Predicting lung cancer survival prognosis based on the conditional survival bayesian network.
BMC Med Res Methodol
; 24(1): 16, 2024 Jan 22.
Article
in En
| MEDLINE
| ID: mdl-38254038
ABSTRACT
Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Lung Neoplasms
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Asia
Language:
En
Journal:
BMC Med Res Methodol
Journal subject:
MEDICINA
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: