ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026
Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- 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
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Authors and funding
7 authors.
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Abstract
backgroundThis study aimed to develop and validate a fully automated magnetic resonance imaging (MRI) deep learning (DL) framework for primary tumor detection and prediction of lymph node metastasis (LNM) in early-stage OTSCC.
methodsA total of 348 patients from two centers were retrospectively enrolled and were divided into a training cohort (n = 163), a validation cohort (n = 65), an internal testing cohort (n = 84) and an external testing cohort (n = 36). The You Only Look Once version 8 (YOLOv8) network was used as the backbone for automated primary tumor detection. Two distinct image encoders were employed to construct the DL models for predicting LNM. The automated DL signature was combined with significant clinical factors to develop the automated DL nomogram. Predictive performance was assessed using the area under the curve (AUC). The prognostic value of automated DL signature was determined by Cox regression analysis.
resultsThe YOLOv8 model demonstrated effective primary tumor detection ability on MRI, achieving mean average precisions (mAPs) of 0.689–0.973 across the validation, internal, and external testing cohorts. The automated DL nomogram, which combined the automated DL signature and clinical T stage, achieved the best performance (AUCs: 0.871, 0.776, 0.909) and significantly outperformed clinical T stage alone (AUCs: 0.653, 0.611, 0.685, all P < 0.01) across the same cohorts. DL signature–predicted positive LNM was significantly associated with worse disease-free survival following surgical treatment (hazard ratio, 1.82; 95% CI: 1.04, 3.19; P = 0.037).
conclusionsThe MRI-based DL framework facilitates predictions of LNM and recurrence risk, thereby assisting in decision-making for patients with early-stage OTSCC.
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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 Socratic graph.