Can quantitative perfusion CT-based biomarkers predict renal cell carcinoma subtypes?

Area of Science:

• Radiology
• Oncology
• Medical Imaging

Background:

• Renal cell carcinoma (RCC) is a significant global health concern.
• Accurate subtyping of RCC, particularly distinguishing clear-cell RCC (ccRCC) from non-ccRCC, is crucial for treatment planning and prognosis.
• Current diagnostic methods may have limitations in non-invasively differentiating RCC subtypes.

Purpose of the Study:

• To evaluate the diagnostic accuracy of perfusion CT (pCT) biomarkers in differentiating ccRCC from non-ccRCC.
• To establish pCT as a reliable imaging biomarker for RCC subtype prediction.

Main Methods:

• Retrospective analysis of 95 patients with RCC (70 ccRCC, 25 non-ccRCC) who underwent pCT before surgery.
• Independent assessment of pCT parameters (blood flow, blood volume, mean transit time, time to peak) by two readers.
• Utilized multivariable logistic regression and ROC analysis to determine predictive value.

Main Results:

• Clear cell RCC exhibited significantly higher Maximum Intensity Projection (MIP) values and lower time to peak (TTP) compared to non-ccRCC (p<0.05).
• RCCs demonstrated higher TTP and MTT, and lower MIP values than normal renal cortex (p<0.05).
• MIP achieved an AUC of 0.78 with 80% sensitivity and 70% specificity at a 129 HU threshold.

Conclusions:

• Perfusion CT demonstrates high diagnostic accuracy in distinguishing ccRCC from non-ccRCC.
• pCT offers a non-invasive, accurate, and reproducible imaging biomarker for RCC subtype prediction.
• This capability is clinically relevant for guiding antiangiogenic therapy response evaluation.