ArticleDiagnostics (Basel, Switzerland)2021
Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Article
- Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [European journal of nuclear medicine and molecular imaging · 2025Article
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.PloS one · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.View (Beijing, China) · 2023Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Article
- Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- The Efficacy of PretreatmentClinical Medicine Insights. Oncology · 2023Article
- Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma.Frontiers in oncology · 2022Article
- Accurate Tumor DelineationFrontiers in oncology · 2021Article
- A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.PloS one · 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
6 authors.
Funding
Abstract
Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular; however, it has been shown that large variations in performance can be obtained by relying on different approaches. The purpose of this study was to evaluate the potential benefit of combining different algorithms into an improved consensus for the final prediction, as it has been shown in other fields.
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.