Predictive Value of Nomogram Model Based on Clinicopathological Factors and Radiomics in Predicting Mortality Risk of Death in Elderly Patients with Advanced Non-small Cell Lung Cancer

Yinghu WANG; Qiang WANG; Yanshan MA; Zhitao XU; Meiyan NIU

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Article Navigation > Journal of Kunming Medical University > 2026 > Accepted Manuscript

Yinghu WANG, Qiang WANG, Yanshan MA, Zhitao XU, Meiyan NIU. Predictive Value of Nomogram Model Based on Clinicopathological Factors and Radiomics in Predicting Mortality Risk of Death in Elderly Patients with Advanced Non-small Cell Lung Cancer[J]. Journal of Kunming Medical University.

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Predictive Value of Nomogram Model Based on Clinicopathological Factors and Radiomics in Predicting Mortality Risk of Death in Elderly Patients with Advanced Non-small Cell Lung Cancer

1.
Department of Pulmonary Diseases
2.
Department of Radiology，Shijiazhuang Hospital of Traditional Chinese Medicine，Shijiazhuang Hebei 050051
3.
Department of Radiology，Shijiazhuang Maternal and Child Health Hospital，Shijiazhuang Hebei 050051，China

Received Date: 2026-01-09

Abstract

Abstract

Objective To explore the value of a nomogram model based on clinicopathological factors and radiomics in predicting mortality risk in elderly patients with advanced non-small cell lung cancer （NSCLC）. Methods A retrospective cohort of 290 elderly patients with advanced NSCLC admitted to Shijiazhuang Hospital of Traditional Chinese Medicine from March 2021 to March 2024 was enrolled and divided into a training set （n = 203） and validation set （n = 87） in a 7:3 ratio. One-year prognostic survival outcomes were assessed in the training set. Personal baseline characteristics, clinicopathological data, computed tomography （CT） radiomics features and scores were compared between patients with different prognoses. Cox regression analysis was performed to identify prognostic factors affecting survival in elderly patients with advanced NSCLC. A nomogram model based on radiomics was constructed to predict the prognosis survival in these patients. The predictive performance was evaluated using receiver operating characteristic （ROC） curves, calibration curves, decision curve analysis （DCA）, and clinical impact curves （CIC）. Results The Eastern Cooperative Oncology Group （ECOG） score, proportion of malnutrition, lesion diameter, differentiation grade, clinical stage, and microwave ablation （MWA） response in the deceased group showed statistically significant differences compared with the survival group （P < 0.05）. Lasso regression identified 8 optimal features. The CT radiomics score in the deceased group （0.58±0.15） was significantly higher than in the survival group （-0.42±0.13）（P < 0.05）. COX regression analysis showed that the ECOG score, lesion diameter, differentiation grade, clinical stage, MWA response, and CT radiomics score were independent prognostic factors （P < 0.05）. The nomogram model demonstrated area under the curve （AUC） values of 0.939 （95%CI: 0.905～0.972） in the training set and 0.942 （95%CI: 0.908～0.975） in the validation set. Calibration curves showed good agreement between predicted and actual outcomes. DCA curves demonstrated high clinical net benefit within the 0.2～0.8 threshold range in both sets. CIC curves showed substantial overlap between predicted and actual prognoses across the 0.8～1.0 range. Conclusion The nomogram model based on clinicopathological factors and radiomics demonstrates predictive value for mortality risk in elderly patients with advanced NSCLC, with good discrimination, calibration and clinical practicability. It can serve as an effective clinical tool for predicting mortality risk and guiding personalized, stratified interventions.
- Old age,
- Advanced non-small cell lung cancer,
- Clinicopathological factors,
- Radiomics,
- Risk of death,
- Nomogram model,
- Prediction

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References(26)

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