Staying Online in Uncertain Times: A Nationwide Canadian Survey of Pathology Resident Uses of and Adaptations to Online Learning During COVID-19.

CONTEXT.­: Because of restrictions as a result of the COVID-19 pandemic, medical educators rapidly transitioned to an online curriculum for pathology resident education. The benefits and challenges of the shift to online learning, as well as strategies to maximize learning, are yet to be fully elucidated. OBJECTIVE.­: To assess learner perception and satisfaction with the move to online learning. Understanding the benefits of online learning will allow future curricular changes to most effectively incorporate online learning. Understanding the common challenges will allow our current learning strategies to rapidly adapt and ideally mitigate these challenges as online learning is incorporated into medical education. DESIGN.­: This was a survey-based study distributed by email to pathology residents nationwide in Canada in anatomic pathology, general pathology, neuropathology, and hematopathology. Thirty residents participated, from anatomic pathology (n = 23; 76%), from general pathology (n = 5; 16%), and 1 participant each from hematopathology and neuropathology. RESULTS.­: All participants indicated that their program had transitioned to online learning at least in part. The majority of participants (n = 16; 53%) did not feel their pathology education was negatively affected by the transition to online learning; however, a significant minority (n = 6; 20%) felt their education had been negatively affected. Convenience and less intimidation were rated as benefits of online learning. Negative effects included technical issues and decreased engagement; we identified a number of strategies used by programs and pathology residents to mitigate these negative effects. CONCLUSIONS.­: Our survey points to a need to use adaptations and best-practice recommendations to maximize the benefits of online learning moving forward.

A Case of Li-Fraumeni Associated Thymoma.

Thymomas are among the most common cancers of the anterior mediastinum. They rarely occur in patients with Li-Fraumeni syndrome (LFS), a hereditary syndrome that predisposes individuals to cancer and is characterized by mutations in the tumor suppressor encoding gene TP53. Here we describe a case of primary thymoma in a woman diagnosed with LFS. We cover the initial presentation and diagnosis, radiological findings, histopathological examination, and management of thymoma. In addition, we review p53 physiology and LFS pathophysiology to explore how TP53 expression might differ between the majority of thymomas and in thymomas associated with LFS. This altered pathophysiology may affect management and prognosis due to emerging evidence of increased resistance to conventional treatment, which suggests a need for close monitoring and consideration of novel treatment strategies such as programmed death-ligand 1 (PD-L1) inhibitors.

A Survey on Changes to the Canadian Anatomical Pathology Certification Examination Due to Coronavirus Disease 2019 and Implications for Competency-Based Medical Education.

The coronavirus disease 2019 pandemic resulted in a dramatic change in the Royal College of Physicians and Surgeons of Canada assessment process through elimination of the oral and practical components of the 2020 Anatomical Pathology examination. Our study sought to determine stakeholder opinions and experiences on these changes in the context of the 2019 implementation of competency-based medical education. Surveys were designed for residents and practicing pathologists. In total, 57 residents (estimated response rate 29%) and 185 pathologists (estimated response rate 19%) participated across Canada; 67% of pathologists disagreed with the 2020 Royal College examination changes, compared with 30% for residents (P = <.00001). When asked whether the Royal College examination should be eliminated, 95% of pathologists indicated they would be against this, compared to only 34% of residents (P = <.00001). Perceptions on changes to and importance of different components of assessment in competency-based medical education were similar between pathologists and residents, with participants perceiving assessment practices to have changed fairly little since its implementation, with the exception of more frequent feedback. Analysis of narrative comments identified several common themes around assessment, including the need for objectivity and standardization and the problem of failure-to-fail. However, residents identified numerous elements of their performance that can be assessed only through longitudinal evaluation. Pathologists, on the other hand, tended to view these aspects of performance as laden with bias. Our results will hopefully help guide future innovation in assessment by characterizing different stakeholder perspectives on key issues in medical education.

Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells.

AIMS: The objective of this study was to develop and validate an open-source digital pathology tool, QuPath, to automatically quantify CD138-positive bone marrow plasma cells (BMPCs). METHODS: We analysed CD138-scanned slides in QuPath. In the initial training phase, manual positive and negative cell counts were performed in representative areas of 10 bone marrow biopsies. Values from the manual counts were used to fine-tune parameters to detect BMPCs, using the positive cell detection and neural network (NN) classifier functions. In the testing phase, whole-slide images in an additional 40 cases were analysed. Output from the NN classifier was compared with two pathologist's estimates of BMPC percentage. RESULTS: The training set included manual counts ranging from 2403 to 17 287 cells per slide, with a median BMPC percentage of 13% (range: 3.1%-80.7%). In the testing phase, the quantification of plasma cells by image analysis correlated well with manual counting, particularly when restricted to BMPC percentages of <30% (Pearson's r=0.96, p<0.001). Concordance between the NN classifier and the pathologist whole-slide estimates was similarly good, with an intraclass correlation of 0.83 and a weighted kappa for the NN classifier of 0.80 with the first rater and 0.90 with the second rater. This was similar to the weighted kappa between the two human raters (0.81). CONCLUSIONS: This represents a validated digital pathology tool to assist in automatically and reliably counting BMPC percentage on CD138-stained slides with an acceptable error rate.

A Case of Synchronous Malignant and Benign Granular Cell Tumor of the Vulva.

Granular cell tumors (GCT) are rare soft tissue neoplasms, which seldom occur in the vulva. They are more commonly benign, but malignant GCT do occur. We report a case of a 50-yr-old postmenopausal woman who presented with a vulvar lesion that was diagnosed as GCT on biopsy. Imaging and clinical examination revealed an enlarged, likely positive lymph node. Pathology of the subsequently resected total deep vulvectomy specimen showed 2 histologically distinct GCTs. The larger lesion met criteria for malignancy and histologically corresponded to metastatic deposits seen in the pelvic lymph nodes. The separate smaller lesion was histologically benign. This case illustrates a malignant GCT with a synchronous, likely benign GCT both occurring in the vulva. Our case demonstrates the application of histologic criteria in the diagnosis of malignant and benign GCT with discussion on the diagnosis and treatment of this rare tumor.

Overexpression of Long Noncoding RNA HOTAIR Is a Unique Epigenetic Characteristic of Myxopapillary Ependymoma.

Ependymomas are a heterogeneous group of central nervous system tumors. Despite the recent advances, there are no specific biomarkers for ependymomas. In this study, we explored the role of homeobox (HOX) genes and long noncoding RNA (LncRNA) HOTAIR in ependymomas along the neural axis. Bioinformatics analysis was performed on publicly available gene expression data. Quantitative RT-PCR was used to determine the mRNA expression level among different groups of ependymomas. RNA in situ hybridization (ISH) with probes specific to HOTAIR was performed on tumor tissue microarray (TMA) constructed with 19 ependymomas formalin-fixed paraffin-embedded tissue. Gene expression analysis revealed higher expression of posterior HOX genes and HOTAIR in myxopapillary ependymoma (MPE), in comparison to other spinal and intracranial ependymoma. qRT-PCR confirmed the high HOXD10 expression in spinal MPEs. There was a significant upregulation of HOTAIR expression in spinal MPE and elevated HOTAIR expressions were further confirmed by RNA ISH on the TMA. Intriguingly, HOXD10 and HOTAIR expressions were not elevated in nonependymoma spinal tumors. Our collective results suggest an important role for the lncRNA HOTAIR and posterior HOX genes in the tumorigenesis of spinal MPE. HOTAIR may also serve as a potential diagnostic marker for spinal MPE.

Explicit Dialogue About the Purpose of Hospital Admission Is Essential: How Different Perspectives Affect Teamwork, Trust, and Patient Care.

PURPOSE: The authors previously found that attending physicians conceptualize hospital admission purpose according to 3 perspectives: one focused dominantly on discharge, one on monitoring and managing chronic conditions, and one on optimizing overall patient health. Given implications of varying perspectives for patient care and team collaboration, this study explored how purpose of admission is negotiated and enacted within clinical teaching teams. METHOD: Direct observations and field interviews took place in 2 internal medicine teaching units at 2 teaching hospitals in Ontario, Canada, in summer 2017. A constructivist grounded theory approach was used to inform data collection and analysis. RESULTS: The 54 participants included attendings, residents, and medical students. Management decisions were identified across 185 patients. Attendings and senior medical residents (second- and third-year residents) were each observed to enact one dominant perspective, while junior trainees (first-year residents and students) appeared less fixed in their perspectives. Teams were not observed discussing purpose of admission explicitly; however, differing perspectives were present and enacted. These differences became most noticeable when at the extremes (discharge focused vs optimization focused) or between senior medical residents and attendings. Attendings implicitly signaled and enforced their perspectives, using authority to shut down and redirect discussion. Trainees' maneuvers for enacting their perspectives ranged from direct advocacy to covert manipulation (passive avoidance/forgetting and delaying until attending changeover). CONCLUSIONS: Failing to negotiate and explicitly label perspectives on purpose of admission may lead to attendings and senior medical residents working at cross-purposes and to trainees participating in covert maneuvers, potentially affecting trust and professional identify development.

Is It Time to Be More Explicit About the Purpose of a Hospital Admission?

PURPOSE: Patient care suffers when teaching teams fail to achieve a shared understanding of problems to be addressed during a hospital admission. In academic contexts where attending physicians take turns supervising, practice variability may contribute to undermining this shared understanding. Exploring variability around what constitutes the purpose of the hospital admission was the focus of this study. METHOD: Constructivist grounded theory was used to inform data collection and analysis of this two-phase study, conducted in London and Hamilton, Ontario, Canada, in 2012. In phase 1, interviews with 24 attending physicians from 2 academic health centers were conducted. Phase 2 involved analyzing 18 audio-recorded admission case review discussions in relation to the emergent theory from phase 1. Participants for phase 2 were 7 different attendings, 7 medical students, and 5 junior residents. RESULTS: Three dominant perspectives around the purpose of an admission were identified. The most focused perspective characterized the purpose as making patients ready for discharge. By contrast, the most comprehensive perspective characterized admission as an opportunity to identify ways to improve overall patient health status. The third perspective was in-between-treating acute issues while monitoring pertinent chronic conditions. All attendings expressed a sense of discharge pressure but responded with different strategies. Attendings rarely explicitly discussed their perspectives as part of case review. CONCLUSIONS: These extremes of practice and lack of overt dialogue are concerning. Potential effects include mixed messages to trainees and missed opportunities for dialogue and debate around what can and should be achieved during a hospital admission.

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes  BCL2L1, BBC3, FGF2, FN1, and  TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.