1. Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.
- Author
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Kludt C, Wang Y, Ahmad W, Bychkov A, Fukuoka J, Gaisa N, Kühnel M, Jonigk D, Pryalukhin A, Mairinger F, Klein F, Schultheis AM, Seper A, Hulla W, Brägelmann J, Michels S, Klein S, Quaas A, Büttner R, and Tolkach Y
- Subjects
- Humans, Prognosis, Female, Male, Carcinoma, Squamous Cell pathology, Carcinoma, Squamous Cell diagnosis, Lung Neoplasms pathology, Lung Neoplasms diagnosis, Algorithms, Carcinoma, Non-Small-Cell Lung pathology, Carcinoma, Non-Small-Cell Lung diagnosis
- 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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
- Published
- 2024
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