ArticleMolecular imaging and radionuclide therapy2022
Diagnostic Performance of Machine Learning Models Based on
Article in Molecular imaging and radionuclide therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics.JMIR aging · 2025Article
- Review
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers.BMC infectious diseases · 2024Article
- Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.Diseases (Basel, Switzerland) · 2024Article
- Classification of Pulmonary Nodules in 2-[Nuclear medicine and molecular imaging · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Peri- and intra-nodular radiomic features based onFrontiers in medicine · 2024Article
- Revisiting Temozolomide's role in solid tumors: Old is gold?Journal of Cancer · 2024Review
- Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules.Journal of clinical medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to evaluate the ability of Methods: Data of 48 patients with SPN detected on Results: The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%). Conclusion: Machine learning based on
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.