Value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma and type II papillary renal cell carcinoma

Liang, Xiaohong; Xue, Caiqiang; Huang, Xiaoyu; Wei, Jinyan; Zhang, Junlin

doi:10.1177/02841851211002830

Cited by 3 publications

(3 citation statements)

References 25 publications

(28 reference statements)

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“…Various clinical and imaging features help distinguish high-grade ccRCC from type 2 pRCC [15,22,23]. Several clinical characteristics were included in our analysis, although most did not differ statistically between high-grade ccRCC and type 2 pRCC.…”

Section: Discussionmentioning

confidence: 99%

“…TEV1, TEV2, REV1, and REV2 were statistically substantially different in identifying these two tumors (p < 0.001), confirming earlier research findings. According to Zhang et al [22] and Liang et al [15], ccRCC was more enhanced than pRCC because of the high degree of malignancy and the abundance of tumor neovascularization.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Sun et al [14] demonstrated that studies using dynamic contrast-enhanced MRI, ccRCC, and non-ccRCC had different enhancement patterns. Liang et al [15] energy spectrum CT parameters could identify high-grade ccRCC and type 2 pRCC. Nevertheless, on regular CT or MRI images, these two tumor types have similar imaging features.…”

Section: Introductionmentioning

confidence: 99%

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A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC

Gao

Zhao

Wang

et al. 2022

Journal of Oncology

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Purpose. A nomogram was constructed by combining clinical factors and a CT-based radiomics signature to discriminate between high-grade clear cell renal cell carcinoma (ccRCC) and type 2 papillary renal cell carcinoma (pRCC). Methods. A total of 142 patients with 71 in high-grade ccRCC and seventy-one in type 2 pRCC were enrolled and split into a training cohort (n = 98) and a testing cohort (n = 44). A clinical factor model containing patient demographics and CT imaging characteristics was designed. By extracting the radiomics features from the precontrast phase, corticomedullary phase (CMP), and nephrographic phase (NP) CT images, a radiomics signature was established, and a Rad-score was computed. By combining the Rad-score and significant clinical factors using multivariate logistic regression analysis, a clinical radiomics nomogram was subsequently developed. The diagnostic performance of these three models was evaluated by using data from both the training and testing groups using a receiver operating characteristic (ROC) curve analysis. Results. The radiomics signature contained eight validated features from the CT images. The relative enhancement value of CMP (REV1) was an independent risk factor in the clinical factor model. The area under the curve (AUC) value of the clinical radiomics nomogram was 0.974 and 0.952 in the training and testing cohorts, respectively. In the training cohort, the decision curves of the nomogram demonstrated an added overall net advantage compared to the clinical factor model. Conclusion. A noninvasive prediction tool termed radiomics nomogram, combining clinical criteria and the radiomics signature, may accurately predict high-grade ccRCC and type 2 pRCC before surgery. It also has some importance in assisting clinicians in determining future treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%