ArticleJournal of digital imaging2018
A Decision-Support Tool for Renal Mass Classification.
Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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.
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
25 citing papers in PubMed.
- Multiphase CT-Based Tumor and Peritumoral Radiomics for Characterization of Clear Cell Renal Cell Carcinoma.Journal of imaging informatics in medicine · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Influence of scan mode, tilt, and radiation dose on CT radiomic metrics.Journal of applied clinical medical physics · 2026Article
- Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.Journal of cancer research and clinical oncology · 2025Article
- CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults.2024 International Conference on Activity and Behavior Computing · 2024Article
- Application of artificial intelligence in the diagnosis and treatment of urinary tumors.Frontiers in oncology · 2024Review
- Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.European radiology · 2023Article
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review.NPJ digital medicine · 2022Review
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
- Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding.JMIR formative research · 2022Article
- Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?Frontiers in surgery · 2022Review
- Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information.Journal of digital imaging · 2021Article
- A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors.Sensors (Basel, Switzerland) · 2021Article
- Application of radiomics and machine learning in head and neck cancers.International journal of biological sciences · 2021Review
- Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.World journal of urology · 2020Review
- Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics.Abdominal radiology (New York) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.
Indexed as
Identifiers
What OpenQuestion holds
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.