Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis

Table 5

Result of multiple subgroup analysis of machine learning-based radiomics for grading gliomas.

Subgroup	Study number	Patient number	Sensitivity	Specificity	PLR	NLR	Diagnostic odds ratio
All combined	5	629	0.96 (0.93–0.98)	0.90 (0.85–0.93)	9.53 (3.55–25.57)	0.07 (0.02–0.20)	153.85 (32.36–731.44)
Populations
>100	2	507	0.98 (0.95–0.99)	0.90 (0.85–0.94)	12.099 (1.37–107.12)	0.03 (0.02–0.06)	393.81 (80.89–1917.3)_
<100	3	122	0.88 (0.78–0.95)	0.90 (0.77–0.96)	7.89 (2.21–28.15)	0.14 (0.05–0.39)	65.13 (7.84–540.95)
Sequence
Single (CS or advanced)	2	262	0.96 (0.93–0.98)	0.81 (0.72–0.88)	4.61 (3.14–6.77)	0.09 (0.02–0.44)	66.75 (10.33–431.19)
Multiple (CS and advanced)	3	367	0.96 (0.91–0.99)	0.97 (0.92–0.99)	30.391 (11.585–79.726)	0.04 (0.017–0.09)	774.25 (202.54–2959.77)
Feature number
≥Sample size	2	82	0.85 (0.72–0.94)	0.85 (0.69–0.95)	5.71 (1.39–23.46)	0.18 (0.07–0.52)	33.76 (3.36–339.14)
<Sample size	3	547	0.97 (0.95–0.99)	0.90 (0.85–0.94)	13.48 (2.56–71.12)	0.03 (0.02–0.06)	369.98 (19.68–6956.0)
Training and testing set
Training set	3	331	0.94 (0.88–0.97)	0.95 (0.89–0.98)	12.91 (2.02–82.22)	0.09 (0.02–0.47)	154.56 (7.30–3276.9)
Training + testing set	2	298	0.97 (0.94–0.99)	0.81 (0.72–0.89)	5.32 (2.55–11.09)	0.05 (0.1–0.22)	176.99 (63.76–491.30)