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A nomogram-based survival prediction model for non-small cell lung cancer patients based on clinical risk factors and
R Nishioka1, D Kawahara1, N Imano2
1Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
Aim:
This study focuses on developing a nomogram-based overall survival (OS) prediction model for non-small cell lung cancer (NSCLC) patients by integrating clinical factors with multiregion radiomics features extracted from pretreatment CT images. The proposed nomogram aims to assist clinicians in stratifying patients into high- and low-risk groups for personalised treatment strategies.
Materials And Methods:
From 2008 to 2018, 77 NSCLC patients were included. The radiomics feature was extracted from the internal and peripheral tumour region of pretreatment computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) and the univariable Cox regression model were used to select the radiomics features. The Rad-score was defined as a linear combination of the selected radiomics features and the Cox proportional hazards regression coefficients. The combined model was constructed based on the clinicopathological factors and the Rad-score. The discrimination capacity of the prediction model was evaluated by Harrell's concordance index (C-index), the calibration curve, and the Kaplan-Meier survival curve.
Results:
We found that nine radiomics features and histology were independent predictors. The combined model showed the best performance (C-index: 0.799 [95% CI: 0.726-0.872]) compared with the clinical model (C-index: 0.692 [95% CI: 0.625-0.759]) and Rad-score (C-index: 0.663 [95% CI: 0.580-0.746]), and could significantly stratify into high-risk and low-risk NSCLC patients. The calibration curve also showed good consistency between the observation and the prediction.
Conclusions:
The multregion radiomics features have the potential for predicting OS in NSCLC patients. The nomogram-based survival prediction model demonstrates significant potential in guiding clinical decision-making, allowing for precise and personalised treatment for NSCLC patients.
