Desroches Shelby T, Huang Alice, Ghankot Rithvik, Tommasini Steven M, Wiznia Daniel H, Buono Frank D
Department of Orthopaedics and Rehabilitation, Yale School of Medicine, New Haven, CT, United States.
Department of Mechanical Engineering, Yale University, New Haven, CT, United States.
JMIR Hum Factors. 2025 Jul 30;12:e71728. doi: 10.2196/71728.
Accurate monitoring of tumor progression is crucial for optimizing outcomes in neurofibromatosis type 2-related schwannomatosis. Standard 2D linear analysis on magnetic resonance imaging is less accurate than 3D volumetric analysis, but since 3D volumetric analysis is time-consuming, it is not widely used. To shorten the time required for 3D volumetric analysis, our lab has been developing an automated artificial intelligence-driven 3D volumetric tool.
The objective of the study was to survey and interview clinicians treating neurofibromatosis type 2-related schwannomatosis to understand their views on current 2D analysis and to gather insights for the design of an artificial intelligence-driven 3D volumetric analysis tool.
Interviews examined for the following themes: (1) shortcomings of the currently used linear analysis, (2) utility of 3D visualizations, (3) features of an interactive 3D modeling software, and (4) lack of a gold standard to assess the accuracy of 3D volumetric analysis. A Likert scale questionnaire was used to survey clinicians' levels of agreement with 25 statements related to 2D and 3D tumor analyses.
A total of 14 clinicians completed a survey, and 12 clinicians were interviewed. Specialties ranged across neurosurgery, neuroradiology, neurology, oncology, and pediatrics. Overall, clinicians expressed concerns with current linear techniques, with clinicians agreeing that linear measurements can be variable with the possibility of two different clinicians calculating 2 different tumor sizes (mean 4.64, SD 0.49) and that volumetric measurements would be more helpful for determining clearer thresholds of tumor growth (mean 4.50, SD 0.52). For statements discussing the capabilities of a 3D volumetric analysis and visualization software, clinicians expressed strong interest in being able to visualize tumors with respect to critical brain structures (mean 4.36, SD 0.74) and in forecasting tumor growth (mean 4.77, SD 0.44).
Clinicians were overall in favor of the adoption of 3D volumetric analysis techniques for measuring vestibular schwannoma tumors but expressed concerns regarding the novelty and inexperience surrounding these techniques. However, clinicians felt that the ability 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 vestibular schwannoma tumor growth. These findings may also apply to other central nervous system tumors, offering broader utility in tumor growth assessments.
准确监测肿瘤进展对于优化2型神经纤维瘤病相关的神经鞘瘤病的治疗效果至关重要。磁共振成像的标准二维线性分析不如三维容积分析准确，但由于三维容积分析耗时，未得到广泛应用。为缩短三维容积分析所需时间，我们实验室一直在开发一种人工智能驱动的自动化三维容积工具。
本研究的目的是对治疗2型神经纤维瘤病相关神经鞘瘤病的临床医生进行调查和访谈，以了解他们对当前二维分析的看法，并收集有关设计人工智能驱动的三维容积分析工具的见解。
访谈围绕以下主题展开：（1）当前使用的线性分析的缺点，（2）三维可视化的效用，（3）交互式三维建模软件的特点，以及（4）缺乏评估三维容积分析准确性的金标准。使用李克特量表问卷来调查临床医生对与二维和三维肿瘤分析相关的25项陈述的认同程度。
共有14名临床医生完成了调查，12名临床医生接受了访谈。专业涵盖神经外科、神经放射学、神经病学、肿瘤学和儿科学。总体而言，临床医生对当前的线性技术表示担忧，他们一致认为线性测量可能存在差异，两名不同的临床医生可能计算出两种不同的肿瘤大小（平均值4.64，标准差0.49），而容积测量对于确定更清晰的肿瘤生长阈值会更有帮助（平均值4.50，标准差0.52）。对于讨论三维容积分析和可视化软件功能的陈述，临床医生对能够相对于关键脑结构可视化肿瘤（平均值4.36，标准差0.74）以及预测肿瘤生长（平均值4.77，标准差0.44）表现出浓厚兴趣。
临床医生总体上赞成采用三维容积分析技术来测量前庭神经鞘瘤，但对这些技术的新颖性和缺乏经验表示担忧。然而，临床医生认为能够参照关键结构可视化肿瘤，并叠加结构、与三维模型交互以及以三维方式可视化缓慢与快速生长区域，将对临床实践有重要贡献。总体而言，临床医生为设计用于评估前庭神经鞘瘤肿瘤生长的三维容积分析工具提供了有价值的见解。这些发现也可能适用于其他中枢神经系统肿瘤，在肿瘤生长评估中具有更广泛的用途。
