Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial.

BACKGROUND: Preclinical studies have demonstrated that tumour cell death can be enhanced 10- to 40-fold when radiotherapy is combined with focussed ultrasound-stimulated microbubble (FUS-MB) treatment. The acoustic exposure of microbubbles (intravascular gas microspheres) within the target volume causes bubble cavitation, which induces perturbation of tumour vasculature and activates endothelial cell apoptotic pathways responsible for the ablative effect of stereotactic body radiotherapy. Subsequent irradiation of a microbubble-sensitised tumour causes rapid increased tumour death. The study here presents the mature safety and efficacy outcomes of magnetic resonance (MR)-guided FUS-MB (MRgFUS-MB) treatment, a radioenhancement therapy for breast cancer. METHODS AND FINDINGS: This prospective, single-center, single-arm Phase 1 clinical trial included patients with stages I-IV breast cancer with in situ tumours for whom breast or chest wall radiotherapy was deemed adequate by a multidisciplinary team (clinicaltrials.gov identifier: NCT04431674). Patients were excluded if they had contraindications for contrast-enhanced MR or microbubble administration. Patients underwent 2 to 3 MRgFUS-MB treatments throughout radiotherapy. An MR-coupled focussed ultrasound device operating at 800 kHz and 570 kPa peak negative pressure was used to sonicate intravenously administrated microbubbles within the MR-guided target volume. The primary outcome was acute toxicity per Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Secondary outcomes were tumour response at 3 months and local control (LC). A total of 21 female patients presenting with 23 primary breast tumours were enrolled and allocated to intervention between August/2020 and November/2022. Three patients subsequently withdrew consent and, therefore, 18 patients with 20 tumours were included in the safety and LC analyses. Two patients died due to progressive metastatic disease before 3 months following treatment completion and were excluded from the tumour response analysis. The prescribed radiation doses were 20 Gy/5 fractions (40%, n = 8/20), 30 to 35 Gy/5 fractions (35%, n = 7/20), 30 to 40 Gy/10 fractions (15%, n = 3/20), and 66 Gy/33 fractions (10%, n = 2/20). The median follow-up was 9 months (range, 0.3 to 29). Radiation dermatitis was the most common acute toxicity (Grade 1 in 16/20, Grade 2 in 1/20, and Grade 3 in 2/20). One patient developed grade 1 allergic reaction possibly related to microbubbles administration. At 3 months, 18 tumours were evaluated for response: 9 exhibited complete response (50%, n = 9/18), 6 partial response (33%, n = 6/18), 2 stable disease (11%, n = 2/18), and 1 progressive disease (6%, n = 1/18). Further follow-up of responses indicated that the 6-, 12-, and 24-month LC rates were 94% (95% confidence interval [CI] [84%, 100%]), 88% (95% CI [75%, 100%]), and 76% (95% CI [54%, 100%]), respectively. The study's limitations include variable tumour sizes and dose fractionation regimens and the anticipated small sample size typical for a Phase 1 clinical trial. CONCLUSIONS: MRgFUS-MB is an innovative radioenhancement therapy associated with a safe profile, potentially promising responses, and durable LC. These results warrant validation in Phase 2 clinical trials. TRIAL REGISTRATION: clinicaltrials.gov, identifier NCT04431674.

Correlation of Mammographic Microcalcifications with Final Surgical Pathology After Neoadjuvant Chemotherapy for Breast Cancer.

INTRODUCTION: Imaging guidelines for post-neoadjuvant chemotherapy (NAC) breast cancer patients lack specificity on appropriateness and utility of individual modalities for surgical planning. Microcalcifications confound mammographic interpretation. We examined the correlation between the mammographic extent of microcalcifications present post-NAC, corresponding magnetic resonance imaging (MRI) lesions, and definitive surgical pathology. METHODS: In this retrospective cohort study, patients with calcifications on mammography were collected from a database of consecutive breast cancer patients receiving NAC. The primary objective was to determine the correlation between maximum dimension of post-NAC calcifications with surgical pathology (invasive disease, tumor bed, and ductal carcinoma in situ [DCIS]), stratified by tumor receptor subgroup. Secondarily, we examined the correlation of residual disease with MRI mass enhancement (ME) and non-ME (NME). Pearson's correlation coefficient was used to evaluate statistical significance (strong: R2 ≥70%; moderate: R2=25-70%; weak: R2 ≤25%). RESULTS: Overall, 186 patients met the inclusion criteria. Mammographic calcifications correlated poorly with invasive disease (R2 = 10.8%), overestimating by 57%. In patients with calcifications on mammography, MRI ME and NME correlated weakly with the maximum dimension of invasive disease and DCIS. In triple-negative breast cancer (TNBC) patients, invasive disease correlated strongly with the maximum dimension of calcifications (R2 = 83%) and moderately with ME (R2 = 37.7%) and NME (R2 = 28.4%). CONCLUSION: Overall, current imaging techniques correlate poorly and overestimate final surgical pathology. This poor correlation may lead to uncertainty in the extent of required surgical excision and the exclusion of potential candidates for non-surgical management in ongoing trials. TNBCs would be good candidates for these trials given the stronger observed correlations between pathology and imaging.

Budget Impact Analysis of Preoperative Radioactive Seed Localization.

BACKGROUND: This study models costs in implementing a radioactive seed localization (RSL) program for nonpalpable breast lesions at a large Canadian tertiary hospital to replace existing wire-guided localization (WGL). METHODS: All direct and indirect operating costs of localization per lesion from the hospital's perspective were determined by retrospectively reviewing patient data and costs from January 2014 to December 2016. A budget impact analysis and sensitivity analysis were performed to calculate the mean cost per lesion, the minimum and maximum cost per lesion, operational costs, and initial costs. RESULTS: There were 265 WGL lesions in 2014 and 170 RSL lesions in 2016 included in cost calculation. The mean cost per localization was $185 CAD for WGL ($148-$311) and $283 CAD ($245-$517) for RSL using preloaded seeds, adjusted to 2016 Canadian dollars. The annual operational expenditure including all localizations and overhead costs was $49,835 for WGL and $80,803 for RSL. Initial costs for RSL were $22,000, including external training and new equipment purchases. CONCLUSIONS: Our budget impact analysis shows that RSL using preloaded radioactive seeds was more expensive than WGL when considering per-lesion localization costs and specific costs related to radiation safety. Manually loading radioactive seed could be a cost-saving alternative to purchasing preloaded seeds. Our breakdown of costs can provide a framework for other centres to determine which localization method best suit their departments.

Adequacy of invasive and in situ breast carcinoma margins in radioactive seed and wire-guided localization lumpectomies.

Image-guided preoperative localizations help surgeons to completely resect nonpalpable breast cancers. The objective of this study is to compare the adequacy of specimen margins for both invasive breast cancer (IBC) and ductal carcinoma in situ (DCIS) after radioactive seed localization (RSL) vs wire-guided localization (WGL). We retrospectively reviewed 600 cases at a single Canadian academic center from January 2014 to September 2017, comparing surgical margins, re-excisions and reoperations, localization accuracy and major complications (migration, accidental deployment, vasovagal reaction), as well as operative duration between RSL and WGL cases. IBC margins were positive in 7% of RSL and 6% of WGL cases (P = .57). Tumor size (P = .039) and association with DCIS (P = .036) predicted positive margins in invasive carcinoma. DCIS margins were positive in 6% and 8%, and close (≤2 mm) in 37% and 36% of cases (P = .45) for RSL and RSL cases respectively. The presence of extensive intraductal component predicted positive DCIS margins (P < .0001). There was no significant difference between intraoperative re-excisions (P = .54), localization accuracy (P = .34), and operation duration (P = .81). Reoperation for lumpectomies and mastectomies was marginally higher for WGL than RSL (P = .049). There were 11 (4%) WGL and no RSL complications (P = .03). Overall, positive margins for IBC, close or positive margins for DCIS, intraoperative re-excision, localization accuracy, and operation duration were similar between RSL and WGL. The reoperation rate was higher in WGL than RSL, which may reflect practice changes over time. RSL was safer than WGL with lower complication rates.

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis.In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.

Ischemic necrosis of the breast following septic shock: A case report.

Fat necrosis of the breast is a common and benign entity, often secondary to trauma, surgery, radiation therapy, or unknown etiologies. Critically ill patients with septic shock may experience end-organ hypoperfusion and tissue infarction and necrosis, which may result in breast fat necrosis, however, to the best of our knowledge this has not been previously described. We report a case of biopsy-proven breast fat necrosis secondary to septic shock following an emergency surgery in a postmenopausal female.

Rapid MRI of the breast in evaluating lesions discovered on screening.

BACKGROUND: In Canada, breast MRI has traditionally been reserved for evaluation of disease extent in patients with known breast malignancy. More recently, MRI has been emerging as an instrument for breast screening. However, its utilization is limited by increased relative cost and increased reader time. In this study, we evaluate a rapid MRI protocol for breast cancer screening within a breast screening population. METHODS: A series of 100 MRI studies performed in a high-risk breast cancer population were selected, ensuring a mix of malignant and benign pathology and normal cases. These were presented as full and abbreviated MRI protocols to 3 breast-trained radiologists. Each case was evaluated for Breast Imaging Reporting and Data Systems (BIRADS) category and the presence or absence of cancer. The time taken to complete and interpret each study was also recorded. RESULTS: Of the 100 cases, 17 were of histopathology-proven invasive carcinoma, 6 were ductal carcinoma in situ, 33 were benign, and 44 were normal cases. Sensitivity using the rapid protocol was 69.6% (CI: 47.1-86.8) vs 83% (CI: 61.2-95.1) using the full protocol. Specificity using the rapid protocol was 77.9% (CI: 67.0-86.6) vs 83% (CI: 61.2-95.1) using the full protocol. Intra-observer agreement of BIRADS category and cancer detection was very good (0.82-0.93 weighted Kappa and 0.81-0.9 weighted Kappa, respectively). Inter-observer variability of BIRADS category and cancer detection was moderate (0.54-0.59 and 0.53-0.58, respectively). CONCLUSION: Our study suggests that a rapid MRI protocol is comparable in performance to that of a standard MRI protocol. In addition, breast imagers are unlikely to change their BIRADS assessment of a study based on the additional sequences provided by the lengthier study. The use of a rapid MRI protocol can improve accessibility, thus making breast MRI a more utilized tool for breast cancer screening.

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis.

BACKGROUND: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pretreatment DOS functional maps for predicting LABC response to NAC. METHODS: Locally advanced breast cancer patients (n=37) underwent DOS breast imaging before starting NAC. Breast tissue parametric maps were constructed and texture analyses were performed based on grey-level co-occurrence matrices for feature extraction. Ground truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS textural features computed on volumetric tumour data before the start of treatment (i.e., 'pretreatment') to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naive Bayes, and k-nearest neighbour classifiers. RESULTS: Data indicated that textural characteristics of pretreatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2 homogeneity resulted in the highest accuracy among univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5% and 89.0%, respectively, and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-contrast+HbO2-homogeneity, which resulted in a %Sn/%Sp=78.0/81.0% and an accuracy of 79.5%. CONCLUSIONS: This study demonstrated that the pretreatment DOS texture features can predict breast cancer response to NAC and potentially guide treatments.

Breast MRI as an adjunct to mammography for breast cancer screening in high-risk patients: retrospective review.

OBJECTIVE: In July 2011, the provincial government of Ontario, Canada, approved funding for the addition of annual breast MRI to mammography screening for all women 30-69 years old considered to be at high risk for breast cancer. The purpose of this study was to evaluate the diagnostic performance of screening breast MRI as compared with mammography in a population-based high-risk screening program. MATERIALS AND METHODS: A retrospective review identified 650 eligible high-risk women who underwent screening breast MRI and mammography between July 2011 and January 2013 at one institution. Results of 806 screening rounds (comprising both MRI and mammography) were reviewed. RESULTS: Malignancy was diagnosed in 13 patients (invasive cancer in nine, ductal carcinoma in situ in three [one with microinvasion], and chest wall metastasis in one). Of the 13 cancers, 12 (92.3%) were detected by MRI and four (30.8%) by mammography. In nine of these patients, the cancer was diagnosed by MRI only, resulting in an incremental cancer detection rate of 10 cancers per 1000 women screened. MRI screening had significantly higher sensitivity than mammography (92.3% vs 30.8%) but lower specificity (85.9% vs 96.8%). MRI also resulted in a higher callback rate for a 6-month follow-up study (BI-RADS category 3 assessment) than mammography (119 [14.8%] vs 13 [1.6%]) and more image-guided biopsies than mammography (95 [11.8%] vs 19 [2.4%]). CONCLUSION: MRI is a useful adjunct to mammography for screening in high-risk women, resulting in a significantly higher rate of cancer detection. However, this was found to be at the cost of more imaging and biopsies for lesions that ultimately proved to be benign.

3D CT Radiomic Analysis Improves Detection of Axillary Lymph Node Metastases Compared to Conventional Features in Patients With Locally Advanced Breast Cancer.

OBJECTIVE: Preoperative detection of axillary lymph node metastases (ALNMs) from breast cancer is suboptimal; however, recent work suggests radiomics may improve detection of ALNMs. This study aims to develop a 3D CT radiomics model to improve detection of ALNMs compared to conventional imaging features in patients with locally advanced breast cancer. METHODS: Retrospective chart review was performed on patients referred to a specialty breast cancer center between 2015 and 2020 with US-guided biopsy-proven ALNMs and pretreatment chest CT. One hundred and twelve patients (224 lymph nodes) met inclusion and exclusion criteria and were assigned to discovery (n = 150 nodes) and testing (n = 74 nodes) cohorts. US-biopsy images were referenced in identifying ALNMs on CT, with contralateral nodes taken as negative controls. Positive and negative nodes were assessed for conventional features of lymphadenopathy as well as for 107 radiomic features extracted following 3D segmentation. Diagnostic performance of individual and combined radiomic features was evaluated. RESULTS: The strongest conventional imaging feature of ALNMs was short axis diameter ≥ 10 mm with a sensitivity of 64%, specificity of 95%, and area under the curve (AUC) of 0.89 (95% CI, 0.84-0.94). Several radiomic features outperformed conventional features, most notably energy, a measure of voxel density magnitude. This feature demonstrated a sensitivity, specificity, and AUC of 91%, 79%, and 0.94 (95% CI, 0.91-0.98) for the discovery cohort. On the testing cohort, energy scored 92%, 81%, and 0.94 (95% CI, 0.89-0.99) for sensitivity, specificity, and AUC, respectively. Combining radiomic features did not improve AUC compared to energy alone (P = .08). CONCLUSION: 3D radiomic analysis represents a promising approach for noninvasive and accurate detection of ALNMs.

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.

Objective: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT). Materials and methods: Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes. Results: Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network. Conclusion: Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study.

Background: In patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy. Methods: The phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination. Results: Between June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUS-radiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation. Conclusion: The study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study. Clinical trial registration: clinicaltrials.gov, ID NCT04050228.

Breast ductography: to do or not to do? A pictorial essay.

Nipple discharge is a frequent breast disease clinical presentation. Although most cases of nipple discharge are physiologic, pathologic nipple discharge is not uncommon. Eight to 15% of pathological nipple discharge is associated with malignancy, requiring investigation. Some specialists believe that ductography is a challenging procedure that is better to be substituted by other methods, such as MRI. However, an experienced physician can perform ductography quickly and easily and still play an essential role in some clinical scenarios. Conventional imaging, such as mammography and sonography, commonly fails to detect the underlying causes of pathological nipple discharge. MRI has limitations of low specificity, cost, lengthy exam duration, accessibility, and patient factors such as claustrophobia. In addition, we can make a specific diagnosis and appropriate treatment by coupling ductography with other methods, such as ultrasound-guided or stereotactic biopsy. This study aims to present the ductography technique, possible findings, and the clinical settings where ductography is useful.Critical relevance statement Although ductography is currently less used in breast imaging, it still plays an essential role in some clinical scenarios. These clinical scenarios include pathological nipple discharge with negative conventional imaging, contraindicated MRI, unavailable MRI, unremarkable MRI results, and multiple MRI findings.Key points• Conventional imaging commonly fails to detect the underlying causes of pathological nipple discharge.• MRI in the setting of nipple discharge has some limitations.• Ductography still plays an essential role in some clinical scenarios.• Coupling ductography with other methods helps make a specific diagnosis.

How Does Diagnostic Accuracy Evolve with Increased Breast MRI Experience?

Introduction: Our institution is part of a provincial program providing annual breast MRI screenings to high-risk women. We assessed how MRI experience, background parenchymal enhancement (BPE), and the amount of fibroglandular tissue (FGT) affect the biopsy-proven predictive value (PPV3) and accuracy for detecting suspicious MRI findings. Methods: From all high-risk screening breast MRIs conducted between 1 July 2011 and 30 June 2020, we reviewed all BI-RADS 4/5 observations with pathological tissue diagnoses. Overall and annual PPV3s were computed. Radiologists with fewer than ten observations were excluded from performance analyses. PPV3s were computed for each radiologist. We assessed how MRI experience, BPE, and FGT impacted diagnostic accuracy using logistic regression analyses, defining positive cases as malignancies alone (definition A) or malignant or high-risk lesions (definition B). Findings: There were 536 BI-RADS 4/5 observations with tissue diagnoses, including 77 malignant and 51 high-risk lesions. A total of 516 observations were included in the radiologist performance analyses. The average radiologist's PPV3 was 16 ± 6% (definition A) and 25 ± 8% (definition B). MRI experience in years correlated significantly with positive cases (definition B, OR = 1.05, p = 0.03), independent of BPE or FGT. Diagnostic accuracy improved exponentially with increased MRI experience (definition B, OR of 1.27 and 1.61 for 5 and 10 years, respectively, p = 0.03 for both). Lower levels of BPE significantly correlated with increased odds of findings being malignant, independent of FGT and MRI experience. Summary: More extensive MRI reading experience improves radiologists' diagnostic accuracy for high-risk or malignant lesions, even in MRI studies with increased BPE.

Pseudoangiomatous stromal hyperplasia of the breast, imaging and clinical perspective: A review.

Pseudoangiomatous stromal hyperplasia (PASH) is a benign breast pathology, which most commonly presents incidentally along with other breast pathologies. The etiology and pathogenesis of PASH are still unknown; however, there is some evidence suggesting PASH is hormone dependent. The clinical history, presentation, and imaging appearance of PASH are variable. Clinically, PASH has a wide spectrum of presentations, from being silent to gigantomastia. On imaging, PASH demonstrates various benign to suspicious features. Here we summarize PASH's clinical presentation, histopathology, imaging features, and management.

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.

OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to its increased risk of micrometastatic spread necessitating early treatment and for guiding targeted therapies. This study aimed to evaluate the diagnostic performance of machine learning (ML) classification of newly diagnosed breast masses into TN versus non-TN (NTN) and HER2+ versus HER2 negative (HER2-) breast cancer, using radiomic features extracted from grayscale ultrasound (US) b-mode images. MATERIALS AND METHODS: A retrospective chart review identified 88 female patients who underwent diagnostic breast US imaging, had confirmation of invasive malignancy on pathology and receptor status determined on immunohistochemistry available. The patients were classified as TN, NTN, HER2+ or HER2- for ground-truth labelling. For image analysis, breast masses were manually segmented by a breast radiologist. Radiomic features were extracted per image and used for predictive modelling. Supervised ML classifiers included: logistic regression, k-nearest neighbour, and Naïve Bayes. Classification performance measures were calculated on an independent (unseen) test set. The area under the receiver operating characteristic curve (AUC), sensitivity (%), and specificity (%) were reported for each classifier. RESULTS: The logistic regression classifier demonstrated the highest AUC: 0.824 (sensitivity: 81.8%, specificity: 74.2%) for the TN sub-group and 0.778 (sensitivity: 71.4%, specificity: 71.6%) for the HER2 sub-group. CONCLUSION: ML classifiers demonstrate high diagnostic accuracy in classifying TN versus NTN and HER2+ versus HER2- breast cancers using US images. Identification of more aggressive breast cancer subtypes early in the diagnostic process could help achieve better prognoses by prioritizing clinical referral and prompting adequate early treatment.

Novel MRI-guided focussed ultrasound stimulated microbubble radiation enhancement treatment for breast cancer.

Preclinical studies have demonstrated focused ultrasound (FUS) stimulated microbubble (MB) rupture leads to the activation of acid sphingomyelinase-ceramide pathway in the endothelial cells. When radiotherapy (RT) is delivered concurrently with FUS-MB, apoptotic pathway leads to increased cell death resulting in potent radiosensitization. Here we report the first human trial of using magnetic resonance imaging (MRI) guided FUS-MB treatment in the treatment of breast malignancies. In the phase 1 prospective interventional study, patients with breast cancer were treated with fractionated RT (5 or 10 fractions) to the disease involving breast or chest wall. FUS-MB treatment was delivered before 1st and 5th fractions of RT (within 1 h). Eight patients with 9 tumours were treated. All 7 evaluable patients with at least 3 months follow-up treated for 8 tumours had a complete response in the treated site. The maximum acute toxicity observed was grade 2 dermatitis in 1 site, and grade 1 in 8 treated sites, at one month post RT, which recovered at 3 months. No RT-related late effect or FUS-MB related toxicity was noted. This study demonstrated safety of combined FUS-MB and RT treatment. Promising response rates suggest potential strong radiosensitization effects of the investigational modality.Trial registration: clinicaltrials.gov, identifier NCT04431674.