Evidence map›Paper›PMID 41359129›Full record

ArticleInsights into imaging2025

A preoperative CT-based radiological score for predicting recurrence in papillary renal cell carcinoma: a multicenter validation study.

Xiaoxia Li, Chenchen Dai, Jianyi Qu, Shaoting Zhang, Fan Meng, Jinglai Lin, Qi Sun, Weigen Yao, Dengqiang Lin, Ying Xiong and 1 more

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Article in Insights into imaging, 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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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

11 authors.

Xiaoxia Li *Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Chenchen Dai *Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Jianyi Qu *Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Shaoting ZhangDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Fan MengDepartment of Radiology, Affiliated Hospital of Nantong University, Nantong University, Nantong, China.
Jinglai LinDepartment of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Qi SunDepartment of Pathology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Weigen YaoDepartment of Radiology, Yuyao People's Hospital, Ningbo, China.
Dengqiang LinDepartment of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. lin.dengqiang@zsxmhospital.com.
Ying XiongDepartment of Urology, Zhongshan Hospital, Fudan University, Shanghai, China. xiong.ying@zs-hospital.sh.cn.
Jianjun ZhouDepartment of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China. zhoujianjunzs@126.com.ORCID http://orcid.org/0000-0003-4175-0513

Funding

Fujian Provincial Natural Science Foundation of China 2025J011500Fujian Provincial Natural Science Foundation of China 2025J011506the Scientific Research Project of Fujian for Youth 2020QNB061the Scientific Research Project of Fujian for Youth 2020QNB062
6 · The paper itself

Abstract

objectivesThis study aims to establish a radiological model derived from preoperative computed tomography (CT) to predict the likelihood of papillary renal cell carcinoma (PRCC) recurrence after surgical intervention. MATERIALS AND

methodsA retrospective multicenter study initially enrolled 384 patients, with 266 eligible for analysis from four centers following partial nephrectomy or radical resection for PRCC. Twelve distinct categories of CT features were evaluated. To assess reproducibility, interobserver variability in radiological assessment was evaluated. A Cox proportional hazards model was employed to identify significant radiological predictors and construct a risk score system. The model's performance was evaluated through Harrell's Concordance Index (C-index), and its effectiveness was compared with that of several histopathologic prognostic systems.

resultsA total of 266 patients were included, comprising a training dataset from one center (n = 152) and an external validation dataset from three other centers (n = 114). Inter-reader agreement was moderate to excellent for the radiological parameters (k = 0.43-0.94). Tumor margin regularity and regional lymph node size on CT scans were found to be independently associated with tumor recurrence (subdistribution hazard ratios ranging from 5.34 to 28.11; p-values ranging from < 0.001 to 0.028) and were incorporated into the predictive model. The model demonstrated superior predictive accuracy for tumor recurrence in the validation set compared to existing prognostic systems (C-index: 0.95 vs. 0.74-0.92; p-values ranging from < 0.001 to 0.08).

conclusionA radiological score that combines tumor margin regularity and regional lymph node size predicts PRCC recurrence, demonstrating superior performance compared to existing prognostic systems. CRITICAL RELEVANCE STATEMENT: This CT-based scoring system outperforms existing models in prognostic accuracy, aiding clinicians in personalized risk stratification and optimizing treatment decisions for patients. KEY POINTS: The preoperative CT features are associated with the prognosis of papillary renal cell carcinoma (PRCC). Tumor irregularity and lymph node size on CT scans independently predict the postoperative recurrence of PRCC. A CT scoring system that incorporates these two features demonstrates superior prognostic accuracy compared to existing models.

Indexed as

Computed tomographyPapillary renal cell carcinomaPrognosisRecurrenceScoring model

Identifiers

PMID41359129
PMCPMC12686295

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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.