Bi Shaowei, Chen Rongxin, Zhang Kai, Xiang Yifan, Wang Ruixin, Lin Haotian, Yang Huasheng
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
School of Computer Science and Technology, Xidian University, Xi'an, China.
Ann Transl Med. 2020 Jun;8(11):710. doi: 10.21037/atm.2020.03.150.
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.
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 consisted of classification models trained to make the final diagnosis.
First, internal four-fold cross-validation processes were conducted for all the models. During the validation of the first group, the 32 eye-positioning models were able to localize the position of the eyes with an average precision of 100%. In the second group, the 28 tumor-positioning models were able to reach an average precision above 90%. Subsequently, using the third group, the accuracy of all 32 tumor classification models reached nearly 90%. Next, external validation processes of 32 tumor classification models were conducted. The results showed that the accuracy of the transverse T1-weighted contrast-enhanced sequence reached 91.13%; the accuracy of the remaining models was significantly lower compared with the ground truth.
The findings of this retrospective study show that an artificial intelligence framework can achieve high accuracy, sensitivity, and specificity in automated differential diagnosis between cavernous hemangioma and schwannoma in a real-world setting, which can help doctors determine appropriate treatments.
海绵状血管瘤和神经鞘瘤均为发生于眼眶的肿瘤。由于这两种肿瘤的治疗策略不同，因此在治疗开始时需要对它们进行区分。磁共振成像（MRI）通常用于区分这两种肿瘤类型；然而，它们在MRI图像中呈现出相似的特征，这增加了鉴别诊断的难度。本研究旨在设计并开发一种人工智能框架，通过自动区分海绵状血管瘤和神经鞘瘤来提高临床医生诊断的准确性，并做出更有效的治疗决策。
材料：作为研究材料，我们选择MRI图像，这些图像代表了来自中国不同地区、从45多家不同医院转诊至我们中心的患者。所有图像最初是在胶片上获取的，我们将其扫描成数字版本并重新裁剪。最终，收集了11489张海绵状血管瘤图像（来自33家不同医院）和3478张神经鞘瘤图像（来自16家不同医院）。标注：所有图像均使用标准解剖学知识和病理诊断进行标注。训练：依次训练三种类型的模型（共96个模型），每个模型都有特定的改进。前两组模型是眼部和肿瘤定位模型，旨在缩小识别范围，而第三组模型是经过训练以做出最终诊断的分类模型。
首先，对所有模型进行内部四折交叉验证过程。在第一组验证中，32个眼部定位模型能够定位眼睛位置，平均精度达到100%。在第二组中，28个肿瘤定位模型的平均精度达到90%以上。随后，使用第三组模型，所有32个肿瘤分类模型的准确率接近90%。接下来，对32个肿瘤分类模型进行外部验证过程。结果显示，横向T1加权对比增强序列的准确率达到91.13%；与真实情况相比，其余模型的准确率显著较低。
这项回顾性研究的结果表明，人工智能框架在现实环境中对海绵状血管瘤和神经鞘瘤进行自动鉴别诊断时能够实现高准确率、敏感性和特异性，这有助于医生确定合适的治疗方案。
