The predictive value of dual-energy computed tomography radiomics in microvessel of clear cell renal cell carcinoma.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiological Imaging, Shandong First Medical University, Jinan, China.
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
- Department of Oncology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
- Department of Radiological Imaging, Shandong Second Medical University, Weifang, China.
Abstract
This retrospective study aimed to assess the potential of radiomic features extracted from dual-energy computed tomography (DECT) images, combined with machine learning algorithms, for the noninvasive prediction of microvessel density (MVD) in clear cell renal cell carcinoma (ccRCC). We manually segmented regions of interest (ROIs) on corticomedullary phase (CMP) images to extract radiomic features. Tumor microvessel parameters were determined by immunohistochemical staining. Prediction models for MVD were developed using both multi-energy and monoenergetic sequence DECT images. Subsequently, a combined model was constructed based on the best-performing radiomics score and statistically significant clinical features, and was visualized as a nomogram. Furthermore, an external validation cohort was recruited from Center II to evaluate the performance of the nomogram. The support vector machine (SVM) classifier achieved the best performance for the multi-energy sequence MVD prediction model, with an AUC of 0.914 in the validation set. The MVD prediction model based on iodine-based material decomposition images (IMDI), constructed using the SVM classifier, achieved an AUC of 0.889 in the validation set. The nomogram showed good calibration, achieving an AUC of 0.757 in the external validation cohort. DECT-based radiomic features show potential for noninvasive predicting microangiogenesis in patients with ccRCC.