Dataset Information

MRI Screening in Vestibular Schwannoma: A Deep Learning-based Analysis of Clinical and Audiometric Data.

ABSTRACT:

Objective

To find a more objective method of assessing which patients should be screened for a vestibular schwannoma (VS) with magnetic resonance imaging (MRI) using a deep-learning algorithm to assess clinical and audiometric data.

Materials and methods

Clinical and audiometric data were collected for 592 patients who received an audiogram between January 2015 and 2020 at Duke University Health Center with and without VS confirmed by MRI. These data were analyzed using a deep learning-based analysis to determine if the need for MRI screening could be assessed more objectively with adequate sensitivity and specificity.

Results

Patients with VS showed slightly elevated, but not statistically significant, mean thresholds compared to those without. Tinnitus, gradual hearing loss, and aural fullness were more common in patients with VS. Of these, only the presence of tinnitus was statistically significant. Several machine learning algorithms were used to incorporate and model the collected clinical and audiometric data, but none were able to distinguish ears with and without confirmed VS. When tumor size was taken into account the analysis was still unable to distinguish a difference.

Conclusions

Using audiometric and clinical data, deep learning-based analyses failed to produce an adequately sensitive and specific model for the detection of patients with VS. This suggests that a specific pattern of audiometric asymmetry and clinical symptoms may not necessarily be predictive of the presence/absence of VS to a level that clinicians would be comfortable forgoing an MRI.

SUBMITTER: Kortebein S

PROVIDER: S-EPMC10950172 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

MRI Screening in Vestibular Schwannoma: A Deep Learning-based Analysis of Clinical and Audiometric Data.

Kortebein Sarah S Gu Shoujun S Dai Kathy K Zhao Elizabeth E Riska Kristal K Kaylie David D Hoa Michael M

Otology & neurotology open 20230309 1

<h4>Objective</h4>To find a more objective method of assessing which patients should be screened for a vestibular schwannoma (VS) with magnetic resonance imaging (MRI) using a deep-learning algorithm to assess clinical and audiometric data.<h4>Materials and methods</h4>Clinical and audiometric data were collected for 592 patients who received an audiogram between January 2015 and 2020 at Duke University Health Center with and without VS confirmed by MRI. These data were analyzed using a deep lea ...[more]

PMID: 38516318

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Project description:BackgroundWe retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery.MethodsAccelerated iMRI was performed using dual surface coils positioned around the area of resection. A DL model was trained on the fastMRI neuro dataset to mimic the data from the iMRI protocol. The evaluation was performed on imaging material from 40 patients imaged from Nov 1, 2021, to June 1, 2023, who underwent iMRI during tumor resection surgery. A comparative analysis was conducted between the conventional compressed sense (CS) method and the trained DL reconstruction method. Blinded evaluation of multiple image quality metrics was performed by two neuroradiologists and one neurosurgeon using a 1-to-5 Likert scale (1, nondiagnostic; 2, poor; 3, acceptable; 4, good; and 5, excellent), and the favored reconstruction variant.ResultsThe DL reconstruction was strongly favored or favored over the CS reconstruction for 33/40, 39/40, and 8/40 of cases for readers 1, 2, and 3, respectively. For the evaluation metrics, the DL reconstructions had a higher score than their respective CS counterparts for 72%, 72%, and 14% of the cases for readers 1, 2, and 3, respectively. Still, the DL reconstructions exhibited shortcomings such as a striping artifact and reduced signal.ConclusionDL shows promise in allowing for high-quality reconstructions of iMRI. The neuroradiologists noted an improvement in the perceived spatial resolution, signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and spatial resolution compared to CS, while the neurosurgeon preferred the CS reconstructions across all metrics.Relevance statementDL shows promise to allow for high-quality reconstructions of iMRI, however, due to the challenging setting of iMRI, further optimization is needed.Key pointsiMRI is a surgical tool with a challenging image setting. DL allowed for high-quality reconstructions of iMRI. Additional optimization is needed due to the challenging intraoperative setting.

Project description:AimVestibular schwannomas (VSs) are generally considered benign tumors, and malignant transformation of VSs (MTVSs) are rare findings. The clinical features, treatment strategy, outcomes and prognostic factors remain unclear. We endeavored to analyze the natural history, management, outcomes and prognostic factors of MTVSs.Materials and methodsThe clinical features, radiologic findings, pathological investigations and surgical outcomes of 4 patients with MTVSs treated at the authors' institution between 2010 and 2019 were retrospectively collected. Related literature published until December 2019 (63 articles, 67 patients) was evaluated. The authors also made a pooled analysis to evaluate the risk factors for overall survival (OS) time.ResultsOf the 4 cases in our series, 3 cases were malignant transformation following previous treatment (surgery and radiosurgery) and 1 was primary MTVS. Of the 71 MTVSs from the literature, 27 were male and 39 were female, with the mean age of 47.2 ± 17.5 years old. Twelve patients (18.5%) were diagnosed with NF2 (15.4%) or NF1 (3.1%). Forty-three (61.4%) patients underwent previous treatment (surgery and/or radiotherapy) prior to the pathological diagnosis of MTVSs. The mean size of the MTVSs was 35.1 ± 13.2mm. The mean Ki-67 index was 30.6% ± 18.8%. Twenty-four (49.0%) patients underwent gross total resection, 25 (51.0%) patients underwent incomplete resection. Twenty-five (44.6%) underwent adjuvant radiotherapy (RT) postoperatively. During the average follow-up of 9.9 ± 9.5 months (range, 0-40 months), 37 (82.2%) patients developed a local recurrence or metastasis. Forty-seven (73.4%) patients died of tumor progression or postoperative complications. The overall 1-year and 2-year survival rate was 42.3% and 18.6% respectively. Log-rank testing for Kaplan-Meier survival analysis identified that size (P = 0.047) and adjuvant radiotherapy (P=0.001) were significant prognostic factors for OS. Multivariate analysis revealed that adjuvant RT was the only prognostic factor for longer OS (P = 0.005).ConclusionsMTVSs are rare, fatal disease, prone to recur and metastasize rapidly, resulting in death in most of the cases. We found that GTR did not improve the survival in MTVSs but postoperative adjuvant RT can significantly improve the OS, and we recommend early postoperative RT in MTVSs regardless of extent of resection.

Dataset Information

MRI Screening in Vestibular Schwannoma: A Deep Learning-based Analysis of Clinical and Audiometric Data.

Objective

Materials and methods

Results

Conclusions

Publications

MRI Screening in Vestibular Schwannoma: A Deep Learning-based Analysis of Clinical and Audiometric Data.

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