Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi‐task Model

Wang, Kai; George‐Jones, Nicholas A.; Chen, Liyuan; Hunter, Jacob B.; Wang, Jing

doi:10.1002/lary.30516

Cited by 12 publications

(4 citation statements)

References 39 publications

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“…The current work describes a stratification grouping whereby a certain subset of patients can almost be assured of future tumor growth (those with a volumetric growth rate of ≥100% per year), whereas those with a volumetric growth rate less than 25% per year demonstrate more saltatory growth patterns with a greater likelihood of long-term quiescence. Consideration for use of a classification schema that accounts for the dynamic biological behavior of these tumors is warranted during wait-and-scan management (17), and the stratification grouping described in the current work may provide this clinical utility as volumetric measurements become increasingly common clinically (22). Moreover, these data further illustrate that some degree of tumor growth should be expected for all patients with sporadic vestibular schwannoma during wait-and-scan management—even among the slowest-growing subset of tumors, nearly 70% displayed additional growth within 5 years of the first episode of growth.…”

Section: Discussionmentioning

confidence: 99%

Stratifying Risk of Future Growth Among Sporadic Vestibular Schwannomas

et al. 2023

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Objective In certain cases, clinicians may consider continued observation of a vestibular schwannoma after initial growth is detected. The aim of the current work was to determine if patients with growing sporadic vestibular schwannomas could be stratified by the likelihood of subsequent growth based on initial growth behavior. Study Design Slice-by-slice volumetric tumor measurements from 3,505 serial magnetic resonance imaging studies were analyzed from 952 consecutively treated patients. Setting Three tertiary-referral centers. Patients Adults with sporadic vestibular schwannoma. Interventions Wait-and-scan. Main Outcome Measures Composite end point of subsequent growth- or treatment-free survival rates, where growth is defined as an additional increase of at least 20% in tumor volume from the volume at the time of initial growth. Results Among 405 patients who elected continued observation despite documented growth, stratification, of volumetric growth rate into less than 25% (reference: n = 107), 25 to less than 50% (hazard ratio [HR], 1.39; p = 0.06; n = 96), 50 to less than 100% (HR, 1.71; p = 0.002; n = 112), and at least 100% (HR, 2.01; p < 0.001; n = 90) change per year predicted the likelihood of future growth or treatment. Subsequent growth- or treatment-free survival rates (95% confidence interval) at year 5 after detection of initial growth were 31% (21–44%) for those with less than 25% growth per year, 18% (10–32%) for those with 25 to less than 50%, 15% (9–26%) for those with 50 to less than 100%, and 6% (2–16%) for those with at least 100%. Neither patient age (p = 0.15) nor tumor volume at diagnosis (p = 0.95) significantly differed across stratification groups. Conclusions At the time of diagnosis, clinical features cannot consistently predict which tumors will ultimately display aggressive behavior. Stratification by volumetric growth rate at the time of initial growth results in a stepwise progression of increasing likelihood of subsequent growth. When considering continued observation after initial growth detection, almost 95% of patients who have tumors that double in volume between diagnosis and the first detection of growth demonstrate further tumor growth or undergo treatment if observed to 5 years.

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Section: Discussionmentioning

confidence: 99%

Stratifying Risk of Future Growth Among Sporadic Vestibular Schwannomas

et al. 2023

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“…Predictive models could be used to guide management decisions for VS by providing clinicians and patients with quantitative estimates of risk. For example, predictive models have been developed to predict VS growth, 18,19 VS recurrence following surgery, 20,21 and VS treatment response following radiation treatment. 22,23 In our study, the binary classification model could provide a probabilistic estimate of having vestibular dysfunction at the 3-, 6-, or 12-month marks; and the 4-way classification model would provide individual probabilities for each of these time points.…”

Section: Discussionmentioning

confidence: 99%

Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery

et al. 2023

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ObjectiveIn an era of vestibular schwannoma (VS) surgery where functional preservation is increasingly emphasized, persistent postoperative dizziness is a relatively understudied functional outcome. The primary objective was to develop a predictive model to identify patients at risk for developing persistent postoperative dizziness after VS resection.MethodsRetrospective review of patients who underwent VS surgery at our institution with a minimum of 12 months of postoperative follow‐up. Demographic, tumor‐specific, preoperative, and immediate postoperative features were collected as predictors. The primary outcome was self‐reported dizziness at 3‐, 6‐, and 12‐month follow‐up. Binary and multiclass machine learning classification models were developed using these features.ResultsA total of 1,137 cases were used for modeling. The median age was 67 years, and 54% were female. Median tumor size was 2 cm, and the most common approach was suboccipital (85%). Overall, 63% of patients did not report postoperative dizziness at any timepoint; 11% at 3‐month follow‐up; 9% at 6‐months; and 17% at 12‐months. Both binary and multiclass models achieved high performance with AUCs of 0.89 and 0.86 respectively. Features important to model predictions were preoperative headache, need for physical therapy on discharge, vitamin D deficiency, and systemic comorbidities.ConclusionWe demonstrate the feasibility of a machine learning approach to predict persistent dizziness following vestibular schwannoma surgery with high accuracy. These models could be used to provide quantitative estimates of risk, helping counsel patients on what to expect after surgery and manage patients proactively in the postoperative setting.Level of evidenceLevel IV Laryngoscope, 2023

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“…AI-assisted radiomics can be extremely useful in the follow-up of specific diseases, such as vestibular schwannoma, whose surveillance is nowadays performed through analogical segmentation and an analysis of serial MRI scans to detect tumor enlargement. A deep learning approach can be applied for tumor detection and segmentation in treatment-naïve patients [120], both after radiosurgery [121], in evaluating residual disease [122], and in predicting tumor enlargement based on radiomics parameters during follow-up [123].…”

Section: Imaging In Otologymentioning

confidence: 99%

Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review

Tsilivigkos,

Athanasopoulos,

Micco

et al. 2023

JCM

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Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords ‘otorhinolaryngology’, ‘imaging’, ‘computer vision’, ‘artificial intelligence’, and ‘deep learning’, and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.

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Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi‐task Model

Cited by 12 publications

References 39 publications

Stratifying Risk of Future Growth Among Sporadic Vestibular Schwannomas

Stratifying Risk of Future Growth Among Sporadic Vestibular Schwannomas

Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery

Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review

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