Enhanced choroid plexus segmentation with 3D UX-Net and its association with disease progression in multiple sclerosis

作者全名："Wang, Xiaohua; Wang, Xiaolong; Yan, Zichun; Yin, Feiyue; Li, Yongmei; Liu, Xiaojuan; Liu, Yanbing"

作者地址："[Wang, Xiaohua; Liu, Yanbing] Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China; [Wang, Xiaohua; Yan, Zichun; Yin, Feiyue; Li, Yongmei] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China; [Wang, Xiaolong; Liu, Xiaojuan] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400054, Peoples R China"

通信作者："Liu, YB (通讯作者)，Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China.; Li, YM (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China.; Liu, XJ (通讯作者)，Southwest Univ, Coll Comp & Informat Sci, Chongqing 400054, Peoples R China."

来源：MULTIPLE SCLEROSIS AND RELATED DISORDERS

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001325550400001

JCR分区：Q2

影响因子：4

年份：2024

卷号：88

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Multiple sclerosis (MS); Choroid plexus (CP); Deep learning (DL); Automatic segmentation; Disease-modifying therapy (DMT)

摘要："Background: The choroid plexus (CP) is suggested to be closely associated with the neuroinflammation of multiple sclerosis (MS). Segmentation based on deep learning (DL) could facilitate rapid and reproducible volume assessment of the CP, which is crucial for elucidating its role in MS. Purpose: To develop a reliable DL model for the automatic segmentation of CP, and further validate its clinical significance in MS.<br /> Methods: The 3D UX-Net model (3D U-Net used for comparison) was trained and validated on T1-weighted MRI from a cohort of 216 relapsing-remitting MS (RRMS) patients and 75 healthy subjects. Among these, 53 RRMS with baseline and 2-year follow-up scans formed an internal test set (dataset1b). Another 58 RRMS from multicenter data served as an external test set (dataset2). Dice coefficient was computed to assess segmentation performance. Compare the correlation of CP volume obtained through automatic and manual segmentation with clinical outcomes in MS. Disability and cognitive function of patients were assessed using the Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT).<br /> Results: The 3D UX-Net model achieved Dice coefficients of 0.875 f 0.030 and 0.870 f 0.044 for CP segmentation on dataset1b and dataset2, respectively, outperforming 3D U-Net's scores of 0.809 f 0.098 and 0.601 f 0.226. Furthermore, CP volumes segmented by the 3D UX-Net model aligned consistently with clinical outcomes compared to manual segmentation. In dataset1b, both manual and automatic segmentation revealed a significant positive correlation between normalized CP volume (nCPV) and EDSS scores at baseline (manual: r = 0.285, p = 0.045; automatic: r = 0.287, p = 0.044) and a negative correlation with SDMT scores (manual: r = -0.331, p = 0.020; automatic: r = -0.329, p = 0.021). In dataset2, similar correlations were found with EDSS scores (manual: r = 0.337, p = 0.021; automatic: r = 0.346, p = 0.017). Meanwhile, in dataset1b, both manual and automatic segmentation revealed a significant increase in nCPV from baseline to follow-up (p < 0.05). The increase of nCPV was more pronounced in patients with disability worsened than stable patients (manual: p = 0.023; automatic: p = 0.018). Patients receiving disease-modifying therapy (DMT) exhibited a significantly lower nCPV increase than untreated patients (manual: p = 0.004; automatic: p = 0.004).<br /> Conclusion: The 3D UX-Net model demonstrated strong segmentation performance for the CP, and the automatic segmented CP can be directly used in MS clinical practice. CP volume can serve as a surrogate imaging biomarker for monitoring disease progression and DMT response in MS patients."

基金机构：General Program of National Nat-ural Science Foundation of China [62272074]; Key Project of Technological Innovation and Application Development of Chongq-ing Science and Technology Bureau [CSTC2021 jscx-gksb-N0008]

基金资助正文："This study was supported by the General Program of National Nat-ural Science Foundation of China (Grant No: 62272074) , the Key Project of Technological Innovation and Application Development of Chongq-ing Science and Technology Bureau (Grant No: CSTC2021 jscx-gksb-N0008) ."