Integrating Histologic Descriptors into the Ninth Edition TNM Staging Improves Prognostic Stratification of Lung Adenocarcinoma
Abstract
Abstract Background Histologic descriptors such as lymphovascular invasion (LVI), visceral pleural invasion (VPI), spread through air spaces (STAS), and grading system have each been associated with adverse outcomes in lung adenocarcinoma (LUAD). However, with the exception of VPI, these features are not formally incorporated into the TNM staging system. We evaluated the prognostic value and incremental contribution of these histologic descriptors within the framework of the 9 th edition TNM staging system. Methods In total, 1,745 individuals diagnosed with stage I–III invasive non-mucinous lung adenocarcinoma (NM-LUAD) were included in this study, comprising 1139 French-Canadian patients who underwent surgical resection at IUCPQ–Université Laval (discovery cohort) and 606 patients from the National Cancer Center Hospital in Tokyo, Japan (validation cohort). The objective of this study was to assess the prognostic contribution of histologic descriptors, including STAS, and LVI, as complements to conventional 9 th edition TNM staging. Results Grade 3 tumors, LVI, and STAS were identified in 880 (50.4%), 809 (46.4%), and 775 (44.4%) of 1745 cases, respectively. Histologic grade and LVI demonstrated the strongest associations, particularly in early-stage disease, while STAS exhibited a stage-dependent effect, being more impactful in stages II-III. VPI showed less consistent prognostic value. Incorporating these histologic descriptors into TNM staging improved prognostic model performance, with the largest gains driven by histologic grade and LVI, while STAS provided additional, complementary prognostic refinement. Conclusion These findings demonstrate that key histologic descriptors—including grading system, LVI, and STAS—represent robust and reproducible prognostic parameters. Importantly, these descriptors provide complementary, stage-dependent information that may enhance risk stratification and inform refinement of future TNM staging frameworks, including the forthcoming 10 th edition.
Fields of Study & Subjects
Lung Cancer Diagnosis and Treatment
Radiomics and Machine Learning in Medical Imaging
MRI in cancer diagnosis
Publication details
- Journal / source
- medRxiv
- Publisher
- openRxiv
- Publication date
- 18 February 2026
- Document type
- Preprint