ArticleJAMA network open2025
Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer.
Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Emerging applications of artificial intelligence for risk stratification in head and neck cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Systematic review of artificial intelligence and radiomics for preoperative prediction of extranodal extension and lymph node metastasis in oropharyngeal cancer.Frontiers in oncology · 2025Pooled it
- External Validation of Pre-Treatment Primary Tumor Volume as a Prognostic Factor in Head and Neck Cancer Treated With (Chemo)Radiotherapy.Head & neck · 2026Article
- Deep learning-based computed tomography detection of early lymph node metastasis in head and neck cancer.Quantitative imaging in medicine and surgery · 2026Article
- Hypothesis Generation via Interpretable Machine Learning: A Case Study on Risk Factors for Postradiation Therapy Lung Cancer Recurrence.Advances in radiation oncology · 2026Article
- Decoding Occult Cervical Lymph Node Metastasis in Head and Neck Squamous Cell Carcinoma: From AI-Driven Multimodal Fusion to Clinical Translation.Current oncology reports · 2026Review
- Deep Learning-Based Detection of Malignant and Equivocal Cervical Lymph Nodes on CT Imaging Using a 2D U-Net++ Model.Maedica · 2026Article
- Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.Biomedical engineering online · 2026Article
- MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.European journal of cancer (Oxford, England : 1990) · 2026Article
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- Leveraging vision transformer for histological grade prediction in laryngeal and hypopharyngeal squamous cell carcinoma: a large-scale multicenter study.Neuroradiology · 2026Article
- Machine learning-driven multi-omics integration of urinary organic acids and ions enables precision risk stratification for calcium oxalate nephrolithiasis.Frontiers in medicine · 2026Article
- MRI-based risk stratification for predicting overall survival in pancreatic ductal adenocarcinoma.Insights into imaging · 2025Article
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15 authors.
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Abstract
Importance: Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging characteristics of both regions for predictions of p16+ OPSCC outcomes. Objectives: To develop and validate a computed tomography (CT)-based deep learning classifier that integrates PT and LN features to predict outcomes in p16+ OPSCC and to identify patients with stage I disease who may derive added benefit associated with chemotherapy. Design, Setting, and Participants: In this retrospective prognostic study, radiographic CT scans were analyzed of 811 patients with p16+ OPSCC treated with definitive radiotherapy or chemoradiotherapy from 3 independent cohorts. One cohort from the Cancer Imaging Archive (1998-2013) was used for model development and validation and the 2 remaining cohorts (2002-2015) were used to externally test the model performance. The Swin Transformer architecture was applied to fuse the features from both PT and LN into a multiregion imaging risk score (SwinScore) to predict survival outcomes across and within subpopulations at various stages. Data analysis was performed between February and July 2024. Exposures: Definitive radiotherapy or chemoradiotherapy treatment for patients with p16+ OPSCC. Main Outcomes and Measures: Hazard ratios (HRs), log-rank tests, concordance index (C index), and net benefit were used to evaluate the associations between multiregion imaging risk score and disease-free survival (DFS), overall survival (OS), and locoregional failure (LRF). Interaction tests were conducted to assess whether the association of chemotherapy with outcome significantly differs across dichotomized multiregion imaging risk score subgroups. Results: The total patient cohort comprised 811 patients with p16+ OPSCC (median age, 59.0 years [IQR, 47.4-70.6 years]; 683 men [84.2%]). In the external test set, the multiregion imaging risk score was found to be prognostic of DFS (HR, 3.76 [95% CI, 1.99-7.10]; P < .001), OS (HR, 4.80 [95% CI, 2.22-10.40]; P < .001), and LRF (HR, 4.47 [95% CI, 1.43-14.00]; P = .01) among all patients with p16+ OPSCC. The multiregion imaging risk score, integrating both PT and LN information, demonstrated a higher C index (0.63) compared with models focusing solely on PT (0.61) or LN (0.58). Chemotherapy was associated with improved DFS only among patients with high scores (HR, 0.09 [95% CI, 0.02-0.47]; P = .004) but not those with low scores (HR, 0.83 [95% CI, 0.32-2.10]; P = .69). Conclusions and Relevance: This prognostic study of p16+ OPSCC describes the development of a CT-based imaging risk score integrating PT and metastatic cervical LN features to predict recurrence risk and identify suitable candidates for treatment tailoring. This tool could optimize treatment modulations of p16+ OPSCC at a highly granular level.
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