BACKGROUND:Primary pulmonary salivary gland-type tumors (PSGTs) are rare but clinically significant tumors that originate from the submucosal glands of the tracheobronchial tree. Cytologic samples taken during bronchoscopy are a key component of preoperative evaluation. However, cytologic diagnosis remains challenging because of the submucosal growth and morphologic overlap of PSGTs. In addition, current knowledge of the cytohistologic correlation of PSGTs is fragmented. The objective of this study was to assess the effectiveness of cytologic diagnoses of PSGTs. METHODS:A comprehensive, systematic literature search of the PubMed database was conducted to identify studies with cytologic and histologic diagnoses of PSGTs. Comprehensive data on diagnostic and clinical factors, when available, were collected for all individual patients. The data were tabulated in Microsoft Excel and analyzed using OpenMeta (Analyst) software. RESULTS:In total, 49 studies comprising 106 patients were identified. Final cytohistologic concordance was demonstrated in 48.1% of cases. Fine-needle aspiration showed the highest sensitivity (75.0%), followed by bronchial/tracheal washing (38.1%), and bronchial brushing (34.2%). Adenoid cystic carcinoma was the most common histologic subtype, accounting for 67 cases, followed by mucoepidermoid carcinoma, which accounted for 27 cases. CONCLUSIONS:The cytologic diagnosis of rare PSGTs remains challenging. Overall, cytohistologic concordance was 48.1%. However, fine-needle aspiration demonstrated greater diagnostic accuracy than exfoliative cytology and may facilitate a more accurate preoperative assessment.
BACKGROUND:This study aimed to evaluate Bladder EpiCheck (BE) performance in detecting urothelial carcinoma (UC) and HG (high-grade) events in patients with indeterminate cytology and negative cystoscopy. METHODS:This prospective study included 63 patients who presented with indeterminate urinary cytology and negative cystoscopy between October 2022 and April 2024. A repeated urine sample was obtained and analyzed via both urinary cytology and BE test. Patients were followed for 1 year. The diagnostic performance of each test was assessed. Logistic regression models were developed to estimate the risk of UC and HG events on the basis of repeated cytology and BE results. RESULTS:Thirty-two patients (51%) experienced a UC event, of which 25 (78%) were HG events. Sensitivity (SN) for detecting UC events was significantly higher (80% vs. 17%; p < .001) for BE compared to repeated urinary cytology, whereas repeated cytology demonstrated higher specificity (SP) (59% vs. 96%; p = .02). Similar SN differences were observed for HG disease (80% vs. 25%; p < .001). However, when suspicious for high-grade urothelial carcinoma was considered a positive cytology result, no statistically significant differences were observed between BE and repeated cytology in either SN or SP. A prognostic model combining repeated cytology and BE outperformed models using baseline cytology combined with either test alone in predicting UC and HG events. CONCLUSIONS:In patients with indeterminate urinary cytology and negative cystoscopy, BE provided complementary diagnostic information for the detection of UC events and HG disease. The combination of BE and repeated urinary cytology provided the most accurate risk assessment.
BACKGROUND:Precision oncology requires diagnostic sampling that delivers molecularly actionable material quickly and safely. In non-small cell lung cancer (NSCLC), core-needle biopsy (CNB) remains common. Yet fine-needle aspiration (FNA) has re-emerged with rapid on-site evaluation (ROSE), optimized cell blocks, and next-generation sequencing. The objective of this study was to compare the economic costs of FNA versus CNB with respect to an FNA-first with predefined CNB escalation strategy for NSCLC clinical pathways. METHOD:The authors synthesized evidence on diagnostic adequacy, complication rates, and molecular performance of cytology versus core tissue. A microcosting framework decomposed total diagnostic episode costs into sampling, processing, complications, and remedial procedures. This framework was applied to Sweden's Standardized Cancer Care Pathway for NSCLC using decision-tree modeling and sensitivity analyses. RESULTS:Modern cytology workflows with ROSE achieve a nondiagnostic sampling rate ≤6% and support programmed death-ligand 1 and broad next-generation sequencing with high mutation concordance to surgical specimens. Compared with CNB, FNA incurs fewer major complications and facilitates repeat sampling. In the Swedish illustration, an FNA-first pathway with ROSE reduced expected per-patient diagnostic cost by approximately 33% (7748 vs. 11,637 Swedish krona). The results were robust to ±50% variation in complication rates and ±5%-10% variation in adequacy. CONCLUSIONS:Reframing adequacy around molecular fitness and time to actionability supports FNA where ROSE exists, with codified, same-episode CNB escalation. This approach advances safety, capacity, and timeliness without compromising molecular yield. The authors provide a transferable cost-consequence model and implementation checklist for health systems. Local recalibration is required for absolute cost values, and multisite calibration is encouraged to establish generalizability across the different settings.
Large language models (LLMs) and vision-language models represent a fundamentally different category of artificial intelligence (AI) compared to prior image analysis approaches in digital pathology, which have largely been based on convolutional neural network architectures. This review from the American Society of Cytopathology Clinical Practice Committee examines the current evidence for LLM and vision-language model applications in cytopathology, including structured reporting, diagnostic assistance, quality control, education, and workflow integration. The distinction between applications with preliminary evidence and those that remain hypothetical is described. A detailed assessment of the challenges that must be addressed before clinical deployment, including hallucination risk, limited explainability, bias, data privacy, validation gaps, and infrastructure barriers is discussed. A review of the regulatory landscape in the United States and European Union as it applies to AI-enabled software as a medical device is provided. Recommendations addressing cytopathology-specific benchmarks, multi-institutional validation, transparent governance, and incremental deployment beginning with low-risk applications are suggested. In the current environment, LLMs have the potential to augment cytopathology practice, but responsible adoption requires rigorous validation and sustained collaboration among cytopathologists, AI researchers, and regulatory bodies.
BACKGROUND:Deep learning has shown promising performance in cervical cytology; however, many studies have relied on presegmented single-cell images rather than the more complex morphologic patterns encountered in routine practice. Here, scattered cells were defined as isolated or dissociated, nonoverlapping single cells. This study quantified the performance loss when artificial intelligence (AI) models trained on these cells were applied to hyperchromatic crowded cell groups (HCGs). METHODS:Binary convolutional neural network models were developed to differentiate between negative for intraepithelial lesion or malignancy cases and high-grade squamous intraepithelial lesion cases via a scattered cell data set composed of institutional and public liquid-based cytology images. The scattered cell data set comprised 101 cases, with 1062 images; the independent HCG data set comprised 48 cases, with 330 images. ResNet-50, ResNeXt-50, ConvNeXt-Tiny, EfficientNet-B3, VGG-19, and GoogLeNet were trained on scattered cell images, and then directly applied to HCGs without retraining or threshold recalibration. RESULTS:All models showed high performance on the scattered cell data set, with the area under the receiver operating characteristic curve (AUC) ranging from 0.950 to 0.996. When directly applied to HCGs, performance declined across all architectures, with the AUC ranging from 0.385 to 0.683. ConvNeXt-Tiny showed the highest AUC on HCGs (0.683); however, this remained substantially lower than its performance on scattered cells (0.996). For all architectures, the AUC was significantly lower on HCGs than on the scattered cell data set. CONCLUSIONS:Binary AI models trained on scattered cell images achieved excellent discrimination in the original setting but their performance was not preserved when directly applied to HCGs. These findings underscore the need for direct validation and HCG-aware model design in cervical cytology AI.
BACKGROUND:The Lung Cancer Compact Panel (cPANEL) is a recently approved highly sensitive multiplex gene panel in Japan that supports both DNA- and RNA-based next-generation sequencing. Although cytological specimens are acceptable for cPANEL, unfixed cell pellets or dedicated preservation tubes are typically recommended. However, evidence remains limited regarding whether residual liquid-based cytology (LBC) cell suspensions prepared for routine cytological diagnosis can be used directly for cPANEL testing without dedicated molecular preservation or additional preanalytical processing. In this study, we evaluated the feasibility of applying LBC specimens that are widely used in contemporary clinical practice to cPANEL. METHODS:We analyzed DNA and RNA quality in 69 clinical LBC specimens. Among these, 51 specimens containing non-small cell lung cancer cells with previously determined driver alteration status were subjected to cPANEL testing to evaluate assay concordance with clinical companion diagnostic results. RESULTS:DNA integrity was generally well preserved (DNA Integrity Number [DIN]: 6.2 ± 1.5). In contrast, RNA integrity showed greater variability (DV200: 16.4 ± 12.1%). ThinPrep-fixed specimens demonstrated lower DIN and DV200 values compared with CytoRich Red-fixed specimens. Although all samples successfully passed the DNA-based cPANEL assay, six cases (11.8%) failed the RNA-based assay, with RNA yield being a major contributing factor. Among the 46 evaluable specimens, concordance was 95.7% and sensitivity was 92.3%, or 88.9% including RNA module failures as cPANEL-negative. CONCLUSIONS:With appropriate fixative selection and adequate cellularity, cPANEL using clinical LBC specimens may serve as a practical diagnostic platform. We demonstrated that routine LBC specimens can be directly applied to cPANEL without special preanalytical processing.
Two new studies now make the most substantial case yet that the framework of the International System for Reporting Serous Fluid Cytopathology can be carried across the biologic boundary to include cerebrospinal fluid, and current work on the second edition of the system will address cerebrospinal fluid for the first time as a special category of body fluid-an expansion the international community has explicitly endorsed. The contributions of these authors will stand among the empirical foundations on which a coherent, cerebrospinal fluid-specific-and, ultimately, fluid-agnostic-reporting standard can be built.
Recognition of high-risk human papillomavirus (HPV) as the etiologic agent in nearly all cervical cancers has fundamentally reshaped screening strategies, driving the development and adoption of highly sensitive molecular HPV assays. Over the past several decades, cervical cancer screening guidelines have shifted from primarily cytology-based approaches toward HPV-based modalities, with primary HPV testing now recommended as the preferred method by the American Cancer Society and anticipated to be similarly endorsed by the United States Preventive Services Task Force. This article traces the evolution of cervical cancer screening, from the original Papanicolaou test to current guideline recommendations, and discusses the logistical requirements for transitioning to a primary HPV screening system, including triage strategies for HPV-positive cases. The authors additionally present an institutional experience of initiating primary HPV testing, highlighting its impact on cytopathology laboratory workflow and specimen volumes.
This news section is written by a medical journalist and offers Cancer Cytopathology readers timely information on events, issues, and personalities of interest to the subspecialty. In this second of a three‐part series on how new US policies and funding cuts are affecting cancer research, scientists warn that restrictive funding policies may turn off key partnerships.
BACKGROUND:Malignant effusions in breast cancer are a minimally invasive source for molecular profiling. Effusion-derived cytology specimens, cell blocks (CB) and post-ThinPrep PreservCyt (PTPC) supernatant, are reliable sources for molecular testing but differ in DNA concentration and turnaround time (TAT). This study evaluates mutational profiles, cytology characteristics, and clinical correlates to assess the utility of effusion-based molecular testing in breast cancer. METHODS:The authors screened 2400 malignant effusion cases, identifying 22 cases of breast adenocarcinoma effusions that underwent next-generation sequencing (NGS). Both CB and PTPC supernatant were evaluated for tumor cellularity, fluid volume, DNA concentration, TAT, and mutation detection. Associations with clinicopathological features and outcomes were explored. RESULTS:NGS was successful in all 22 cases (mean volume, 426.5 mL; mean tumor cellularity, 50%). Successful sequencing was achieved in low volume samples with high tumor cellularity. Higher tumor cellularity (>50%) was associated with bloody effusions (p = .016). PTPC supernatant yielded 5-fold higher DNA (mean 177.4 ng/µL vs. 33.8 ng/µL) and shorter TAT by 9 days (15 vs. 24 days). TP53 mutations were more common in estrogen receptor (ER)-negative and triple-negative breast effusions (p = .025). Mutations in PIK3CA, TP53, CCND1, and AKT were reliably detected. Time from diagnosis to effusion development was significantly shorter in ER negative disease (p = .001) CONCLUSIONS: Effusion cytology using PTPC supernatant, support successful NGS even in low-volume samples. ER-negative and triple-negative effusions demonstrate more aggressive molecular features including enrichment of TP53 mutations and earlier effusion development. Effusion-based molecular testing yields clinically relevant genomic information in advanced breast cancer.
BACKGROUND:Bladder cancer is a common and highly recurrent malignancy requiring lifelong surveillance. Urine cytology serves as a noninvasive triage tool to guide cystoscopy but is limited by variable sensitivity and manual review. Although deep learning enables quantitative cell-level assessment, limited work has examined three-dimensional urine cytology preparations (e.g., SurePath) containing residual cellular fragments, where restricting analysis to a single nominal focal plane may obscure diagnostically relevant features. This study aimed to quantify focal-plane heterogeneity, measure degradation of nuclear-to-cytoplasmic (NC) ratio and nuclear area estimates off plane, and evaluate focal-plane selection algorithms for performance recovery. METHODS:A total of 325 SurePath whole-slide images scanned as 11-plane Z-stacks were analyzed that spanned negative through high-grade urothelial carcinoma cases. A detection model identified cells and clusters across planes, and 343 clusters (2435 urothelial cells) were reannotated at the optimal nuclear and cytoplasmic focal depths. Classical focus metrics and vision-transformer models were evaluated for focal-plane prediction. A U-Net segmentation model generated NC ratios and nuclear areas, and Spearman correlations compared annotated and predicted measurements across optimal, off-plane, and algorithm-selected conditions. RESULTS:Focal-plane prediction accuracy ranged from 42% to 88%, with classical focus metrics outperforming deep-learning approaches. NC ratio correlation was 0.774 at optimal focus and declined progressively off plane (∼0.50 at ±5 planes). Algorithm-selected planes partially recovered performance (up to 0.748). Similar trends were observed for nuclear area estimation. CONCLUSIONS:Accurate focal-plane selection is critical for artificial intelligence-based assessment of three-dimensional urine cytology. Future work will extend this analysis to cluster- and patient-level outcomes in multi-institutional validation studies.
BACKGROUND:Cerebrospinal fluid (CSF) cytopathology lacks standardized reporting, leading to diagnostic variability and inconsistent clinical management. Although the International System for Reporting Serous Fluid Cytopathology (TIS) has been established for effusions, its utility in CSF remains underexplored. For this study, the authors applied TIS categories to CSF specimens from three academic centers across two continents and evaluated their association with overall survival (OS). METHODS:A retrospective review of pathology databases (2019-2022) across three institutions was performed. Data included demographics, cytologic diagnosis, and OS. Diagnoses were reclassified using TIS criteria. OS was analyzed using Kaplan-Meier curves and Cox proportional hazards models, and multivariable analysis was used for predictors of mortality. RESULTS:In total, 1137 patients underwent CSF cytologic evaluation. Diagnostic distribution was as follows: 3.2% nondiagnostic, 67.1% negative for malignancy, 6.2% atypia of undetermined significance, 2.7% suspicious for malignancy, and 20.8% malignant. Among 579 patients with follow-up, OS differed across categories (p < .001). Mean survival was longest in patients who were diagnosed as negative for malignancy (53.9 months), followed by atypia of undetermined significance (48.4 months), and suspicious for malignancy (40.3 months), and it was shortest in patients who were diagnosed with malignancy (18.7 months). CONCLUSIONS:Leveraging a large, multi-institutional cohort with up to 5 years of follow-up, this study demonstrated that TIS categories effectively stratify CSF specimens into distinct prognostic groups. The authors identified a significant correlation between diagnostic classification and OS, with a clear decline in patient outcomes from those who had diagnoses of negative for malignancy/atypia of undetermined significance to those who had diagnoses of suspicious for malignancy and malignancy. These findings support the TIS system as a standardized, evidence-based framework that enhances risk stratification and clinical decision-making in CSF cytopathology.
BACKGROUND:Accurate peritoneal staging is critical in pancreatic ductal adenocarcinoma (PDAC), as the presence of peritoneal metastases significantly alters prognosis and treatment strategy. In this setting, some centers use peritoneal washings (PWs), which allow the sampling of a large area of the peritoneal surface in the absence of a visible lesion, to improve peritoneal staging and improve selection for curative-intent surgery. METHODS:The authors conducted a retrospective review of 28 patients who underwent cytopathologic evaluation of intraoperative PWs collected between July 2019 to July 2024. RESULTS:There was high concordance between PWs and concomitant biopsy. Six patients had positive PWs. Of these six patients, five of them had concordant findings noted on biopsy. There was one patient who had benign findings on biopsy despite having a malignant PW. In a similar fashion, 22 patients had PWs that were negative. Nineteen of these patients had concordant findings noted on biopsy. The remaining three patients had biopsy-confirmed metastasis despite benign findings noted on PW. CONCLUSION:Peritoneal washings can enhance the detection of peritoneal involvement by PDAC while minimizing false-positive diagnoses.
