The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non–Small Cell Lung Cancer
| Autor: | Won Gi Jeong, Jinheum Kim, Hyemi Choi, Kum Ju Chae |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Oncology
Cancer Research medicine.medical_specialty Lung Neoplasms Disease stages business.industry Prognosis Malignancy medicine.disease Disease-Free Survival Smoking history Disease course Carcinoma Non-Small-Cell Lung Internal medicine medicine Humans In patient Disease process Non small cell Neoplasm Recurrence Local Lung cancer business Neoplasm Staging Retrospective Studies |
| Zdroj: | Cancer Research and Treatment. 54:996-1004 |
| ISSN: | 2005-9256 1598-2998 |
| DOI: | 10.4143/crt.2021.902 |
| Popis: | Purpose The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model.Materials and Methods We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model.Results The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model.Conclusion Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients. |
| Databáze: | OpenAIRE |
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