JMIR Publications

ABSTRACT

Background:

Accurate monitoring of tumor progression is crucial for optimizing outcomes in NF2-related schwannomatosis (NF2-SWN). Standard 2D linear analysis on MRIs is less accurate than 3D volumetric analysis, but 3D volumetric analysis is time-consuming, so it is not widely utilized. To shorten the time required for 3D volumetric analysis, our lab has been developing an automated AI-driven 3D volumetric tool.

Objective:

The objective of our study was to survey and interview clinicians treating NF2-SWN to understand their views on current 2D analysis and to gather insights for our 3D volumetric analysis tool's design.

Methods:

Surveys and interviews were conducted with clinicians experienced in treating NF2. Interviews were examined for the following themes: (1) the state of tumor monitoring and 2D analysis, (2) utility of 3D visualization, (3) features for interactive 3D modeling, and (4) lack of a gold standard for 3D accuracy. A Likert scale questionnaire was used to survey clinician’s level of agreement with 25 statements related to 2D and 3D tumor analyses.

Results:

14 clinicians completed a survey, and 12 clinicians were interviewed. Specialties ranged across neurosurgery, neuroradiology, neurology, oncology and pediatrics. Both surveys and interviews revealed concerns around the variability and subjectivity of 2D analysis. Clinicians felt that 3D volumetric analysis addresses these concerns but expressed uncertainty about how it could be interpreted clinically. Clinicians recommended features for the tool, such as interactive 3D models, visualization of neighboring anatomic structures, and the ability to monitor growth using visual cues.

Conclusions:

Clinicians were overall in favor of adoption of 3D volumetric analysis techniques for measuring analyze VS tumors but expressed concerns regarding the novelty and inexperience surrounding these techniques. However, clinicians felt that the abilities to visualize tumors with reference to critical structures, to overlay structures, to interact with 3D models, and to visualize areas of slow versus rapid growth in 3D would be valuable contributions to clinical practice. Overall, clinicians provided valuable insights for designing a 3D volumetric analysis tool for VS tumor growth. These findings may also apply to other CNS tumors, offering broader utility in tumor growth assessments.