ArticleLa Radiologia medica2022
A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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29 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Potential Application of Hydrops MR Imaging: A Systematic Review.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-facialePooled it
- Comparative performance of radiomics models based on 3D-FLAIR-CUBE and MPRAGE sequences gadolinium-induced labyrinth MRI protocols for predicting endolymphatic hydrops in Ménière's disease.European journal of medical research · 2026Article
- Development and validation of an explainable machine learning-based risk prediction model for obesity in Chinese children and adolescents: a population-based study.Frontiers in nutrition · 2026Article
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- The Endolymph Signal in Non-contrast Enhanced 3D-real IR Image Differs between the Ears with and without Significant Endolymphatic Hydrops.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Review
- [Artificial intelligence applications in Ménière's disease].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Review
- Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.Sensors (Basel, Switzerland) · 2025Review
- Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- The relationship between endolymphatic hydrops features and hearing loss in Bilateral Meniere's disease.Head & face medicine · 2024Article
- Article
- Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.Biomedical engineering online · 2024Article
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Mass Spectrometric Interrogation of Earwax: Toward the Detection of Ménière's Disease.ACS omega · 2023Article
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Magnetic resonance imaging of endolymphatic hydrops: a comparison of methods with and without gadolinium-based contrast agent administration.Nagoya journal of medical science · 2023Article
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning at the edge for AI-enabled multiplexed pathogen detection.Scientific reports · 2023Article
- MRI-based artificial intelligence to predict infection following total hip arthroplasty failure.La Radiologia medica · 2023Article
- Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.Current oncology (Toronto, Ont.) · 2023Article
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13 authors at 6 institutions in 2 countries.
Funding
Abstract
purposeThis study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease. MATERIALS AND
methodsA retrospective, multicentric diagnostic case-control study was performed. This study included 120 patients with unilateral or bilateral Menière's disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière's disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model.
resultsThe classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively.
conclusionThe multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière's disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière's disease.
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