Logo Logo
Switch Language to German

Kazdal, Daniel; Rempel, Eugen; Oliveira, Cristiano; Allgaeuer, Michael; Harms, Alexander; Singer, Kerstin; Kohlwes, Elke; Ormanns, Steffen; Fink, Ludger; Kriegsmann, Joerg; Leichsenring, Michael; Kriegsmann, Katharina; Stoegbauer, Fabian; Tavernar, Luca; Leichsenring, Jonas; Volckmar, Anna-Lena; Longuespee, Remi; Winter, Hauke; Eichhorn, Martin; Heussel, Claus Peter; Herth, Felix; Christopoulos, Petros; Reck, Martin; Muley, Thomas; Weichert, Wilko; Budczies, Jan; Thomas, Michael; Peters, Solange; Warth, Arne; Schirmacher, Peter; Stenzinger, Albrecht and Kriegsmann, Mark (2021): Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma. In: Translational Lung Cancer Research, Vol. 10, No. 4: pp. 1666-1678

Full text not available from 'Open Access LMU'.


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.

Actions (login required)

View Item View Item