Digital Examination of LYmph node CYtopathology Using the Sydney system (DELYCYUS): An international, multi-institutional study

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct histologic patterns impacting prognosis and treatment. The current pathological assessment methods face limitations such as interobserver variability, necessitating more reliable techniques. This study seeks to predict lymph node metastasis in LUSC using deep learning models applied to histopathology images of primary tumors, offering a more accurate and objective method for diagnosis and prognosis. Whole slide images (WSIs) from the Outdo-LUSC and the cancer genome atlas cohorts were used to train and validate deep learning models. Multiinstance learning was applied, with patch-level predictions aggregated into WSI-level outcomes. The study employed the ResNet-18 network, transfer learning, and rigorous data preprocessing. To represent WSI features, innovative techniques like patch likelihood histogram and bag of words were used, followed by training of machine learning classifiers, including the ExtraTrees algorithm. The diagnostic model for lymph node metastasis showed strong performance, particularly using the ExtraTrees algorithm, as demonstrated by receiver operating characteristic curves and gradient-weighted class activation mapping visualizations. The signature generated by the ExtraTrees algorithm, named lymph node status-related in situ LUSC histopathology (LN_ISLUSCH), achieved an area under the curve of 0.941 (95% CI: 0.926-0.955) in the training set and 0.788 (95% CI: 0.748-0.827) in the test set. Kaplan-Meier analyses confirmed that the LN_ISLUSCH model was a significant prognostic factor (P = .02). This study underscores the potential of artificial intelligence in enhancing diagnostic precision in pathology. The LN_ISLUSCH model stands out as a promising tool for predicting lymph node metastasis and prognosis in LUSC. Future studies should focus on larger and more diverse cohorts and explore the integration of additional omics data to further refine predictive accuracy and clinical utility.

Leveraging deep learning for identification and segmentation of "CAF-1/p60-positive" nuclei in oral squamous cell carcinoma tissue samples

In the current study, we introduced a unique method for identifying and segmenting oral squamous cell carcinoma (OSCC) nuclei, concentrating on those predicted to have significant CAF-1/p60 protein expression. Our suggested model uses the StarDist architecture, a deep-learning framework designed for biomedical image segmentation tasks. The training dataset comprises painstakingly annotated masks created from tissue sections previously stained with hematoxylin and eosin (H&E) and then restained with immunohistochemistry (IHC) for p60 protein. Our algorithm uses subtle morphological and colorimetric H&E cellular characteristics to predict CAF-1/p60 IHC expression in OSCC nuclei. The StarDist-based architecture performs exceptionally well in localizing and segmenting H&E nuclei, previously identified by IHC-based ground truth. In summary, our innovative approach harnesses deep learning and multimodal information to advance the automated analysis of OSCC nuclei exhibiting specific protein expression patterns. This methodology holds promise for expediting accurate pathological assessment and gaining deeper insights into the role of CAF-1/p60 protein within the context of oral cancer progression.

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity

BACKGROUND

Cytopathological examination serves as a tool for diagnosing solid tumors and hematologic malignancies. Artificial intelligence (AI)-assisted methods have been widely discussed in the literature for increasing sensitivity, specificity and accuracy in the diagnosis of cytopathological clinical samples. Many of these tools are also used in clinical practice. There is a growing body of literature describing the role of AI in clinical settings, particularly in improving diagnostic accuracy and providing predictive and prognostic insights.

METHODS

A comprehensive search for this systematic review was conducted using databases Google, PUBMED (n = 450) and Google Scholar (n = 1067) with the keywords "Artificial Intelligence" AND "cytopathological" and "fine needle aspiration" AND "Deep Learning" AND "Machine Learning" AND "Hematologic Disorders" AND "Lung Cancer" AND "Pap Smear" and "cervical cancer screening" AND "Thyroid Cancer" AND "Breast Cancer" and "Sensitivity" and "Specificity". The search focused on literature reviews and systematic reviews published in English language between 2020 and 2024. PRISMA guidelines were adhered to with studies included and excluded as depicted in a flowchart. 417 results were screened with 34 studies were chosen for this review.

RESULTS

In the screening of patients with cervical cancer, bone marrow and peripheral blood smears and benign and malignant lesions in the lung, AI-assisted methods, particularly machine learning and deep learning (a subset of machine learning) methods, were applied to cytopathological data. These methods yielded greater diagnostic accuracy, specificity and sensitivity and decreased interobserver variability. Data sets were collected for both training and validation. Human machine combined performance was also found to be comparable to standalone performance in comparison with medical performance as well.

CONCLUSIONS

The use of AI in the analysis of cytopathological samples in research and clinical settings is increasing, and the involvement of pathologists in AI workflows is becoming increasingly important.

Current role of cytopathology in the molecular and computational era: The perspective of young pathologists

Cytopathology represents a well established diagnostic approach because of its limited cost, reliability, and minimal invasiveness with respect to other methodologies. The evolving complexity of the different classifications systems and the implementation of ancillary techniques to refine the diagnosis is progressively helping in the risk of malignancy stratification, and the adoption of next-generation sequencing techniques contributes to enrich this valuable tool with predictive information, which is always more essential in the tailored medicine era. The recent introduction of digital and computational pathology is further boosting the potentialities of cytopathology, aiding in the interpretation of samples to improve the cost effectiveness of large screening programs and the diagnostic efficiency within intermediate/atypical categories. Moreover, the adoption of artificial intelligence tools is promising to complement molecular investigations, representing a stimulating perspective in the cytopathology field. In this work, the authors tried to summarize the multifaceted nature of this complex and evolving field of pathology, synthesizing the most recent advances and providing the young pathologists' perspective on this fascinating world.

Computer-assisted urine cytology: Faster, cheaper, better?

Recent advancements in computer-assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes.

Parotid squamous cell carcinoma metastases: Application of human papillomavirus-DNA test on liquid-based cytology to recognize oropharyngeal origin of the neoplasm

Malignancies of the parotid gland are relatively uncommon and in most cases are primary neoplasms; intraparotid metastases are rare. Oral and oropharyngeal squamous cell carcinoma (O- and OP-SCC) can potentially metastasize to the parotid gland or intraparotid lymph nodes. Fine-needle aspiration cytology (FNAC) serves as the initial diagnostic approach for this purpose. HPV status in FNAC specimens is relevant and can guide the diagnostic workup, indicating a potential oropharyngeal origin of the primary tumor. A small series of occult SCC metastases is presented below, in which HPV-DNA testing of FNAC specimens helped identify primary neoplasms located in the oropharynx. US-guided FNAC of parotid nodules was conducted by an experienced interventional cytopathologist in three cases. Each patient underwent assessment of direct smears, cell blocks, and liquid-based samples for HPV testing. The morphological and immunocytochemical features of SCC were documented, and real-time PCR was employed for the detection and genotyping of HPV. The role of HPV testing on FNAC specimens in pinpointing the primary neoplasms in the oropharynx is highlighted. Consequently, FNAC samples emerge as valuable diagnostic and prognostic tools in this context, providing essential insights for patient management.

The Current and Future Impact of Lymph Node Fine-Needle Aspiration Cytology on Patient Care

Lymph node (LN) fine-needle aspiration cytology (FNAC) is a common diagnostic procedure for lymphadenopathies. Despite the qualities and potentialities of LN-FNAC, the number of possible pathologies and the variety of clinical contexts represent a challenge and require a continuous upgrading of the procedure according to the emerging clinical requests and new technologies. This study presents an overview of the current and future impact of LN-FNAC on the care of patients with lymphadenopathy.

Peripheral lymphadenopathy of unknown origin in adults: a diagnostic approach emphasizing the malignancy hypothesis

The term lymphadenopathy refers to an abnormality in size, consistency or morphological aspect of one or several lymph nodes. Although lymphadenopathies are commonly observed in everyday clinical practice, the difficulty of differentiating benign and malignant disease may delay therapeutic approaches. The present review aims to update diagnostic algorithms in different clinical situations based on the currently available literature. A literature review was performed to assess current knowledge of and to update the diagnostic approach. A short clinical vignette was used as an example of a typical clinical presentation. This case of metastatic lymphadenopathy with incomplete patient history demonstrates how misleading such lymphadenopathy may be, leading to a delayed diagnosis and even a fatal outcome. Any lymphadenopathy persisting for more than 2 weeks should be considered suspicious and deserves further investigation. Precise clinical examination, meticulous history-taking and a search for associated symptomatology are still cornerstones for diagnosing the origin of the condition. The next diagnostic step depends on the anatomical region and the specific patient's situation. Imaging starts with ultrasound, while computed tomography (CT) and magnetic resonance imaging (MRI) allow assessment of the surrounding structures. If the diagnosis remains uncertain, tissue sampling and histological analyses should be performed. Except for head and neck loco-regional lymphadenopathy, there are no methodical guidelines for persistent lymphadenopathy. The present review clarifies several confusing and complex situations. The accuracy of fine needle aspiration cytology could be increased by using core needle biopsy with immunocytologic and flow cytometric methods. Notably, except in the head and neck area, open biopsy remains the best option when lymphoma is suspected or when inconclusive results of previous fine needle aspiration cytology or core needle biopsy are obtained. The incidence of malignant lymphadenopathy varies with its location and the various diagnostic strategies. In metastatic lymphadenopathy of unknown primary origin, European Society for Medical Oncology (ESMO) guidelines and modern methods like next-generation sequencing (NGS) may help to manage such complex cases.

Exploring the landscape of urinary tract melanomas: A review for pathologists and clinicians

Melanomas originating within the urinary tract represent a rare and clinically challenging subset of malignancies. Despite extensive research on cutaneous melanomas, urinary tract melanomas remain relatively unexplored, presenting diagnostic dilemmas and limited treatment consensus. In this comprehensive review, we synthesize current knowledge on the epidemiology, risk factors, clinical presentation, histopathological characteristics, and treatment strategies specific to this disease. Enhancing clinical awareness, refining diagnostic approaches, and exploring novel therapeutic interventions hold promise for improving outcomes in this challenging malignancy subset.

Interobserver variability in cytopathology: How much do we agree?

Interobserver variability remains a major challenge for cytopathologists despite the development of standardized reporting and classification systems. Indeed, whereas moderate-to-good interobserver agreement is generally achievable when the differential diagnosis between benign and malignant entities is straightforward, high levels of variability make the diagnostic interpretation of atypical and suspicious samples not consistent. This review explores the landscape of interobserver agreement in cytopathology across different anatomical sites.

Uses and limitations of small-volume biopsies for the diagnosis of lymphoma

Although surgical biopsy remains the gold standard for the diagnosis of lymphoma, small-volume biopsies including fine-needle aspiration and core needle biopsy are increasingly being used as a first line diagnostic tool. Small-volume biopsies are safe, rapid and cost effective; however, diagnostic utility varies by lymphoma subtype. It is important for pathologists and clinicians to recognize both the strengths and limitations of such biopsies.

How many more slides to go until we fully adopt digital cytology?

Two‐liner/synopsis: The digital cytology hub (DCH) has been established under the umbrella of the Cytopathology journal. DCH will help bring about the crucial changes needed to make digital cytology the way of practicing cytology in our laboratories.

Digital odyssey: lessons learnt from a reverse transition from a digital to a manual pathology workflow

In the fully digital Caltagirone pathology laboratory, a reverse shift from a digital to a manual workflow occurred due to a server outage in September 2023. Here, insights gained from this unplanned transition are explored. Surveying the affected pathologists and technicians revealed unanimous preferences for the time-saving and error-reducing capabilities of the digital methodology. Conversely, the return to manual methods highlighted increased dissatisfaction and reduced efficiency, emphasising the superiority of digital workflows. This case study underscores that transition challenges are not inherent to digital workflows but to transitioning itself, advocating for the adoption of digital technologies in all pathology practices.

Upcoming World Health Organization system for reporting lymph node cytopathology-A preliminary outline

Purple book for WHO reporting for lymph node cytopathology - 2023. Lymph node cytopathology reporting system may use standardized nomenclature and usage of the terminologies harmonizing with the WHO Blue Book on hematolymphoid tumors.

Combined fine needle aspiration cytology and core needle biopsy in the same setting: A two-years' experience

INTRODUCTION

Fine needle aspiration cytology (FNAC) combined with rapid on-site evaluation (ROSE) and ancillary techniques is an accurate diagnostic tool for many pathologies. However, in some cases, it may not be sufficient for actionable diagnoses or molecular testing, especially for cases that require large immunohistochemical panels or cases in which histological features are mandatory for the diagnosis. Core needle biopsy (CNB), on the contrary, provides samples that are suitable for histological features and sufficient for all ancillary studies. However, CNB is often performed by radiologists or clinicians without the direct participation of cytopathologists, which can lead to missed or delayed diagnoses. This study reports on the experience of combining FNAC and CNB performed in one setting by cytopathologists. The aim was to evaluate the impact of CNB on FNAC and the diagnostic efficiency of the combined procedures.

MATERIALS AND METHODS

One hundred forty-two FNAC and CNB procedures performed in the same setting over a period of 2 years were analysed. The FNAC diagnoses were compared and integrated with the subsequent CNB diagnoses. The impact of CNB was categorized as follows: non-contributory, in cases of inadequate samples; confirmed, when the CNB and FNAC diagnoses were the same; improved, when the CNB diagnosis was consistent with the FNAC diagnosis and further specified the corresponding entity; allowed, when CNB produced a diagnosis that could not be reached by FNAC; changed, when the CNB changed the previous FNAC diagnosis.

RESULTS

CNB confirmed the FNAC diagnosis in 40.1% of cases (n = 57/142). CNB improved the FNAC diagnosis in 47.2% of cases (n = 67/142). CNB allowed a diagnosis that could not be performed on FNAC in 2.1% of cases (n = 3/142). CNB changed a previous FNAC diagnosis in 2.1% of cases (n = 3/142). CNB was non-contributory in 8.4% of cases (n = 12/142). CNB produced a positive impact on the whole diagnostic procedure in 51.4% of total cases (n = 73/142). The combined FNAC and CNB resulted in actionable diagnoses in 91.5% of all cases (n = 130/142). A complete molecular assessment was successfully performed in 14.7% of cases (n = 21/142) utilizing either FNAC or CNB material.

CONCLUSIONS

The combined use of FNAC and CNB in one setting improves the diagnostic accuracy of both procedures. This approach exploits the advantages of each procedure, enhancing the accuracy of the final diagnosis.

Challenges and opportunities in lymph node FNA: Insights from the DELYCYUS study

The DELYCYUS study represents a significant step forward in the field of lymph node cytology, providing evidence that digital cytopathology, when applied using the Sydney system, yields reliable and reproducible results.