ArticleAcademic radiology2014
CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.
Article in Academic radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 2 of them syntheses that pooled it.
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Who cites it
68 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- SNMMI/EANM/ACNM Procedure Standard/Procedure Guideline on the Use of Molecular Imaging for Renal Mass Characterization.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Guideline
- Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis.BMJ open · 2021Pooled it
- Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques.Journal of imaging informatics in medicine · 2026Article
- CT-Based Texture Analysis in Indeterminate Pediatric Renal and Pararenal Masses.The Indian journal of radiology & imaging · 2026Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Conventional chest computed tomography-based radiomics for predicting the risk of thoracolumbar osteoporotic vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Using deep learning to differentiate among histology renal tumor types in computed tomography scans.BMC medical imaging · 2025Article
- The use of ADC histogram analysis in the diagnosis and determination of aggressiveness of peripheral zone prostate cancer.Polish journal of radiology · 2025Article
- Radiomics models based on thoracic and upper lumbar spine in chest LDCT to predict low bone mineral density.Scientific reports · 2024Article
- Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment.European radiology · 2024Article
- Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.Scientific reports · 2024Article
- Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs.Journal of imaging informatics in medicine · 2024Article
- Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
- Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis.ACS omega · 2023Review
- Article
- Xanthogranuloma of the Sellar Region: A Comprehensive Review of Neuroimaging in a Rare Inflammatory Entity.Journal of personalized medicine · 2022Review
- Grading of soft tissues sarcomas using radiomics models: Choice of imaging methods and comparison with conventional visual analysis.Research in diagnostic and interventional imaging · 2022Article
- A No-Math Primer on the Principles of Machine Learning for Radiologists.Seminars in ultrasound, CT, and MR · 2022Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
RATIONALE AND
objectivesComputed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model. MATERIALS AND
methodsCTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases.
resultsThe random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%).
conclusionsCTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.
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Registered trials
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