Evidence map›Paper›PMID 41653298›Full record

ArticleAbdominal radiology (New York)2026

Noninvasive renal mass diagnostic models based on radiomic features and clinical data: a multi-center study.

Aiming Xu, Yufan Luo, Jiajun Xing, Yiheng Ding, Shihang Pu, Shijie Qian, Minda Li, Shifeng Su, Yingying Gu, Qianwei Xing and 2 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 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

12 authors.

Aiming XuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Yufan LuoThe First Clinical Medical School, Nanjing Medical University, 210029, Nanjing, China.
Jiajun XingDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Yiheng DingDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Shihang PuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Shijie QianDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Minda LiDepartment of Radiology, Affiliated Hospital of Nantong University, 226001, Nantong, China.
Shifeng SuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Yingying GuDepartment of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China.
Qianwei XingDepartment of Urology, Affiliated Hospital of Nantong University, 226001, Nantong, China.
Sipeng ShenDepartment of Biostatistics, School of Public Health, Nanjing Medical University, 210029, Nanjing, China. sshen@njmu.edu.cn.
Zengjun WangDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China. zengjunwang@njmu.edu.cn.

Funding

Jiangsu Province Capability Improvement Project through Science, Technology and Education ZDXK202219Young Scholars Fostering Fund of the First Affiliated Hospital of Nanjing Medical University PY2023040
6 · The paper itself

Abstract

objectivesAbout 20% of renal masses are proved to be benign after surgery. We aimed to develop a strategy based on radiomics to differentially diagnose renal masses.

methodsIn this retrospective study, 726 renal masses (54 benign and 672 malignant) were collected from two hospitals in China and two public databases. Two diagnostic models, kidney mass diagnosis model (KMDM) and kidney cancer subtypes diagnosis model (KCSDM), were developed for differential diagnosis of renal cell carcinoma and their subtypes. Each model was divided into three units (radiomics model, clinical model, and integrated model) and evaluated in the training set and two independent validation sets.

methodsIn this retrospective study, 726 renal masses (54 benign and 672 malignant) were collected from two hospitals in China and two public databases. Two diagnostic models, kidney mass diagnosis model (KMDM) and kidney cancer subtypes diagnosis model (KCSDM), were developed for differential diagnosis of renal cell carcinoma and their subtypes. Each model was divided into three units (radiomics model, clinical model, and integrated model) and evaluated in the training set and two independent validation sets.

resultsA total of 39 radiomic variables were used for the KMDM, and 78 variables for the KCSDM. The radiomics model performed better than the clinical model in both KMDM and KCSDM. The integrated model of KMDM showed the best performance (AUC of 0.937 in training set, 0.942 in both two validation sets), when compared to the other two models and two experts. A cutoff value of risk of malignant index (RMI=1.661) was designated to distinguish the benign and malignant renal masses with a sensitivity of 0.929 and a specificity of 0.833 in validation sets. Similar results were found in the integrated model of KCSDM.

conclusionThe integrated model can noninvasively distinguish benign from malignant renal masses and renal cell carcinoma subtypes with satisfactory sensitivity and specificity.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsRadiomicsTomography, X-Ray ComputedAdultAgedChinaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityComputed tomographyDiagnosis modelRadiomicsRenal massRetrospective study

Identifiers

PMID41653298

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