Objective The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT. Methods Sixty patients with 39 malignant nodules and 21 benign nodules underwent chest contrast CT scans using spectral imaging mode during pulmonary arterial phase (PP), arterial phase (AP), and venous phase (VP). Iodine concentrations of proximal and distal regions in pulmonary nodules on iodine-based material decomposition images were recorded. Normalized iodine concentration (NIC) and the differences in NIC between the proximal and the distal regions (dNIC) were calculated. The two-sample t-test and Mann–Whitney U-test were performed to compare the multiple parameters generated from spectral CT between malignant and benign nodules. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity. Results NIC in the proximal region (NICpro) and NIC in the distal region (NICdis) between malignant and benign nodules at AP (NICpro, P=0.012; NICdis, P=0.024), and VP (NICpro, P=0.005; NICdis, P =0.004) were significantly different. NICpro at PP (P = 0.037) was also found significantly different between malignant and benign nodules; however, no significant differences were found in NICdis at PP (P = 0.093). In addition, the dNIC of malignant nodules was significantly higher than that of benign ones at PP (median and interquartiles (0.31, 0.11, 0.57 versus -0.26, -0.5, -0.1); p≤0.001), AP (mean dNIC, 0.093 ±0.094 versus -0.075±0.060; p≤0.001), and VP (mean dNIC, 0.171±0.137 versus -0.183±0.127; p≤0.001). The sensitivity and specificity (93%, 95%, respectively) of dNIC during VP were higher than other parameters, with a threshold value of -0.07. Conclusions Spectral CT imaging with multiple parameters such as NICpro, NICdis, and dNIC may be a new method for differentiating malignant SPNs from benign ones.

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Objectives Construct and validate a nomogram model integrating the radiomics features and the clinical risk factors to differentiating axial spondyloarthritis (axSpA) in low back pain patients undergone sacroiliac joint (SIJ)- magnetic resonance imaging (MRI). Methods 638 patients confirmed as axSpA (n= 424) or non-axSpA (n = 214) who were randomly divided into training (n = 447) and validation cohorts (n = 191). Optimal radiomics signatures were constructed from the 3.0T SIJ-MRI using maximum relevance–minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. We also included six clinical risk predictors to build clinical model. Incorporating the independent clinical factors and Rad-score, a nomogram model was constructed by multivariable logistic regression analysis. The performance of the clinical, Rad-score, and nomogram model were evaluated by ROC analysis, calibration curve and decision curve analysis (DCA). Results 1316 features were extracted and reduced to 15 features to build the Rad-score. The Rad-score allowed a good discrimination in the training (AUC, 0.82; 95% CI, 0.77-0.86) and the validation cohort (AUC, 0.82; 95% CI, 0.76-0.88). The clinical-radiomics nomogram model also showed favorable discrimination in the training (AUC, 0.90; 95% CI, 0.86-0.93) and the validation cohort (AUC, 0.90; 95% CI, 0.85-0.94). Calibration curves (p > 0.05) and DCA demonstrated the nomogram was useful for axSpA diagnosis in the clinical environment. Conclusion The study proposed a radiomics model was able to separate axSpA and non-axSpA. The clinical-radiomics nomogram can increase the efficacy for differentiating axSpA, which might facilitate clinical decision-making process.

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