Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system

Abstract

Abstract Background Several studies have proposed grading systems for risk stratification of early‐stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients. Methods Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression‐free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log‐rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index. Results A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high‐risk group showed a 5‐year OS of 52.4% compared to 89.9% and 97.5% in the medium and low‐risk groups, respectively. The 5‐year PFS of patients in the high‐risk group was 38.1% compared to 61.7% and 90.9% in the medium and low‐risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index. Conclusion The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients.

Keywords

• grading system
• histological subtypes
• lung adenocarcinoma
• machine learning
• spread through air spaces