NSCLC Subtyping in Conventional Cytology: Results of the International Association for the Study of Lung Cancer Cytology Working Group Survey to Determine Specific Cytomorphologic Criteria for Adenocarcinoma and Squamous Cell Carcinoma.
Cytology
IASLC
Machine learning
Non–small cell lung carcinoma
Regression tree
Subtyping
Journal
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
ISSN: 1556-1380
Titre abrégé: J Thorac Oncol
Pays: United States
ID NLM: 101274235
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
received:
07
11
2021
revised:
03
02
2022
accepted:
15
02
2022
pubmed:
26
3
2022
medline:
1
6
2022
entrez:
25
3
2022
Statut:
ppublish
Résumé
Accurate subtyping of NSCLC into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is the cornerstone of NSCLC diagnosis. Cytology samples reveal higher rates of classification failures, that is, subtyping as non-small cell carcinoma-not otherwise specified (NSCC-NOS), as compared with histology specimens. This study aims to identify specific algorithms on the basis of known cytomorphologic features that aid accurate and successful subtyping of NSCLC on cytology. A total of 13 expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for nonkeratinizing LUSC. They selected from 23 predefined cytomorphologic features that they used in subtyping. Data were analyzed using machine learning algorithms on the basis of random forest method and regression trees. From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792 of 1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; p < 0.0001). Keratinization, when present, recognized LUSC with high accuracy. In its absence, the machine learning algorithms developed on the basis of experts' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates. Suboptimal recognition of LUSC in the absence of keratinization remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS seems to be an inevitable morphologic diagnosis emphasizing that ancillary immunochemistry is necessary to achieve accurate subtyping on cytology.
Identifiants
pubmed: 35331963
pii: S1556-0864(22)00145-9
doi: 10.1016/j.jtho.2022.02.013
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
793-805Investigateurs
Mary Beth Beasley
(MB)
Sabina Berezowska
(S)
Alain Borczuk
(A)
Elizabeth Brambilla
(E)
Teh-Ying Chou
(TY)
Jin-Haeng Chung
(JH)
Wendy Cooper
(W)
Sanja Dacic
(S)
Yuchen Chan
(Y)
Fred R Hirsch
(FR)
David Hwang
(D)
Philippe Joubert
(P)
Keith Kerr
(K)
Sylvie Lantuejoul
(S)
Dongmei Lin
(D)
Fernando Lopez-Rios
(F)
Daisuke Matsubara
(D)
Mari Mino-Kenudson
(M)
Andrew Nicholson
(A)
Claudia Poleri
(C)
Anja Roden
(A)
Kurt Schalper
(K)
Lynette Sholl
(L)
Erik Thunnissen
(E)
William D Travis
(WD)
Ming Tsao
(M)
Ignacio Wistuba
(I)
Gang Chen
(G)
Informations de copyright
Copyright © 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.