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Table 1 Study characteristics

From: An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

Study Characteristics

Data

Sample size, mean ± standard deviation, median (range)

86.6 ± 45.8, 81 (17–191)

Journal type, n (%)

N = 29

 Imaging

13 (44.8)

 Non-imaging

16 (55.2)

First authorship, n (%)

N = 29

 Radiologist

19 (65.5)

 Non-radiologist

10 (34.5)

Imaging modality, n (%)

N = 29

 CT

9 (31.0)

 MRI

14 (48.3)

 PET

6 (20.7)

Biomarker, n (%)

N = 33

 Diagnostic

3 (9.1)

 Predictive

18 (54.5)

 Prognostic

12 (36.4)

Model type, n (%)

N = 33

 Type 1a: Developed model validated with exactly the same data

8 (24.2)

 Type 1b: Developed model validated with resampling data

8 (24.2)

 Type 2a: Developed model validated with randomly splitting data

12 (36.4)

 Type 2b: Developed model validated with non-randomly splitting data

1 (3.0)

 Type 3: Developed model validated with separate data

4 (12.1)

 Type 4: Validation only

0 (0.0)

  1. There were 33 radiomics models identified in 29 included studies. The model type was determined according to criteria in TRIPOD statement. TRIPOD Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis