Abstract

Recognizing non-invasive growth patterns is necessary for correct diagnosis, invasive size determination and pT-stage in resected non-small cell lung carcinoma. Due to iatrogenic collapse after resection, the distinction between adenocarcinoma in-situ (AIS) and invasive adenocarcinoma may be difficult. The aim of this study is to investigate the complex morphology of non-mucinous non-invasive patterns of AIS in resection specimen with iatrogenic collapse, and to relate this to follow-up.The effects of iatrogenic collapse on the morphology of collapsed AIS were simulated in a mathematical model. Three dimensional related criteria applied in a modified classification using also cytokeratin 7 and elastin as additional stains in two independent retrospective cohorts of primary pulmonary adenocarcinomas ≤3 cm resection specimen with available follow-up information.The model demonstrated that infolding of alveolar walls occurs during iatrogenic collapse and lead to a significant increase in tumor cell heights in maximal collapse areas, compared to less collapsed areas. The morphology of infolded AIS overlaps with patterns described as papillary and acinar adenocarcinoma according to the WHO classification, necessitating an adaptation.The modified classification incorporates recognition of iatrogenic and biologic collapse, tangential cutting effect true invasion and surrogate markers of invasion i.e. grey zone, covering a multilayering falling short of micropapillary, cribriform and solid alveolar filling growth. The use of elastin and CK7 staining aids in the morphologic recognition of iatrogenic collapsed AIS and the distinction from invasive adenocarcinoma. Out of a total of 70 resection specimens 1 case was originally classified as AIS and 9 were reclassified as iatrogenic collapsed AIS. Patients with collapsed AIS showed a 100 % recurrence-free survival after a mean follow-up time of 69.5 months.With the current WHO classification, AIS is overdiagnosed as invasive adenocarcinoma due to infolding. The modified classification facilitates the diagnosis of AIS.

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