Development and clinical validation of a prognostic algorithm for stroma-tumor ratio quantification in non-small cell lung cancer

Waleed K M Ahmad; Tillmann Bedau; Yuan Wang; Sebastian Michels; Anna Rasokat; Jürgen Wolf; Matthias Heldwein; Simon Schallenberg; Alexander Quaas; Reinhard Büttner; Yuri Tolkach

doi:10.1016/j.lungcan.2025.108613

Development and clinical validation of a prognostic algorithm for stroma-tumor ratio quantification in non-small cell lung cancer

Lung Cancer. 2025 Jun 1:205:108613. doi: 10.1016/j.lungcan.2025.108613. Online ahead of print.

Authors

Affiliations

¹ Institute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.
² Institute of Pathology, University Hospital Cologne, Cologne, Germany.
³ Clinic I of Internal Medicine, Center for Integrated Oncology, Germany.
⁴ Department of Cardiothoracic Surgery, University Hospital Cologne, Germany.
⁵ Institute of Pathology, Charité University Hospital, Berlin, Germany.
⁶ Institute of Pathology, University Hospital Cologne, Cologne, Germany. Electronic address: yuri.tolkach@gmail.com.

PMID: 40466465
DOI: 10.1016/j.lungcan.2025.108613

Abstract

Background and aim: Lung cancer is the leading cause of cancer-related mortality worldwide, highlighting the importance of refining diagnostic modalities. This study's main focus is the development of a digital pathology, prognostic algorithm for fully automatized quantification of stroma-tumor ratio (STR) in patients with resectable non-small cell lung cancer (NSCLC).

Materials and methods: The developed STR algorithm is built upon a powerful multi-class tissue segmentation algorithm that generates precise maps of the full tumor region. One retrospective exploration cohort of NSCLC patients (n = 902) and three validation cohorts (n = 784) of patients with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) were included to identify and validate optimal prognostic cut-offs and different risk stratification methods with regard to different clinical endpoints: overall survival (OS), cancer-specific survival (CSS) and progression-free survival (PFS).

Results: For LUAD, we show that the minimal STR value for the whole case is decisive for prognostic evaluation. Different approaches (single STR cut-off, multiple STR cut-offs, using STR as a continuous parameter) allow for robust stratification of patients into prognostic risk groups, independent of the classical clinicopathological variables and conventional histological grading. For LUSC, STR may assist in identifying a small subset of patients with unfavorable prognosis (based on the maximum STR for the whole case), however, its prognostic impact varies between cohorts.

Conclusion: STR quantification in LUAD NSCLC subtype shows a promising role as a prognostic biomarker. It can be easily implemented in routine diagnostics and could be considered as a component of advanced prognostic systems in LUAD. Our results in LUSC cohorts suggest that STR quantification in its current implementation is of limited value in this subtype.

Keywords: Digital pathology; Segmentation algorithm; Stroma-tumor ratio (STR); non-small cell lung cancer (NSCLC).