Evidence map›Paper›PMID 34658350›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2021

[Predicting postoperative recurrence of stage Ⅰ-Ⅲ renal clear cell carcinoma based on preoperative CT radiomics feature nomogram].

H Zhang, F Yin, M Chen, A Qi, L Yang, W Cui, S Yang, G Wen

Abstract read
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Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers 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

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. [An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022
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4 · The record

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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

8 authors.

H ZhangPET/CT Center, First Affiliated Hospital of Shenzhen University, Shenzhen 518052, China.
F YinShenzhen University School of Information Engineering, Shenzhen 518052, China.
M ChenDepartment of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
A QiDepartment of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
L YangDepartment of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
W CuiDepartment of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
S YangDepartment of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
G WenDepartment of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the preoperative radiomics features (RFs) and construct a nomogram for predicting postoperative recurrence of stage Ⅰ-Ⅲ clear cell renal carcinoma (ccRCC).

methodsThe clinicopathological data and preoperative enhanced CT images collected from 256 patients with ccRCC were used as the training dataset (175 patients) and test dataset (81 patients). The enhanced CT images of the tumor were segmented using ITK-SNAP software, and the RFs were extracted using the PyRadiomics computing platform. In the training dataset, the RFs were screened based on Lasso-CV algorithm, and the Rad_score was calculated. The Clinic factors were screened by univariate and multivariate logistic regression analysis of the clinical and pathological factors and CT characteristics. The Rad_score, Clinic、Rad_score + Clinic nomograms were constructed and verified using the test dataset. The performance, discrimination power and calibration of the nomograms were compared, and their clinical value was evaluated using decision curve analysis.

resultsSix RFs were retained to calculate the Rad_score. The Clinic factors included Rad_score, KPS score, platelet, calcification and TNM clinical stage. In terms of discrimination, the Rad_score + Clinic nomogram showed better performance (AUC=0.84 for training set; AUC=0.85 for test set) than the Rad_score nomogram (AUC=0.78 for training set,

conclusionThe nomogram based on preoperative CT RFs has a high value for predicting postoperative recurrence of stage Ⅰ-Ⅲ ccRCC to facilitate individualized treatment of RCC.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsHumansNomogramsRetrospective StudiesTomography, X-Ray Computedclear cell renal carcinomanomogrampostoperative recurrenceradiomics features

Identifiers

PMID34658350
PMCPMC8526308

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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.