Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast‐Enhanced Magnetic Resonance Imaging. Issue 1 (9th December 2021)

• Record Type: Journal Article
• Authors: Zhao, Keyang; Zhang, Mingzi; Xie, Zhaozhi; Yan, Xu; Wu, Shenghui; Liao, Peng; Lu, Hongtao; Shen, Wei; Fu, Chicheng; Cui, Haoyang; Fang, Qu; Mei, Jiong
• Is Part Of: Journal of magnetic resonance imaging. Volume 56:Issue 1(2022)
• Page Start: 99
• Page End: 107
• Publication Date: 2021-12-09
• Subjects: deep learning -- musculoskeletal tumors -- bone tumors -- MRI -- neural networks; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.28025
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death. Purpose: To improve the diagnostic efficacy of musculoskeletal tumors by developing deep learning (DL) models based on contrast‐enhanced magnetic resonance imaging and to quantify the improvement in diagnostic performance obtained by using these models. Study type: Retrospective. Population: Three hundreds and four musculoskeletal tumors, including 212 malignant and 92 benign lesions, were randomized into the training ( n = 180), validation ( n = 62) and testing cohort ( n = 62). Field strength/sequence: A 3 T/T1 ‐weighted (T1 ‐w), T2 ‐weighted (T2 ‐w), diffusion‐weighted imaging (DWI), and contrast‐enhanced T1‐weighted (CET1 ‐w) images. Assessment: Three DL models based, respectively, on the sagittal, coronal, and axial MR images were constructed to predict the malignancy of tumors. Blinded to the prediction results, a group of specialists made independent initial diagnoses for each patient by reading all image sequences. One month after the initial diagnoses, the same group of doctors made another round of diagnoses knowing the malignancy of each tumor predicted by the three models. The reference standard was the pathological diagnosis of malignancy. Statistical tests: Sensitivity, specificity, and accuracy (all with 95% confidential intervals [CI]) corresponding to each diagnostic test were computed. Chi‐square tests were used to assess the differences in those parameters with and without DL models. A P value < 0.05 was considered statistically significant. Results: The developed models significantly improved the diagnostic sensitivities of two oncologists by 0.15 (95% CI: 0.06–0.24) and 0.36 (95% CI: 0.24–0.28), one radiologist by 0.12 (95% CI: 0.04–0.20), and three of the four orthopedists, respectively, by 0.12 (95% CI: 0.04–0.20), 0.29 (95% CI: 0.18–0.40), and 0.23 (95% CI: 0.13–0.33), without impairing any of their diagnostic specificities (all P > 0.128). Data conclusion: The DL models developed can significantly improve the performance of doctors with different training and experience in diagnosing musculoskeletal tumors. Evidence Level: 3 Technical Efficacy: Stage 2