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

• Published in: Lung cancer (Amsterdam, Netherlands) Vol. 205; p. 108613
• Main Authors: Ahmad, Waleed K.M., Bedau, Tillmann, Wang, Yuan, Michels, Sebastian, Rasokat, Anna, Wolf, Jürgen, Heldwein, Matthias, Schallenberg, Simon, Quaas, Alexander, Büttner, Reinhard, Tolkach, Yuri
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.07.2025
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Carcinoma, Non-Small-Cell Lung - diagnosis; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Digital pathology; Female; Humans; Lung Neoplasms - diagnosis; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Male; Middle Aged; non-small cell lung cancer (NSCLC); Prognosis; Retrospective Studies; Segmentation algorithm; Stroma-tumor ratio (STR); Stromal Cells - pathology; Digital pathology; Segmentation algorithm; non-small cell lung cancer (NSCLC); Stroma-tumor ratio (STR)
• ISSN: 0169-5002; 1872-8332; 1872-8332
• DOI: 10.1016/j.lungcan.2025.108613

•Automated tool quantifies stroma-tumor ratio (STR) in non-small cell lung cancer.•Algorithm segments H&E-stained tissues into 11 classes for STR analysis.•Four patient cohorts used to identify and validate prognostic cut-offs for survival.•STR confirmed as an independent prognostic parameter in lung adenocarcinoma. 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). 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). 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. 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.