Discussion
tumor
TUMOR
The results of our study showed that radiomics scores based on 65 keV images of AP and VP from SDCT had the optimal performance within the range of 40-65 keV in the discrimination of benign and malignant SPSNs. Furthermore, we developed and validated a radiomics-based model based on optimal radiomics-based scores derived from 65 keV images of AP and VP, which had better diagnostic performance than conventional model, and the integrated model combining radiomics model and ZTo the best of our knowledge, this is the first time to compare the performance of radiomics scores based on different keV VMIs of SDCT to distinguish benign and malignant SPSNs. Currently, in view of clinical need for image quality and resolution, we could obtain 161 monochromatic images between 40-200 keV from spectral CT. Low keV images could improve the density resolution of images and help optimize the display of low-contrast structures, as one of the important clinical applications of VMIs. Given that the reconstruction of 70 keV VMIs is roughly equivalent to a standard spectral CT acquisition performed at 120 kVp [An additional important observation was that radiomics model was significantly better than conventional model, indicating the advantages of radiomics features obtained from SDCT in the identification of SPSNs. The top two radiomics features with the greatest relative weights obtained from training dataset in radiomics model were GLCM-Cluster-Prominence of AP and GLRLM-Short-Run-High-Gray-Level-Emphasis of VP. The former revealed larger asymmetry [Moreover, we developed an integrated model based on radiomics model and ZThis study still has some limitations. Firstly, the sample size of our study was relatively small along with a large proportion of malignant SPSNs (75%) in our cohort, which may result in selection bias and exaggerate the diagnostic efficacy of predictive models to some extent, the efficacy of our models needs to be further validated in a large population. Secondly, the clinical application of our predictive models to general populations was limited to a single-center study, thus there is still a requirement for further verifying our models in a multi-center and independent validation cohort. Thirdly, no subgroup analysis of SPSNs was conducted in this study, further research based on spectral CT radiomics in the differentiation of tumor subtyping will be carried out. Finally, retrospective data collection may also lead to sample bias, and further prospective studies are still required.