ArticleThe Laryngoscope2023
Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model.
Article in The Laryngoscope, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Deep Learning Models for Radiomics-Based Segmentation of Vestibular Schwannoma on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Beyond the naked eye: a systematic review on the current state of radiomics approaches to the vestibular schwannoma.Journal of neuro-oncology · 2026Pooled it
- Magnetic resonance imaging and the evaluation of vestibular schwannomas: a systematic review.Neuroradiology · 2026Pooled it
- The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Turing problems in otolaryngology: a scoping review of the principal challenges of artificial ıntelligence and large language models.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- Advances in imaging modalities and tumor classification for vestibular schwannomas.Journal of neuro-oncology · 2026Review
- Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study.JAMIA open · 2026Article
- [Skull base surgery-where do we stand?]HNO · 2025Review
- A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images.Physics in medicine and biology · 2025Article
- Evaluation of Vestibular Schwannoma Size across Time: How Well Do the Experts Perform and What Can Be Improved?AJNR. American journal of neuroradiology · 2025Article
- Towards clinical implementation of automated segmentation of vestibular schwannomas: a reliability study comparing AI and human performance.Neuroradiology · 2025Article
- Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.Medical physics · 2024Article
- Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation.ArXiv · 2024Article
- Vision Transformer-Based Multilabel Survival Prediction for Oropharynx Cancer After Radiation Therapy.International journal of radiation oncology, biology, physics · 2024Article
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery.The Laryngoscope · 2023Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveTo develop a deep-learning-based multi-task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast-enhanced T1-weighted (ceT1) magnetic resonance images (MRIs).
methodsInitial ceT1 MRIs for VS patients meeting the inclusion/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow-up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (≥20% volumetric growth) were used as the ground truth for training and evaluating the TS and TEP modules, respectively.
resultsWe performed 5-fold cross-validation with the eligible patients (n = 103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p = 0.03) and marginally lower than the state-of-the-art segmentation model nnU-Net (dice coefficient of 86.45%, p = 0.16). The TEP performance is significantly better than the single-task prediction model (AUC = 0.60, p = 0.01) and marginally better than a radiomics-based prediction model (AUC = 0.70, p = 0.17).
conclusionThe proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management. LEVEL OF EVIDENCE: NA Laryngoscope, 133:2754-2760, 2023.
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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.