Evidence map›Paper›PMID 31506816›Full record

ArticleEuropean radiology2020

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.

Pei Nie, Guangjie Yang, Zhenguang Wang, Lei Yan, Wenjie Miao, Dapeng Hao, Jie Wu, Yujun Zhao, Aidi Gong, Jingjing Cui and 2 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 1 pooled it
–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

78 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

18 more citing papers are in PubMed but not listed here.

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

12 authors.

Pei NieRadiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Guangjie YangPET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
Zhenguang WangPET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China. doctorwzg2002@hotmail.com.ORCID http://orcid.org/0000-0003-1365-1198
Lei YanPET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
Wenjie MiaoPET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
Dapeng HaoRadiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Jie WuPathology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Yujun ZhaoPET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
Aidi GongPET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
Jingjing CuiHuiying Medical Technology Co., Ltd, Beijing, China.
Yan JiaHuiying Medical Technology Co., Ltd, Beijing, China.
Haitao NiuUrology Department, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266005, Shandong, China. niuht0532@126.com.

Funding

Key Research and Development Project of Shandong Province 2018GSF118078National Natural Science Foundation of China 81601527National Natural Science Foundation of China 81701688Natural Science Foundation of Shandong Province ZR2017BH096Natural Science Foundation of Shandong Province ZR2017MH036Postdoctoral Science Foundation of China 2018M642617
6 · The paper itself

Abstract

objectivesTo develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC).

methodsNinety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness.

resultsFourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness.

conclusionsThe CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy. KEY POINTS: • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

Indexed as

NomogramsAlgorithmsAngiomyolipomaCarcinoma, Renal CellDiagnosis, DifferentialFemaleHumansKidney NeoplasmsMaleMiddle AgedReproducibility of ResultsRetrospective StudiesTomography, X-Ray ComputedAngiomyolipomaClear cell renal cell carcinomaRadiomicsTomography, X-ray computed

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.