Derived Neutrophil-Lymphocyte Ratio and C-Reactive Protein as Prognostic Factors for Early-Stage Non-Small Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy

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.

Prognostic utility of blood inflammation biomarkers before and after treatment on the survival of patients with locally advanced non-small cell lung cancer undergoing stereotactic body radiotherapy

BACKGROUND AND OBJECTIVE

The neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) were significant and succinct indicators of systemic inflammation. We assessed the influence of stereotactic body radiotherapy (SBRT) on NLR and PLR in patients with locally advanced non-small cell lung cancer (LA-NSCLC).

METHODS

We reviewed the medical data of patients with LA-NSCLC who underwent SBRT between 1 January 2013 and 31 December 2018. NLR and PLR values recorded at pre- and post-SBRT were examined. We assessed the correlation between pre/post-SBRT NLR and PLR and survival outcomes. The decision tree evaluation was conducted using Chi-square automatic detection.

RESULTS

In total, 213 patients were included in the study with a median follow-up duration of 40.00 (ranging from 5.28 to 100.70) months. Upon dichotomization by a median, we identified that post-SBRT NLR > 5.5 and post-SBRT PLR > 382.0 were negatively associated with shorter overall survival (OS). In the multivariate assessment, post-SBRT PLR > 382.0 was the only factor. Based on post-SBRT PLR, tumor locations, and tumor stage, we categorized patients into low, medium, or high-risk groups.

CONCLUSIONS

Post-SBRT PLR > 382.0 correlated with survival in patients undergoing SBRT. The decision tree model might play a role in future risk stratification to guide the clinical practice of individualized SBRT for LA-NSCLC.

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.

Inflammation index predicts radiation-induced lung injury and prognosis in lung tumors treated with stereotactic body radiation therapy

PURPOSE

To investigate the effect of inflammation-based indexes in predicting radiation pneumonitis (RP) and prognosis in lung tumor patients treated with stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

The data of one hundred and seventy-two patients with 272 lung lesions from November 2015 to December 2020 were retrospectively analyzed. Pretreatment hematological indexes including platelet count, neutrophil count, and lymphocyte count were collected before treatment. Systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were calculated. The receiver operating characteristic (ROC) curve was established to predict the RP and overall survival of patients, and the Youden index was calculated to determine the cutoff values of SII, NLR, and PLR before radiotherapy.

RESULTS

Pretreatment SII, NLR, and PLR could predict RP in lung tumor patients treated with SBRT, the optimal cutoff values of SII, NLR, and PLR were 355.38, 2.04, and 141.09, respectively. Pretreatment PLR could predict survival and the optimal cutoff value of PLR was 166.83, patients with a PLR > 166.83 predict worse overall survival (OS) (P < 0.001). The 1-year and 2-year OS for patients with a PLR ≤ 166.83 were 96.3% and 82.4%, while for those with a PLR > 166.83 were 82.0% and 58.5%, respectively.

CONCLUSION

In lung tumor patients treated with SBRT, pretreatment SII, NLR, and PLR can effectively predict RP and a higher PLR predicts poor OS. These inflammation-based indexes could serve as reliable and convenient predictors to guide treatment for physicians in clinical practice.

Factors associated with failure to start consolidation durvalumab after definitive chemoradiation for locally advanced NSCLC

Introduction

The introduction of consolidation immunotherapy after chemoradiotherapy has improved outcome for patients with locally advanced non-small cell lung cancer. However, not all patients receive this treatment. This study identifies factors associated with failure to start durvalumab as consolidation therapy with the aim of optimizing treatment, supportive care and prehabilitation to ensure that more patients complete the planned treatment.

Materials and methods

Patients from two clinical trials and a named patient use program, were included in this study. All patients received platinum-doublet chemotherapy concomitant with radiotherapy to a total dose of 60-66 gray. Patient characteristics, cancer treatment, toxicity, performance status and laboratory data before and after chemoradiotherapy were recorded and patients who never started durvalumab were compared with those who did.

Results

A total of 101 patients were included, of which 83 started treatments with durvalumab after chemoradiotherapy. The 18 patients who did not start durvalumab had significantly higher lactate dehydrogenase at baseline and a worse performance status, cumulative toxicity and higher c-reactive protein after completed chemoradiotherapy. Data also suggest that pre-treatment diabetes and reduced hemoglobin and/or diffusion capacity of the lungs for carbon monoxide contribute to the risk of treatment abruption.

Conclusion

Treatment plan disruption rate was 18%. Systemic inflammation and performance status were associated with failure to receive durvalumab after chemoradiation. Further studies are needed to confirm findings and prospective trials should investigate whether prehabilitation and supportive treatment could help more patients finishing the planned treatment.

Clinical Trial Registration

clinicaltrials.gov, identifier NCT03798535; NCT04392505.