ArticleEuropean radiology2020
Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.BMC medical imaging · 2026Article
- Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026Review
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.Scientific reports · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Role ofCancers · 2024Review
- Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.Frontiers in oncology · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- An Intelligent System to Improve Diagnostic Support for Oral Squamous Cell Carcinoma.Healthcare (Basel, Switzerland) · 2023Article
- An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.Scientific reports · 2023Article
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- The Efficacy of PretreatmentClinical Medicine Insights. Oncology · 2023Article
- Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy.BMC medicine · 2022Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the utility of deep learning analysis using
methodsOne hundred thirteen patients with OCSCC who received pretreatment FDG-PET/CT were included. They were divided into training (83 patients) and test (30 patients) sets. The diagnosis of treatment control/failure and the DFS rate were obtained from patients' medical records. In deep learning analyses, three planes of axial, coronal, and sagittal FDG-PET images were assessed by ResNet-101 architecture. In the training set, image analysis was performed for the diagnostic model creation. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. T-stage, clinical stage, and conventional FDG-PET parameters (the maximum and mean standardized uptake value (SUVmax and SUVmean), heterogeneity index, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were also assessed with determining the optimal cutoff from training dataset and then validated their diagnostic ability from test dataset.
resultsIn dividing into patients with treatment control and failure, the highest diagnostic accuracy of 0.8 was obtained using deep learning classification, with a sensitivity of 0.8, specificity of 0.8, positive predictive value of 0.89, and negative predictive value of 0.67. In the Kaplan-Meier analysis, the DFS rate was significantly different only with the analysis of deep learning-based classification (p < .01).
conclusionsDeep learning-based diagnosis with FDG-PET images may predict treatment outcome in patients with OCSCC. KEY POINTS: • Deep learning-based diagnosis of FDG-PET images showed the highest diagnostic accuracy to predict the treatment outcome in patients with oral cavity squamous cell carcinoma. • Deep learning-based diagnosis was shown to differentiate patients between good and poor disease-free survival more clearly than conventional T-stage, clinical stage, and conventional FDG-PET-based parameters.
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