Evidence map›Paper›PMID 39975590›Full record

ArticleFrontiers in oncology2025

Intratumoral and peritumoral radiomics using multi-phase contrast-enhanced CT for diagnosis of renal oncocytoma and chromophobe renal cell carcinoma: a multicenter retrospective study.

Yongsong Ye, Bei Weng, Yan Guo, Lesheng Huang, Shanghuang Xie, Guimian Zhong, Wenhui Feng, Wenxiang Lin, Zhixuan Song, Huanjun Wang and 1 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Tabular prior-data fitted network in real-world CT radiomics: benignQuantitative imaging in medicine and surgery · 2025
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yongsong Ye *Department of Radiology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China.
Bei Weng *Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Yan GuoDepartment of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Lesheng HuangDepartment of Radiology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Zhuhai, China.
Shanghuang XieLab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Longgang Central Hospital of Shantou University Medical College, Shenzhen, China.
Guimian ZhongDepartment of Radiology, The Affiliated Panyu Central Hospital of Guangzhou Medical University, Guangzhou, China.
Wenhui FengDepartment of Radiology, Zhuhai People's Hospital, Zhuhai, China.
Wenxiang LinDepartment of Radiology, The First Affiliated Hospital of GuangZhou Medical University, HengQin Hospital, Zhuhai, China.
Zhixuan SongClinical and Technical Support, Philips Healthcare, Guangzhou, China.
Huanjun WangDepartment of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Tianzhu LiuDepartment of Radiology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Zhuhai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To construct diagnostic models that distinguish renal oncocytoma (RO) from chromophobe renal cell carcinoma (CRCC) using intratumoral and peritumoral radiomic features from the corticomedullary phase (CMP) and nephrographic phase (NP) of computed tomography, and compare model results with manual and radiological results. Methods: The RO and CRCC cases from five centers were split into a training set (70%) and a validation set (30%). CMP and NP intratumoral and peritumoral (1-3 mm) radiomic features were extracted. Segmentation was performed by radiologists and software. Features with high intraclass correlation coefficients (ICC>0.75) were selected through univariate analysis, followed by the LASSO method to determine the final features for the SVM model. All images were assessed by two radiologists, and radiological reports were also examined. The diagnostic performances of the different methods were compared using several statistical methods. Results: The training set had 65 cases (29 RO, 36 CRCC) and the validation set had 27 cases (12 RO, 15 CRCC). All the training models had excellent performance (area under the curve [AUC]: 0.828-0.942); the AUC values of the validation models ranged from 0.900 (Model 4) to 0.600 (Model 2). CMP models (AUC: 0.811-0.900) generally outperformed NP and fusion models (AUC: 0.728-0.756). SVM models (sensitivity: 62.50-88.89%; specificity: 63.16-77.78%; accuracy: 62.96-81.48%) outperformed manual diagnosis (sensitivity: 46.74-70.59%; specificity: 41.67-46.34%; accuracy: 52.27-59.78%). The clinical reports alone had no diagnostic value. Conclusion: CMP intratumoral and peritumoral radiomics models reliably distinguished RO from CRCC.

Indexed as

chromophobe renal cell carcinomaintratumoralperitumoralradiomicsrenal oncocytoma

Identifiers

PMID39975590
PMCPMC11835681

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