Development and validation of a nomogram for local control prediction in lung cancer patients treated with stereotactic body radiation therapy based on clinical, dosimetric, and inflammation-related parameters

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Abstract
The incidence of local recurrence remains noteworthy among lung cancer patients treated with stereotactic body radiation therapy (SBRT). The aim of the study is to identify the risk factors and develop a nomogram for local control (LC) prediction. One hundred fifty-eight primary or metastatic lung cancer patients treated with SBRT were retrospectively analyzed. The clinical, dosimetric and inflammation-related parameters were collected. The Cox regression analysis was performed to determine the independent prognostic factors. A nomogram based on the prognostic factors was established and internally validated using a bootstrap resampling method. The median follow-up time for the whole cohort was 40 months (95% CI: 34–46) and 35.4% of the patients (56/158) experienced local recurrence. The 1-year, 3-year and 5-year LC rates were 97.4%, 85.8% and 76.1%. Multivariate Cox regression analysis revealed that six independent factors were associated with LC, including age, clinical stage, planning target volume (PTV) volume, BED of the prescription dose (BEDPD), lymphocyte count, and neutrocyte count. The bootstrap-corrected C-index of the developed nomogram was 0.745 (95% CI, 0.663–0.793). The time-dependent AUC indicated the nomogram exhibited strong discriminatory capability. Calibration curves demonstrated a good concordance between the predicted and the observed probabilities. The results of decision curve analysis highlighted the clinical utility of the model. Additionally, the high- and low-risk patients were stratified based on the cut-off point from the nomogram (P < 0.0001). A nomogram based on the clinical, dosimetric, and inflammation-related predictors is developed for LC prediction in lung cancer patients treated with SBRT. External validation is required for further confirm its validity.
Funding Information
  • National Natural Science Foundation of China (81602667)
  • Guangdong Medical Research Foundation (A2015534, B2023390)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2023A1515220015)
  • Shantou Medical and Health Science and Technology Planning Project ([2022] 88-7, [2022] 81-1)

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