Ahmad, Waleed K.M. 
ORCID: 0009-0000-2167-8951, Bedau, Tillmann ORCID: 0000-0001-7510-2261, Wang, Yuan, Michels, Sebastian ORCID: 0000-0003-3164-870X, Rasokat, Anna ORCID: 0009-0008-7850-6374, Wolf, Jürgen ORCID: 0000-0001-8833-8812, Heldwein, Matthias ORCID: 0000-0002-2084-795X, Schallenberg, Simon, Quaas, Alexander ORCID: 0000-0002-3537-6011, Büttner, Reinhard ORCID: 0000-0001-8806-4786 and Tolkach, Yuri ORCID: 0000-0001-5239-2841
(2025).
Development and clinical validation of a prognostic algorithm for stroma-tumor ratio quantification in non-small cell lung cancer.
Lung Cancer, 205.
pp. 1-10.
Elsevier.
ISSN 0169-5002
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Abstract
[Artikel-Nr.: 108613] 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.
| Item Type: | Article |
| Creators: | Creators Email ORCID ORCID Put Code Wang, Yuan UNSPECIFIED UNSPECIFIED UNSPECIFIED Schallenberg, Simon UNSPECIFIED UNSPECIFIED UNSPECIFIED |
| URN: | urn:nbn:de:hbz:38-805607 |
| Identification Number: | 10.1016/j.lungcan.2025.108613 |
| Journal or Publication Title: | Lung Cancer |
| Volume: | 205 |
| Page Range: | pp. 1-10 |
| Number of Pages: | 10 |
| Date: | July 2025 |
| Publisher: | Elsevier |
| ISSN: | 0169-5002 |
| Language: | English |
| Faculty: | Faculty of Medicine |
| Divisions: | Faculty of Medicine > Chirurgie > Klinik und Poliklinik für Herzchirurgie Faculty of Medicine > Innere Medizin > Klinik I für Innere Medizin - Hämatologie und Onkologie Faculty of Medicine > Pathologie und Neuropathologie > Institut für Pathologie Faculty of Medicine > Weitere > Centrum für integrierte Onkologie (CIO) |
| Subjects: | Medical sciences Medicine |
| Uncontrolled Keywords: | Keywords Language Stroma-tumor ratio (STR) ; non-small cell lung cancer (NSCLC) ; Segmentation algorithm ; Digital pathology English |
| ['eprint_fieldname_oa_funders' not defined]: | Publikationsfonds UzK |
| Refereed: | Yes |
| URI: | http://kups.ub.uni-koeln.de/id/eprint/80560 |
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