Prognostic utility of pretreatment neutrophil-lymphocyte ratio in survival outcomes in localized non-small cell lung cancer patients treated with stereotactic body radiotherapy: Selection of an ideal clinical cutoff point

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

PURPOSE

The study aims to investigate whether including the inflammation-related parameters would enhance the accuracy of a nomogram for local control (LC) prediction in lung cancer patients undergoing stereotactic body radiation therapy (SBRT).

METHODS

158 primary or metastatic lung cancer patients treated with SBRT were retrospectively analyzed. The clinical, dosimetric and inflammation-related parameters were collected for the Cox regression analysis. The ACPB model was constructed by employing the clinical and dosimetric factors. And the ACPBLN model was established by adding the inflammation-related factors to the ACPB model. The two models were compared in terms of ROC, Akaike Information Criterion (AIC), C-index, time-dependent AUC, continuous net reclassification index (NRI), integrated discrimination improvement (IDI), calibration plots and decision curve analysis (DCA).

RESULTS

Multivariate Cox regression analysis revealed that six prognostic factors were independently associated with LC, including age, clinical stage, planning target volume (PTV) volume, BED of the prescribed dose (BEDPD), the lymphocyte count and neutrocyte count. The ACPBLN model performed better in AIC, bootstrap-corrected C-index, time-dependent AUC, NRI and IDI than the ACPB model. The calibration plots showed good consistency between the probabilities and observed values in the two models. The DCA curves showed that the ACPBLN nomogram had higher overall net benefit than the ACPB model across a majority of threshold probabilities.

CONCLUSION

The inflammation-related parameters were associated with LC for lung cancer patients treated with SBRT. The inclusion of the inflammation-related parameters improved the predictive performance of the nomogram for LC prediction.

Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy

PURPOSE

The study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters.

METHODS

Clinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction.

RESULTS

With a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735-0.914), 0.886 (95% CI 0.790-0.948), and 0.914 (95% CI 0.825-0.966) in the training cohort, and 0.757 (95% CI 0.580-0.887), 0.818 (95% CI 0.648-0.929), and 0.843 (95% CI 0.677-0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility.

CONCLUSION

A clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.

Association of neutrophil-lymphocyte ratio with survival in peripheral early-stage non-small cell lung cancer after stereotactic body radiation therapy

BACKGROUND

The role of neutrophil-lymphocyte ratio (NLR) as a predictor for survival in single fraction SBRT-treated non-small cell lung cancer (NSCLC) patients remains unclear. We performed an observational cohort study to determine the role of pretreatment NLR in predicting survival of early-stage NSCLC patients after single fraction SBRT.

METHODS

A single-institution database of peripheral early-stage NSCLC patients treated with SBRT from February 2007 to May 2022 was queried. Optimal threshold of neutrophil-lymphocyte ratio (NLR) was defined based on maximally selected rank statistics. Cox multivariable analysis (MVA), Kaplan-Meier, and propensity score matching were performed to evaluate outcomes.

RESULTS

A total of 286 patients were included for analysis with median follow up of 19.7 months. On Cox multivariate analysis, as a continuous variable, NLR was shown to be an independent predictor of OS (adjusted hazards ratio [aHR] 1.06, 95% CI 1.02-1.10, p = 0.005) and PFS (aHR 1.05, 95% CI 1.01-1.09, p = 0.013). In addition, NLR was associated with DF (aHR 1.11, 95% CI 1.05-1.18, p < 0.001). Maximally selected rank statistics determined 3.28 as the cutoff point of high NLR versus low NLR. These findings were confirmed upon propensity matching.

CONCLUSIONS

Pretreatment NLR is an independent predictor for survival outcomes of peripheral early-stage NSCLC patients after single fraction SBRT.

Impact of Pre-Treatment NLR and Other Hematologic Biomarkers on the Outcomes of Early-Stage Non-Small-Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy

Introduction: We evaluated the association of pre-treatment immunologic biomarkers on the outcomes of early-stage non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). Materials and methods: In this retrospective study, all newly diagnosed early-stage NSCLC treated with SBRT between January 2010 and December 2017 were screened and included for further analysis. The pre-treatment neutrophil-lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated by Kaplan–Meier. Multivariable models were constructed to determine the impact of different biomarkers and the Akaike information criterion (AIC), index of adequacy, and scaled Brier scores were calculated. Results: A total of 72 patients were identified and 61 were included in final analysis. The median neutrophil count at baseline was 5.4 × 10⁹/L (IQR: 4.17–7.05 × 10⁹/L). Median lymphocyte count was 1.63 × 10⁹/L (IQR: 1.29–2.10 × 10⁹/L), median monocyte count was 0.65 × 10⁹/L (IQR: 0.54–0.83 × 10⁹/L), median platelet count was 260.0 × 10⁹/L (IQR: 211.0–302.0 × 10⁹/L). The median NLR was 3.42 (IQR: 2.38–5.04), median MLR was 0.39 (IQR: 0.31–0.53), and median PLR was 156.4 (IQR: 117.2–197.5). On multivariable regression a higher NLR was associated with worse OS (p = 0.01; HR-1.26; 95% CI 1.04–1.53). The delta AIC between the two multivariable models was 3.4, suggesting a moderate impact of NLR on OS. On multivariable analysis, higher NLR was associated with poor RFS (p = 0.001; NLR^1 HR 0.36; 0.17–0.78; NLR^2 HR-1.16; 95% CI 1.06–1.26) with a nonlinear relationship. The delta AIC between the two multivariable models was 16.2, suggesting a strong impact of NLR on RFS. In our cohort, MLR and PLR were not associated with RFS or OS in multivariable models. Conclusions: Our study suggests NLR, as a biomarker of systemic inflammation, is an independent prognostic factor for OS and RFS. The nonlinear relationship with RFS may indicate a suitable immunological environment is needed for optimal SBRT action and tumoricidal mechanisms. These findings require further validation in independent cohorts.