Dosiomics and Radiomics Features-Based Model to Predict the Neutrophil-Lymphocyte Ratio in Locally Advanced Non-Small Cell Lung Cancer Treated with Definitive Radiotherapy

Abstract

<h3>Purpose/Objective(s)</h3> The neutrophil-to-lymphocyte (NLR) is a marker of systemic inflammatory that may predict clinical outcomes of many malignancies. This study therefore developed and validated a dosiomics and radiomics model based on three-dimensional (3D) dose distribution map and planning computed tomography (CT) images to predict the post-radiotherapy (post-RT) NLR. <h3>Materials/Methods</h3> We retrospectively collected 242 locally advanced non-small cell lung cancer (LA-NSCLC) patients. These patients were treated with definitive radiotherapy from 2012 to 2016. The model was developed and validated to predict the NLR collected one month after the completion of RT. Patients were randomly assigned (2:1) into development set and independent testing set. We extracted dosiomics features from 3D dose maps and radiomics features from CT images within different region of interest (ROIs). We developed the prediction model with gradient boosting tree (GBDT) using the features with good discriminability and low redundancy. We tuned the hyperparameters of GBDT model by using five-fold cross-validation. At last, we developed a fusion mode which combined dosiomics, radiomics and clinical features with weighted sum of different model's prediction probability. The model's discriminability was evaluated by receiver operating characteristic curve (ROC) and area under the curve AUC). Kaplan-Meier (KM) curves were used to explore the association between the predicted NLR and patient's survival. <h3>Results</h3> The clinical-based model and DVH-based model achieved an AUC of 0.632 and 0.621 in the testing set, respectively. The 8 features-based dosiomics and 4 features-based radiomics model achieved an AUC of 0.730 (95% CI: 0.599 – 0.860) and 0.701 (0.576 – 0.827). The dosiomics and radiomics and clinical fusion model improved the model's generalization ability with an AUC of 0.751 (95% CI: 0.623 – 0.880), sensitivity of 60.9% and specificity of 80.7% in the testing set. Moreover, the fusion model can significantly divide patients into high-risk and low-risk groups of progression free survival (PFS) and overall survival (OS) (log-rank test, p = 0.0056 and 0.0013). <h3>Conclusion</h3> In patients with LA-NSCLC treated with definitive radiotherapy, the dosiomics and radiomics can improve the prediction of the post-RT NLR.