ReviewInternational journal of biological sciences2021
Application of radiomics and machine learning in head and neck cancers.
Review in International journal of biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- MRI-Based Radiomics and Artificial Intelligence for Prediction of Recurrence and Prognostic Outcomes in Oral Tongue Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Application of artificial intelligence and radiomics in the prediction of lymph node metastasis and tumour grading of oral cancer - a systematic review and meta analysis.BMC oral health · 2026Pooled it
- Performance of magnetic-resonance imaging radiomics in prediction of response after neoadjuvant chemotherapy in head and neck squamous cell carcinoma: A systematic review and meta-analysis.European journal of radiology open · 2026Article
- PET/CT imaging for assessment of tumor immunotherapy response: a new perspective on tumor metabolic reprogramming and immune escape.La Radiologia medica · 2026Review
- Programmed cell death ligand 1(PD-L1) association in metastatic and non-metastatic oral squamous cell carcinoma: clinicopathologic and immunohistochemical study.Scientific reports · 2026Article
- Article
- Multiparametric MRI Assessment of Cervical Lymphadenopathy: Combined Diagnostic Performance of Morphological Features and Apparent Diffusion Coefficient.Healthcare (Basel, Switzerland) · 2026Article
- An Interpretable Machine Learning Model With Synthetic MRI-Based Habitat Radiomics for Predicting Lymph Node Metastasis in Oral Cancer.Cancer medicine · 2026Article
- Clinical prognostic prediction ofTranslational cancer research · 2026Article
- Regionally modulated radiomics analysis in PET/CT imaging: application to prognosis prediction of head and neck cancer.Physical and engineering sciences in medicine · 2026Article
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- Article
- Distinguishing granulomatous lobular mastitis from breast cancer using a clinical-radiomics nomogram to improve diagnostic accuracy.Frontiers in oncology · 2026Article
- Unleashing the potential role of tumor-associated NK cells as a novel immunotherapeutic target in triple-negative breast cancer.Breast cancer research : BCR · 2025Article
- Developing a multi-modal MRI radiomics-based model to predict the long-term overall survival of patients with hypopharyngeal cancer receiving definitive radiotherapy.World journal of otorhinolaryngology - head and neck surgery · 2025Article
- Prediction models after hepatectomy for hepatocellular carcinoma-based ultrasonic radiomics: an observational study.European journal of medical research · 2025Observational
- Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics.Physics and imaging in radiation oncology · 2025Article
- Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning.Scientific reports · 2025Article
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- 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
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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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.