Research Article
Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma
Table 1
Characteristics of patients in the TCIA and independent test datasets.
| Characteristic | TCIA () | Huadong () | |
| Ages (years) | | 0.856 | Range | 17-80 | 18-73 | | Median | 57.5 | 54 | | | | Gender, No. (%) | | 0.847 | Female | 40 (39.22%) | 13 (43.33%) | | Male | 62 (60.78%) | 17 (56.67%) | Status, No. (%) | | 0.0011 | Alive | 10 (9.8%) | 11 (36.67%) | | Dead | 92 (90.2%) | 19 (63.33%) | KPS | | 0.2795 | | 75 | 21 | | | 17 | 9 | Not reported | 10 | 0 | Tumour location | | 0.377 | Frontal lobe | 24 | 12 | | Temporal lobe | 43 | 11 | Parietal lobe | 19 | 4 | Occipital lobe | 8 | 3 | Insular lobe | 6 | 0 | Callosum lobe | 2 | 0 | OS (months) | | 0.6516 | Range | 1-74.87 | 3.3-52.43 | | Median | 14.30 | 14.77 | | | | 31-gene prediction result | | | RH | 89 | — | RR | 13 | — | Radiomics prediction result | | 0.8813 | RH | 85 | 24 | | RR | 17 | 6 | |
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