Recommendations for immunocytochemistry in lung cancer typing: An update on a resource‐efficient approach with large‐scale comparative Bayesian analysis

Abstract

Objectives The majority of lung cancer cases are of advanced stage and diagnosis is usually made using minimally invasive small biopsies and cytological specimens. The WHO 2015 classification recommends limiting immunocytochemistry (ICC) to lung cancer typing and molecular testing drives for personalised therapies. An algorithm using Bayes' theorem could be useful for defining antibody profiles. This study aims to assess the impact of different antibody profiles for cytological samples on the accuracy of lung cancer typing with a large‐scale Bayesian analysis. Methods A retrospective examination of 3419 consecutive smears and/or cytospins diagnosed over 2011‐2016 found 1960 primary lung cancer tumours: 972 adenocarcinomas (ADC), 256 squamous carcinomas (SQC), 268 neuroendocrine tumours (NET), and 464 non‐small cell cancer‐not otherwise specified (NSCC‐NOS). The a priori and a posteriori probabilities, before and after ICC using antibodies singly or in combination, were calculated for different lung cancer types. Results TTF‐1 or CK7 alone improved the a posteriori probabilities of correct cytological typing for ADC to 86.5% and 95.8%, respectively. For SQC, using p40 (∆Np63) or CK5/6 together with CK5/14 led to comparable results (78.3% and 90.3%). With synaptophysin or CD56 alone, improvements in a posteriori probabilities to 87.5 and 90.3% for the correct recognition of NET could be achieved. Conclusions Based on morphological and clinical data, the use of two antibodies appears sufficient for reliable detection of the different lung cancer types. This applies to diagnoses that were finalised following ICC both on a clinical or cytological basis and on a histological basis.

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