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

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.