Ergonomic wellness for the trainee in gastrointestinal endoscopy.

PURPOSE OF REVIEW: Endoscopy-related injuries (ERIs) are prevalent in gastrointestinal endoscopy. The aim of this review is to address the growing concern of ERIs by evaluating the ergonomic risk factors and the efficacy of interventions and educational strategies aimed at mitigating these risks, including novel approaches. RECENT FINDINGS: ERIs are highly prevalent, exacerbated by factors such as repetitive strain, nonneutral postures, suboptimal equipment design, and the procedural learning curve. Female sex and smaller hand sizes have been identified as specific risk factors. Recent guidelines underscore the importance of ergonomic education and the integration of ergonomic principles into the foundational training of gastroenterology fellows. Advances in equipment design focus on adaptability to different hand sizes and ergonomic positions. Furthermore, the incorporation of microbreaks and macrobreaks, along with neutral monitor and bed positioning, has shown promise in reducing the incidence of ERIs. Wearable sensors may be helpful in monitoring and promoting ergonomic practices among trainees. SUMMARY: Ergonomic wellness is paramount for gastroenterology trainees to prevent ERIs and ensure a sustainable career. Effective strategies include ergonomic education integrated into curricula, equipment design improvements, and procedural adaptations such as scheduled breaks and optimal positioning. Sensor-based and camera-based systems may allow for education and feedback to be provided regarding ergonomics to trainees in the future.

Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma.

Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.

Patient-reported experience with the use of Mepitel Film for prevention of acute radiation dermatitis in breast cancer.

BACKGROUND AND PURPOSE: Mepitel Film (MF) has been demonstrated to reduce the severity of radiation dermatitis (RD) in patients receiving breast cancer radiotherapy (RT). The objective of this study was to characterize patient-reported experience with MF use, including its impact on daily activities and wellbeing. MATERIALS AND METHODS: This single-institution study analyzed anonymized responses to a questionnaire completed by patients who used MF for the prevention of RD during breast cancer RT. RESULTS: Of the 254 patients contacted, 192 patients completed the survey. Most patients disagreed or strongly disagreed that MF limited their ability to perform their daily activities, including household chores (88%, n = 169/191), their ability to work (83%, n = 157/189), or their ability to sleep (85%, n = 163/191). Furthermore, patients agreed or strongly agreed MF was comfortable on their skin (67%, n = 126/189) and protected their skin from rubbing against clothing (86%, n = 161/188). Some patients agreed or strongly agreed that MF affected their ability to shower (31%, n = 50/162), wear bras (28%, n = 51/185), and impacted their level of pruritus (35%, n = 67/189). However, most patients agreed or strongly agreed that their overall experience with MF was positive (92%, n = 173/189) and would recommend MF to a friend undergoing breast cancer RT (88%, n = 166/188). CONCLUSION: MF use is associated with positive patient-reported experience during breast RT with minimal impact on daily activities.

Perceptions of healthcare professionals on the use of Mepitel Film for the prevention of acute radiation dermatitis in breast cancer.

INTRODUCTION: Randomized clinical trials support Mepitel Film (MF) as a prophylactic treatment for radiation dermatitis (RD) in patients undergoing breast radiotherapy. Although several studies have canvassed the opinion of patients on using MF, no such studies have been done to investigate the perception of healthcare professionals (HCPs). The objective of this study was therefore to investigate the perceptions of HCPs on MF as a treatment option for RD. METHODS: Anonymized responses to a web-based survey sent to HCPs at a single institution managing patients using MF during breast radiotherapy were analyzed. RESULTS: Of the 28 HCPs contacted, 22 completed the survey, including 6 radiation oncologists (ROs), 11 radiation therapists (RTTs), and 5 nurses. Most HCPs reported MF was better at preventing severe RD than the standard of care and improved radiation-induced skin reactions (n = 20/22, 91%, and n = 19/22, 86%, respectively). MF was recommended for mastectomy patients without reconstruction (n = 15/21, 71%). The majority of HCPs believed that patients' families could be trained to apply and remove MF (n = 19/22, 86%). Many HCPs perceived that implementation of MF would be difficult in terms of maintaining patient flow and wide-scale implementation within their institution (n = 11/22, 50%, and n = 10/22, 46%, respectively). Most HCPs perceived that fewer than 50% of their patients could afford MF if priced at $100 CAD (n = 15/20, 75%). CONCLUSION: These findings provide insights into the possibility of MF to be incorporated into standard practice of care for RD. Although most HCPs were satisfied with MF as a prophylactic treatment for RD, there are concerns about its resource-intensive operationalization and financial accessibility to patients. Future research should focus on ways to improve HCP experience with MF and to improve its implementation into clinical settings as standard of care.

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning.

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

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.

Glass microspargers as effective frit spargers in single use bioreactors.

For multiple-use bench scale and larger bioreactors, sintered stainless steel frit spargers are commonly used as microspargers. For bench-scale single-use bioreactors (SUBs), existing microspargers are sintered plastics, such as polyethylene. However, though plastics are readily sterilized by irradiation making them convenient for single use, these designs overlook surface energy properties of the materials of construction. For these sintered plastic spargers, forces at the water-air-surface interface cause bubble coalescence, leading to lower effective mass transfer, higher gas flow rates, and differing pCO2 profiles in cell culture. Alternative materials of construction were evaluated based on contact angle information and bubble formation observations. Sintered glass was chosen over thermoplastic polymers for higher surface wettability as described in the glass/water contact angle, its history as a commonly sintered material, and availability at costs suitable for single use applications. Glass sintered spargers and traditional stainless steel frit spargers were compared by porosity, bubble size, and kL a studies. Mass transfer (kL a) and cell culture performance equal or greater than a standard 20 µm stainless steel microsparger mass transfer efficiency was achieved by a glass frit sparger, of international porosity standard "P40" according to ISO 4793-80, which corresponds to a range of 16-40 µm.

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival. PURPOSE: This study, for the first time, proposes a hierarchical self-attention-guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre-treatment biopsy specimens. METHODS: Digitized hematoxylin and eosin-stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch-level and tumor-level processing modules followed by a patient-level response prediction component. A combination of convolutional layers and transformer self-attention blocks were utilized in the patch-level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor-level processing and the patient-level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five-fold cross-validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework. RESULTS: The obtained results on the test set showed an AUC of 0.89 and an F1-score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch-level, patch-level + tumor-level, and patch-level + patient-level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1-scores of 86%, 87%, and 89%, respectively. CONCLUSIONS: The results demonstrate a high potential of the proposed hierarchical deep-learning methodology for analyzing digital pathology images of pre-treatment tumor biopsies to predict the pathological response of breast cancer to NAC.

Individualising radiation therapy decisions in breast cancer patients based on tumour infiltrating lymphocytes and genomic biomarkers.

Radiation therapy (RT) has long been fundamental for the curative treatment of breast cancer. While substantial progress has been made in the anatomical and technological precision of RT delivery, and some approaches to de-escalate or omit RT based on clinicopathologic features have been successful, there remain substantial opportunities to refine individualised RT based on tumour biology. A major area of clinical and research interest is to ascertain the individualised risk of loco-regional recurrence to direct treatment decisions regarding escalation and de-escalation of RT. Patient-tailored treatment with RT is considerably lagging behind compared with the massive progress made in the field of personalised medicine that currently mainly applies to decisions on the use of systemic therapy or targeted agents. Herein we review select literature surrounding the use of tumour genomic biomarkers and biomarkers of the immune system, including tumour infiltrating lymphocytes (TILs), within the management of breast cancer, specifically as they relate to progress in moving toward analytically validated and clinically tested biomarkers utilized in RT.

Drivers of Emergency Department Use Among Oncology Patients in the Era of Novel Cancer Therapeutics: A Systematic Review.

BACKGROUND: Patients diagnosed with cancer are frequent users of the emergency department (ED). While many visits are unavoidable, a significant portion may be potentially preventable ED visits (PPEDs). Cancer treatments have greatly advanced, whereby patients may present with unique toxicities from targeted therapies and are often living longer with advanced disease. Prior work focused on patients undergoing cytotoxic chemotherapy, and often excluded those on supportive care alone. Other contributors to ED visits in oncology, such as patient-level variables, are less well-established. Finally, prior studies focused on ED diagnoses to describe trends and did not evaluate PPEDs. An updated systematic review was completed to focus on PPEDs, novel cancer therapies, and patient-level variables, including those on supportive care alone. METHODS: Three online databases were used. Included publications were in English, from 2012-2022, with sample sizes of ≥50, and reported predictors of ED presentation or ED diagnoses in oncology. RESULTS: 45 studies were included. Six studies highlighted PPEDs with variable definitions. Common reasons for ED visits included pain (66%) or chemotherapy toxicities (69.1%). PPEDs were most frequent amongst breast cancer patients (13.4%) or patients receiving cytotoxic chemotherapy (20%). Three manuscripts included immunotherapy agents, and only one focused on end-of-life patients. CONCLUSION: This updated systematic review highlights variability in oncology ED visits during the last decade. There is limited work on the concept of PPEDs, patient-level variables and patients on supportive care alone. Overall, pain and chemotherapy toxicities remain key drivers of ED visits in cancer patients. Further work is needed in this realm.

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.

Mepitel Film for the Prevention of Acute Radiation Dermatitis in Breast Cancer: A Randomized Multicenter Open-Label Phase III Trial.

PURPOSE: Radiation dermatitis (RD) is common in patients undergoing breast radiotherapy. Mepitel film (MF) can reduce RD, but the results from two randomized controlled trials are conflicting. We aimed to conduct a confirmatory randomized controlled trial in patients at risk of RD. METHODS: Patients were randomly assigned to receive MF or standard care (2:1 ratio). Patients with large breasts after lumpectomy (bra size ≥ 36 inches or cup size ≥ C) or after mastectomy were eligible. Stratification factors included surgery type, dose fractionation, and administration of boost/bolus. The primary end point was grade (G) 2 or 3 RD using the Common Terminology Criteria for Adverse Events v5.0. Secondary end points included patient- and clinician-reported outcomes. RESULTS: Between January 2020 and May 2022, 376 patients were included in the modified intention-to-treat analysis. The incidence of G2 or 3 RD was significantly lower in MF patients compared with standard care (n = 39/251, 15.5%; 95% CI, 11.3 to 20.6% v n = 57/125, 45.6%; 95% CI, 36.7 to 54.8% respectively, odds ratio (OR): 0.20, P < .0001). Benefits of MF remained significant in patients who developed G 3 RD (n = 7, 2.8%; 95% CI, 1.1 to 5.7% v n = 17, 13.6%; 95% CI, 8.1 to 20.9%, OR: 0.19) and moist desquamation (n = 20, 8.0%; 95% CI, 4.9 to 12.0% v n = 24, 19.2%; 95% CI, 12.7 to 27.1%, OR: 0.36). When evaluating the combined patient and health care provider score using Radiation-Induced Skin Reaction Assessment Scale, the MF arm had significantly lower scores (P < .0001). Individual items on the Radiation-Induced Skin Reaction Assessment Scale also favored the MF for both patient- and clinician-reported outcomes. Blistering/peeling, erythema, pigmentation, and edema were significantly reduced in the MF arm. Three patients removed the film prematurely because of rash (n = 2) and excessive pruritus (n = 1). CONCLUSION: MF significantly reduces RD in patients undergoing breast radiotherapy.

Comparative Evaluation of Tumor-Infiltrating Lymphocytes in Companion Animals: Immuno-Oncology as a Relevant Translational Model for Cancer Therapy.

Despite the important role of preclinical experiments to characterize tumor biology and molecular pathways, there are ongoing challenges to model the tumor microenvironment, specifically the dynamic interactions between tumor cells and immune infiltrates. Comprehensive models of host-tumor immune interactions will enhance the development of emerging treatment strategies, such as immunotherapies. Although in vitro and murine models are important for the early modelling of cancer and treatment-response mechanisms, comparative research studies involving veterinary oncology may bridge the translational pathway to human studies. The natural progression of several malignancies in animals exhibits similar pathogenesis to human cancers, and previous studies have shown a relevant and evaluable immune system. Veterinary oncologists working alongside oncologists and cancer researchers have the potential to advance discovery. Understanding the host-tumor-immune interactions can accelerate drug and biomarker discovery in a clinically relevant setting. This review presents discoveries in comparative immuno-oncology and implications to cancer therapy.

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images.

Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recent advances in automated frameworks for nuclei segmentation, this task is still challenging. Specifically, detecting small nuclei in large-scale histology images and delineating the border of touching nuclei accurately is a complicated task even for advanced deep neural networks. In this study, a cascaded deep learning framework is proposed to segment nuclei accurately in digitized microscopy images of histology slides. A U-Net based model with customized pixel-wised weighted loss function is adapted in the proposed framework, followed by a U-Net based model with VGG16 backbone and a soft Dice loss function. The model was pretrained on the Post-NAT-BRCA public dataset before training and independent evaluation on the MoNuSeg dataset. The cascaded model could outperform the other state-of-the-art models with an AJI of 0.72 and a F1-score of 0.83 on the MoNuSeg test set.

Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence.

Highly complex and multi-dimensional medical data containing clinical, radiologic, pathologic, and sociodemographic information have the potential to advance precision oncology [...].

Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies.

Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic factor for breast cancer (BC) patients and is correlated with improved survival. However, pCR rates are variable to standard NAC, depending on BC subtype. This study investigates quantitative digital histopathology coupled with machine learning (ML) to predict NAC response a priori. Clinicopathologic data and digitized slides of BC core needle biopsies were collected from 149 patients treated with NAC. The nuclei within the tumor regions were segmented on the histology images of biopsy samples using a weighted U-Net model. Five pathomic feature subsets were extracted from segmented digitized samples, including the morphological, intensity-based, texture, graph-based and wavelet features. Seven ML experiments were conducted with different feature sets to develop a prediction model of therapy response using a gradient boosting machine with decision trees. The models were trained and optimized using a five-fold cross validation on the training data and evaluated using an unseen independent test set. The prediction model developed with the best clinical features (tumor size, tumor grade, age, and ER, PR, HER2 status) demonstrated an area under the ROC curve (AUC) of 0.73. Various pathomic feature subsets resulted in models with AUCs in the range of 0.67 and 0.87, with the best results associated with the graph-based and wavelet features. The selected features among all subsets of the pathomic and clinicopathologic features included four wavelet and three graph-based features and no clinical features. The predictive model developed with these features outperformed the other models, with an AUC of 0.90, a sensitivity of 85% and a specificity of 82% on the independent test set. The results demonstrated the potential of quantitative digital histopathology features integrated with ML methods in predicting BC response to NAC. This study is a step forward towards precision oncology for BC patients to potentially guide future therapies.

Effect of Supine vs Prone Breast Radiotherapy on Acute Toxic Effects of the Skin Among Women With Large Breast Size: A Randomized Clinical Trial.

Importance: Women with large breast size treated with adjuvant breast radiotherapy (RT) have a high rate of acute toxic effects of the skin. Breast RT in the prone position is one strategy that may decrease these toxic effects. Objective: To determine if breast RT in the prone position reduces acute toxic effects of the skin when compared with treatment in the supine position. Design, Setting, and Participants: This phase 3, multicenter, single-blind randomized clinical trial accrued patients from 5 centers across Canada from April 2013 to March 2018 to compare acute toxic effects of breast RT for women with large breast size (bra band ≥40 in and/or ≥D cup) in the prone vs supine positions. A total of 378 patients were referred for adjuvant RT and underwent randomization. Seven patients randomized to supine position were excluded (5 declined treatment and 2 withdrew consent), and 14 patients randomized to prone position were excluded (4 declined treatment, 3 had unacceptable cardiac dose, and 7 were unable to tolerate being prone). Data were analyzed from April 2019 through September 2020. Interventions: Patients were randomized to RT in the supine or prone position. From April 2013 until June 2016, all patients (n = 167) received 50 Gy in 25 fractions (extended fractionation) with or without boost (range, 10-16 Gy). After trial amendment in June 2016, the majority of patients (177 of 190 [93.2%]) received the hypofractionation regimen of 42.5 Gy in 16 fractions. Main Outcomes and Measures: Main outcome was moist desquamation (desquamation). Results: Of the 357 women (mean [SD] age, 61 [9.9] years) included in the analysis, 182 (51.0%) were treated in the supine position and 175 (49.0%) in prone. There was statistically significantly more desquamation in patients treated in the supine position compared with prone (72 of 182 [39.6%] patients vs 47 of 175 [26.9%] patients; OR, 1.78; 95% CI, 1.24-2.56; P = .002), which was confirmed on multivariable analysis (OR, 1.99; 95% CI, 1.48-2.66; P < .001), along with other independent factors: use of boost (OR, 2.71; 95% CI, 1.95-3.77; P < .001), extended fractionation (OR, 2.85; 95% CI, 1.41-5.79; P = .004), and bra size (OR, 2.56; 95% CI, 1.50-4.37; P < .001). Conclusions and Relevance: This randomized clinical trial confirms that treatment in the prone position decreases desquamation in women with large breast size receiving adjuvant RT. It also shows increased toxic effects using an RT boost and conventional fractionation. Trial Registration: ClinicalTrials.gov Identifier: NCT01815476.

A Canadian national guideline on the neoadjuvant treatment of invasive breast cancer, including patient assessment, systemic therapy, and local management principles.

PURPOSE: The neoadjuvant treatment of breast cancer (NABC) is a rapidly changing area that benefits from guidelines integrating evidence with expert consensus to help direct practice. This can optimize patient outcomes by ensuring the appropriate use of evolving neoadjuvant principles. METHODS: An expert panel formulated evidence-based practice recommendations spanning the entire neoadjuvant breast cancer treatment journey. These were sent for practice-based consensus across Canada using the modified Delphi methodology, through a secure online survey. Final recommendations were graded using the GRADE criteria for guidelines. The evidence was reviewed over the course of guideline development to ensure recommendations remained aligned with current relevant data. RESULTS: Response rate to the online survey was almost 30%; representation was achieved from various medical specialties from both community and academic centres in various Canadian provinces. Two rounds of consensus were required to achieve 80% or higher consensus on 59 final statements. Five additional statements were added to reflect updated evidence but not sent for consensus. CONCLUSIONS: Key highlights of this comprehensive Canadian guideline on NABC include the use of neoadjuvant therapy for early stage triple negative and HER2 positive breast cancer, with subsequent adjuvant treatments for patients with residual disease. The use of molecular signatures, other targeted adjuvant therapies, and optimal response-based local regional management remain actively evolving areas. Many statements had evolving or limited data but still achieved high consensus, demonstrating the utility of such a guideline in helping to unify practice while further evidence evolves in this important area of breast cancer management.

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade.

BACKGROUND: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. METHODS: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. RESULTS: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. CONCLUSION: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.

Treatment Patterns and Outcomes of Women with Symptomatic and Asymptomatic Breast Cancer Brain Metastases: A Single-Center Retrospective Study.

BACKGROUND: Breast cancer is the most common cancer among women worldwide and the second leading cause of brain metastases (BrM). We assessed the treatment patterns and outcomes of women treated for breast cancer BrM at our institution in the modern era of stereotactic radiosurgery (SRS). MATERIALS AND METHODS: We conducted a retrospective analysis of women (≥18 years of age) with metastatic breast cancer who were treated with surgery, whole brain radiotherapy (WBRT), or SRS to the brain at the Sunnybrook Odette Cancer Centre, Toronto, Canada, between 2008 and 2018. Patients with a history of other malignancies and those with an uncertain date of diagnosis of BrM were excluded. Descriptive statistics were generated and survival analyses were performed with subgroup analyses by breast cancer subtype. RESULTS: Among 683 eligible patients, 153 (22.4%) had triple-negative breast cancer, 188 (27.5%) had HER2+, 246 (36.0%) had hormone receptor (HR)+/HER2-, and 61 (13.3%) had breast cancer of an unknown subtype. The majority of patients received first-line WBRT (n = 459, 67.2%) or SRS (n = 126, 18.4%). The median brain-specific progression-free survival and median overall survival (OS) were 4.1 months (interquartile range [IQR] 1.0-9.6 months) and 5.1 months (IQR 2.0-11.7 months) in the overall patent population, respectively. Age >60 years, presence of neurological symptoms at BrM diagnosis, first-line WBRT, and HER2- subtype were independently prognostic for shorter OS. CONCLUSION: Despite the use of SRS, outcomes among patients with breast cancer BrM remain poor. Strategies for early detection of BrM and central nervous system-active systemic therapies warrant further investigation. IMPLICATIONS FOR PRACTICE: Although triple-negative breast cancer and HER2+ breast cancer have a predilection for metastasis to the central nervous system (CNS), patients with hormone receptor-positive/HER2- breast cancer represent a high proportion of patients with breast cancer brain metastases (BrM). Hence, clinical trials should include patients with BrM and evaluate CNS-specific activity of novel systemic therapies when feasible, irrespective of breast cancer subtype. In addition, given that symptomatic BrM are associated with shorter survival, this study suggests that screening programs for the early detection and treatment of breast cancer BrM warrant further investigation in an era of minimally toxic stereotactic radiosurgery.