SynthesisPloS one2023
CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis.
Synthesis in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.Annals of surgical oncology · 2026Article
- ASO Author Reflections: From Four Spatial-Input Strategies to a Full Exhaustive Paradigm-Refining Deep Learning Radiomics for Challenging Renal Masses.Annals of surgical oncology · 2026Article
- A nomogram model for challenging cases: differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma in uncertain or misdiagnosed tumors.Abdominal radiology (New York) · 2026Article
- Utility of Tumor Sphericity on CT for Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma.Journal of the Korean Society of Radiology · 2026Article
- Development and validation of a machine learning-based radiomics model using 45-keV virtual monoenergetic images for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma.Translational andrology and urology · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Multiphase CT-derived markers for the characterization of large solid and cystic renal masses by histology and grade.Polish journal of radiology · 2026Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Fully automated segmentation and classification of renal tumors on CT scans via machine learning.BMC cancer · 2025Article
- Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.Journal of clinical medicine · 2024Review
- The radiomics-clinical nomogram for predicting the response to initial superselective arterial embolization in renal angiomyolipoma, a preliminary study.Frontiers in oncology · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDifferentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs.
methodsWe conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034).
resultsOur literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan.
conclusionThis study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.
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