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Document type:
Journal Article
Author(s):
Kazdal, Daniel; Rempel, Eugen; Oliveira, Cristiano; Allgäuer, Michael; Harms, Alexander; Singer, Kerstin; Kohlwes, Elke; Ormanns, Steffen; Fink, Ludger; Kriegsmann, Jörg; Leichsenring, Michael; Kriegsmann, Katharina; Stögbauer, Fabian; Tavernar, Luca; Leichsenring, Jonas; Volckmar, Anna-Lena; Longuespée, Rémi; Winter, Hauke; Eichhorn, Martin; Heußel, Claus Peter; Herth, Felix; Christopoulos, Petros; Reck, Martin; Muley, Thomas; Weichert, Wilko; Budczies, Jan; Thomas, Michael; Peters, Solange; Wa...     »
Title:
Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma.
Abstract:
Background: Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification. Methods: TCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath). Results: Agreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%. Conclusions: Our results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions.
Journal title abbreviation:
Transl Lung Cancer Res
Year:
2021
Journal volume:
10
Journal issue:
4
Pages contribution:
1666-1678
Fulltext / DOI:
doi:10.21037/tlcr-20-1168
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34012783
Print-ISSN:
2218-6751
TUM Institution:
Institut für Allgemeine Pathologie und Pathologische Anatomie
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