Dataset Information

Underreporting of Vestibular Schwannoma Incidence Within National Brain Tumor and Cancer Registries in the United States.

ABSTRACT:

Objective

Reports using large brain tumor and cancer registries suggest that the incidence of vestibular schwannoma is considerably lower in the United States compared with other countries. The current study compares the incidence and disease characteristics of vestibular schwannoma within a large tertiary referral center's brain tumor and cancer registry using nationally mandated reporting protocols to disease incidence in the same population using an externally validated population-based consortium.

Study design

Population-based study spanning 1,945,007 person-years.

Setting

Large tertiary referral center.

Patients

Adults with sporadic vestibular schwannoma.

Main outcome measure

Disease incidence rates from 2004 to 2016.

Results

From 2004 to 2016, the incidence of vestibular schwannoma in the tumor registry was 1.3 per 100,000 person-years whereas the population-based cohort had an incidence of 4.4 per 100,000 person-years. From 2012 to 2016, the incidence in the tumor registry was 1.4 per 100,000 person-years compared with 5.2 in the population-based cohort. Patients within the population-based cohort were significantly more likely to have smaller tumors at diagnosis (78% intracanalicular versus 45%; p = 0.004) and consequently more likely to undergo management consisting of observation with serial imaging as opposed to treatment with either microsurgery or radiosurgery (71% versus 28%; p = 0.001).

Conclusions

The reliance on pathology specimens and cancer-related treatment data for the national registration of new cancer and brain tumor diagnoses may introduce selection bias and underreporting of benign brain tumors that frequently involve observation as a primary treatment modality. This selection bias likely accounts for the discrepant incidence rates of vestibular schwannoma reported between the United States and other countries.

SUBMITTER: Marinelli JP

PROVIDER: S-EPMC8192892 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Underreporting of Vestibular Schwannoma Incidence Within National Brain Tumor and Cancer Registries in the United States.

Marinelli John P JP Nassiri Ashley M AM Habermann Elizabeth B EB Lohse Christine M CM Holton Sara J SJ Carlson Matthew L ML

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology 20210701 6

<h4>Objective</h4>Reports using large brain tumor and cancer registries suggest that the incidence of vestibular schwannoma is considerably lower in the United States compared with other countries. The current study compares the incidence and disease characteristics of vestibular schwannoma within a large tertiary referral center's brain tumor and cancer registry using nationally mandated reporting protocols to disease incidence in the same population using an externally validated population-bas ...[more]

PMID: 34111053

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Project description:BackgroundGiven concerns about risks associated with the growing use of mobile phones over recent decades, the authors analyzed temporal trends in incidence rates of nonmalignant meningioma and vestibular schwannoma in the United States.MethodsThe incidence of nonmalignant meningioma and vestibular schwannoma among adults in the Surveillance, Epidemiology, and End Results 18 registries during 2004 through 2017 was evaluated according to the method of diagnosis: microscopically (MC) or radiographically confirmed (RGC). Annual percent changes (APCs) and 95% CIs were estimated using log-linear models.ResultsOverall meningioma rates (n = 108,043) increased significantly from 2004 to 2009 (APC, 5.4%; 95% CI, 4.4%-6.4%) but subsequently rose at a slower pace through 2017 (APC, 1.0%; 95% CI, 0.6%-1.5%). Rates for MC meningiomas changed little from 2004 to 2017 (APC, -0.3%; 95% CI, -0.7%, 0.1%) but rose rapidly for RGC meningiomas until 2009 (APC, 9.5%; 95% CI, 7.8%-11.1%) and rose more modestly thereafter (APC, 2.3%; 95% CI, 1.5%-3.0%). Overall vestibular schwannoma rates (n = 17,475) were stable (APC, 0.4%; 95% CI, -0.2%, 1.0%), but MC vestibular schwannoma rates decreased (APC, -1.9%; 95% CI, -2.7%, -1.1%), whereas RGC vestibular schwannoma rates rose (2006-2017: APC, 1.7%; 95% CI, 0.5%-3.0%). For each tumor, the trends by diagnostic method were similar for each sex and each racial/ethnic group, but RGC diagnosis was more likely in older patients and for smaller tumors. Meningioma trends and the proportion of RGC diagnoses varied notably by registry.ConclusionsOverall trends obscured differences by diagnostic method in this first large, detailed assessment, but the recent stable rates argue against an association with mobile phone use. Variation among registries requires evaluation to improve the registration of these nonmalignant tumors.Lay summaryThe etiology of most benign meningiomas and vestibular schwannomas is poorly understood, but concerns have been raised about whether mobile phone use contributes to risk of developing these tumors. Descriptive studies examining temporal trends could provide insight; however, globally, few registries collect these nonmalignant cases. In the United States, reporting benign meningiomas and vestibular schwannomas became required by law in 2004. This was the first large, systematic study to quantify and characterize incidence trends for meningioma and vestibular schwannoma according to whether the tumors were diagnosed microscopically or only radiographically. Differential trends across registries and by diagnostic method suggest that caution should be used when interpreting the patterns.

Project description:ObjectiveThe Koos grading scale is a frequently used classification system for vestibular schwannoma (VS) that accounts for extrameatal tumor dimension and compression of the brain stem. We propose an artificial intelligence (AI) pipeline to fully automate the segmentation and Koos classification of VS from MRI to improve clinical workflow and facilitate patient management.MethodsWe propose a method for Koos classification that does not only rely on available images but also on automatically generated segmentations. Artificial neural networks were trained and tested based on manual tumor segmentations and ground truth Koos grades of contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) MR images from subjects with a single sporadic VS, acquired on a single scanner and with a standardized protocol. The first stage of the pipeline comprises a convolutional neural network (CNN) which can segment the VS and 7 adjacent structures. For the second stage, we propose two complementary approaches that are combined in an ensemble. The first approach applies a second CNN to the segmentation output to predict the Koos grade, the other approach extracts handcrafted features which are passed to a Random Forest classifier. The pipeline results were compared to those achieved by two neurosurgeons.ResultsEligible patients (n = 308) were pseudo-randomly split into 5 groups to evaluate the model performance with 5-fold cross-validation. The weighted macro-averaged mean absolute error (MA-MAE), weighted macro-averaged F1 score (F1), and accuracy score of the ensemble model were assessed on the testing sets as follows: MA-MAE = 0.11 ± 0.05, F1 = 89.3 ± 3.0%, accuracy = 89.3 ± 2.9%, which was comparable to the average performance of two neurosurgeons: MA-MAE = 0.11 ± 0.08, F1 = 89.1 ± 5.2, accuracy = 88.6 ± 5.8%. Inter-rater reliability was assessed by calculating Fleiss' generalized kappa (k = 0.68) based on all 308 cases, and intra-rater reliabilities of annotator 1 (k = 0.95) and annotator 2 (k = 0.82) were calculated according to the weighted kappa metric with quadratic (Fleiss-Cohen) weights based on 15 randomly selected cases.ConclusionsWe developed the first AI framework to automatically classify VS according to the Koos scale. The excellent results show that the accuracy of the framework is comparable to that of neurosurgeons and may therefore facilitate management of patients with VS. The models, code, and ground truth Koos grades for a subset of publicly available images (n = 188) will be released upon publication.

Dataset Information

Underreporting of Vestibular Schwannoma Incidence Within National Brain Tumor and Cancer Registries in the United States.

Objective

Study design

Setting

Patients

Main outcome measure

Results

Conclusions

Publications

Underreporting of Vestibular Schwannoma Incidence Within National Brain Tumor and Cancer Registries in the United States.

Similar Datasets

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