Abstract:

Objective To explore the predictive efficacy of a combined model integrating intratumoral and peritumoral CT radiomic features and clinical signatures for bone metastasis in non-small cell lung cancer（NSCLC）. Methods A retrospective analysis was conducted on chest arterial-phase CT images and clinical data from 537 pathologically confirmed NSCLC patients at Tangshan People's Hospital between January 2017 and December 2019. Patients were divided into a training cohort（n=376）and a validation set（n=161）in a 7∶3 ratio. Predictive models were developed within the training set， and the predictive efficacy was evaluated in both the training and validation sets respectively， and the clinical application value was verified as well. Using univariate and multivariate logistic regression analysis， the influencing factors for bone metastasis in NSCLC patients were investigated. Radiomics models were established for the tumor interior group， the tumor interior combined with a 3 mm peritumoral group， and the simple 3 mm peritumoral group. The best model was selected and combined with clinical signatures to construct a combined model. The diagnostic efficacy and clinical application value of the model were evaluated using receiver operator characteristic（ROC）curves， the calibration curve， and decision curve analysis（DCA）. Results Among the 537 NSCLC patients， 414 had bone metastasis（290 in training set， 124 in validation set）. Univariate analysis showed that， smoking history， tumor location， T stage， N stage， pathological type， D-dimer， carcinoembryonic antigen（CEA）， cytokeratin fragment antigen 21-1， squamous cell carcinoma antigen， spiculation sign， lobulation sign， pleural indentation sign， and vascular convergence sign were all influencing factors predicting bone metastasis in NSCLC patients（all P<0.05）. Multivariate analysis showed that， T stage（OR=0.69， 95%CI：0.52-0.87， P<0.001）， N stage（OR=0.24， 95%CI：0.13-0.43， P<0.001）， pathological type（OR=6.01， 95%CI：2.83-12.77， P<0.001）， D-dimer（OR=0.32， 95%CI：0.17-0.59， P<0.001）， CEA（OR=0.25， 95%CI：0.14-0.44， P<0.001）， spiculation sign（OR=0.21， 95%CI：0.07-0.65， P=0.007）， and pleural indentation sign（OR=0.32， 95%CI：0.18-0.56， P<0.001）were all independent influencing factors predicting bone metastasis in NSCLC patients. ROC curve analysis showed that， the area under the curve（AUC）for predicting bone metastasis in the training set were 0.81， 0.79， and 0.74 for the models in the tumor interior group， the tumor interior combined with a 3 mm peritumoral group， and the simple 3 mm peritumoral group， respectively. The predictive value of the model in the tumor interior group was higher than that of the models in the tumor interior combined with a 3 mm peritumoral group and the simple 3 mm peritumoral group（Z=1.46， P=0.032； Z=3.01， P=0.024）. In the validation set， the AUC were 0.66， 0.63， and 0.53， respectively， with the predictive value of the model in the tumor interior group being higher than that of the models in the tumor interior combined with a 3 mm peritumoral group and the simple 3 mm peritumoral group（Z=2.37， P=0.025； Z=4.12， P=0.012）. A combined model was established using the radiomic features in the tumor interior group and the influencing factors with statistical significance from the multivariate analysis. The AUC of the combined model was 0.94 in the training set， which was higher than that of the model in the tumor interior group alone（Z=2.43， P=0.023）. In the validation set， the combined model's AUC was 0.92， which was also higher than that of the model in the tumor interior group（Z=3.76， P=0.007）. The calibration curve showed that the actual probabilities of both the training set and the validation set were in relatively good agreement with the predicted probabilities. DCA showed good discrimination ability for the combined model. Conclusions T stage， N stage， pathological type， D-dimer， CEA， spiculation sign， pleural indentation sign are all independent influencing factors predicting bone metastasis in NSCLC patients. Among the radiomics models， the model in the tumor interior group demonstrates higher predictive efficacy for NSCLC bone metastasis. The combined model constructed based on the above factors can further improve the predictive efficacy of bone metastasis in NSCLC patients，showing promising potential for clinical application.

Key words: Carcinoma, non-small-cell lung, Neoplasm metastasis, Radiomics, Chest-enhanced computed tomography, Prediction model