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.

• Kludt, C.
• Wang, Y.
• Ahmad, W.
• Bychkov, A.
• Fukuoka, J.
• Gaisa, N.
• Kühnel, M.
• Jonigk, D.
• Pryalukhin, A.
• Mairinger, F.
• Klein, F.
• Schultheis, A. M.
• Seper, A.
• Hulla, W.
• Brägelmann, J.
• Michels, S.
• Klein, S.
• Quaas, A.
• Büttner, R.
• Tolkach, Y.