ArticleFrontiers in oncology2022
An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ultrasound Radiomics for Differential Diagnosis of Renal Masses: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
- B-Mode Ultrasound Radiomics for Differentiating Benign and Malignant Small Hyperechoic Renal Masses: An Exploratory Single-Center Experience.Journal of imaging · 2026Article
- Development and validation of a novel echocardiography-based nomogram for the streamlined classification of cardiac tumors in cancer patients.Quantitative imaging in medicine and surgery · 2025Article
- A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm).Translational oncology · 2023Article
- A narrative review on the application of artificial intelligence in renal ultrasound.Frontiers in oncology · 2023Review
- Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review.Frontiers in medicine · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study was conducted in order to develop and validate an ultrasonic-based radiomics nomogram for diagnosing solid renal masses. Methods: Six hundred renal solid masses with benign renal lesions ( Results: The radiomics signature model showed satisfactory discrimination in the training set [area under the ROC (AUC), 0.887; 95% confidence interval (CI), 0.860-0.915] and the validation set (AUC, 0.874; 95% CI, 0.816-0.932). The radiomics nomogram also demonstrated good calibration and discrimination in the training set (AUC, 0.911; 95% CI, 0.886-0.936) and the validation set (AUC, 0.861; 95% CI, 0.802-0.921). In addition, the radiomics nomogram model showed higher accuracy in discriminating benign and malignant renal masses compared with the evaluations by junior physician (DeLong Conclusions: The ultrasonic-based radiomics nomogram shows favorable predictive efficacy in differentiating solid renal masses.
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Registered trials
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