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A Novel Approach to Classification and Reporting of Lymph Node Fine-Needle Cytology: Application of the Proposed Sydney System

Elena Vigliar, Gennaro Acanfora, Antonino Iaccarino, Massimo Mascolo, Daniela Russo, Giulia Scalia, Roberta Della Pepa, Claudio Bellevicine, Marco Picardi, Giancarlo Troncone

2021Diagnostics45 citationsDOIOpen Access PDF

Abstract

Fine-needle cytology (FNC) is a useful diagnostic tool in the first line evaluation of lymphadenopathy of unknown aetiology. Nevertheless, considering the large number of conditions presenting as lymphadenopathy, lymph node cytology represents a challenging scenario. Recently, an expert panel published the proposal of the Sydney system for performing classification and reporting of lymph node cytopathology; the aim of the present study was to evaluate the applicability of this system. Thus, 300 lymph node FNCs performed over 1 year were reviewed and categorized according to the Sydney system classification. Overall, n = 20 cases (6.7%) were categorized as L1-inadequate/non-diagnostic; n = 104 (34.7%) as benign (L2); n = 25 (8.3%) as atypical (L3); n = 13 (4.3%) as suspicious (L4), and n = 138 (46%) as malignant (L5). FNC diagnoses were correlated with histopathologic and clinical follow-up to assess the diagnostic accuracy and the risk of malignancy (ROM) for each diagnostic category. Statistical analysis showed the following results: sensitivity 98.47%, specificity 95.33%, positive predictive value 96.27%, negative predictive value 98.08%, and accuracy 97.06%. The ROM was 50% for the category L1, 1.92% for L2, 58.3% for L3, and 100% for L4 and L5. In conclusion, FNC coupled with ancillary techniques ensures satisfactory diagnostic accuracy and the implementation of the Sydney system may improve the practice of cytopathologists.

Topics & Concepts

MedicineCytopathologyLymph nodeMalignancyMedical diagnosisCytologyPredictive valueRadiologyFine needle aspiration cytologyDiagnostic accuracyFalse positive paradoxFirst linePathologyInternal medicineComputer scienceArtificial intelligenceLymphadenopathy Diagnosis and AnalysisCervical Cancer and HPV ResearchInfectious Diseases and Mycology