A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma. Issue 10 (15th May 2020)

• Record Type: Journal Article
• Authors: Maki, Satoshi; Furuya, Takeo; Horikoshi, Takuro; Yokota, Hajime; Mori, Yasukuni; Ota, Joji; Kawasaki, Yohei; Miyamoto, Takuya; Norimoto, Masaki; Okimatsu, Sho; Shiga, Yasuhiro; Inage, Kazuhide; Orita, Sumihisa; Takahashi, Hiroshi; Suyari, Hiroki; Uno, Takashi; Ohtori, Seiji
• Is Part Of: Spine. Volume 45:Issue 10(2020)
• Publication Date: 2020-05-15
• Subjects: artificial intelligence -- deep convolutional neural network -- deep learning -- meningioma -- schwannoma -- spinal cord tumor; Spine -- Abnormalities -- Periodicals; Spine -- Diseases -- Periodicals; Spine -- Surgery -- Periodicals; 616.73005
• DOI: 10.1097/BRS.0000000000003353
• Languages: English
• ISSNs: 0362-2436

Abstract

Abstract : Study Design: Retrospective analysis of magnetic resonance imaging (MRI). Objective: The aim of this study was to evaluate the performance of our convolutional neural network (CNN) in differentiating between spinal schwannoma and meningioma on MRI. We compared the performance of the CNN and that of two expert radiologists. Summary of Background Data: Preoperative discrimination between spinal schwannomas and meningiomas is crucial because different surgical procedures are required for their treatment. A deep-learning approach based on CNNs is gaining interest in the medical imaging field. Methods: We retrospectively reviewed data from patients with spinal schwannoma and meningioma who had undergone MRI and tumor resection. There were 50 patients with schwannoma and 34 patients with meningioma. Sagittal T2-weighted magnetic resonance imaging (T2WI) and sagittal contrast-enhanced T1-weighted magnetic resonance imaging (T1WI) were used for the CNN training and validation. The deep learning framework Tensorflow was used to construct the CNN architecture. To evaluate the performance of the CNN, we plotted the receiver-operating characteristic (ROC) curve and calculated the area under the curve (AUC). We calculated and compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN and two board-certified radiologists. Results: . The AUC of ROC curves of the CNN based on T2WI and contrast-enhanced T1WI were 0.876 and 0.870, respectively. The sensitivity of the CNN based on T2WI was 78%; 100% for radiologist 1; and 95% for radiologist 2. The specificity was 82%, 26%, and 42%, respectively. The accuracy was 80%, 69%, and 73%, respectively. By contrast, the sensitivity of the CNN based on contrast-enhanced T1WI was 85%; 100% for radiologist 1; and 96% for radiologist 2. The specificity was 75%, 56, and 58%, respectively. The accuracy was 81%, 82%, and 81%, respectively. Conclusion: We have successfully differentiated spinal schwannomas and meningiomas using the CNN with high diagnostic accuracy comparable to that of experienced radiologists. Level of Evidence: 4 Abstract : We evaluated and compared the performance of our convolutional neural network (CNN) and that of two expert radiologists in differentiating between spinal schwannoma and meningioma on magnetic resonance imaging. We have successfully differentiated meningiomas and schwannomas using the CNN with high diagnostic accuracy comparable to that of experienced radiologists.