PDF(Pubmed)
Abstract：
UNASSIGNED: It is still a challenge to differentiate space-occupying brain lesions such as tumefactive demyelinating lesions (TDLs), tumefactive primary angiitis of the central nervous system (TPACNS), primary central nervous system lymphoma (PCNSL), and brain gliomas. Convolutional neural networks (CNNs) have been used to analyze complex medical data and have proven transformative for image-based applications. It can quickly acquire diseases\' radiographic features and correct doctors\' diagnostic bias to improve diagnostic efficiency and accuracy. The study aimed to assess the value of CNN-based deep learning model in the differential diagnosis of space-occupying brain diseases on MRI.
UNASSIGNED: We retrospectively analyzed clinical and MRI data from 480 patients with TDLs (n = 116), TPACNS (n = 64), PCNSL (n = 150), and brain gliomas (n = 150). The patients were randomly assigned to training (n = 240), testing (n = 73), calibration (n = 96), and validation (n = 71) groups. And a CNN-implemented deep learning model guided by clinical experts was developed to identify the contrast-enhanced T1-weighted sequence lesions of these four diseases. We utilized accuracy, sensitivity, specificity, and area under the curve (AUC) to evaluate the performance of the CNN model. The model\'s performance was then compared to the neuroradiologists\' diagnosis.
UNASSIGNED: The CNN model had a total accuracy of 87% which was higher than senior neuroradiologists (74%), and the AUC of TDLs, PCNSL, TPACNS and gliomas were 0.92, 0.92, 0.89 and 0.88, respectively.
UNASSIGNED: The CNN model can accurately identify specific radiographic features of TDLs, TPACNS, PCNSL, and gliomas. It has the potential to be an effective auxiliary diagnostic tool in the clinic, assisting inexperienced clinicians in reducing diagnostic bias and improving diagnostic efficiency.
UNASSIGNED: We retrospectively analyzed clinical and MRI data from 480 patients with TDLs (n = 116), TPACNS (n = 64), PCNSL (n = 150), and brain gliomas (n = 150). The patients were randomly assigned to training (n = 240), testing (n = 73), calibration (n = 96), and validation (n = 71) groups. And a CNN-implemented deep learning model guided by clinical experts was developed to identify the contrast-enhanced T1-weighted sequence lesions of these four diseases. We utilized accuracy, sensitivity, specificity, and area under the curve (AUC) to evaluate the performance of the CNN model. The model\'s performance was then compared to the neuroradiologists\' diagnosis.
UNASSIGNED: The CNN model had a total accuracy of 87% which was higher than senior neuroradiologists (74%), and the AUC of TDLs, PCNSL, TPACNS and gliomas were 0.92, 0.92, 0.89 and 0.88, respectively.
UNASSIGNED: The CNN model can accurately identify specific radiographic features of TDLs, TPACNS, PCNSL, and gliomas. It has the potential to be an effective auxiliary diagnostic tool in the clinic, assisting inexperienced clinicians in reducing diagnostic bias and improving diagnostic efficiency.
摘要：
UNASSIGNED：区分占位性脑病变，如肿瘤脱髓鞘病变（TDL），仍然是一个挑战，中枢神经系统原发性血管炎（TPACNS），原发性中枢神经系统淋巴瘤（PCNSL），和脑胶质瘤。卷积神经网络（CNN）已用于分析复杂的医疗数据，并已被证明对基于图像的应用具有变革性。它可以快速获取疾病的影像学特征并纠正医生的诊断偏见，以提高诊断效率和准确性。该研究旨在评估基于CNN的深度学习模型在MRI鉴别诊断占位性脑疾病中的价值。
UNASSIGNED：我们回顾性分析了480例TDL患者（n=116）的临床和MRI数据，TPACNS（n=64），PCNSL（n=150），和脑胶质瘤（n=150）。患者被随机分配到培训(n=240)，测试(n=73)，校准（n=96），和验证组(n=71)。并开发了由临床专家指导的CNN实现的深度学习模型，以识别这四种疾病的对比增强T1加权序列病变。我们利用准确性，灵敏度,特异性，和曲线下面积(AUC)来评估CNN模型的性能。然后将模型的性能与神经放射学家的诊断进行比较。
UNASSIGNED：CNN模型的总准确度为87％，高于高级神经放射科医生（74％）。和TDL的AUC，PCNSL,TPACNS和胶质瘤分别为0.92、0.92、0.89和0.88。
UNASSIGNED：CNN模型可以准确识别TDL的特定射线照相特征，TPACNS,PCNSL,和神经胶质瘤。它有可能成为临床上有效的辅助诊断工具，协助没有经验的临床医生减少诊断偏倚，提高诊断效率。
UNASSIGNED：我们回顾性分析了480例TDL患者（n=116）的临床和MRI数据，
UNASSIGNED：CNN模型的总准确度为87％，高于高级神经放射科医生（74％）。
UNASSIGNED：CNN模型可以准确识别TDL的特定射线照相特征，
