Copyright: ©Author(s) 2026.
World J Radiol. Mar 28, 2026; 18(3): 116826
Published online Mar 28, 2026. doi: 10.4329/wjr.v18.i3.116826
Published online Mar 28, 2026. doi: 10.4329/wjr.v18.i3.116826
Table 1 Key characteristics of the included studies
| Ref. | Year | Country | Number of patients | Center | Field strength (T) | Study design (Re/Pr) | Sequences | Segmentation software | Lesion segmentation | Code/data availability | Biological correlation analysis | Cut-off analyses conducted | Reference standard |
| Bertelli et al[4] | 2021 | Italy | 112 | Single | 1.5T | Re | mpMRI without DCE | Manual segmentation | 2D | Yes | No | Yes | TRUSGB and MRGB |
| Wu et al[5] | 2019 | Canada | 90 | Single | 3.0T | Re | mpMRI with DCE | ImageJ (v.1.48, National Institutes of Health, Bethesda, MD) | 2D | Yes | Yes | Yes | RP |
| Urakami et al[6] | 2022 | Japan | 101 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Nketiah et al[7] | 2021 | Norway | 96 | Multicenter | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Jensen et al[8] | 2019 | Denmark | 182 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | TRUSGB |
| Gong et al[23] | 2022 | China | 489 | Single | 3.0T | Re | mpMRI without DCE | ITK-SNAP, version 3.4.0 | 3D | Yes | No | Yes | RP |
| Castillo T et al[13] | 2021 | Netherlands | 644 | Multicenter | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | STRUSGB |
| Castillo T et al[14] | 2021 | Netherlands | 204 | Multicenter | 3.0T and 1.5T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Orczyk et al[15] | 2019 | United Kingdom | 20 | Multicenter | 1.5T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Damascelli et al[50] | 2021 | Italy | 102 | Single | 1.5T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Niu et al[51] | 2018 | China | 184 | Single | 1.5T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | NR |
| Peng et al[52] | 2021 | China | 194 | Single | 1.5T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Zhang et al[24] | 2021 | China | 139 | Single | 3.0T | Re | mpMRI without DCE | Open source, ITK-SNAP | 3D | Yes | Yes | Yes | RP |
| Han et al[25] | 2021 | China | 176 | Single | 3.0T | Re | mpMRI with DCE | ITK-SNAP, Toolbox v3.6.0 | 3D | Yes | No | Yes | MRI-TRUS biopsy |
| Gong et al[26] | 2020 | China | 489 | Single | 3.0T | Re | mpMRI without DCE | ITK-SNAP v.3.4.0 | 3D | Yes | Yes | Yes | RP |
| Cheng et al[27] | 2023 | China | 226 | Single | 3.0T | Re | mpMRI with DCE | ITKSNAP software | 3D | Yes | No | Yes | STRUSGB and MRGB |
| Min et al[28] | 2019 | China | 280 | Single | 3.0T | Re | mpMRI without DCE | ITK-SNAP software | 3D | Yes | Yes | Yes | RP |
| Li et al[29] | 2020 | China | 381 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Bonekamp et al[10] | 2018 | Germany | 316 | Single | 3.0T | Pr | mpMRI without DCE | The medical imaging toolkit | 3D | Yes | No | Yes | RP |
| Zhang et al[30] | 2022 | China | 142 | Single | 3.0T | Re | mpMRI with DCE | ITK-SNAP software | 3D | Yes | Yes | Yes | STRUSGB and MRGB |
| Liu et al[31] | 2021 | United States | 402 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Hou et al[32] | 2020 | China | 263 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Bleker et al[11] | 2021 | Netherlands | 206 | Single | 3.0T | Pr | mpMRI with DCE | Manual segmentation | 2D | Yes | No | Yes | RP |
| Xiong et al[53] | 2021 | China | 85 | Single | 1.5T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Yang et al[33] | 2023 | China | 392 | Single | 3.0T | Re | mpMRI without DCE | ITK-SNAP software | 3D | Yes | Yes | Yes | RP |
| Bleker et al[16] | 2020 | Netherlands | 262 | Multicenter | 3.0T | Re | mpMRI without DCE | Manual segmentation | 2D | Yes | No | Yes | STRUSGB and MRGB |
| Woźnicki et al[20] | 2020 | Germany | 191 | Single | 3.0T | Re | mpMRI without DCE | Board-certified radiologist (D.N.) | 3D | Yes | Yes | Yes | RP |
| Bevilacqua et al[34] | 2021 | Italy | 76 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Donisi et al[21] | 2021 | Italy | 299 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 2D | Yes | Yes | Yes | TRUSGB |
| Zhou et al[17] | 2023 | China | 170 | Multicenter | 3.0T | Re | mpMRI without DCE | ITK-SNAP software | 3D | Yes | No | Yes | TRUSGB |
| Chen et al[35] | 2019 | China | 381 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | TRUSGB |
| Fehr et al[36] | 2015 | United States | 147 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Roest et al[18] | 2023 | Netherlands | 1513 | Multicenter | 3.0T and 1.5T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Algohary et al[37] | 2018 | United States | 301 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Zhang et al[38] | 2021 | China | 140 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 2D | Yes | No | Yes | TTSB |
| Isaksson et al[39] | 2020 | Italy | 121 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Tanadini-Lang et al[40] | 2018 | Switzerland | 47 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Bourbonne et al[41] | 2020 | United States | 195 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| McGarry et al[12] | 2022 | United States | 48 | Single | 3.0T | Pr | mpMRI with DCE | Manual segmentation | 2D | Yes | No | Yes | RP |
| Penzias et al[42] | 2018 | United States | 34 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | NR |
| Daniel et al[19] | 2019 | Austria | 42 | Multicenter | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | NR |
| Wang et al[22] | 2017 | China | 54 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | TRUSGB |
| Jung et al[43] | 2020 | Korea | 68 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | TRUSGB and RP |
| Stoyanova et al[44] | 2016 | United States | 46 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | No | Yes | TRUSGB and RP |
| Schieda et al[45] | 2021 | Canada | 76 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Ginsburg et al[46] | 2017 | United States | 54 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | No | Yes | RP |
| Shiradkar et al[47] | 2018 | United States | 120 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | RP |
| Zhang et al[48] | 2019 | China | 140 | Single | 3.0T | Re | mpMRI with DCE | Manual segmentation | 3D | Yes | No | Yes | TRUSGB and MRGB |
| Toivonen et al[49] | 2019 | Finland | 72 | Single | 3.0T | Re | mpMRI without DCE | Manual segmentation | 3D | Yes | Yes | Yes | TRUSGB and RP |
Table 2 Results of the subgroup analysis, 95% confidence intervals
| Number of studies | Sensitivity | P value | Specificity | P value | AUC | |||||
| Sensitivity | P value | I2 | Specificity | P value | I2 | |||||
| Overall | 49 | 0.84 (0.81-0.87) | < 0.01 | 93.08 (91.76-94.40) | 0.78 (0.70-0.84) | < 0.01 | 94.68 (93.75-95.62) | 0.88 (0.85-0.91) | ||
| Sequences | < 0.01 | < 0.01 | ||||||||
| mpMRI with DCE | 20 | 0.88 (0.84-0.91) | < 0.01 | 72.75 (60.62-84.87) | 0.83 (0.76-0.88) | < 0.01 | 84.66 (78.82-90.50) | 0.82 (0.81-0.97) | ||
| mpMRI without DCE | 29 | 0.82 (0.77-0.87) | < 0.01 | 94.41 (93.11-95.71) | 0.73 (0.67-0.83) | < 0.01 | 95.90 (95.03-96.77) | 0.87 (0.81-0.94) | ||
| Lesion segmentation | < 0.01 | < 0.01 | ||||||||
| 2D | 7 | 0.79 (0.71-0.83) | < 0.01 | 92.54 (88.50-96.59) | 0.76 (0.73-0.83) | < 0.01 | 93.33 (89.82-96.84) | 0.78 (0.73-0.81) | ||
| 3D | 42 | 0.85 (0.82-0.88) | < 0.01 | 92.21 (91.43-94.38) | 0.86 (0.77-0.89) | < 0.01 | 94.56 (93.52-95.60) | 0.86 (0.82-0.95) | ||
| Tumor aggressiveness | < 0.01 | < 0.01 | ||||||||
| csPCa | 30 | 0.85 (0.82-0.88) | < 0.01 | 95.02 (93.92-96.12) | 0.87 (0.84-0.92) | < 0.01 | 96.21 (95.44-96.96) | 0.93 (0.81-0.95) | ||
| Non-csPCa | 19 | 0.71 (0.63-0.77) | < 0.01 | 69.89 (55.77-84.00) | 0.78 (0.73-0.82) | < 0.01 | 83.84 (77.44-90.25) | 0.76 (0.72-0.83) | ||
- Citation: Zhong SY, Deng XR, Han BC, Yang LQ, Ye ST, Niu XK. Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis. World J Radiol 2026; 18(3): 116826
- URL: https://www.wjgnet.com/1949-8470/full/v18/i3/116826.htm
- DOI: https://dx.doi.org/10.4329/wjr.v18.i3.116826
