Evidence map›Paper›PMID 40106107›Full record

ArticleAnnals of surgical oncology2025

Intra- and Peritumoral CT-Based Radiomics for Assessing Pathologic T-Staging in Clear Cell Renal Cell Carcinoma: A Multicenter Study.

Yuanhao Xia, Zehua Sun, Zhongyi Wang, Xin Zhang, Jiakang Xu, Min Li, Ning Mao, Chang Xu, Xianglin Li, Hui Xu and 3 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Annals of surgical 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.

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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

13 authors.

Yuanhao Xia *School of Medical Imaging, Binzhou Medical University, Yantai, China.
Zehua Sun *Department of Radiology, The First Hospital of Jilin University, Changchun, China.
Zhongyi Wang *Department of Radiology, Qingdao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, China.
Xin Zhang *Department of Personnel, Lianshui County People's Hospital, Huai'an, China.
Jiakang XuDepartment of Radiology, Qingdao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, China.
Min LiSchool of Medical Imaging, Binzhou Medical University, Yantai, China.
Ning MaoDepartment of Radiology, Qingdao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, China.
Chang XuSchool of Medical Imaging, Binzhou Medical University, Yantai, China.
Xianglin LiSchool of Medical Imaging, Binzhou Medical University, Yantai, China.
Hui XuDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Zhenghan YangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Heng MaSchool of Medical Imaging, Binzhou Medical University, Yantai, China.
Hao GuoSchool of Medical Imaging, Binzhou Medical University, Yantai, China. guohao112@163.com.

Funding

Beijing Science and Technology Innovation Medical Devclopment Foundation KC2023-JX-0186-BM084Shandong Postdoctoral Science Project SDCX-ZG-202303059Shandong ProvinceNatural Science Foundation ZR2022MH274Taishan Scholars tsqn202103197Yantai Science and Technology Innovation project 2023YD002
6 · The paper itself

Abstract

backgroundA radiomics model constructed from the intratumoral region of computed tomography (CT) can predict the pathologic T stage of clear cell renal cell carcinoma (ccRCC). However, the predictive capability of the radiomics model that incorporates both intra- and peritumoral regions of CT for the pathologic T stage in ccRCC patients has not been reported to date.

methodsThis study enrolled 250 patients with ccRCC who underwent laparoscopic surgery. Three radiomics models were developed based on the intra- and peritumoral regions. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were analyzed. Decision curve analysis (DCA) and calibration curves were used to assess the net benefit and calibration ability of the models. Additionally, the diagnostic performance of the different models were compared with that of radiologists.

resultsThe radiomics model based on the intra- and peritumoral regions at 5 mm exhibited the strongest performance, with area under curve values of 0.91 (95 % confidence interval [CI], 0.8551-0.9650), 0.85 (95 % CI, 0.7490-0.9517), and 0.873 (95 % CI, 0.7612-0.9839) in distinguishing high and low T stages of ccRCC across the training, validation, and test sets, respectively. The model's accuracy in the training, validation, and test sets was 0.798, 0.732, and 0.769, with corresponding sensitivity values of 0.921, 0.857, and 0.882, and specificity values of 0.747, 0.690, and 0.729. The calibration curve demonstrated a high level of agreement between the predicted and actual outcomes, whereas the DCA showed that the model provided a meaningful net benefit.

conclusionsThe radiomics model based on the intra- and peritumoral regions of CT has certain value in distinguishing between high and low T stages of ccRCC.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsTomography, X-Ray ComputedAdultAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedNeoplasm StagingPrognosisRadiomicsRetrospective StudiesROC CurveClear cell renal cell carcinomaCTPathological T stagingPeritumoralRadiomics

Identifiers

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Registered trials

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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 Socratic graph.