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Updated: Apr 24, 2026
Endobronchial Ultrasound-guided Intratumoral Injection of Cisplatin for the Treatment of Isolated Mediastinal Recurrence of Lung Cancer
Published on: February 12, 2017
Radiological, Molecular, and Pathological Factors Unite: A Model for Predicting Recurrence-Free Survival in
Hui Zeng1,2,3, Yufei Huang1, Zehao Song1
1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
The tumor and node metastasis (TNM) staging and pathological grading systems are currently insufficient for accurately predicting recurrence-free survival (RFS) in patients with pathological stage (p-stage) I lung adenocarcinoma (LUAD). Therefore, there is an urgent need for a more economical and applicable clinical prediction model to assess the risk of recurrence and guide clinical postoperative care.
Methods:
This retrospective study included 544 patients with p-stage I LUAD who were randomly allocated to development (272 patients) and validation (272 patients) cohorts. Cox regression and backward model selection were used to develop the prediction model. The predictive performance of the model was then compared with that of the current TNM staging system and two major pathological grading systems. The primary endpoint was RFS.
Results:
A total of 79 out of 544 patients with p-stage I LUAD experienced recurrence after surgery. Four risk factors were incorporated into a weighted risk index-high-grade patterns ratio, epidermal growth factor receptor mutation status, spread through air spaces status and consolidation tumor ratio-to establish the "CEHS" RFS prediction model. This model demonstrated superior predictive accuracy compared with existing staging and grading systems. High-risk patients had significantly shorter RFS than low-risk patients did. An online algorithm based on the CEHS model was also developed.
Conclusion:
We established and validated a novel model that integrates radiological, molecular and pathological features to predict RFS in patients with p-stage I LUAD. This new model exhibited excellent discriminatory power for classifying early-stage LUAD patients at different risks of recurrence.