Prediction of Progression-free Survival for Stage IB-IIA Non-small Cell Lung Cancer Based on CT Morphological Features and Clinicopathological Characteristics
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objectives: The aim of this study is to evaluate the feasibility of computed tomography (CT) morphological features and clinicopathological characteristics in predicting progression-free survival (PFS) for patients with stage IB-IIA non-small cell lung cancer (NSCLC). Methods: A total of 95 patients with stage IB-IIA NSCLC who underwent CT scans and surgical resection were retrospectively included in our study. The CT morphological features and clinicopathological characteristics were assessed by two observers. Univariate and multivariate cox proportional hazards regression analysis were used to identify significant PFS predictors and construct prediction model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive ability of the model for progression-free survival at 1-, 2- and 3-years. In internal validation, the predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the overall relative C-index. Results: Progression were found in 17 patients (17.9%, 17/95). Four risk factors were determined in the multiple stepwise Cox regression analysis, including male, age, sublobectomy, and lobulation, as well as CT diameter. The AUC of the model for predicting 1, 2, and 3 years PFS was 0.899, 0.799 and 0.833, respectively. In the internal validation cohort, the overall relative C index of the model was 0.82. The model showed good prediction performance. Conclusion: The model constructed from CT morphological features and clinicopathological characteristics helps predict PFS in patients with stage IB-IIA NSCLC and distinguish between high-risk and low-risk patients, which could help the doctor make Postoperative decisions.
Authors: Junjun Liang, Yunjin Long, Haotian Zhu, Xin Yang, Huanlong Lu, Xun Wang
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4407919123