دورية أكاديمية
Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning
| العنوان: | Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning |
|---|---|
| المؤلفون: | Zhuo, Zhizheng, Zhang, Jie, Duan, Yunyun, Qu, Liying, Feng, Chenlu, Huang, Xufang, Cheng, Dan, Xu, Xiaolu, Sun, Ting, Li, Zhaohui, Guo, Xiaopeng, Gong, Xiaodong, Wang, Yongzhi, Jia, Wenqing, Tian, Decai, Zhang, Xinghu, Shi, Fudong, Haller, Sven, Barkhof, Frederik, Ye, Chuyang, Liu, Yaou |
| المصدر: | Radiology: Artificial Intelligence , 4 (6) , Article e210292. (2022) |
| بيانات النشر: | Radiological Society of North America (RSNA) |
| سنة النشر: | 2022 |
| المجموعة: | University College London: UCL Discovery |
| مصطلحات موضوعية: | Astrocytoma, Deep Learning, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Spinal Cord MRI |
| الوصف: | Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A retrospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neuromyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual labeling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%-95% (AUC, 0.78-0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis. Supplemental material is available for this article. © RSNA, 2022 Keywords: Spinal Cord MRI, Astrocytoma, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Deep Learning. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text |
| اللغة: | English |
| العلاقة: | https://discovery.ucl.ac.uk/id/eprint/10162244/1/Barkhof_Zhou%20-%20Automated%20Classification%20of%20Intramedullary%20Spinal%20Cord%20Tumors%20and%20Inflammatory%20Demyelinating%20Lesions%20Using%20Deep%20Learning%20-%20RYAI_R2%20Main_text_revis.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10162244Test/ |
| الإتاحة: | https://discovery.ucl.ac.uk/id/eprint/10162244/1/Barkhof_Zhou%20-%20Automated%20Classification%20of%20Intramedullary%20Spinal%20Cord%20Tumors%20and%20Inflammatory%20Demyelinating%20Lesions%20Using%20Deep%20Learning%20-%20RYAI_R2%20Main_text_revis.pdfTest https://discovery.ucl.ac.uk/id/eprint/10162244Test/ |
| حقوق: | open |
| رقم الانضمام: | edsbas.D04D8C56 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |