Evidence map›Paper›PMID 39688674›Full record

ArticleAbdominal radiology (New York)2025

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

Anjali Sah, Amit Gupta, Sanil Garg, Neel Yadav, Maroof Ahmad Khan, Chandan J Das

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Anjali SahAll India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India.
Amit GuptaAll India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India.
Sanil GargAll India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India.
Neel YadavAll India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India.
Maroof Ahmad KhanAll India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India.
Chandan J DasAll India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India. dascj@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess diagnostic accuracy of perfusion CT (pCT) based biomarkers in differentiating clear-cell renal cell carcinoma (ccRCC) from non-ccRCC. MATERIALS AND

methodThis retrospective study comprised 95 patients with RCCs (70 ccRCCs and 25 non-ccRCCs) who had perfusion CT (pCT) before surgery between January 2017 and December 2022. Two readers independently recorded PCT parameters [blood flow (BF), blood volume (BV), mean transit time (MTT), and time to peak (TTP)] by drawing a circular ROI on the tumor. The open-source program "Labelme" was used to create a polygonal bounding box to outline tumor borders. The intraclass correlation coefficient (ICC) was used to determine interreader agreement. The pCT model was evaluated using multivariable logistic regression analysis with the STATA 18 program to determine the importance of each of these characteristics in predicting the type of tumor.

resultsClear cell RCC had significantly greater MIP and lower TTP values than non-clear cell RCC (p < 0.05). RCCs showed considerably higher TTP, MTT, and lower MIP values than the normal renal cortex (p < 0.05). At a threshold of 129 HU, MIP had an AUC of 0.78, sensitivity and specificity of 80% and 70%, respectively, according to ROC analysis.

conclusionspCT has a high diagnostic accuracy in distinguishing between ccRCC and non-ccRCC tumors; Clinical relevance: A non-invasive, accurate, reliable, and reproducible imaging biomarker for RCC subtype prediction is possible on pCT, which may be significant for evaluating the response to antiangiogenic therapy.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsTomography, X-Ray ComputedAdultAgedAged, 80 and overBiomarkers, TumorContrast MediaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesSensitivity and SpecificityBiomarkers, TumorContrast MediaAngiogenesisClear cellCT perfusionQuantitative imagingRenal cell carcinoma

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.