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Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults
Author(s) -
Shu ZhenYu,
Shao Yuan,
Xu YuYun,
Ye Qin,
Cui SiJia,
Mao DeWang,
Pang PeiPei,
Gong XiangYang
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26813
Subject(s) - medicine , nomogram , hyperintensity , receiver operating characteristic , cohort , magnetic resonance imaging , area under the curve , radiology , white matter , nuclear medicine
Background White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need. Purpose To develop and validate a radiomics nomogram for the prediction of WMH progression. Study Type Retrospective. Population Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old. Field Strength/Sequence T 1 ‐fluid attenuated inversion recovery images were acquired using a 3T scanner. Assessment WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow‐up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction. Statistical Tests A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer–Lemeshow test was performed. Results The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer–Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort ( P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively. Data Conclusion This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535–546.