Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

Abstract

Objective: To explore the CT radiomic features and clinical imaging features of the primary tumor in patients with nonsmall cell lung cancer (NSCLC) before treatment and their predictive value for the occurrence of bone metastases.

Methods: From June 2017 to June 2021, 195 patients with NSCLC who were pathologically diagnosed without any treatment in the Cancer Hospital Affiliated to Hainan Medical College were retrospectively analyzed, and they were divided into a bone metastasis group and a nonbone metastasis group. The relationship between clinical imaging features and bone metastasis in patients was analyzed by the t-test, rank sum test, and χ ² test. At the same time, ITK software was used to extract the radiomic characteristics of the primary tumor of the patients, and the patients were randomly divided into a training group and a validation group in a ratio of 7 : 3. The training model was validated in the validation group, and the performance of the model for predicting bone metastases in NSCLC patients was verified by the ROC curve, and a multivariate logistic regression prediction model was established based on the omics parameters extracted from the best prediction model combined with clinical image features.

Results: Seven features were screened from the primary tumor by LASSO to establish a model for predicting metastasis. The area under the curve was 0.82 and 0.73 in the training and validation sets. The best omics signature and univariate analysis suggested clinical imaging factors (P < 0.05) associated with bone metastases were included in multivariate binary logistic analysis to obtain clinical characteristics of the primary tumor such as gender (OR = 0.141, 95% CI: 0.022-0.919, P = 0.04), increased Cyfra21-1 (OR = 0.12, 95% CI: 0.018-0.782, P = 0.027), Fe content in blood (OR = 0.774, 95% CI: 0.626-0.958, P = 0.018), CT signs such as lesion homogeneity (OR = 0.052, 95% CI: 0.006-0.419, P = 0.006), pleural indentation sign (OR = 0.007, 95% CI: 0.001-0.696, P = 0.034), and omics characteristics glszm_Small Area High Gray Level Emphasis (OR = 0.016, 95% CI: 0.001-0.286, P = 0.005) were independent risk factors for bone metastasis in patients.

Conclusion: The prediction model established based on radiomics and clinical imaging features has high predictive performance for the occurrence of bone metastasis in NSCLC patients.