Automatic identification algorithm of meniscus tear based on radiomics of knee MRI_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

LI Yuanzhe ¹ , LAI Qingquan ¹ , HUANG Jing ¹ , HU Weiyi ¹ ,  WANG Yi ¹ , FANG Kaibin ²

1. CT/MRI Department, the Second Affiliated Hospital of Fujian Medical University, Quanzhou Fujian, 362000, P. R. China;
2. Department of Orthopedics, the Second Affiliated Hospital of Fujian Medical University, Quanzhou Fujian, 362000, P. R. China;

Corresponding author：

WANG Yi, Email: fjmuwang@sina.cn

Keywords：

Radiomics; meniscus tear; MRI; knee

DOI：

10.7507/1002-1892.202206016

Video：

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Objective To establish a classification model based on knee MRI radiomics, realize automatic identification of meniscus tear, and provide reference for accurate diagnosis of meniscus injury. Methods A total of 228 patients (246 knees) with meniscus injury who were admitted between July 2018 and March 2021 were selected as the research objects. There were 146 males and 82 females; the age ranged from 9 to 76 years, with a median age of 53 years. There were 210 cases of meniscus injury in one knee and 18 cases in both knees. All the patients were confirmed by arthroscopy, among which 117 knees with meniscus tear and 129 knees with meniscus non-tear injury. The proton density weighted-spectral attenuated inversion recovery (PDW-SPAIR) sequence images of sagittal MRI were collected, and two doctors performed radiomics studies. The 246 knees were randomly divided into training group and testing group according to the ratio of 7∶3. First, ITK-SNAP3.6.0 software was used to extract the region of interest (ROI) of the meniscus and radiomic features. After retaining the radiomic features with intraclass correlation coefficient (ICC)>0.8, the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for filtering the features to establish an automatic identification model of meniscus tear. The receiver operator characteristic curve (ROC) and the corresponding area under the ROC curve (AUC) was obtained; the model performance was comprehensively evaluated by calculating the accuracy, sensitivity, and specificity. Results A total of 1 316-dimensional radiomic features were extracted from the meniscus ROI, and the ICC within the group and ICC between the groups of the 981-dimensional radiomic features were both greater than 0.80. The redundant information in the 981-dimensional radiomic features was eliminated by mRMR, and the 20-dimensional radiomic features were retained. The optimal feature subset was further selected by LASSO, and 8-dimensional radiomic features were selected. The average ICC within the group and the average ICC between the groups were 0.942 and 0.920, respectively. The AUC of the training group was 0.889±0.036 [95%CI (0.845, 0.942), P<0.001], and the accuracy, sensitivity, and specificity were 0.873, 0.869, and 0.842, respectively; the AUC of the testing group was 0.876±0.036 [95%CI (0.875, 0.984), P<0.001], and the accuracy, sensitivity, and specificity were 0.862, 0.851, and 0.845, respectively. Conclusion The model established by the radiomics method has good automatic identification performance of meniscus tear.

Citation： LI Yuanzhe, LAI Qingquan, HUANG Jing, HU Weiyi, WANG Yi, FANG Kaibin. Automatic identification algorithm of meniscus tear based on radiomics of knee MRI. Chinese Journal of Reparative and Reconstructive Surgery, 2022, 36(11): 1395-1399. doi: 10.7507/1002-1892.202206016 Copy

1.	Lee NH, Seo HY, Sung MJ, et al. Does meniscectomy have any advantage over conservative treatment in middle-aged patients with degenerative medial meniscus posterior root tear? BMC Musculoskelet Disord, 2021, 22(1): 742. doi: 10.1186/s12891-021-04632-8.
2.	Berg B, Roos EM, Kise NJ, et al. On a trajectory for success-9 in every 10 people with a degenerative meniscus tear have improved knee function within 2 years after treatment: A secondary exploratory analysis of a randomized controlled trial. J Orthop Sports Phys Ther, 2021, 51(6): 289-297.
3.	Pache S, Aman ZS, Kennedy M, et al. Meniscal root tears: Current concepts review. Arch Bone Jt Surg, 2018, 6(4): 250-259.
4.	Lecouvet F, Van Haver T, Acid S, et al. Magnetic resonance imaging (MRI) of the knee: Identification of difficult-to-diagnose meniscal lesions. Diagn Interv Imaging, 2018, 99(2): 55-64.
5.	Naraghi AM, White LM. Imaging of athletic injuries of knee ligaments and menisci: Sports imaging series. Radiology, 2016, 281(1): 23-40.
6.	Crawford R, Walley G, Bridgman S, et al. Magnetic resonance imaging versus arthroscopy in the diagnosis of knee pathology, concentrating on meniscal lesions and ACL tears: a systematic review. Br Med Bull, 2007, 84: 5-23.
7.	Chen H, Zhang X, Wang X, et al. MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol, 2021, 31(10): 7913-7924.
8.	Ubaldi L, Valenti V, Borgese RF, et al. Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples. Phys Med, 2021, 90: 13-22.
9.	Yang Y, Li J, Liu Y, et al. Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer. Breast, 2021, 60: 90-97.
10.	Bang M, Eom J, An C, et al. An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum. Transl Psychiatry, 2021, 11(1): 462. doi: 10.1038/s41398-021-01586-2.
11.	Wang R, Xu L. Coronary computed tomography angiography radiomics: A potential noninvasive tool for detecting myocardial fibrosis. Int J Cardiol, 2021, 338: 276-277.
12.	Couteaux V, Si-Mohamed S, Nempont O, et al. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging, 2019, 100(4): 235-242.
13.	Fritz B, Marbach G, Civardi F, et al. Correction to: Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol, 2020, 49(8): 1219. doi: 10.1007/s00256-020-03458-0.
14.	Saygılı A, Albayrak S. An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images. Artif Intell Med, 2019, 97: 118-130.
15.	Song Z, Tang Z, Liu H, et al. A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage. Eur Radiol, 2021, 31(7): 4949-4959.
16.	Xie H, Ma S, Wang X, et al. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model. Eur Radiol, 2020, 30(1): 87-98.
17.	Jiang PQ. Diagnosis of knee meniscus injury by low field MRI. Guide of China Medicine, 2017, 13(13): 95-96.
18.	Qiu X, Liu Z, Zhuang M, et al. Fusion of CNN1 and CNN2-based magnetic resonance image diagnosis of knee meniscus injury and a comparative analysis with computed tomography. Comput Methods Programs Biomed, 2021, 211: 106297. doi: 10.1016/j.cmpb.2021.106297.
19.	Fritz B, Marbach G, Civardi F, et al. Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol, 2020, 49(8): 1207-1217.
20.	Li Z, Ren S, Zhang X, et al. Deep learning-based image feature with arthroscopy-aided early diagnosis and treatment of meniscus injury of knee joint. J Healthc Eng, 2021, 2021: 2254594. doi: 10.1155/2021/2254594.
21.	Bien N, Rajpurkar P, Ball RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med, 2018, 15(11): e1002699. doi: 10.1371/journal.pmed.1002699.
22.	Rizk B, Brat H, Zille P, et al. Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation. Phys Med, 2021, 83: 64-71.
23.	Astuto B, Flament I, Namiri KN, et al. Automatic deep learning-assisted detection and grading of abnormalities in knee MRI studies. Radiol Artif Intell, 2021, 3(3): e200165. doi: 10.1148/ryai.2021200165.
24.	张新民, 曹结水. 膝关节半月板损伤的MRI分级分类及临床应用. 安徽卫生职业技术学院学报, 2020, 19(1): 43-44.

1. Lee NH, Seo HY, Sung MJ, et al. Does meniscectomy have any advantage over conservative treatment in middle-aged patients with degenerative medial meniscus posterior root tear? BMC Musculoskelet Disord, 2021, 22(1): 742. doi: 10.1186/s12891-021-04632-8.
2. Berg B, Roos EM, Kise NJ, et al. On a trajectory for success-9 in every 10 people with a degenerative meniscus tear have improved knee function within 2 years after treatment: A secondary exploratory analysis of a randomized controlled trial. J Orthop Sports Phys Ther, 2021, 51(6): 289-297.
3. Pache S, Aman ZS, Kennedy M, et al. Meniscal root tears: Current concepts review. Arch Bone Jt Surg, 2018, 6(4): 250-259.
4. Lecouvet F, Van Haver T, Acid S, et al. Magnetic resonance imaging (MRI) of the knee: Identification of difficult-to-diagnose meniscal lesions. Diagn Interv Imaging, 2018, 99(2): 55-64.
5. Naraghi AM, White LM. Imaging of athletic injuries of knee ligaments and menisci: Sports imaging series. Radiology, 2016, 281(1): 23-40.
6. Crawford R, Walley G, Bridgman S, et al. Magnetic resonance imaging versus arthroscopy in the diagnosis of knee pathology, concentrating on meniscal lesions and ACL tears: a systematic review. Br Med Bull, 2007, 84: 5-23.
7. Chen H, Zhang X, Wang X, et al. MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol, 2021, 31(10): 7913-7924.
8. Ubaldi L, Valenti V, Borgese RF, et al. Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples. Phys Med, 2021, 90: 13-22.
9. Yang Y, Li J, Liu Y, et al. Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer. Breast, 2021, 60: 90-97.
10. Bang M, Eom J, An C, et al. An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum. Transl Psychiatry, 2021, 11(1): 462. doi: 10.1038/s41398-021-01586-2.
11. Wang R, Xu L. Coronary computed tomography angiography radiomics: A potential noninvasive tool for detecting myocardial fibrosis. Int J Cardiol, 2021, 338: 276-277.
12. Couteaux V, Si-Mohamed S, Nempont O, et al. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging, 2019, 100(4): 235-242.
13. Fritz B, Marbach G, Civardi F, et al. Correction to: Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol, 2020, 49(8): 1219. doi: 10.1007/s00256-020-03458-0.
14. Saygılı A, Albayrak S. An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images. Artif Intell Med, 2019, 97: 118-130.
15. Song Z, Tang Z, Liu H, et al. A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage. Eur Radiol, 2021, 31(7): 4949-4959.
16. Xie H, Ma S, Wang X, et al. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model. Eur Radiol, 2020, 30(1): 87-98.
17. Jiang PQ. Diagnosis of knee meniscus injury by low field MRI. Guide of China Medicine, 2017, 13(13): 95-96.
18. Qiu X, Liu Z, Zhuang M, et al. Fusion of CNN1 and CNN2-based magnetic resonance image diagnosis of knee meniscus injury and a comparative analysis with computed tomography. Comput Methods Programs Biomed, 2021, 211: 106297. doi: 10.1016/j.cmpb.2021.106297.
19. Fritz B, Marbach G, Civardi F, et al. Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol, 2020, 49(8): 1207-1217.
20. Li Z, Ren S, Zhang X, et al. Deep learning-based image feature with arthroscopy-aided early diagnosis and treatment of meniscus injury of knee joint. J Healthc Eng, 2021, 2021: 2254594. doi: 10.1155/2021/2254594.
21. Bien N, Rajpurkar P, Ball RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med, 2018, 15(11): e1002699. doi: 10.1371/journal.pmed.1002699.
22. Rizk B, Brat H, Zille P, et al. Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation. Phys Med, 2021, 83: 64-71.
23. Astuto B, Flament I, Namiri KN, et al. Automatic deep learning-assisted detection and grading of abnormalities in knee MRI studies. Radiol Artif Intell, 2021, 3(3): e200165. doi: 10.1148/ryai.2021200165.
24. 张新民, 曹结水. 膝关节半月板损伤的MRI分级分类及临床应用. 安徽卫生职业技术学院学报, 2020, 19(1): 43-44.

Chinese Journal of Reparative and Reconstructive Surgery

Automatic identification algorithm of meniscus tear based on radiomics of knee MRI

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