Evidence map›Paper›PMID 42183967›Full record

ArticleAnnals of surgical oncology2026

Improving Clear Cell Renal Cell Carcinoma Diagnosis: Modified CT Algorithms with Simplified Heterogeneity Scoring.

Wenjie Zhang, Qichun Chen, Xianli Zhu, Xinyan Li, Xiaofei Wang, Heng Ma, Xinhong Song, Zhaofeng Zheng, Jianyi Qu

Abstract readMulticenter Study
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Article in Annals of surgical oncology, 2026. 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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5 · Who and what money

Authors and funding

9 authors.

Wenjie Zhang *Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Qichun Chen *Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Xianli Zhu *Department of General Practice, Community Health Service Center of Xinjing Town, Shanghai, China.
Xinyan LiDepartment of Pathology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Xiaofei WangDepartment of Urology, Yantaishan Hospital, Binzhou Medical University, Yantai, China.
Heng MaDepartment of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Xinhong SongDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. 819231985@qq.com.
Zhaofeng ZhengDepartment of Radiology, Weifang People's Hospital, Shandong Second Medical University, Weifang, China. zyawen2008@163.com.
Jianyi QuDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. JianyiQu2020@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo refine a computed tomography (CT)-based algorithm for distinguishing clear cell renal cell carcinoma (ccRCC) from other small renal masses (SRMs ≤ 4 cm) through a simplified heterogeneity score and optimized attenuation criteria. PATIENTS AND

methodsThis multicenter retrospective study included 257 SRMs as an initial cohort and 149 for validation. A simplified heterogeneity score was developed incorporating persistent hypodense areas and tumor-parenchymal enhancement heterogeneity. Attenuation score was re-stratified using classification and regression tree analysis. Two modified systems were created: Modified Score 1 (original attenuation score + simplified heterogeneity score) and Modified Score 2 (new attenuation score + simplified heterogeneity score), compared against the original CT score.

resultsBoth modified scores outperformed the original system. Modified Score 1 achieved higher sensitivity (cutoff ≥ 4: 86.3% versus 69.1%, p < 0.001) and area under the receiver operating characteristic curve (AUC) (0.880 versus 0.793, p < 0.001); Modified Score 2 showed better specificity (85.6% versus 75.4%, p = 0.017) and AUC (0.907 versus 0.793, p < 0.001). The simplified heterogeneity score improved interobserver agreement (kappa: 0.736 versus 0.282) and drove improvements in the corresponding CT scores from 0.466 to 0.663 (score 1) and 0.577 (score 2) and from 0.459 to 0.833 and 0.712 in the five-tiered and dichotomous systems, respectively. The validation cohort confirmed the superior performance of the modified scores, including for SRMs ≤ 3 cm. Moreover, a simplified heterogeneity score of 3 demonstrated high specificity (90.6-94.0%) for diagnosing malignant SRMs.

conclusionsThe modified CT algorithms significantly improve diagnostic accuracy and reproducibility for ccRCC, supporting clinical decisions between active surveillance and intervention.

Indexed as

AlgorithmsCarcinoma, Renal CellKidney NeoplasmsTomography, X-Ray ComputedAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedNeoplasm StagingPrognosisRetrospective StudiesROC CurveClear cell renal cell carcinomaCTDiagnostic performanceHeterogeneitySmall renal mass

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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.