|Title||MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Shew M, Wichova H, Bur A, Koestler DC, St Peter M, Warnecke A, Staecker H|
|Journal||Otolaryngol Head Neck Surg|
|Date Published||2021 02|
|Keywords||Aged, Female, Humans, Machine Learning, Male, Meniere Disease, MicroRNAs, Middle Aged, Perilymph, Prospective Studies|
OBJECTIVE: Diagnosis and treatment of Ménière's disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a "liquid biopsy" equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of Ménière's disease.
STUDY DESIGN: Prospective cohort study.
SETTING: Tertiary academic hospital.
SUBJECTS AND METHODS: Perilymph was collected during labyrinthectomy (Ménière's disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80/20 train/test split and cross-validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models.
RESULTS: In terms of miRNA profiles for conductive hearing loss versus Ménière's, 4 models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top-performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus Ménière's performed significantly worse, with the best models achieving 66% accuracy. Ménière's models showed unique features distinct from SNHL.
CONCLUSIONS: We can use ML to build Ménière's-specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from Ménière's, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.
|Alternate Journal||Otolaryngol Head Neck Surg|
|Grant List||P20 GM103418 / GM / NIGMS NIH HHS / United States |
P30 CA168524 / CA / NCI NIH HHS / United States
MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning.
Helena Wichova, MD