{"title":"基于 T1ρ-MR 的椎间盘变性阶段的深度学习辅助分类。","authors":"Yanrun Li, Meiyu Hu, Junhong Chen, Zemin Ling, Xuenong Zou, Wuteng Cao, Fuxin Wei","doi":"10.1002/jmri.29499","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T1- and T2-MR, T1ρ-MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1ρ-MR based IDD phases.</p><p><strong>Purpose: </strong>To build a deep learning model to achieve the classification of T1ρ-MR based IDD phases from routine T1-MR images.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Sixty (M/F: 35/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N = 50) and test (N = 10) sets.</p><p><strong>Field strength/sequences: </strong>1.5 T MR scanner; T1-, T2-, and T1ρ-MR sequence (spin echo).</p><p><strong>Assessment: </strong>The T1ρ values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1ρ value: pre-degeneration phase (mean T1ρ value >110 msec), rapid degeneration phase (mean T1ρ value: 80-110 msec), and late degeneration phase (mean T1ρ value <80 msec). After measurement, the T1ρ values, phases, and levels of IVDs were input into the model as labels.</p><p><strong>Statistical tests: </strong>Intraclass correlation coefficient, area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall (P < 0.05 was considered significant).</p><p><strong>Results: </strong>In the test dataset, the model achieved a mean average precision of 0.996 for detecting IVD levels. The diagnostic accuracy of the T1ρ-MR based IDD phases was 0.840 and the AUC was 0.871, the average AUC of 5-folds cross validation was 0.843.</p><p><strong>Data conclusion: </strong>The proposed deep learning model achieved the classification of T1ρ-MR based IDD phases from routine T1-MR images, which may provide a method to facilitate the application of T1ρ-MR in IDD.</p><p><strong>Evidence level: </strong>4 TECHNICAL EFFICACY: Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.\",\"authors\":\"Yanrun Li, Meiyu Hu, Junhong Chen, Zemin Ling, Xuenong Zou, Wuteng Cao, Fuxin Wei\",\"doi\":\"10.1002/jmri.29499\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T1- and T2-MR, T1ρ-MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1ρ-MR based IDD phases.</p><p><strong>Purpose: </strong>To build a deep learning model to achieve the classification of T1ρ-MR based IDD phases from routine T1-MR images.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Sixty (M/F: 35/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N = 50) and test (N = 10) sets.</p><p><strong>Field strength/sequences: </strong>1.5 T MR scanner; T1-, T2-, and T1ρ-MR sequence (spin echo).</p><p><strong>Assessment: </strong>The T1ρ values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1ρ value: pre-degeneration phase (mean T1ρ value >110 msec), rapid degeneration phase (mean T1ρ value: 80-110 msec), and late degeneration phase (mean T1ρ value <80 msec). After measurement, the T1ρ values, phases, and levels of IVDs were input into the model as labels.</p><p><strong>Statistical tests: </strong>Intraclass correlation coefficient, area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall (P < 0.05 was considered significant).</p><p><strong>Results: </strong>In the test dataset, the model achieved a mean average precision of 0.996 for detecting IVD levels. The diagnostic accuracy of the T1ρ-MR based IDD phases was 0.840 and the AUC was 0.871, the average AUC of 5-folds cross validation was 0.843.</p><p><strong>Data conclusion: </strong>The proposed deep learning model achieved the classification of T1ρ-MR based IDD phases from routine T1-MR images, which may provide a method to facilitate the application of T1ρ-MR in IDD.</p><p><strong>Evidence level: </strong>4 TECHNICAL EFFICACY: Stage 2.</p>\",\"PeriodicalId\":16140,\"journal\":{\"name\":\"Journal of Magnetic Resonance Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Magnetic Resonance Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/jmri.29499\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Magnetic Resonance Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jmri.29499","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.
Background: According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T1- and T2-MR, T1ρ-MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1ρ-MR based IDD phases.
Purpose: To build a deep learning model to achieve the classification of T1ρ-MR based IDD phases from routine T1-MR images.
Study type: Retrospective.
Population: Sixty (M/F: 35/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N = 50) and test (N = 10) sets.
Field strength/sequences: 1.5 T MR scanner; T1-, T2-, and T1ρ-MR sequence (spin echo).
Assessment: The T1ρ values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1ρ value: pre-degeneration phase (mean T1ρ value >110 msec), rapid degeneration phase (mean T1ρ value: 80-110 msec), and late degeneration phase (mean T1ρ value <80 msec). After measurement, the T1ρ values, phases, and levels of IVDs were input into the model as labels.
Statistical tests: Intraclass correlation coefficient, area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall (P < 0.05 was considered significant).
Results: In the test dataset, the model achieved a mean average precision of 0.996 for detecting IVD levels. The diagnostic accuracy of the T1ρ-MR based IDD phases was 0.840 and the AUC was 0.871, the average AUC of 5-folds cross validation was 0.843.
Data conclusion: The proposed deep learning model achieved the classification of T1ρ-MR based IDD phases from routine T1-MR images, which may provide a method to facilitate the application of T1ρ-MR in IDD.
期刊介绍：
The Journal of Magnetic Resonance Imaging (JMRI) is an international journal devoted to the timely publication of basic and clinical research, educational and review articles, and other information related to the diagnostic applications of magnetic resonance.
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