Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. In this study, we develop a clinically useful computational pathology platform for NSCLC that can be a foundation for multiple downstream applications and provide immediate value for patient care optimization and individualization. We train the primary multi-class tissue segmentation algorithm on a substantial, high-quality, manually annotated dataset of whole-slide images with lung adenocarcinoma and squamous cell carcinomas. We investigate two downstream applications. NSCLC subtyping algorithm is trained and validated using a large, multi-institutional (n = 6), multi-scanner (n = 5), international cohort of NSCLC cases (slides/patients 4,097/1,527). Moreover, we develop four AI-derived, fully explainable, quantitative, prognostic parameters (based on tertiary lymphoid structure and necrosis assessment) and validate them for different clinical endpoints. The computational platform enables the high-precision, quantitative analysis of H&E-stained slides. The developed prognostic parameters facilitate robust and independent risk stratification of patients with NSCLC.

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Publication Details

Journal
Cell Reports Medicine
Published
2024-08-23
DOI
https://doi.org/10.1016/j.xcrm.2024.101697
Citations
20
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
6.95
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article

Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms

Mark Kühnel, Reinhard Büttner, Fabian Mairinger, Sebastian Yves Friedrich Michels et al.
20 citations
Cell Reports Medicine
AI in cancer detection
6.95
article

Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms

Mark Kühnel, Reinhard Büttner, Fabian Mairinger, Sebastian Yves Friedrich Michels, Junya Fukuoka, Alexander Quaas, Andrey Bychkov, Wolfgang Hulla, Yuri Tolkach, Anne M. Schultheis, Danny Jonigk, Nadine Therese Gaisa, Johannes Brägelmann, Waleed Ahmad, Yuan Wang, Sebastian Klein, Carina Kludt, Franziska Klein, Alexander Seper, Alexey Pryalukhin
article en
20 citations

Abstract

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. In this study, we develop a clinically useful computational pathology platform for NSCLC that can be a foundation for multiple downstream applications and provide immediate value for patient care optimization and individualization. We train the primary multi-class tissue segmentation algorithm on a substantial, high-quality, manually annotated dataset of whole-slide images with lung adenocarcinoma and squamous cell carcinomas. We investigate two downstream applications. NSCLC subtyping algorithm is trained and validated using a large, multi-institutional (n = 6), multi-scanner (n = 5), international cohort of NSCLC cases (slides/patients 4,097/1,527). Moreover, we develop four AI-derived, fully explainable, quantitative, prognostic parameters (based on tertiary lymphoid structure and necrosis assessment) and validate them for different clinical endpoints. The computational platform enables the high-precision, quantitative analysis of H&E-stained slides. The developed prognostic parameters facilitate robust and independent risk stratification of patients with NSCLC.

Cell Reports MedicineVol. 5(9)
Danube Private University (AT), University of Cologne (DE), Fachhochschule Wiener Neustadt (AT), Düsseldorf University Hospital (DE), Kameda Medical Center (JP), Essen University Hospital (DE), Universitätsklinikum Aachen (DE), German Center for Lung Research (DE), University Hospital Cologne (DE), University Hospital Ulm (DE), Nagasaki University (JP), Heinrich Heine University Düsseldorf (DE)
Good health and well-being
Openalex Percentile: Top 3%
AI in cancer detection
6.95
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