US category of 4A-5. The results indicate that TNBC has some distinctive ultrasound characteristics. Ultrasound is a useful adjunct in early detection of breast cancer. A combination of ultrasound with mammography is excellent for detecting breast cancer.

An Integrative Clinical Model for the Prediction of Pathological Complete Response in Patients with Operable Stage II and Stage III Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

Simple Summary

Neoadjuvant chemotherapy (NAC) is widely used to treat stage II and III primary, operable triple-negative breast cancer (TNBC). The response to NAC critically affects the subsequent treatment plan, including not only curative surgical planning but also adjuvant therapy. There is no standard prediction model that accurately predicts NAC response. Therefore, the development of an easy-to-apply and cost-effective clinical prediction model for NAC treatment response would improve clinical practice. We propose an integrative clinical prediction model for the prediction of pathologically complete response in patients with operable stage II and stage III TNBC receiving NAC based on findings from tumor ultrasound and blood tests. All included parameters were readily available during and before NAC. This clinical prediction model could provide a reference to guide clinicians’ decisions in planning a patient’s NAC treatment as early as after the first cycle of NAC.

Abstract

Triple-negative breast cancer (TNBC) is treated with neoadjuvant chemotherapy (NAC). The response to NAC, particularly the probability of a complete pathological response (pCR), guides the surgical approach and adjuvant therapy. We developed a prediction model using a nomogram integrating blood tests and pre-treatment ultrasound findings for predicting pCR in patients with stage II or III operable TNBC receiving NAC. Clinical data before and after the first cycle of NAC collected from patients between 2012 and 2019 were analyzed using univariate and multivariate analyses to identify correlations with pCR. The coefficients of the significant parameters were calculated using logistic regression, and a nomogram was developed based on the logistic model to predict the probability of pCR. Eighty-eight patients were included. Five parameters correlated with the probability of pCR, including the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte (PLR) ratio, percentage change in PLR, presence of echogenic halo, and tumor height-to-width ratio. The discrimination performance of the nomogram was indicated by an area under the curve of 87.7%, and internal validation showed that the chi-square value of the Hosmer–Lemeshow test was 7.67 (p = 0.363). Thus, the integrative prediction model using clinical data can predict the probability of pCR in patients with TNBC receiving NAC.

Multimodality imaging in lobular breast cancer: Differences in mammography, ultrasound, and MRI in the assessment of local tumor extent and correlation with molecular characteristics

Introduction

Invasive lobular breast cancer (ILC) is a diagnostic challenge due to the diversity of morphological features. The objective of the study was to investigate the presentation and local extent of ILC using various imaging techniques and to assess the correlation between imaging and molecular profile.

Materials and methods

We reviewed 162 consecutive patients with ILC found on vacuum-assisted biopsy, who underwent evaluation of the lesion morphology and extent using ultrasound (US), mammography (MMG), and magnetic resonance imaging (MRI). Radiographic features were compared with ILC intrinsic subtype based on the expression of Ki-67 and estrogen, progesterone, and HER2 receptors.

Results

A total of 113 mass lesions and 49 non-mass enhancements (NMEs) were found in MRI. Masses were typically irregular and spiculated, showing heterogeneous contrast enhancement, diffusion restriction, and type III enhancement curve. NMEs presented mainly as the area of focal or multiregional distribution with heterogeneous or clumped contrast enhancement, diffusion restriction, and type III enhancement curve. Lesion extent significantly varied between MRI and MMG/ultrasonography (USG) (P < 0.001) but did not differ between MGF and ultrasonography (USG). The larger the ILC, the higher the disproportion when lesion extent in MRI was compared with MMG (P < 0.001) and ultrasonography (USG) (P < 0.001). In the study group, there were 97 cases of luminal A subtype (59.9%), 54 cases of luminal B HER2− (33.3%), nine cases of luminal B HER2+ (5.5%), and two cases of triple negative (1.2%). The HER2 type was not found in the study group. We did not observe any significant correlation between molecular profile and imaging.

Conclusion

MRI is the most effective technique for the assessment of ILC local extent, which is important for optimal treatment planning. Further studies are needed to investigate if the intrinsic subtype of ILC can be predicted by imaging features on MRI.

Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma

OBJECTIVE

This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma.

METHODS

A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA).

RESULTS

The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram.

CONCLUSION

The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

ADVANCES IN KNOWLEDGE

Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

Diagnostic delay in moderately/poorly differentiated breast cancer types

BACKGROUND

Diagnostic delay of breast cancer related to the false-negative assessment of the healthcare provider leads to tumor progression and might worsen the outcome. Previous studies found some factors associated with provider-related diagnostic delay; however, tumor biology has tended not to be considered. The aim of our study was to find differences in diagnostic delay of poorly differentiated breast cancer types.

METHODS

Data of 970 patients with newly diagnosed moderately/poorly differentiated (G2/3) breast cancer at the age ≥40 years was retrospectively analyzed regarding breast cancer type, diagnostic delay and its consequence, clinical factors and physician's assessment. Multivariate analysis was used to evaluate associated factors with diagnostic delay.

RESULTS

We observed a diagnostic delay in 3.8% (n = 37) of all patients. Mean delay time was 128 days, and clinically relevant tumor growth was observed in 43.2% of these cases. Delay was significantly higher in the group of triple-negative breast cancer (9.9% versus 2.7, 5.3 and 1.8% in hormonal receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, HR-/Her2+ and HR+/Her2+, respectively; P value <0.001). Age, breast density and reason for presentation were not correlated to diagnostic delay.

CONCLUSION

Patients with triple-negative breast cancer are at higher risk of receiving a false-negative assessment and experiencing a diagnostic delay. Our results emphasize the importance of a detailed consideration of clinical risk factors and provider training and suggest a broad indication for a core needle biopsy.

Flubendazole Plays an Important Anti-Tumor Role in Different Types of Cancers

Flubendazole, belonging to benzimidazole, is a broad-spectrum insect repellent and has been repurposed as a promising anticancer drug. In recent years, many studies have shown that flubendazole plays an anti-tumor role in different types of cancers, including breast cancer, melanoma, prostate cancer, colorectal cancer, and lung cancer. Although the anti-tumor mechanism of flubendazole has been studied, it has not been fully understood. In this review, we summarized the recent studies regarding the anti-tumor effects of flubendazole in different types of cancers and analyzed the related mechanisms, in order to provide the theoretical reference for further studies in the future.

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.

Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study

Background

Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images.

Methods

This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB).

Finding

The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89–0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81–0.84) and the monomodal ACNN model (macroaverage AUC: 0.73–0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89–0.99 vs. 0.67–0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87–0.94 vs. 0.78-0.81 vs. 0.71–0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934–0.970 vs. 0.688–0.830 vs. 0.536–0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957–0.958 and 0.932–0.985).

Interpretation

The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice.

Funding

This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).

Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network

Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resnet50 deep convolutional neural network was fine-tuned. The results showed that the averaged area under the receiver operating characteristic curve (AUC) of discriminating malignant from benign ones were 0.9789 (benign vs. TN), 0.9689 (benign vs. NTN). To discriminate TN from NTN breast cancer, the AUC was 0.9000, the accuracy was 88.89%, the sensitivity was 87.5%, and the specificity was 90.00%. It showed that the computer-aided system based on DCNN is expected to be a promising noninvasive clinical tool for ultrasound diagnosis of TN breast cancer.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms

OBJECTIVES

To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes.

METHODS

We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images.

RESULTS

The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048.

CONCLUSIONS

This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists.

KEY POINTS

• Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study

OBJECTIVES

To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC).

METHODS AND MATERIALS

A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness.

RESULTS

The patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71-0.80), an internal validation C-index of 0.73 (95% CI, 0.69-0.78) and an external validation 0.71 (95% CI,0.66-0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model.

CONCLUSIONS

The ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately.

ADVANCES IN KNOWLEDGE

The radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.

Screening mammography mitigates breast cancer disparities through early detection of triple negative breast cancer

PURPOSE

Screening mammography improves breast cancer survival through early detection, but Triple Negative Breast Cancer (TNBC) is more difficult to detect on mammography and has lower survival compared to non-TNBC, even when detected at early stages. TNBC is twice as common among African American (AA) compared to White American (WA) women, thereby contributing to the 40% higher breast cancer mortality rates observed in AA women. The role of screening mammography in addressing breast cancer disparities is therefore worthy of study.

METHODS

Outcomes were evaluated for TNBC patients treated in the prospectively-maintained databases of academic cancer programs in two metropolitan cities of the Northeast and Midwest, 1998-2018.

RESULTS

Of 756 TNBC cases, 301 (39.8%) were mammographically screen-detected. 46% of 189 AA and 38.5% of 460 WA patients had screen-detected TNBC (p = 0.16). 25.3% of 257 TNBC cases ≤50 years old had screen-detected disease compared to 47.3% of 499 TNBC cases >50 years old (p < 0.0001). 220/301 (73.1%) screen-detected TNBC cases were T1 lesions versus 118/359 (32.9%) non-screen-detected cases (p < 0.0001). Screen-detected TNBC was more likely to be node-negative (51.9% v. 40.4%; p < 0.0001). Five-year overall survival was better in screen-detected TNBC compared to nonscreen-detected TNBC (92.8% v. 81.5%; p < 0.0001) in the entire cohort. The magnitude of this effect was most significant among AA patients (Fig. 1). Screening-related survival patterns were similar among AA and WA patients in both cities.

CONCLUSION

Data from two different cities demonstrates the value of screening mammography to mitigate breast cancer disparities in AA women through the early detection of TNBC.

Breast Imaging After Dark

Patients may present to the emergency department with breast complaints due to traumatic or nontraumatic changes in the breast. Benign and malignant breast pathologies may mimic each other both in clinical presentation and imaging appearance. A complex cystic and solid mass seen on ultrasound in a patient with a palpable mass can represent breast cancer, abscess, or hematoma. A unilateral swollen breast may result from inflammatory breast cancer, mastitis, or other benign etiologies; correlation with clinical history, physical exam, and close follow-up are required to ensure complete resolution of symptoms. Uncommon breast entities such as granulomatous mastitis and breast implant-associated anaplastic large-cell lymphoma may cause changes in the appearance of the breast that prompt a patient to seek initial evaluation in the emergency department. Imaging evaluation of the breast in the emergency department is limited, and it is important that patients with a breast complaint be referred to a dedicated breast center for complete evaluation at an appropriate time interval after their discharge from the emergency department.

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

OBJECTIVE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Are baseline mammographic and ultrasound features associated with metastasis free survival in women receiving neoadjuvant chemotherapy for invasive breast cancer?

OBJECTIVES

To identify associations between baseline ultrasound (US) and mammographic features and metastasis free survival (MFS) in women receiving neo-adjuvant chemotherapy (NACT) for breast cancer.

METHODS

The data were collected as part of an ethically approved prospective study. Women with invasive breast cancer receiving NACT who were metastasis free at diagnosis were included. Baseline US and mammography were performed. Imaging was assessed by an experienced breast radiologist who was blinded to outcomes. US imaging features documented included posterior effect, skin thickening, size and stiffness using shear wave elastography (SWE). The mammographic features documented were spiculation and microcalcification. The development of metastatic disease was ascertained from computer records. Statistical analysis was performed using Kaplan Meier survival curves and Receiver Operator Characteristic (ROC) analysis.

RESULTS

171 women with 172 cancers were included in the study and 55 developed metastatic disease. Mean follow-up was 6.0 years. Women with mammographic calcification had significantly poorer metastasis free survival (MFS) compared to women without calcification (p = 0.043, 6 yr MFS 50 % vs 69 %). Women bearing cancer with distal shadowing had poorer MFS than women without shadowing (p = 0.025, 6 yr MFS 47 % vs. 73 %). Women with US skin thickening had poorer MFS compared to women without skin thickening (p = 0.032, 6 yr MFS 52 % vs. 68 %). Mammographic spiculation, US size and stiffness at SWE had no significant association with MFS.

CONCLUSION

We have identified mammographic and US features associated with MFS in women receiving NACT. Such information may be useful when counselling patients about the benefits and risks of NACT.

Accuracy of MRI for prediction of response to neo-adjuvant chemotherapy in triple negative breast cancer compared to other subtypes of breast cancer

Purpose:

The aim of this study was to compare the accuracy of magnetic resonance imaging (MRI) for the prediction of response to neo-adjuvant chemotherapy in triple negative (TN) breast cancer, with respect to other subtypes.

Materials and Methods:

There were a total of 1610 breast cancers diagnosed between March 2009 and August 2014, out of which 82 patients underwent MRI before and after neo-adjuvant chemotherapy but just before surgery. TN cancers were analyzed with respect to others subtypes. Accuracy of MRI for prediction of pathological complete response was compared between different subtypes by obtaining receiver operating characteristic (ROC) curves. The Statistical Package for the Social Sciences version 21 was used for all data analysis, with P value of 0.05 as statistically significant.

Results:

Out of 82 patients, 29 were luminal (HR+/HER2−), 23 were TN (HR−, HER2−), 11 were HER2 positive (HR−, HER2+), and 19 were of hybrid subtype (HR+/HER2+). TN cancers presented as masses on the pre-chemotherapy MRI scan, were grade 3 on histopathology, and showed concentric shrinkage following chemotherapy. TN cancers were more likely to have both imaging and pathological complete response following chemotherapy (P = 0.055) in contrast to luminal cancers, which show residual cancer. ROC curves were constructed for the prediction of pathological complete response with MRI. For the TN subgroup, MR had a sensitivity of 0.745 and specificity of 0.700 (P = 0.035), with an area under curve of 0.745 (95% confidence interval: 0.526–0.965), which was significantly better compared to other subtypes.

Conclusion:

TN breast cancers present as masses and show concentric shrinkage following chemotherapy. MRI is most accurate in predicting response to chemotherapy in the TN group, compared to others subtypes. MRI underestimates residual disease in luminal cancers.

Application value of conventional ultrasound combined with shear wave elastography in diagnosing triple negative breast cancer

Background

At present, most ultrasound (US) studies on triple negative breast cancer (TNBC) are limited to conventional US features, so it is necessary to develop new joint diagnostic methods. The study aims to explore the values of conventional US and shear wave elastography (SWE) in differential diagnoses of TNBC and non-TNBC.

Methods

A total of 120 breast cancer (BC) patients involving 120 lesions that were pathologically verified were retrospectively analyzed in this study. All participants had received both conventional US and SWE before surgery. Meanwhile, the participants were divided into a TNBC group or a non-TNBC group according to their immunohistochemical (IHC) results. The differences between the conventional US features (including lesion shape, growth location, margin, boundary, internal echo, micro-calcification, posterior echo, and internal blood supply) and the SWE image features [including mean lesion hardness (Emean), maximum (Emax), minimum (Emin), standard deviation (SD), and ratio to normal gland (Eratio)] of 2 groups were compared. The receiver operating characteristic (ROC) curve of the diagnosed lesion was calculated by the area under the curve (AUC).

Results

According to the findings of conventional US, the TNBC group mostly manifested as a micro-lobulated margin, with a clear boundary and no internal micro-calcification; the non-TNBC group mainly manifested as marginal angulation or burr, and hyper-echo halo in the boundary, accompanied with internal micro-calcification, and the difference was statistically significant (P<0.05); the internal thrombolysis in myocardial infarction (TIMI) and resistance index between the TNBC group and non-TNBC group were similar, and the differences were not statistically significant (P>0.05). The findings of SWE were as follows: differences in Emax, Emean, and Eratio values between 2 groups were statistically significant (P<0.05); and the areas under the ROC curve (AUC) of these three in diagnosing the lesions were 0.811, 0.781 and 0.770, respectively.

Conclusions

Conventional US combined with SWE can comprehensively analyze the morphological, blood supply, and hardness features of breast lesions, and provide more reliable information for the differential diagnosis between TNBC and non-TNBC.

Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.

Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.

Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.

Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

Diagnostic and Predictive Values of Strain Ratios in the Regions of Interests in Reference Tissue for Breast Tumor

PURPOSE

To investigate the diagnostic and predictive value of strain ratios in the regions of interests (ROIs) in reference tissue for breast tumor.

PATIENTS AND METHODS

A total of 707 lesions in 665 consecutive patients were examined with B-mode Breast Imaging-Reporting and Data System (BI-RADS) and Ultrasonic elastography (UE). Elasticity score (ES) and strain ratio (SR) in each lesion were calculated. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of BI-RADS, ES, SR1, SR2, BI-RADS combined with ES (BI-RADS+ES), BI-RADS combined with SR1 (BI-RADS+SR1), and BI-RADS combined with SR2 (BI-RADS+SR2). The sensitivity, specificity, and areas under the ROC curves (Az) were obtained. Scatter plots were generated to demonstrate the correlation between SR1 and SR2. Kruskal-Walls H-test, Mann-Whitney U-test and one-way ANOVA were performed to evaluate SRs and tumor-related variables. Multiple linear regression analysis was carried out to determine variables independently associated with SRs.

RESULTS

BI-RADS had high sensitivity and low specificity in the diagnosis of breast tumor. The specificity of BI-BADS combined with ES or SR was even higher. The Az value of BI-RADS+ES or BI-RADS+SRs was higher than that of BI-RADS (P < 0.001). The Az value of ES was higher than those of SR1 and SR2 (P < 0.001), and those of SR1 and SR2 were similar. SR1 and SR2 were highly positively correlated. There was no statistical difference between Az values of BI-RADS+ES, BI-RADS+SR1, and BI-RADS+SR2. Indistinct margin, high histologic grade, histological type, and negative human epidermal growth factor receptor (Her-2) were associated with SR1 and SR2. Progesterone receptor (PR) status and molecular subtype were associated with SR2. Histologic grade and tumor margin were significantly associated with SR1, and tumor margin was associated with SR2.

CONCLUSION

SRs in different ROIs in the reference tissue at the same depth showed no different diagnostic value for breast tumor. Both SR1 and SR2 could be useful in assessing the biological characteristics of invasive breast carcinoma.

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

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

Role of ultrasound in predicting the molecular subtypes of invasive breast ductal carcinoma

Background

Breast cancer is a heterogeneous disease that was explained recently by the presence of multiple molecular subtypes. These subtypes are the luminal A (LA), luminal B (LB), human epidermal growth factor receptor 2 (HER2), and triple-negative (TN). In this study, we aim to investigate whether ultrasound imaging features can play a role in predicting the molecular subtypes of invasive ductal breast cancer (IDC) and to assess whether the nodal metastasis is an independent predictor for each subtype.

Results

The predictive sonographic signs for each subtype are as follows: echogenic halo, posterior shadowing, angular or spiculated margin, and unifocal mass for LA subtype; irregular shape for LB subtype; unifocal mass, abrupt interface of the tumor boundary, and posterior enhancement or no posterior change for HER2 subtype; and circumscribed or lobulated margin, oval or rounded shape, posterior enhancement or no posterior change, abrupt interface of the tumor boundary, and parallel orientation of the mass for TN. By multivariate logistic regression, presence of nodal metastasis is the strongest independent predictor for HER2 subtype, and its absence is the strongest independent predictor for LA subtype.

Conclusions

Certain sonographic signs are predictors for each molecular subtype of IDC. Nodal metastasis is an independent predictor for HER2 subtype when present and for LA subtype when absent.

Accuracy of tumor size measurement on shear wave elastography (SWE): Correlation with histopathologic factors of invasive breast cancer

The aim of this study is to investigate the accuracy of tumor size assessment by shear wave elastography (SWE) in invasive breast cancer and also evaluated histopathologic factors influencing the accuracy.A total of 102 lesions of 102 women with breast cancers of which the size was 3 cm or smaller were included and retrospectively analyzed. Tumor size on B-mode ultrasound (US) and SWE were recorded and compared with the pathologic tumor size. If tumor size measurements compared to pathological size were within ±3 mm, they were considered as accurate. The relationship between the accuracy and histopathologic characteristics were evaluated.The mean pathologic tumor size was 16.60 ± 6.12 mm. Tumor sizes on SWE were significantly different from pathologic sizes (18.00 ± 6.71 mm, P < 0.001). The accuracy of SWE (69.6%) was lower than that by B-mode US (74.5%). There was more size overestimation than underestimation (23.5% vs 6.9%) using SWE. Conversely, there was more size underestimation than overestimation (18.6% vs 6.9%) using B-mode US. The accuracy of SWE was associated with ER positivity (P = .004), PR positivity (P = .02), molecular subtype (P = .02), and histologic grade (P = .03). In the multivariate analysis, ER positivity (P = .002) and molecular subtype (P = .027) significantly influenced the accuracy of tumor size measurement by SWE.In conclusion, the accuracy of the tumor size measured with SWE was lower than that measured with B-mode US and SWE tends to overestimate the size. ER positivity and molecular subtype are significantly associated with the accuracy of SWE in tumor size assessment.

Systematic Review and Meta-analysis of the Malignant Ultrasound Features of Triple-Negative Breast Cancer

OBJECTIVES

The malignant ultrasound (US) features of breast cancer are known to include an irregular shape, a noncircumscribed margin, an echogenic halo, a nonparallel orientation, posterior acoustic attenuation, microcalcification, and others. However, these US features are uncertain and controversial for the diagnosis of triple-negative breast cancer (TNBC). This study aimed to analyze the diagnostic value of malignant US features for TNBC by a systematic review and meta-analysis, analyze the US characteristics of TNBC, and provide US evidence for clinical diagnosis.

METHODS

A comprehensive search was performed to identify relevant English articles concerning the US diagnosis of TNBC (from the date of database establishment to November 2019). The pooled sensitivity, pooled specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio with 95% confidence interval, summary receiver operating characteristic curve, and area under the curve for the different malignant US features were calculated.

RESULTS

Ten studies (620 patients) met the eligibility criteria. The sensitivity (range, 0.14-0.68) and specificity (range, 0.19-0.66) of the malignant US features were not high. Summary receiver operating characteristic curves showed that the area under the curve (range, 0.25-0.47) of the malignant US features was low, demonstrating that these features have poor diagnostic value for TNBC. The positive likelihood ratio (range, 0.4-to 0.9) of the malignant US features was low, and the negative likelihood ratio (range, 1.09-2.02) was not low, revealing that these features had a poor ability to confirm or exclude TNBC.

CONCLUSIONS

Triple-negative breast cancer lacks the typical malignant US features of breast cancer and has its own US features.

Study Processes and Applications of Ultrasomics in Precision Medicine

Ultrasomics is the science of transforming digitally encrypted medical ultrasound images that hold information related to tumor pathophysiology into mineable high-dimensional data. Ultrasomics data have the potential to uncover disease characteristics that are not found with the naked eye. The task of ultrasomics is to quantify the state of diseases using distinctive imaging algorithms and thereby provide valuable information for personalized medicine. Ultrasomics is a powerful tool in oncology but can also be applied to other medical problems for which a disease is imaged. To date there is no comprehensive review focusing on ultrasomics. Here, we describe how ultrasomics works and its capability in diagnosing disease in different organs, including breast, liver, and thyroid. Its pitfalls, challenges and opportunities are also discussed.

Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics

PURPOSE

To examine the relationship between ultrasonic findings and positive expression of Ki67 and P53 in breast cancer.

MATERIAL AND METHODS

Surgical resection specimens of 263 breast cancer lesions were examined. Ultrasound examination and pathological examination were performed on each lesion for retrospective analysis. We applied regression analysis to the ultrasonic features related to the positive expression of Ki67 and P53 and obtained the corresponding models. To analyze diagnostic efficiency, we calculated the area under the curve (AUC). Additionally, we created a heat map to show the results of the cluster analysis.

RESULTS

Lesions with higher Ki67 expression were associated with posterior acoustic enhancement, absence of an echo halo and a higher Breast Imaging Reporting and Data System (BI-RADS) category. P53-positive cancer were associated with an absence of an echo halo and a higher BI-RADS category. The AUC of the regression models of Ki67 and P53 was 0.78 and 0.71, respectively.

CONCLUSIONS

Our study revealed that breast cancer ultrasonic findings were closely related to expression of molecular indicators, suggesting that ultrasound can be used to provide useful information to clinicians.

Construction of Nucleolin-Targeted Lipid Nanobubbles and Contrast-Enhanced Ultrasound Molecular Imaging in Triple-Negative Breast Cancer

PURPOSE

To construct aptamer AS1411-functionalized targeted lipid nanobubbles that could simultaneously target abnormally highly expressed nucleolin (NCL) on tumor tissue and neovasculature. Additionally, the study of their contrast-enhanced ultrasound molecular imaging capabilities in vitro and in vivo to explore new methods and approaches for the early and accurate diagnosis of triple-negative breast cancer (TNBC).

METHODS

First, the targeted lipid-nucleic acid molecules were constructed by an amide reaction. Then, the targeted lipid nanobubbles (AS1411-NBs) and nontargeted lipid nanobubbles (NBs) were prepared by membrane hydration, mechanical vibration and centrifugal floatation. The physicochemical characteristics and contrast-enhanced ultrasound imaging capabilities of AS1411-NBs and NBs were compared and analyzed in vitro and in vivo.

RESULTS

There were no significant differences between the AS1411-NBs and NBs in their concentration, average particle size or ultrasound imaging capabilities in vitro (P > 0.05). However, AS1411-NBs could simultaneously target NCL in tumor tissue and neovasculature to effectively prolong the duration of contrast-enhanced ultrasound imaging compared to NBs in vivo. The area under the time-intensity curve was significantly different between AS1411-NBs and NBs (P < 0.001), and the drug loading capacity of the AS1411-NBs was also significantly higher than that of the NBs (P < 0.05).

CONCLUSIONS

Aptamer AS1411-functionalized targeted lipid nanobubbles could significantly prolong the duration of contrast-enhanced ultrasound imaging to achieve dual-targeted ultrasound molecular imaging of tumor tissue and neovasculature. AS1411-NBs also have higher drug loading and targeted drug delivery capabilities compared with NBs, which can provide new methods and approaches for the early accurate diagnosis and effective treatment of TNBC.

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.

Triple Negative Breast Cancer Detection Using LC-MS/MS Lipidomic Profiling

Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early-stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific, and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls, and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least-squares discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, and specificity = 0.76 and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, and specificity = 0.79 and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier MSV000085324).

Heterogeneity of Triple-Negative Breast Cancer: Differences in Clinicopathologic and Ultrasound Features Between Premenopausal and Postmenopausal Patients

OBJECTIVES

The aim of this study was to determine whether any salient differences exist in triple-negative breast cancer (TNBC) between premenopausal and postmenopausal patients by comprehensively comparing the ultrasound (US) and clinicopathologic features of these patients.

METHODS

A detailed evaluation of the US and clinicopathologic features of 60 tumors from 60 patients with a diagnosis of TNBC (36 premenopausal and 24 postmenopausal women) was conducted in this retrospective study.

RESULTS

Triple-negative breast cancer in premenopausal patients was more likely to show a round or oval shape and microlobulated margins, more commonly exerted effects on surrounding tissue, and showed grade 3 blood flow according to the method of Adler et al (Ultrasound Med Biol 1990; 16:553-559) compared with tumors in postmenopausal women. Triple-negative breast cancer in postmenopausal patients showed some similar US features as those observed in estrogen receptor-positive tumors, including irregular shapes, angular or spiculated margins, and nonparallel growth to the skin (P < .05 for all).

CONCLUSIONS

Although US cannot be used to unequivocally identify TNBC, it can distinguish some salient US features regarding TNBC between premenopausal and postmenopausal women. Such information can then provide some valuable predictive information on the diagnosis of TNBC for clinicians.

Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound

Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to determine immunohistochemistry index. However, this examination is invasive and commonly causes discomfort in patients. There has been a lack of noninvasive method capable of predicting immunohistochemistry index for breast cancer patients. This paper proposes a machine learning method to predict the immunohistochemical index of breast cancer patients by using noninvasive contrast-enhanced ultrasound. A total of 119 breast cancer patients were included in this retrospective study. Each patient implemented the pathological examination of immunohistochemical expression and underwent contrast-enhanced ultrasound imaging of breast tumor. The multi-dimension features including 266 three-dimension features and 837 two-dimension dynamic features were extracted from the contrast-enhanced ultrasound sequences. Using the machine learning prediction method, 21 selected multi-dimension features were integrated to generate a model for predicting the immunohistochemistry index noninvasively. The immunohistochemical index of human epidermal growth factor receptor-2 (HER2) was predicted based on multi-dimension features in contrast-enhanced ultrasound sequence with the sensitivity of 71%, and the specificity of 79% in the testing cohort. Therefore, the noninvasive contrast-enhanced ultrasound can be used to predict the immunohistochemical index. To our best knowledge, no studies have been reported about predicting immunohistochemical index by using contrast-enhanced ultrasound sequences for breast cancer patients. Our proposed method is noninvasive and can predict immunohistochemical index by using contrast-enhanced ultrasound in several minutes, instead of relying totally on the invasive and biopsy-based histopathological examination. Graphical abstract Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound.

Imaging Features of Triple Negative Breast Cancer and the Effect of BRCA Mutations

OBJECTIVE

The purpose of this study is to review the mammographic and the ultrasound features of triple negative breast cancer (TNBC) patients and to investigate the potential effect of BRCA mutations on the imaging features of these patients.

METHODS

One hundred and seven patients with TNBC were enrolled in a retrospective study following IRB approval and approval of waiver of informed consent. BRCA mutations were assessed using genetic testing. Imaging features on mammography and ultrasound (US) as well as pathology and clinical information were retrospectively reviewed and characterized according to the BI-RADS lexicon (fifth edition). The relationships between BRCA mutations and the imaging findings were examined.

RESULTS

TNBC commonly presented as an irregular mass with obscured margins on mammography and as an irregular hypoechoic mass with microlobulated or angular margins on US. Approximately two thirds of TNBC cases had a parallel orientation and approximately one third had posterior enhancement, features often associated with benign masses. There was no statistically significant difference in the mammographic and the US features of BRCA positive and BRCA negative triple negative tumors.

CONCLUSION

TNBC may have a parallel orientation and posterior enhancement, which are features often seen with benign masses. BRCA mutations do not affect the imaging features of triple negative breast tumors.

Multiple Bilateral Circumscribed Breast Masses Detected at Imaging: Review of Evidence for Management Recommendations

OBJECTIVE. Historically, management recommendations for multiple bilateral circumscribed breast masses encountered with breast imaging have varied. This article reviews the evidence and provides best-practice recommendations for managing these masses. CONCLUSION. Meticulous imaging technique and interpretation are required to correctly diagnose multiple bilateral circumscribed breast masses. Radiologists should classify such masses identified at mammography, digital breast tomosynthesis, or bilateral whole-breast sonography as benign and recommend annual follow-up. Elucidating the significance of these masses on MRI, contrast-enhanced mammography, or nuclear breast imaging requires further study.

Are baseline ultrasound and mammographic features associated with rates of pathological completes response in patients receiving neoadjuvant chemotherapy for breast cancer?

Background

Increasing numbers of breast cancer patients receive neoadjuvant chemotherapy (NACT). We seek to investigate whether baseline mammographic and ultrasound features are associated with complete pathological response (pCR) after NACT.

Methods

A database of NACT patients was reviewed. Baseline imaging parameters assessed were ultrasound: posterior effect; echo pattern; margin and lesion diameter; mammography: spiculation and microcalcification. Core biopsy grade and immunophenotype were documented. Data were analysed for the whole study group and by immunophenotype.

Results

Of the 222 cancers, 83 (37%) were triple negative (TN), 61 (27%) ER positive/HER-2 negative and 78 (35%) HER-2 positive. A pCR occurred in 46 of 222 cancers (21%). For the whole group, response was associated with high core biopsy grade (grade 3 vs. grade 1 or 2) (26% vs. 9%, p = 0.0044), absence of posterior shadowing on ultrasound (26% vs. 10%, p <  0.001) and the absence of mammographic spiculation (26 vs. 6%, p <  0.001). Within the HER-2 positive group; the absence of shadowing and spiculation remained highly associated with pCR, in addition to small ultrasound size (AUC = 0.71, p < 0.001) and the absence of microcalcification (39% vs. 21%, p < 0.02). On multivariable analysis absence of spiculation and core grade remained significant for the whole cohort, size and absence of spiculation remained significant for HER-2 positive tumours. No feature predicted pCR in TN tumours.

Conclusion

A pCR is less likely when there is mammographic spiculation. Small ultrasound size is associated with pCR in HER-2 positive tumours. These findings may be helpful when discussing NACT and surgical options with patients.

Trial registration

UK Clinical Trials Gateway: registration number 16712.

Clinical Value of Shear Wave Elastography Added to Targeted Ultrasound (Second-Look Ultrasound) in the Evaluation of Breast Lesions Suspicious of Malignancy Detected on Magnetic Resonance Imaging

OBJECTIVES

To determine the value of shear wave elastography (SWE) added to targeted ultrasound (US) after breast magnetic resonance imaging (MRI).

METHODS

From July 2015 to October 2017, 40 patients who underwent targeted US evaluations of suspicious MRI-detected American College of Radiology Breast Imaging Reporting and Data System category 4 lesions (mass or nonmass enhancement) were enrolled in this prospective study. B-mode US and SWE examinations were performed to detect US correlates to MRI-detected lesions; their Breast Imaging Reporting and Data System categories were recorded; lesions that were dark blue on a 6-point color scale or had maximum elasticity of 30 kPa or less were categorized as soft. Biopsy was performed with US or MRI guidance, with the pathologic findings correlated with MRI, US, and SWE findings. The value of SWE for lesion detection and identification of benign lesions was determined.

RESULTS

The mean age of the 40 patients was 51.1 years. There were 48 MRI-detected lesions (20 cancers, 3 high-risk lesions, and 25 benign lesions). Ultrasound correlates (8 category 3 and 25 category 4) were shown for 33 lesions (69%; P < .0001), with 16 cancers (80%; P < .0001) and 17 benign lesions. Shear wave elastography assisted detection of 3 (19%) cancers on US imaging. All 7 soft US category 3 lesions were benign (7 of 33 [21%]; P = .0014).

CONCLUSIONS

Shear wave elastography was useful with targeted US after breast MRI to increase cancer detection by US. A significant number of US correlates to MRI-detected lesions could have been identified as benign (category 3 and soft) before biopsy, with the potential of short-interval follow-up of MRI-detected lesions with benign US correlates instead of biopsy.

Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy

OBJECTIVES

To evaluate whether multiparametric breast-MRI, obtained before the initiation of neoadjuvant systemic therapy (NST) for operable breast cancer, predicts which cancer will achieve a pathological complete response (pCR) after the completion of NST.

METHODS

This was an IRB-approved retrospective study on 31 consecutive patients (median age, 56 years) with operable invasive breast cancer (median size: 22 mm; triple-negative: 11/31 [35%], HER2-positive: 7/31 [23%], triple-positive: 13/31 [42%]) who underwent multiparametric DCE-MRI before the initiation of NST. The MRI protocol consisted of high-resolution dynamic contrast-enhanced MRI (DCE-MRI), T2-TSE, and DWI (b-values 0, 100, 800 s/mm²). The results of surgical pathology after the completion of NST served as a standard of reference. Patient characteristics (age and menopausal status), pathological tumor characteristics (type, stage, nuclear grade, ER/PR and HER2 receptor status, and Ki-67 staining), and MRI characteristics (size, morphology, T2 signal intensity, enhancement kinetics, and ADC values) before NST were evaluated and compared between patients achieving pCR vs. non-pCR.

RESULTS

Among 31 patients, 17 achieved pCR (55%) and 14 non-pCR (45%). No correlation was observed between patient- or tumor pathology-derived characteristics and pCR vs. non-pCR. Among MRI-derived tumor characteristics, tumor growth orientation parallel to Cooper's ligaments (p = 0.002) and wash-out rates (p = 0.019) correlated with pCR. Pre-NST ADC values were lower in patients achieving pCR (P = 0.086).

CONCLUSIONS

A tumor growth pattern parallel with Cooper's ligaments and a fast wash-out rate on pre-treatment multiparametric MRI are predictive of pCR and more closely associated with pCR than ADC values.