ReviewInternational journal of environmental research and public health2022
Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review.
Review in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Exploring the Efficacy of Combining Radiofrequency Thermal Ablation or Microwave Ablation with Vertebroplasty for Pain Control and Disease Management in Metastatic Bone Disease-A Systematic Review.Current oncology (Toronto, Ont.) · 2024Pooled it
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Bibliometric analysis of bone metastasis from prostate cancer research from 2007 to 2024.Discover oncology · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Biomarker-Driven Approaches to Bone Metastases: From Molecular Mechanisms to Clinical Applications.Biomedicines · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- AI diagnostics in bone oncology for predicting bone metastasis in lung cancer patients using DenseNet-264 deep learning model and radiomics.Journal of bone oncology · 2024Article
- Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- The progressive trend of modeling and drug screening systems of breast cancer bone metastasis.Journal of biological engineering · 2024Review
- Large-Scale assessment of ChatGPT's performance in benign and malignant bone tumors imaging report diagnosis and its potential for clinical applications.Journal of bone oncology · 2024Article
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- Review
- MRI-based radiomics nomogram for differentiation of solitary metastasis and solitary primary tumor in the spine.BMC medical imaging · 2023Article
- Radiomics in Lung Metastases: A Systematic Review.Journal of personalized medicine · 2023Review
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studies were MRI-based, and two (15%) were CT-based. The sample size was <200 patients for all studies. There is significant heterogeneity in the literature, as evidenced by the relatively low RQS value (average score = 22.6%). There is not a homogeneous protocol used for MRI sequences among the different studies, although the highest predictive ability was always obtained in T2W-FS. Six articles (46%) reported on the potential application of the model in a clinical setting with a decision curve analysis (DCA). (4) Conclusions: Despite the variability in the radiomics method application, the similarity of results and conclusions observed is encouraging. Substantial limits were found; prospective and multicentric studies are needed to affirm the role of radiomics as a supporting tool.
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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.