肺癌
病理
癌症
医学
算法
肺
计算机科学
内科学
作者
Carina Kludt,Yuan Wang,Waleed Ahmad,Andrey Bychkov,Junya Fukuoka,Nadine T. Gaisa,Mark Kühnel,Danny Jonigk,Alexander Seper,Fabian Mairinger,Florian Klein,Anne M. Schultheis,Alexander Seper,Wolfgang Hulla,Johannes Brägelmann,Sebastian Michels,Sebastian Klein,Alexander Quaas,Reinhard Büttner,Yuri Tolkach
标识
DOI:10.1016/j.xcrm.2024.101697
摘要
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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