Cell Reports Medicine (Sep 2024)

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

  • Carina Kludt,
  • Yuan Wang,
  • Waleed Ahmad,
  • Andrey Bychkov,
  • Junya Fukuoka,
  • Nadine Gaisa,
  • Mark Kühnel,
  • Danny Jonigk,
  • Alexey Pryalukhin,
  • Fabian Mairinger,
  • Franziska Klein,
  • Anne Maria Schultheis,
  • Alexander Seper,
  • Wolfgang Hulla,
  • Johannes Brägelmann,
  • Sebastian Michels,
  • Sebastian Klein,
  • Alexander Quaas,
  • Reinhard Büttner,
  • Yuri Tolkach

Journal volume & issue
Vol. 5, no. 9
p. 101697

Abstract

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Summary: 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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