ReviewTomography (Ann Arbor, Mich.)2020
Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma.
Review in Tomography (Ann Arbor, Mich.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Texture analysis of [European journal of nuclear medicine and molecular imaging · 2026Observational
- Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases.Journal of the anus, rectum and colon · 2025Article
- Differential diagnosis of clear cell renal cell carcinoma with low signal intensity on T2WI from angiomyolipoma without visible fat on MR imaging.Frontiers in oncology · 2025Article
- Rate of benign histology after resection of suspected renal cell carcinoma: multicenter comparison between Korea and the United States.BMC cancer · 2024Article
- Review
- Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study.Tomography (Ann Arbor, Mich.) · 2021Article
- The Potential of Visceral Adipose Tissue in Distinguishing Clear Cell Renal Cell Carcinoma from Renal Angiomyolipoma with Minimal Fat.Cancer management and research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
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Registered trials
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.