GRAND(ER) ROUNDS: Expanding the universe of topics and speakers in a pathology department seminar series.

This article reports on a unique pathology department seminar series that included talks on social science and humanities topics in addition to traditional biomedical subjects. The goals, achievements, and potential of such a series are addressed via review of selected speakers and topics.

Rare immune-related adverse events in a patient with metastatic melanoma: a case report highlighting sarcoidosis-like reactions triggered by immune-checkpoint inhibitors.

Pembrolizumab and ipilimumab/nivolumab (ipi/nivo) combination are FDA-approved immune checkpoint inhibitor (ICI) therapies for metastatic melanoma. ICIs could result in various inflammation responses known as immune-related adverse events (IRAEs). We report a patient with metastatic melanoma who developed multiple IRAEs including sarcoidosis-like reaction (SLR), diabetic ketoacidosis (DKA), and worsening hypothyroidism on ICIs. A 71-year-old man with stage IIIC melanoma and lymph node metastasis began adjuvant therapy with pembrolizumab in May 2021. A surveillance positron emission tomography-computed tomography (PET-CT) scan four months later showed diffuse nodal uptake indicating potential metastases although the patient remained asymptomatic. His treatment was temporarily switched to ipi/nivo before biopsy was obtained for definitive diagnosis, which revealed non-caseating granulomas consistent with SLR. After resuming pembrolizumab, he developed DKA and worsening hypothyroidism in November 2021, both of which were attributed to IRAEs. His surveillance PET scan in March 2022 again revealed new hypermetabolic activity in several bones, subcutaneous tissue, and the left inguinal lymph node. Left inguinal node biopsy showed disease recurrence, while biopsies of hypermetabolic subcutaneous nodules and bone demonstrated non-caseating granulomas. Our case described a patient on ICIs who developed several IRAEs. SLR is often asymptomatic but remains a diagnostic challenge due to its indistinguishable appearance on imaging studies compared to metastasis. Better understanding of IRAEs and improved surveillance strategies are needed for optimal patient outcomes.

Does ROSE matter? Evaluation of final diagnostic, PD-L1 immunohistochemistry, and molecular testing yields of CT-guided lung biopsies performed before and after the onset of the COVID-19 pandemic.

INTRODUCTION: The onset of the COVID-19 pandemic, with urgent implementation of safety protocols limiting the number of on-site personnel, essentially terminated the use of rapid on-site evaluation (ROSE) for computed tomography (CT)--guided lung biopsies at our institution. The diminished use of ROSE during the pandemic prompted us to reevaluate the potential value of ROSE for CT-guided lung biopsies. MATERIALS AND METHODS: We retrospectively identified all CT-guided lung biopsies from 2017 to 2022. Associations between the use of ROSE, adequate diagnostic and ancillary testing (programmed death-ligand 1 immunohistochemistry and next-generation sequencing) outcomes, and other factors such as the number of passes performed and lesion size, were evaluated. RESULTS: Nine hundred twelve CT-guided lung biopsies were performed from 2017 to 2022; 171 (19%) utilized ROSE. The use of ROSE had been steadily decreasing prior to the pandemic but was essentially eliminated with the onset of the pandemic. By univariable analysis, the employment of ROSE was more likely to be associated with an adequate final diagnosis (odds ratio = 2.14, 95% confidence interval: [1.24-3.70], P = 0.006) and successful molecular testing (odds ratio = 2.16, 95% confidence interval: [1.11-4.21], P = 0.024). However, those associations were not present in multivariable analyses that incorporated the number of passes performed or lesion size. There were no differences in diagnostic adequacy or ancillary testing yields when comparing the periods 2017-2019 and 2020-2022, despite declining use of ROSE. CONCLUSIONS: If ROSE is not requested for CT-guided lung biopsies, proceduralists should err on the side of performing more, rather than fewer, passes, particularly for smaller lesions.

Sensitive mutation detection in heterogeneous cancer specimens by massively parallel picoliter reactor sequencing.

The sensitivity of conventional DNA sequencing in tumor biopsies is limited by stromal contamination and by genetic heterogeneity within the cancer. Here, we show that microreactor-based pyrosequencing can detect rare cancer-associated sequence variations by independent and parallel sampling of multiple representatives of a given DNA fragment. This technology can thereby facilitate accurate molecular diagnosis of heterogeneous cancer specimens and enable patient selection for targeted cancer therapies.

Use of molecular testing results to analyze the overuse of atypia of undetermined significance in thyroid cytology.

INTRODUCTION: The suggested atypia of undetermined significance (AUS) rate for thyroid fine-needle aspiration biopsies is 10% or less. Prompted by a high institutional AUS rate, we examined using molecular testing results (MTR) as a potential quality metric tool to reduce the AUS rate. We correlated MTR with AUS cytologic findings, surgical pathology follow-up, and individual pathologist AUS rates. MATERIALS AND METHODS: Demographic data, cytologic diagnoses, MTR, and surgical pathology diagnoses were retrospectively obtained. MTR were classified as either positive or negative. AUS rates and MTR proportions were compared among pathologists. The cytomorphologic features of 143 AUS cases were assessed and correlated with MTR. RESULTS: Between 2017 and 2022, 710 of 3247 thyroid fine-needle aspirations were classified as AUS, with a yearly average rate of 22% (range = 19%-26%). AUS cases included: 331 (47%) with architectural atypia; 204 (29%) with oncocytic (Hürthle cell) atypia; 99 (14%) with combined architectural and cytologic atypia; and 76 (10%) with isolated cytologic atypia. Most AUS cases with molecular testing had negative MTR (360/492, 73%). AUS with cytologic atypia had higher positive MTR risk (logarithm of odds ratio = 1.27, 95% credible interval [0.5-2.04], P = 0.001). The average positive MTR rate by pathologist was 21.5% (range 0%-35%); higher positive MTR rates had better correlation with subsequent neoplastic/malignant histologic diagnoses. The MTR sensitivity for malignant disease was 89% and the negative predictive value was 91%. CONCLUSIONS: MTR analysis reveals the importance of cytologic atypia as a determinant of malignancy risk in AUS cases. Periodic analysis of MTR data alongside individual pathologist AUS rates can help refine diagnostic criteria and potentially reduce AUS overuse.

Uncovering additional predictors of urothelial carcinoma from voided urothelial cell clusters through a deep learning-based image preprocessing technique.

BACKGROUND: Urine cytology is commonly used as a screening test for high-grade urothelial carcinoma for patients with risk factors or hematuria and is an essential step in longitudinal monitoring of patients with previous bladder cancer history. However, the semisubjective nature of current reporting systems for urine cytology (e.g., The Paris System) can hamper reproducibility. For instance, the incorporation of urothelial cell clusters into the classification schema is still an item of debate and perplexity among expert cytopathologists because several previous works have disputed their diagnostic relevance. METHODS: In this work, an automated preprocessing tool for urothelial cell cluster assessment was developed that divides urothelial cell clusters into meaningful components for downstream assessment (ie, population-based studies, workflow automation). RESULTS: In this work, an automated preprocessing tool for urothelial cell cluster assessment was developed that divides urothelial cell clusters into meaningful components for downstream assessment (ie, population-based studies, workflow automation). Results indicate that cell cluster atypia (i.e., defined by whether the cell cluster harbored multiple atypical cells, thresholded by a minimum number of cells), cell border overlap and smoothness, and total number of clusters are important markers of specimen atypia when considering assessment of urothelial cell clusters. CONCLUSIONS: Markers established through techniques to separate cell clusters may have wider applicability for the design and implementation of machine learning approaches for urine cytology assessment.

Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.

BACKGROUND: Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting. METHODS: In this study, the authors report on the development and large-scale validation of a deep-learning tool, AutoParis-X, which can facilitate rapid, semiautonomous examination of urine cytology specimens. RESULTS: The results of this large-scale, retrospective validation study indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide variety of cell-related and cluster-related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm ratio for cells in these clusters. CONCLUSIONS: The authors developed a publicly available, open-source, interactive web application that features a simple, easy-to-use display for examining urine cytology whole-slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head-to-head clinical trials.

Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology.

BACKGROUND: Urine cytology is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological examinations for assessment and early detection of recurrence, beyond identifying a positive finding that requires more invasive methods to confirm recurrence and decide on therapeutic options. Because screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists, and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk-stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer. METHODS: In this study, a computational machine learning tool, AutoParis-X, was leveraged to extract imaging features from urine cytology examinations longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk. RESULTS: Results indicate that imaging predictors extracted using AutoParis-X can predict recurrence as well or better than traditional cytological/histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence. CONCLUSIONS: Further research will clarify how computational methods can be effectively used in high-volume screening programs to improve recurrence detection and complement traditional modes of assessment.