Evidence map›Paper›PMID 39838024›Full record

ArticleScientific reports2025

CT-based scoring system for diagnosing eosinophilic solid and cystic renal cell carcinoma versus clear cell renal cell carcinoma.

Sunya Fu, Dawei Chen, Yuqin Zhang, Yuguo Wei, Yuning Pan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sunya FuDepartment of Radiology, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan road, Ningbo, 315040, Zhejiang, People's Republic of China.
Dawei ChenDepartment of Gastroenterology, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan road, Ningbo, 315040, Zhejiang, People's Republic of China. c040149@163.com.
Yuqin ZhangDepartment of Radiology, Ningbo Medical Center LiHuiLi Hospital, 1111 Jiangnan road, Ningbo, 315040, Zhejiang, People's Republic of China. zyqa666888@163.com.
Yuguo WeiAdvanced Analytics, Global Medical Service, GE Healthcare, Shanghai, China.
Yuning PanDepartment of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Eosinophilic solid and cystic renal cell carcinoma (ESC-RCC) is rare and often misdiagnosed as clear cell renal cell carcinoma (ccRCC). Therefore, a CT-based scoring system was developed to improve differential diagnosis. Retrospectively, 25 ESC-RCC and 176 ccRCC cases, were collected. The two groups were matched on a 1:2 basis using the propensity-score-matching (PSM) method, with matching factors including sex and age. Finally, 25 ESC-RCC and 50 ccRCC cases were included and randomly divided into a training cohort (52 cases) and a validation cohort (23 cases). Logistic regression identified significant factors, constructed the primary model, and assigned weights for the scoring model. Diagnostic performance was compared using receiver operating characteristic curves, dividing points into three intervals. Multifactorial logistic regression identified three independent factors: intra-tumour necrosis (3 points), degree of corticomedullary phase (CMP) enhancement (3 points), and pseudocapsule (2 points). The primary model's area under the curve (AUC) value was 0.954 (95% confidence interval [CI]: 0.857-0.993, P < 0.001), with 85.7% sensitivity and 94.1% specificity. The scoring model's AUC value for the training cohort was 0.950 (95% CI: 0.852-0.991, P < 0.001), with 77.1% sensitivity and 100% specificity at a cut-off of 4 points. The validation cohort's AUC was 0.942 (95% CI: 0.759-0.997, P < 0.001). The scoring system intervals were: ≥0 to < 2 points, ≥ 2 to ≤ 3 points, and > 3 to ≤ 8 points. Higher scores correlated with increased ccRCC incidence and decreased ESC-RCC incidence.The limitation of this study is the small sample size. A CT-based scoring system effectively differentiates ESC-RCC from ccRCC.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsTomography, X-Ray ComputedAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRetrospective StudiesROC CurveClear cell renal cell carcinomaCTDifferential diagnosisEosinophilic solid and cystic renal cell carcinomaPropensity score matchingScoring system

Identifiers

PMID39838024
PMCPMC11751170

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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.