Cytologic features of differentiated high-grade thyroid carcinoma: A multi-institutional study of 40 cases.

BACKGROUND: Differentiated high-grade thyroid carcinoma (DHGTC) is recently recognized by the World Health Organization (WHO) as a subgroup of thyroid carcinomas with high-grade features while retaining the architectural and/or cytologic features of well-differentiated follicular-cell-derived tumors. The cytomorphology of DHGTC is not well documented despite potential implications for patient triage and management. METHODS: The pathology archives of six institutions were searched for cases diagnosed on resection as "high-grade thyroid carcinoma" using WHO criteria. The fine-needle aspiration (FNA) cohort represents a 10-year period (2013-2023); all were reviewed to confirm DHGTC classification. The corresponding FNAs were assessed for 32 cytomorphologic features. RESULTS: Forty cases of DHGTC with prior FNA were identified. The mean patient age was 64.2 years. The average lesion size was 4.9 cm, and the majority demonstrated a TI-RADS score of 4 or 5 (95.2%). Three main high-grade subsets of DHGTC based on corresponding histology included papillary thyroid carcinoma (65%), follicular carcinoma (22.5%), and oncocytic carcinoma (12.5%). Over 97% of FNA cases were classified as Bethesda category IV or above. Approximately 25% of DHGTC showed cytologic features that included marked cytologic atypia, increased anisonucleosis, large oval nuclei, mitotic activity, or necrosis (p < .05); 68% of DHGTC cases were associated with high-risk molecular alterations. TERT mutations occurred in 41%, of which 89% of these were associated with a second mutation, usually RAS or BRAF p.V600E. CONCLUSIONS: Cytology has a low sensitivity for DHGTC, although a subset of DHGTCs have cytologic features raising the possibility of a high-grade thyroid carcinoma. Other findings include high-risk molecular changes and clinicopathologic features such as older patient age and larger lesion size.

Second opinion for pulmonary and pleural cytology is valuable for patient care.

INTRODUCTION: Thoracic cytology can be challenging due to limited procured material or overlapping morphology between benign and malignant entities. In such cases, expert consultation might be sought. This study aimed to characterize all pulmonary and pleural cytology consult cases submitted to our practice and provide recommendations on approaching difficult cases. MATERIALS AND METHODS: All thoracic (pulmonary and pleural) cytology cases submitted for expert consultation to the University of Michigan (MLabs) from 2013 to mid-2022 were reviewed. Cases where cytology was only part of a hematopathology or surgical pathology consult were excluded. Patient demographics, specimen location, procedure performed, referring diagnosis, and our diagnoses were recorded for each case. Diagnoses were categorized according to the Papanicolaou Society of Cytopathology recommendations for pulmonary and effusion cytology. Discordant diagnoses were stratified as major or minor. Data was analyzed using chi-square analysis and logistic models. RESULTS: We received 784 thoracic cytology consult cases, including 530 exfoliative samples and 307 fine-needle aspirations. The most common anatomic locations sampled were the bronchial wall (n = 194, 23%), lung nodule (n = 322, 38%), and pleura (n = 296, 35%). 413 cases had a diagnostic discrepancy (48.3%), with 274 (66%) minor and 139 (34%) major discrepancies. By location, pleural effusion specimens had the highest probability of a discrepant diagnosis (P = 0.003). By specimen type, fine-needle aspiration samples were significantly more likely to have a discrepant diagnosis (P = 0.06). CONCLUSION: Nearly half of the thoracic cytology cases submitted for expert second opinion had diagnostic discrepancies. Consequently, consulting a tertiary medical care center with cytopathology expertise for challenging thoracic cytology diagnoses is beneficial.

Metastases to the parotid glands: An institutional experience.

BACKGROUND: Metastatic lesions to the salivary gland are rare and mostly affect the parotids. Metastases represent 8% of all malignant lesions of the parotid gland. Around 80% originate from squamous cell carcinomas (SCC) of the head and neck region. Fine needle aspiration (FNA) plays a crucial role in distinguishing primary salivary gland lesions from metastases. Herein we describe our series of metastases to the parotid glands. MATERIALS AND METHODS: We analysed 630 parotid gland FNAs over a decade including conventional and liquid-based cytology specimens. Ancillary techniques such as immunocytochemistry (ICC) were conducted on cell blocks. RESULTS: Eighty (12.4%) cases were malignant lesions, of which 53 (63.75%) were metastases including 24% melanoma, 22.6% SCC, 19% renal carcinomas, 7.5% breast carcinomas, 11.3% lung, 9% intestinal and 1.8% testicular, malignant solitary fibrous tumour and Merkel cell carcinoma. The 53 cases, classified according to the Milan system for salivary cytopathology, belonged to 5 Suspicious for malignancy (SFM) and 48 malignant (M) categories. Forty had a known history of primary malignancy (75.4%), while 13 were suspicious to be a metastatic localisation (24.5%), distributed as 5SFM (2SCC and 3Melanoma) and 8 M. A combination of clinical history, cytomorphology and ICC identified 100% of them. CONCLUSIONS: Fine needle aspiration plays a central role in the diagnostic workup of patients with metastatic lesions to their parotid glands, thereby defining the correct management. Diagnostic accuracy may be enhanced by applying ICC. Although melanoma and SCC are the most common histological types, several other malignancies may also metastasize to the parotid glands and should be kept into consideration.

The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force.

INTRODUCTION: The integration of whole slide imaging (WSI) and artificial intelligence (AI) with digital cytology has been growing gradually. Therefore, there is a need to evaluate the current state of digital cytology. This study aimed to determine the current landscape of digital cytology via a survey conducted as part of the American Society of Cytopathology (ASC) Digital Cytology White Paper Task Force. MATERIALS AND METHODS: A survey with 43 questions pertaining to the current practices and experiences of WSI and AI in both surgical pathology and cytology was created. The survey was sent to members of the ASC, the International Academy of Cytology (IAC), and the Papanicolaou Society of Cytopathology (PSC). Responses were recorded and analyzed. RESULTS: In total, 327 individuals participated in the survey, spanning a diverse array of practice settings, roles, and experiences around the globe. The majority of responses indicated there was routine scanning of surgical pathology slides (n = 134; 61%) with fewer respondents scanning cytology slides (n = 150; 46%). The primary challenge for surgical WSI is the need for faster scanning and cost minimization, whereas image quality is the top issue for cytology WSI. AI tools are not widely utilized, with only 16% of participants using AI for surgical pathology samples and 13% for cytology practice. CONCLUSIONS: Utilization of digital pathology is limited in cytology laboratories as compared to surgical pathology. However, as more laboratories are willing to implement digital cytology in the near future, the establishment of practical clinical guidelines is needed.

Performance of preferentially expressed antigen in melanoma (PRAME) immunohistochemistry for metastatic melanoma in cytology specimens.

BACKGROUND: Preferentially expressed antigen in melanoma (PRAME) has been introduced as a new melanoma marker and potential target for immunotherapy. While PRAME immunohistochemistry (IHC) is well documented in surgical pathology, similar data in cytology are limited. Metastatic melanoma is frequently diagnosed via cytology samples in which IHC plays an important role. We aimed to accordingly evaluate the performance of PRAME IHC in diagnosing metastatic melanoma in cytology samples relative to other commonly used melanoma markers. MATERIALS AND METHODS: The study included 156 archival cytology cases, of which 93 were melanoma cases and 63 nonmelanoma cases (controls). All cases underwent PRAME IHC staining on cell blocks. Nuclear staining of PRAME was evaluated using a quantitative and qualitative scale. Other melanocytic IHC stain results (SOX10, S-100, Melan-A, and HMB45) were also documented. RESULTS: PRAME was detected in tumor cells in 86% of melanoma cases, which was significantly lower than SOX10 (100%) (p < .01), and similar to HMB45 (84%) and Melan-A (82%). S-100 had the lowest sensitivity of 71%. In comparison to other types of melanomas, spindle cell melanoma exhibited higher negativity for PRAME IHC (4/10 = 40%). PRAME was also expressed in some nonmelanocytic malignancies including carcinoma (5/22 = 23%), sarcoma (5/15 = 33%), and hematologic malignancies (1/9 = 11%). Overall, PRAME showed a sensitivity of 86%, specificity of 82%, positive predictive value of 70%, and negative predictive value of 92% for metastatic melanoma. CONCLUSIONS: PRAME is a useful marker for the diagnosis of melanoma in cytology material, but it is less sensitive than SOX10. PRAME is also expressed in other nonmelanocytic tumors which limits its specificity.

Benchmarking cytology support for ROSE during endoscopic and bronchoscopic procedures.

INTRODUCTION: There has been an increase in endoscopic and bronchoscopic biopsies as minimally invasive methods to obtain specimens from gastrointestinal (GI) or pancreatobiliary lesions and thoracic or mediastinal lesions, respectively. As hospitals undertake more of these procedures, it is important to consider the staffing implications that this has on cytopathology laboratories with respect to support for rapid on-site evaluation (ROSE). MATERIALS AND METHODS: Volume and time data from endoscopic ultrasound and bronchoscopic procedures (including endobronchial ultrasound-guided transbronchial needle aspirations and small biopsies with touch preparation) in the GI suite, bronchoscopy suite, or operating room were reviewed for 2 months at 2 different medical centers with ROSE services provided by cytologists or fellows physically present at the procedure and cytopathologists located remotely using telecytology. Statistical analysis was performed to investigate significant trends based on the location of the biopsies and other factors. RESULTS: A total of 16 proceduralists performed 159 procedures and submitted 276 different specimens during 16 total weeks at 2 institutions. The total ROSE time for the on-site personnel to cover these procedures was 109.3 hours (bronchoscopy, 62.3 hours [57%]; GI, 29.8 hours [27%]; OR, 17.2 hours [16%]), which represents an average of 0.69 hour (41.4 minutes) per procedure or 0.40 hour (24.0 minutes) per part, with the shortest procedure times per sample recorded during bronchoscopy. When stratified by practice volume for individual proceduralists, the average time per specimen sample submitted was shorter for proceduralists with high volume practices and was most pronounced during bronchoscopy procedures. CONCLUSIONS: Endoscopic and bronchoscopic procedures account for an increasing amount of the ROSE time for the cytology team. On average, each ROSE procedure takes 0.69 hour (41.4 minutes) or approximately 0.40 hour (24.0 minutes) per specimen, with shorter time requirements for specimens obtained in bronchoscopy procedures and for operators with high volume practices for endobronchial ultrasound-guided transbronchial needle aspirations. This provides important benchmarking data to calculate staffing needs for cytology to provide ROSE support for different proceduralists.

Changing digital and telecytology practices post COVID-19 comparing ASC survey results from 2016 to 2023.

INTRODUCTION: During the COVID-19 pandemic, the need for digital pathology tools became more urgent. However, there needs to be more knowledge of the use in cytology. We aimed to evaluate current digital cytology practices and attitudes and compare the results with a pre-COVID-19 American Society of Cytopathology (ASC) survey. MATERIALS AND METHODS: Fourteen survey questions assessing current attitudes toward digital cytology were developed from a 2016 ASC Digital Pathology Survey. Ten new survey questions were also created to evaluate telecytology use. The survey was e-mailed to ASC members over 6 weeks in 2023. RESULTS: A total of 123 individuals responded (116 in 2016). Attitudes toward digital cytology were unchanged; most participants stated digital cytology is beneficial (87% 2023 versus 90% 2016). The percentage of individuals using digital cytology was unchanged (56% in 2016 and 2023). However, telecytology for rapid onsite assessment (ROSE) is now considered the best application (55% 2023 versus 31% 2016). Forty-three institutions reported using digital and telecytology tools; 40% made implementations after 2020; most did not feel that COVID-19 affected digital cytology (56%). Telecytology for ROSE is the most common application now (78%) compared with education (30%) in 2016. Limitations for implementing digital imaging in cytology included inability to focus (38%) and expense (33%). CONCLUSIONS: General attitudes toward digital tools by the cytology community have essentially remained the same between 2016 and now. However, telecytology for ROSE is increasingly being used, which supports a need for validation and competency guidelines.

Automated Quantitative CD8+ Tumor-Infiltrating Lymphocytes and Tumor Mutation Burden as Independent Biomarkers in Melanoma Patients Receiving Front-Line Anti-PD-1 Immunotherapy.

BACKGROUND: CD8+ tumor-infiltrating lymphocyte (TIL) predicts response to anti-PD-(L)1 therapy. However, there remains no standardized method to assess CD8+ TIL in melanoma, and developing a specific, cost-effective, reproducible, and clinically actionable biomarker to anti-PD-(L)1 remains elusive. We report on the development of automatic CD8+ TIL density quantification via whole slide image (WSI) analysis in advanced melanoma patients treated with front-line anti-PD-1 blockade, and correlation immunotherapy response. METHODS: Seventy-eight patients treated with PD-1 inhibitors in the front-line setting between January 2015 and May 2023 at the University of Pittsburgh Cancer Institute were included. CD8+ TIL density was quantified using an image analysis algorithm on digitized WSI. Targeted next-generation sequencing (NGS) was performed to determine tumor mutation burden (TMB) in a subset of 62 patients. ROC curves were used to determine biomarker cutoffs and response to therapy. Correlation between CD8+ TIL density and TMB cutoffs and response to therapy was studied. RESULTS: Higher CD8+ TIL density was significantly associated with improved response to front-line anti-PD-1 across all time points measured. CD8+ TIL density ≥222.9 cells/mm2 reliably segregated responders and non-responders to front-line anti-PD-1 therapy regardless of when response was measured. In a multivariate analysis, patients with CD8+ TIL density exceeding cutoff had significantly improved PFS with a trend toward improved OS. Similarly, increasing TMB was associated with improved response to anti-PD-1, and a cutoff of 14.70 Mut/Mb was associated with improved odds of response. The correlation between TMB and CD8+ TIL density was low, suggesting that each represented independent predictive biomarkers of response. CONCLUSIONS: An automatic digital analysis algorithm provides a standardized method to quantify CD8+ TIL density, which predicts response to front-line anti-PD-1 therapy. CD8+ TIL density and TMB are independent predictors of response to anti-PD-1 blockade.

On image search in histopathology.

Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.

Utility of artificial intelligence in a binary classification of soft tissue tumors.

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review.

OBJECTIVES: The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. METHODS: A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. RESULTS: Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. CONCLUSIONS: The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential.

Evaluation of TRIM63 RNA in situ hybridization (RNA-ISH) as a potential biomarker for alveolar soft-part sarcoma (ASPS).

Alveolar soft-part sarcoma (ASPS) is a rare soft tissue tumor with a broad morphologic differential diagnosis. While histology and immunohistochemistry can be suggestive, diagnosis often requires exclusion of other entities followed by confirmatory molecular analysis for its characteristic ASPSCR1-TFE3 fusion. Current stain-based biomarkers (such as immunohistochemistry for cathepsin K and TFE3) show relatively high sensitivity but may lack specificity, often showing staining in multiple other entities under diagnostic consideration. Given the discovery of RNA in situ hybridization (RNA-ISH) for TRIM63 as a sensitive and specific marker of MiTF-family aberration renal cell carcinomas, we sought to evaluate its utility in the workup of ASPS. TRIM63 RNA-ISH demonstrated high levels (H-score greater than 200) of expression in 19/20 (95%) cases of ASPS (average H-score 330) and was weak or negative in cases of paraganglioma, clear cell sarcoma, rhabdomyosarcoma, malignant epithelioid hemangioendothelioma, as well as hepatocellular and adrenal cortical carcinomas. Staining was also identified in tumors with known subsets characterized by TFE3 alterations such as perivascular epithelioid cell neoplasm (PEComa, average H-score 228), while tumors known to exhibit overexpression of TFE3 protein without cytogenetic alterations, such as melanoma and granular cell tumor, generally showed less TRIM63 ISH staining (average H-scores 147 and 96, respectively). Quantitative assessment of TRIM63 staining by RNA-ISH is potentially a helpful biomarker for tumors with molecular TFE3 alterations such as ASPS.

American Society of Cytopathology Telecytology validation recommendations for rapid on-site evaluation (ROSE).

Telecytology has multiple applications, including rapid onsite evaluation (ROSE) of fine-needle aspiration (FNA) specimens. It can enhance cytopathology practice by increasing productivity, reducing costs, and providing subspecialty expertise in areas with limited access to a cytopathologist. However, there are currently no specific validation guidelines to ensure safe practice and compliance with regulations. This initiative, promoted by the American Society of Cytopathology (ASC), intends to propose recommendations for telecytology implementation. These recommendations propose that the validation process should include testing of all hardware and software, both separately and as a whole; training of all individuals who will participate in telecytology with regular competency evaluations; a structured approach using retrospective slides with defined diagnoses for validation and prospective cases for verification and quality assurance. Telecytology processes must be integrated into the laboratory's quality management system and benchmarks for discrepancy rates between preliminary and final diagnoses should be established and monitored. Special attention should be paid to minimize discrepancies that downgrade malignant cases to benign (false positive on telecytology). Currently, billing and reimbursement codes for telecytology are not yet available. Once, they are, recommendation of the appropriate usage of these codes would be a part of the recommendations. These proposed guidelines are intended to be a resource for laboratories that are considering implementing telecytology. These recommendations can help to ensure the safe and effective use of telecytology and maximize its benefits for patients.

Artificial intelligence for human gunshot wound classification.

Certain features are helpful in the identification of gunshot entrance and exit wounds, such as the presence of muzzle imprints, peripheral tears, stippling, bone beveling, and wound border irregularity. Some cases are less straightforward and wounds can thus pose challenges to an emergency room doctor or forensic pathologist. In recent years, deep learning has shown promise in various automated medical image classification tasks. This study explores the feasibility of using a deep learning model to classify entry and exit gunshot wounds in digital color images. A collection of 2418 images of entrance and exit gunshot wounds were procured. Of these, 2028 entrance and 1314 exit wounds were cropped, focusing on the area around each gunshot wound. A ConvNext Tiny deep learning model was trained using the Fastai deep learning library, with a train/validation split ratio of 70/30, until a maximum validation accuracy of 92.6% was achieved. An additional 415 entrance and 293 exit wound images were collected for the test (holdout) set. The model achieved an accuracy of 87.99%, precision of 83.99%, recall of 87.71%, and F1-score 85.81% on the holdout set. Correctly classified were 88.19% of entrance wounds and 87.71% of exit wounds. The results are comparable to what a forensic pathologist can achieve without other morphologic cues. This study represents one of the first applications of artificial intelligence to the field of forensic pathology. This work demonstrates that deep learning models can discern entrance and exit gunshot wounds in digital images with high accuracy.

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.