Incorporating the inflammation-related parameters enhances the performance of the nomogram for predicting local control in lung cancer patients treated with stereotactic body radiation therapy
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Summary
This summary is machine-generated.Adding inflammation markers to nomograms improves local control prediction for lung cancer patients undergoing stereotactic body radiation therapy (SBRT). This enhances accuracy in predicting treatment outcomes.
Area Of Science
- Oncology
- Radiation Oncology
- Medical Physics
Background
- Stereotactic body radiation therapy (SBRT) is a key treatment for lung cancer.
- Accurate prediction of local control (LC) is crucial for optimizing SBRT outcomes.
- Existing predictive models may not fully capture all relevant prognostic factors.
Purpose Of The Study
- To evaluate if incorporating inflammation-related parameters enhances a nomogram's accuracy for predicting local control (LC) in lung cancer patients treated with SBRT.
- To compare the predictive performance of a nomogram with and without inflammation markers.
Main Methods
- Retrospective analysis of 158 lung cancer patients treated with SBRT.
- Collected clinical, dosimetric, and inflammation-related parameters.
- Developed two Cox regression models: ACPB (clinical and dosimetric factors) and ACPBLN (including inflammation factors).
- Compared model performance using ROC, AIC, C-index, time-dependent AUC, NRI, IDI, calibration plots, and DCA.
Main Results
- Six factors, including lymphocyte and neutrocyte counts, were independently associated with LC.
- The ACPBLN model demonstrated superior performance over the ACPB model in AIC, C-index, AUC, NRI, and IDI.
- Calibration plots indicated good consistency in both models.
- Decision curve analysis showed higher net benefit for the ACPBLN nomogram.
Conclusions
- Inflammation-related parameters are significantly associated with local control in lung cancer patients receiving SBRT.
- Including these parameters substantially improves the predictive accuracy of nomograms for LC in SBRT.
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