CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma
| Autor: | Ge Wen, Yuanmeng Yu, Fu Yin, Menglin Chen, Haijie Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Clear cell renal cell carcinoma
Multi phase R895-920 LASSO regression Medical physics. Medical radiology. Nuclear medicine Lasso (statistics) Humans Medicine Radiology Nuclear Medicine and imaging Radiometry Carcinoma Renal Cell Clinical treatment RC254-282 Retrospective Studies Radiomics Improved enhanced parameters Radiological and Ultrasound Technology Receiver operating characteristic business.industry Neoplasms. Tumors. Oncology. Including cancer and carcinogens Cell Differentiation General Medicine Middle Aged medicine.disease Tumor tissue Kidney Neoplasms Oncology Excretory phase Tomography X-Ray Computed business Nuclear medicine Clear cell Research Article |
| Zdroj: | Cancer Imaging Cancer Imaging, Vol 21, Iss 1, Pp 1-13 (2021) |
| ISSN: | 1470-7330 |
| Popis: | Background The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs). Methods A total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p Results The three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748–0.823, 0.776–0.887 and 0.864–0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p Conclusions The predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options. |
| Databáze: | OpenAIRE |
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