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Updated: May 21, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
Published on: March 30, 2015
Multiphase CT-derived markers for the characterization of large solid and cystic renal masses by histology and grade
1Voxel Medical Diagnostic Center, Katowice, Poland.
Purpose:
This study aimed to evaluate the diagnostic performance of multiphase contrast-enhanced computed tomography (MCECT) for differentiating benign and malignant renal masses > 4 cm, and in predicting renal cell carcinoma (RCC) subtypes and grade, using signal intensity (SI) and tumor-to-cortex signal intensity ratio (TCSI).
Material And Methods:
A retrospective analysis was performed on 190 patients with renal tumors > 4 cm (120 solid and 70 cystic lesions). All solid tumors and 49 cystic lesions (Bosniak IIF-IV) underwent histopathological verification. MCECT was performed in four phases: pre-contrast, corticomedullary (CMP), nephrographic (NP), and excretory (EP). SI and TCSI were measured and analyzed using receiver operating characteristic analysis. Cluster and principal component analyses were applied to evaluate enhancement-based classification.
Results:
For solid masses, excretory phase SI reached an area under the curve (AUC) of 0.844 for distinguishing RCC from other tumors (98.8% sensitivity, 69.4% specificity). Differentiating RCC from benign tumors using EP SI achieved an AUC of 0.745. CMP SI enabled separation of RCC subtypes, especially chRCC vs. pRCC (AUC = 0.983). SI of NP differentiated low- from high-grade ccRCC with an AUC of 0.969 (100% sensitivity, 90.9% specificity). Among Bosniak IIF cysts, EP TCSI ≥ 0.40 identified malignancy with 88.9% sensitivity and 100% specificity (AUC = 0.951). Cluster analysis grouped tumors by vascularity-based enhancement. Lymph node assessment showed no significant SI differences between pN1 and pN0.
Conclusions:
SI and TCSI from MCECT are accurate, non-invasive markers for histologic and biologic characterization of large renal masses. CMP and EP provide the highest diagnostic value. These enhancement parameters may improve radiologic workflows and support clinical decision-making.

