TY - JOUR
T1 - Quality Assessment of Radiomics Studies on Functional Outcomes After Acute Ischemic Stroke–A Systematic Review
AU - Gupta, Rishabh
AU - Bilgin, Cem
AU - Jabal, Mohamed S.
AU - Kandemirli, Sedat
AU - Ghozy, Sherief
AU - Kobeissi, Hassan
AU - Kallmes, David F.
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2024/3
Y1 - 2024/3
N2 - Objective: Radiomics is a machine-learning method that extracts features from medical images. The objective of the present systematic review was to assess the quality of existing studies that use radiomics methods to predict functional outcomes in patients after acute ischemic stroke. Methods: Studies using radiomics-extracted features to predict functional outcomes among patients with acute ischemic stroke using the modified Rankin Scale were included. PubMed, Scopus, Web of Science, and Embase were screened using the terms “radiomics” and “texture” in combination with “stroke.” Quality scores were calculated based on Radiomics Quality Score, the IBSI (Image Biomarkers Standardization Initiative), and the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). Results: Fourteen studies were included. The median total Radiomics Quality Score was 14.5 (13–16) out of 36. Domains 1, 5, and 6 on protocol quality and stability of imaging and segmentation, level of evidence, and use of open science and data, respectively, were poor. Median IBSI score was 2.5 (1–5) out of 6. Few studies included bias-field correction algorithms, isovoxel resampling, skull stripping, or gray-level discretization. Of 14 studies, none received +6 points, 1 received +5 points, 5 received +4 points, 1 study received +3 points, 5 received +2 points, 2 received +1 points, and none received 0 points. As per the QUADAS-2, 6/14 (42.9%) studies had a risk of bias concern and 0/14 (0%) had applicability concern. Conclusions: The quality of the included studies was low to moderate. With increasing use of radiomics, future studies should attempt to adhere to and report established radiomics quality guidelines.
AB - Objective: Radiomics is a machine-learning method that extracts features from medical images. The objective of the present systematic review was to assess the quality of existing studies that use radiomics methods to predict functional outcomes in patients after acute ischemic stroke. Methods: Studies using radiomics-extracted features to predict functional outcomes among patients with acute ischemic stroke using the modified Rankin Scale were included. PubMed, Scopus, Web of Science, and Embase were screened using the terms “radiomics” and “texture” in combination with “stroke.” Quality scores were calculated based on Radiomics Quality Score, the IBSI (Image Biomarkers Standardization Initiative), and the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). Results: Fourteen studies were included. The median total Radiomics Quality Score was 14.5 (13–16) out of 36. Domains 1, 5, and 6 on protocol quality and stability of imaging and segmentation, level of evidence, and use of open science and data, respectively, were poor. Median IBSI score was 2.5 (1–5) out of 6. Few studies included bias-field correction algorithms, isovoxel resampling, skull stripping, or gray-level discretization. Of 14 studies, none received +6 points, 1 received +5 points, 5 received +4 points, 1 study received +3 points, 5 received +2 points, 2 received +1 points, and none received 0 points. As per the QUADAS-2, 6/14 (42.9%) studies had a risk of bias concern and 0/14 (0%) had applicability concern. Conclusions: The quality of the included studies was low to moderate. With increasing use of radiomics, future studies should attempt to adhere to and report established radiomics quality guidelines.
KW - Acute ischemic stroke
KW - Imaging
KW - Radiomics
KW - Systematic review
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U2 - 10.1016/j.wneu.2023.11.154
DO - 10.1016/j.wneu.2023.11.154
M3 - Review article
C2 - 38056625
AN - SCOPUS:85184760964
SN - 1878-8750
VL - 183
SP - 164
EP - 171
JO - World neurosurgery
JF - World neurosurgery
ER -