The diagnostic performance of combined conventional cytology with smears and cell block preparation obtained from endoscopic ultrasound-guided fine needle aspiration for intra-abdominal mass lesions

Enhancing diagnostic precision in thyroid nodule assessment: evaluating the efficacy of a novel cell preservation technique in fine-needle aspiration cytology

Objectives

This study aimed to evaluate the effectiveness of thyroid fine needle aspiration cytology (FNAC) using a novel-cell preserving matrix called Cytomatrix in improving diagnostic accuracy for thyroid nodules.

Materials and methods

Fifty patients undergoing thyroidectomy were enrolled and FNAC was performed on the excised thyroid glands, with the collected sample being placed on the Cytomatrix. The results were compared with histopathological analysis, and diagnostic performance was assessed statistically.

Results

Cytomatrix demonstrated an accuracy of 96%, sensitivity of 84.61%, and specificity of 100%. Concordance between cytological and histopathological findings highlighted Cytomatrix's potential to enhance thyroid FNAC accuracy.

Conclusion

FNAC using Cytomatrix shows promise in improving diagnostic accuracy for thyroid nodules. Its application, marked by faster processing and efficient resource utilization, coupled with the preservation of cellular architecture, holds considerable potential in enhancing cytological diagnosis, thus optimizing patient management strategies.

Diagnostic role of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in abdominal lymphadenopathy of unknown etiology

Background

Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is an established method for the evaluation of abdominal organ lesions. However, there are few studies on EUS-FNA for abdominal lymph node (LN) lesions. The purpose of this study was to evaluate the diagnostic role of EUS-FNA in isolated abdominal lymphadenopathy (LAP).

Methods

A retrospective analysis was performed on patients with isolated abdominal LAP who underwent a EUS-FNA examination. The diagnosis was made based on cytology, histology, and immunohistochemical (IHC) studies. The area under curve (AUC) value, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated.

Results

A total of 99 patients were included in this study. The final diagnoses were metastatic tumor (n = 32), lymphoma (n = 32), tuberculosis (n = 17), sarcoidosis (n = 5), castleman's disease (n = 1), and reactive LAP (n = 12). The AUC value, sensitivity, specificity, PPV, NPV, and accuracy of EUS-FNA in the diagnosis of malignant LAP were 0.9531, 90.6, 100, 100, 85.4, and 93.9%, respectively. For the diagnosis of lymphoma, the accuracy of EUS-FNA combined with IHC staining was 94.9%. Retroperitoneal LN enlargement is more commonly associated with lymphoma, while hepatic hilar LN enlargement predominantly suggests benign conditions or metastatic tumors. Malignant lymph nodes are more likely to be regular border, circular/quasi-circular, and fusion. Lymphomas are more likely to present with fusion and heterogeneous echogenicity than metastatic tumors.

Conclusion

EUS-FNA is a safe and effective method to diagnose isolated abdominal LAP.

Minimally Invasive Cytopathology and Accurate Diagnosis: Technical Procedures and Ancillary Techniques

In recent years, the demand for cytopathological accurate diagnoses has increased as expanding minimally invasive procedures obtain materials from patients with advanced cancer for diagnostic, prognostic, and predictive purposes. However, inadequate knowledge of cytopathological technical procedures and ancillary techniques by clinicians remains the most common reason for the limited availability of cytopathology. The objectives of this review were to understand the technical procedures, ancillary techniques, and application and effectiveness of various types of tests in cytopathology. Each of the many ancillary technologies described in the literature has specific advantages and limitations and laboratories select one or more methods depending on their infrastructure and expertise to achieve the goal from initial screening of the disease to the final diagnosis of the cytopathology. This paper systematically reviews the development of cytopathology, summarizes the existing problems in cytopathology and the new progress of auxiliary examination, to provide a theoretical basis for the advanced development of cytopathological diagnostic technologies and to consolidate the minimally invasive and accurate diagnosis of cytopathologies for clinicians. Cytopathology offers many advantages over other clinical examinations, particularly for minimally invasive and accurate diagnosis.

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.