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.

Gynecologic pathology services in low- and middle-income countries.

OBJECTIVE: The International Gynecologic Cancer Society (IGCS) offers multidisciplinary conferences to underserved communities. Mentor pathologists have become an integral part of these tumor boards, as pathology services in low-to-middle-income countries are often inadequate and disjointed. The IGCS Pathology Working Group conducted a survey to assess barriers to quality pathology services in low-to-middle-income countries and identified potential solutions. METHODS: A 69-question cross-sectional survey assessing different aspects of pathology services was sent to 15 IGCS Extension for Community Healthcare Outcomes (ECHO) training sites in Africa, Asia, Central America, and the Caribbean. Local gynecologic oncologists distributed the survey to their pathology departments for review. The responses were tabulated in Microsoft Excel. RESULTS: Responses were received from nine training sites: five sites in Africa, two in Asia, one in Central America, and one in the Caribbean. There were no pathologists with subspecialty training in gynecologic pathology. Most (7/9, 78%) surveyed sites indicated that they have limited access to online education and knowledge transfer resources. Of the eight sites that responded to the questions, 50% had an electronic medical system and 75% had a cancer registry. Synoptic reporting was used in 75% of the sites and paper-based reporting was predominant (75%). Most (6/7, 86%) laboratories performed limited immunohistochemical stains on site. None of the sites had access to molecular testing. CONCLUSIONS: Initial goals for collaboration with local pathologists to improve diagnostic pathology in low- and middle-income countries could be defining minimal gross, microscopic, and reporting pathology requirements, as well as wisely designed educational programs intended to mentor local leaders in pathology. Larger studies are warranted to confirm these observations.

Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC. METHODS: Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined. RESULTS: In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR). CONCLUSION: Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.

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.

Ductal carcinoma in situ of the breast: an update for the pathologist in the era of individualized risk assessment and tailored therapies.

Ductal carcinoma in situ (DCIS) is a neoplastic proliferation of mammary ductal epithelial cells confined to the ductal-lobular system, and a non-obligate precursor of invasive disease. While there has been a significant increase in the diagnosis of DCIS in recent years due to uptake of mammography screening, there has been little change in the rate of invasive recurrence, indicating that a large proportion of patients diagnosed with DCIS will never develop invasive disease. The main issue for clinicians is how to reliably predict the prognosis of DCIS in order to individualize patient treatment, especially as treatment ranges from surveillance only, breast-conserving surgery only, to breast-conserving surgery plus radiotherapy and/or hormonal therapy, and mastectomy with or without radiotherapy. We conducted a semi-structured literature review to address the above issues relating to "pure" DCIS. Here we discuss the pathology of DCIS, risk factors for recurrence, biomarkers and molecular signatures, and disease management. Potential mechanisms of progression from DCIS to invasive cancer and problems faced by clinicians and pathologists in diagnosing and treating this disease are also discussed. Despite the tremendous research efforts to identify accurate risk stratification predictors of invasive recurrence and response to radiotherapy and endocrine therapy, to date there is no simple, well-validated marker or group of variables for risk estimation, particularly in the setting of adjuvant treatment after breast-conserving surgery. Thus, the standard of care to date remains breast-conserving surgery plus radiotherapy, with or without hormonal therapy. Emerging tools, such as pathologic or biologic markers, may soon change such practice. Our review also includes recent advances towards innovative treatment strategies, including targeted therapies, immune modulators, and vaccines.

Endometriosis-associated Ovarian Cancer is a Subset With a More Favorable Outcome and Distinct Clinical-pathologic Characteristics.

There is a controversy about whether endometriosis-associated ovarian cancer (EAOC) might represent a different entity from the corresponding ovarian cancer occurring de novo, in the absence of endometriosis. This study investigated the clinical-pathologic characteristics and outcome of EAOC compared with other ovarian carcinomas that are not associated with endometriosis (non-EAOC) in a large cohort. Seven hundred two patients meeting the inclusion criteria were further subclassified as group I when patients had ovarian carcinoma associated with or arising within endometriosis (EAOC) and group II when patients had non-EAOC. Age, gross features, histologic type, International Federation of Gynecology and Obstetrics stage, and disease-free survival (DFS) were compared between the groups. One hundred sixty-eight (23.9%) patients had EAOC, whereas 534 (76.1%) patients had non-EAOC. EAOCs were mostly endometrioid and clear cell type. Patients with EAOC were younger, present early, and had a lower rate of recurrence when compared with patients with non-EAOC, P<0.001. Patients with EAOC had longer DFS time, 51.9 mo (95% confidence interval, 44.9-58.8) versus 30.5 mo (95% confidence interval, 27.7-33.3) in non-EAOC patients. The 5 yr Kaplan-Meier estimate of DFS rate was 70% in 166 patients of group I and was 39.3% in 532 patients of group II, P<0.001. On multivariate analysis, International Federation of Gynecology and Obstetrics staging, histologic type, and treatment were the only significant factors affecting the hazards of recurrence. Patients with tumors associated with endometriosis are usually, younger, present early, have lower rate of recurrence, longer DFS, and their tumors are of lower grade and are more likely endometrioid or clear cell carcinoma.

Atypical ductal hyperplasia on core needle biopsy: Development of a predictive model stratifying carcinoma upgrade risk on excision.

BACKGROUND: Although the rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable, current standard treatment consists of surgical excision (SE) for all ADH CNB diagnoses. Our objective was to identify features of ADH on CNB that may stratify carcinoma upgrade risk on SE. METHODS: We retrospectively analyzed cases diagnosed as ADH on CNB. An independent slide review and detailed analysis of radiological and clinical data was performed. Statistical analyses were used to identify predictors for upgrade. Using variables predictive of upgrade, a model to stratify the probability of upgrade of ADH diagnosed on CNB was constructed. RESULTS: We identified 124 ADH cases with subsequent SE. Of these, 62 cases (50%) were upgraded to carcinoma. Features predictive of upgrade were as follows: diagnosis of "At least ADH", percentage of cores involved by ADH, radiologic lesion size, presence of ipsilateral carcinoma, and patient age. A 4-tiered predictive model using percentage of cores involved by ADH, histologic extent of ADH, radiologic lesion size, and patient age was constructed. This predictive model has a fair accuracy, with an area under the ROC curve of 0.76. CONCLUSION: We have identified several predictors of carcinoma upgrade for ADH diagnosed on CNB. Our predictive model may be used to stratify the risk of carcinoma upgrade on SE.

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.

Fibroepithelial lesions of the breast: a comprehensive morphological and outcome analysis of a large series.

Mammary fibroepithelial lesions encompass a wide spectrum of tumors ranging from an indolent fibroadenoma to potentially fatal malignant phyllodes tumor. The criteria used for their classification based on morphological assessment are often challenging to apply and there is no consensus as to what constitutes an adequate resection margin. We studied a retrospective cohort of 213 fibroepithelial lesions in 178 patients (80 fibroadenomas with unusual features and 133 phyllodes tumors: 63 benign, 41 borderline, and 29 malignant) in order to describe the spectrum of changes within each group, with special emphasis on margin evaluation. Outcome data were available for 153 fibroepithelial lesions in 139 patients (median 56 months, range 3-249 months). Positive final margin (tumor transected), age < 50 years and a predominantly myxoid stroma were statistically significant predictors of local recurrence, while age > 50, stromal overgrowth, diffuse marked atypia, necrosis and mitotic index of ≥ 10 per 10 HPF were predictive of distant metastases. Tumors with satellite/bulging nodules were at a significantly higher risk to have a final positive resection margin. Our findings highlight important aspects of the interpretation and reporting of fibroepithelial lesions: the amount of myxoid stroma and the presence of satellite nodules are clinically relevant and should be routinely assessed and reported; infiltrative border might not be a prerequisite for the diagnosis of malignant phyllodes tumor, while the presence of tumor necrosis, massive stromal overgrowth or mitotic index of ≥ 25 per 10 HPF is diagnostic of malignant phyllodes tumor. On the other hand, increased mitotic index outside of the range of the World Health Organization guidelines in the absence of other worrisome features should be treated with caution, as it can be found in benign tumors.

The role of quantitative estrogen receptor status in predicting tumor response at surgery in breast cancer patients treated with neoadjuvant chemotherapy.

PURPOSE: Estrogen receptor (ER) negative (-) breast cancer (BC) patients have better tumor response rates than ER-positive (+) patients after neoadjuvant chemotherapy (NCT). We conducted a retrospective review using the institutional database "Biomatrix" to assess the value of quantitative ER status in predicting tumor response at surgery and to identify potential predictors of survival outcomes. METHODS: Univariate followed by multivariable regression analyses were conducted to assess the association between quantitative ER and tumor response assessed as tumor size reduction and pathologic complete response (pCR). Predictors of recurrence-free survival (RFS) were identified using a cox proportional hazards model (CPH). A log-rank test was used to compare RFS between groups if a significant predictor was identified. RESULTS: 304 patients were included with a median follow-up of 43.3 months (Q1-Q3 28.7-61.1) and a mean age of 49.7 years (SD 10.9). Quantitative ER was inversely associated with tumor size reduction and pCR (OR 0.99, 95% CI 0.99-1.00, p = 0.027 and 0.98 95% CI 0.97-0.99, p < 0.0001, respectively). A cut-off of 60 and 80% predicted best the association with tumor size reduction and pCR, respectively. pCR was shown to be an independent predictor of RFS (HR 0.17, 95% CI 0.07-0.43, p = 0.0002) in all patients. At 5 years, 93% of patients with pCR and 72% of patients with residual tumor were recurrence-free, respectively (p = 0.0012). CONCLUSIONS: Quantitative ER status is inversely associated with tumor response in BC patients treated with NCT. A cut-off of 60 and 80% predicts best the association with tumor size reduction and pCR, respectively. Therefore, patients with an ER status higher than the cut-off might benefit from a neoadjuvant endocrine therapy approach. Patients with pCR had better survival outcomes independently of their tumor phenotype. Further prospective studies are needed to validate the clinical utility of quantitative ER as a predictive marker of tumor response.

Histological Grade and Immunohistochemical Biomarkers of Breast Cancer: Correlation to Ultrasound Features.

OBJECTIVES: The purpose of this study is to correlate various features of breast cancers on ultrasound to their histological grade and immunohistochemical biomarkers. METHODS: Seventy-three patients with 77 invasive breast cancers, diagnosed between August 2011 and December 2014, were included in this prospective analysis. Margin, posterior features, shape, and vascularity were determined from ultrasound and classified according to the Breast Imaging Reporting and Data System lexicon. Histological grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status (positive [+] or negative [-]) were determined from surgical pathology reports. The cancers were categorized into low grade (grades 1 or 2) and high grade (grade 3). Correlation of ultrasound features of the cancers to their histological grade and receptor status was performed. RESULTS: There were 47 low-grade and 29 high-grade cancers. There was a significant difference in margin and posterior features between the low and high grade, ER + and ER-, and PR + and PR- (all P < .05), but not between HER2 + and HER2- cancers (both P > .05). There was no significant difference in shape and vascularity among the different subtypes (all P > .05). Spiculated margin was significantly associated with low-grade, ER+, PR + status; angular margin with high grade; microlobulated margin with ER- status; shadowing with PR + status; and enhancement with high grade, ER- status (all P < .05, all odds ratios ≥ 3.94). CONCLUSIONS: There was significant association of margin and posterior features of breast cancers with their histological grade and receptor status.

Immunophenotypic Analysis in Early Müllerian Serous Carcinogenesis.

Studies on the immunophenotypes of early forms of serous carcinoma arising from female genital tract are limited. We aimed to examine p53, p16(Ink4a), estrogen receptor (ER), progesterone receptor (PR), ERBB2, WT1, and Ki-67 protein expression in endometrial intraepithelial carcinoma (n=29), serous tubal intraepithelial lesion (n=4) and carcinoma (STIC, n=10), and the putative precursor p53 signature (n=11). Among endometrial intraepithelial carcinoma, 80% demonstrated p53 overexpression and 10% were consistent with a null phenotype. p16(Ink4a) immunostaining were observed in all endometrial intraepithelial carcinoma cases. ER, PR, ERBB2, and WT1 were positive in 54%, 25%, 11%, and 18% of cases, respectively. STIC cases demonstrated p53 overexpression and null phenotype in 90% and 10%, respectively. All STIC cases were p16(Ink4a) and WT1 positive, whereas ER and PR were positive in 70% and 20%, respectively. All STICs were negative for ERBB2. Among serous tubal intraepithelial lesion cases, 75% demonstrated p53 overexpression and 25% a null phenotype. p53 was positive in all 11 p53 signature cases, whereas p16(Ink4a) was universally negative. Finally, ER and PR were positive in 100% and 73% of p53 signature cases, respectively. These results suggest that p16(Ink4a) has a role in early Müllerian serous carcinogenesis but is absent in the earliest noncommitted lesion. p16(Ink4a) immunohistochemistry can be used as an adjunct confirmatory tool in p53-null cases with limited surface area.

Cystic neutrophilic granulomatous mastitis: sensitivity and specificity of 16s rRNA and Sanger sequencing for Corynebacterium spp.

AIMS: Cystic neutrophilic granulomatous mastitis (CNGM) is a subtype of granulomatous mastitis (GM) associated with Corynebacterium spp infection. We aimed to analyse the prevalence of Corynebacteria in CNGM and non-CNGM cases. METHODS: Breast specimens diagnosed as granulomatous inflammation between 2010 and 2020 were reviewed to identify a CNGM cohort and a non-CNGM cohort. Polymerase chain reaction-based identification of Corynebacteria by 16S ribosomal RNA (16S rRNA) primers, followed by confirmatory Sanger sequencing (SS), was performed on all cases. Clinical, radiological and microbiology data were retrieved from the electronic patient records. RESULTS: Twenty-eight CNGM cases and 19 non-CNGM cases were identified. Compared with the non-CNGM cohort, patients in the CNGM cohort were more likely to be multiparous (p=0.01), breast feeding (p=0.01) and presenting with a larger breast mass (p<0.01), spontaneous drainage (p=0.05) and skin irritation (p<0.01). No significant difference in the prevalence of Corynebacteria between the cohorts (7% vs 11%, p=0.68) by microbiological culture was identified. Compared with microbiology culture, the sensitivity and specificity of each Corynebacterial detection method were 50% and 81% for Gram stain, and 25% and 100% for 16S rRNA combined with SS. Regardless of the diagnosis, patients positive for Corynebacteria were more likely to have a persistent disease (p<0.01). CONCLUSION: CNGM presents as a large symptomatic breast mass in multiparous breastfeeding women. The importance of adequate sampling and repeated microbiology culture in conjunction with sequencing on all GM cases with persistent disease is paramount.

Burnout Interventions for Resident Physicians: A Scoping Review of Their Content, Format, and Effectiveness.

CONTEXT.­: Physicians face a high rate of burnout, especially during the residency training period when trainees often experience a rapid increase in professional responsibilities and expectations. Effective burnout prevention programs for resident physicians are needed to address this significant issue. OBJECTIVE.­: To examine the content, format, and effectiveness of resident burnout interventions published in the last 10 years. DESIGN.­: The literature search was conducted on the MEDLINE database with the following keywords: internship, residency, health promotion, wellness, occupational stress, burnout, program evaluation, and program. Only studies published in English between 2010 and 2020 were included. Exclusion criteria were studies on interventions related to the COVID-19 pandemic, studies on duty hour restrictions, and studies without assessment of resident well-being postintervention. RESULTS.­: Thirty studies were included, with 2 randomized controlled trials, 3 case-control studies, 20 pretest and posttest studies, and 5 case reports. Of the 23 studies that used a validated well-being assessment tool, 10 reported improvements postintervention. These effective burnout interventions were longitudinal and included wellness training (7 of 10), physical activities (4 of 10), healthy dietary habits (2 of 10), social activities (1 of 10), formal mentorship programs (1 of 10), and health checkups (1 of 10). Combinations of burnout interventions, low numbers of program participants with high dropout rates, lack of a control group, and lack of standardized well-being assessment are the limitations identified. CONCLUSIONS.­: Longitudinal wellness training and other interventions appear effective in reducing resident burnout. However, the validity and generalizability of the results are limited by the study designs.

Resident Depression and Burnout During the COVID-19 Pandemic: A Survey of Canadian Laboratory Medicine Trainees.

CONTEXT.­: Resident physicians face a higher rate of burnout and depression than the general population. Few studies have examined burnout and depression in Canadian laboratory medicine residents, and none during the COVID-19 pandemic. OBJECTIVE.­: To identify the prevalence of burnout and depression, contributing factors, and the impact of COVID-19 in this population. DESIGN.­: An electronic survey was distributed to Canadian laboratory medicine residents. Burnout was assessed using the Oldenburg Burnout Inventory. Depression was assessed using the Patient Health Questionnaire 9. RESULTS.­: Seventy-nine responses were collected. The prevalence of burnout was 63% (50 of 79). The prevalence of depression was 47% (37 of 79). Modifiable factors significantly associated with burnout included career dissatisfaction, below average academic performance, lack of time off for illness, stress related to finances, lack of a peer or staff physician mentor, and a high level of fatigue. Modifiable factors significantly associated with depression further included a lack of access to wellness resources, lack of time off for leisure, and fewer hours of sleep. Fifty-five percent (41 of 74) of participants reported direct impacts to their personal circumstances by the COVID-19 pandemic. CONCLUSIONS.­: Burnout and depression are significant issues affecting Canadian laboratory medicine residents. As the COVID-19 pandemic continues, we recommend the institution of flexible work arrangements, protected time off for illness and leisure, ongoing evaluation of career satisfaction, formal and informal wellness programming with trainee input, formal mentorship programming, and a financial literacy curriculum as measures to improve trainee wellness.

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.

Ki67 in Breast Cancer Assay: An Ad Hoc Testing Recommendation from the Canadian Association of Pathologists Task Force.

Ki67, a marker of cellular proliferation, is commonly assessed in surgical pathology laboratories. In breast cancer, Ki67 is an established prognostic factor with higher levels associated with worse long-term survival. However, Ki67 IHC is considered of limited clinical use in breast cancer management largely due to issues related to standardization and reproducibility of scoring across laboratories. Recently, both the American Food and Drug Administration (FDA) and Health Canada have approved the use of abemaciclib (CDK4/6 inhibitor) for patients with HR+/HER2: high-risk early breast cancers in the adjuvant setting. Health Canada and the FDA have included a Ki67 proliferation index of ≥20% in the drug monograph. The approval was based on the results from monarchE, a phase III clinical trial in early-stage chemotherapy-naïve, HR+, HER2 negative patients at high risk of early recurrence. The study has shown significant improvement in invasive disease-free survival (IDFS) with abemaciclib when combined with adjuvant endocrine therapy at two years. Therefore, there is an urgent need by the breast pathology and medical oncology community in Canada to establish national guideline recommendations for Ki67 testing as a predictive marker in the context of abemaciclib therapy consideration. The following recommendations are based on previous IKWG publications, available guidance from the monarchE trial and expert opinions. The current recommendations are by no means final or comprehensive, and their goal is to focus on its role in the selection of patients for abemaciclib therapy. The aim of this document is to guide Canadian pathologists on how to test and report Ki67 in invasive breast cancer. Testing should be performed upon a medical oncologist's request only. Testing must be performed on treatment-naïve tumor tissue. Testing on the core biopsy is preferred; however, a well-fixed resection specimen is an acceptable alternative. Adhering to ASCO/CAP fixation guidelines for breast biomarkers is advised. Readout training is strongly recommended. Visual counting methods, other than eyeballing, should be used, with global rather than hot spot assessment preferred. Counting 100 cells in at least four areas of the tumor is recommended. The Ki67 scoring app developed to assist pathologists with scoring Ki67 proposed by the IKWG, available for free download, may be used. Automated image analysis is very promising, and laboratories with such technology are encouraged to use it as an adjunct to visual counting. A score of <5 or >30 is more robust. The task force recommends that the results are best expressed as a continuous variable. The appropriate antibody clone and staining protocols to be used may take time to address. For the time being, the task force recommends having tonsils/+pancreas on-slide control and enrollment in at least one national/international EQA program. Analytical validation remains a pending goal. Until the data become available, using local ki67 protocols is acceptable. The task force recommends participation in upcoming calibration and technical validation initiatives.

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.

Prevalence of loss of expression of DNA mismatch repair proteins in primary epithelial ovarian tumors.

Although different histologic subtypes of epithelial ovarian tumors have long been recognized, their molecular abnormalities have not been fully defined. We examined the prevalence of DNA mismatch repair (MMR) protein loss in these tumors. Tissue microarrays (TMA) of suspected ovarian carcinomas were stained for hMLH1, hMSH2, hMSH6, and hPMS2 and scored separately by 2 groups of investigators. Loss of staining (negative) or discrepant staining results on TMA were verified on whole-section slides. Intact (positive) staining results were also verified for an additional 25 randomly selected cases. Clinical data for cases demonstrating MMR protein loss were collected. A second set of TMA composed purely of mucinous tumors was also stained for antibodies to MMR proteins and scored by 1 group of investigators. TMA was an effective method for screening a large number of ovarian tumors for MMR protein expression, with a sensitivity of 100% for all 4 MMR proteins, and a specificity of 22.2%-53.8% for different MMR proteins. Of the primary epithelial tumors of the ovary, loss of expression of MMR proteins was significantly more common in the endometriosis-associated carcinomas (7/69; 10.1%) than in high-grade serous carcinomas (2/182; 1.1%): P=0.0021. The former group also showed more frequent loss of MMR proteins compared with mucinous intestinal-type carcinomas (0/32; P=0.0940). Cases within the group of endometriosis-associated carcinomas were endometrioid (2/29 cases), clear cell (1/27 cases), undifferentiated (1/8 cases), and mixed carcinomas with an endometrioid, clear cell, and/or undifferentiated component (3/5 cases). No loss of MMR protein expression was identified in epithelial tumors of other histologic subtypes. Our study demonstrated the loss of MMR protein expression in 10.1% of endometriosis-associated ovarian carcinomas. These results raise the possibility of selective screening for Lynch syndrome in patients with these types of ovarian carcinoma.

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.