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Optimised architecture-based grading system as an independent prognostic factor in resected lung adenocarcinoma
- Source :
- Journal of Clinical Pathology; 2022, Vol. 75 Issue: 3 p176-184, 9p
- Publication Year :
- 2022
-
Abstract
- AimsConsidering morphological heterogeneity of lung adenocarcinoma (LUAD) and no objective prognostic grading system existing currently, we aim to establish an ‘optimised architecture-based grading system’ (OAGS) to predict prognosis for resected LUAD.MethodsA multicentral study involving three independent cohorts of LUAD was conducted. Predictive ability of the OAGS for recurrence-free probability (RFP) and overall survival (OS) was assessed in training cohort (n=228) by the area under the receiver operating characteristic curve (AUC), Harrell’s concordance index (C-index) and Kaplan-Meier survival analyses, which was validated in testing (n=135) and validation (n=226) cohorts.ResultsThe OAGS consists of: grade 1 for lepidic, papillary or acinar predominant tumour with no or less than 5% of high-grade patterns (cribriform, solid and or micropapillary), grade 2 for lepidic, papillary or acinar predominant tumour with 5% or more of high-grade patterns, and grade 3 for cribriform, solid or micropapillary predominant tumour. In all stages, the OAGS outperformed the pattern-dominant grading system and IASLC grading system for predicting RFP (C-index, 0.649; AUC, 0.742) and OS (C-index, 0.685; AUC, 0.754). Multivariate analysis identified it as an independent predictor of both (RFP, p<0.001; OS, p<0.001). Furthermore, in pT1-2aN0M0 subgroup, the OAGS maintained its ability to predict recurrence (C-index, 0.699; AUC, 0.769) and stratified patients into different risk groups of RFP (p<0.001). These results were confirmed in testing and validation cohorts.ConclusionsThe OAGS is an independent prognostic factor and shows a robust ability to predict prognosis for resected LUAD.
Details
- Language :
- English
- ISSN :
- 00219746 and 14724146
- Volume :
- 75
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- Journal of Clinical Pathology
- Publication Type :
- Periodical
- Accession number :
- ejs58964417
- Full Text :
- https://doi.org/10.1136/jclinpath-2020-207104