Differentiate cavernous hemangioma from schwannoma with artificial intelligence (AI)

作者：Shaowei Bi, Rongxin Chen, Kai Zhang, Yifan Xiang, Ruixin Wang, Haotian Lin, Huasheng Yang · 发表于：Annals of Translational Medicine · 年份：2020 · DOI：10.21037/atm.2020.03.150 · 被引用次数：26 · 研究领域：Meningioma and schwannoma management、Vascular Malformations Diagnosis and Treatment、Vascular Malformations and Hemangiomas

BACKGROUND: Cavernous hemangioma and schwannoma are tumors that both occur in the orbit. Because the treatment strategies of these two tumors are different, it is necessary to distinguish them at treatment initiation. Magnetic resonance imaging (MRI) is typically used to differentiate these two tumor types; however, they present similar features in MRI images which increases the difficulty of differential diagnosis. This study aims to devise and develop an artificial intelligence framework to improve the accuracy of clinicians' diagnoses and enable more effective treatment decisions by automatically distinguishing cavernous hemangioma from schwannoma. METHODS: Material: As the study materials, we chose MRI images as the study materials that represented patients from diverse areas in China who had been referred to our center from more than 45 different hospitals. All images were initially acquired on films, which we scanned into digital versions and recut. Finally, 11,489 images of cavernous hemangioma (from 33 different hospitals) and 3,478 images of schwannoma (from 16 different hospitals) were collected. Labeling: All images were labeled using standard anatomical knowledge and pathological diagnosis. Training: Three types of models were trained in sequence (a total of 96 models), with each model including a specific improvement. The first two model groups were eye- and tumor-positioning models designed to reduce the identification scope, while the third model group consiste...