Table 2.
Accuracy measures of radiomic studies for the systematic review.
| Study, year | Model basisa | Patients, n | Total sample (PCa+, PCa-)b | Crossvalc/ split/none | MLd methodse | TP,f n | FN,g n | FP,h n | TN,i n | Senj (lower-upper) | Spek (lower-upper) |
| Zhao, 2015 [44] | LB | 71 | 238 (92, 146) | 120 (60, 60) | ANN | 57 | 35 | 16 | 130 | 0.620 (0.517-0.713) |
0.890 (0.829-0.932) |
| Valerio, 2016 [45] | LB | 53 | 106 (53, 53) | None | LDA | 51 | 2 | 1 | 53 | 0.962 (0.861-0.991) |
0.981 (0.880-0.997) |
| Lay, 2017 [46] | LB | 224 | 410 (123, 287) | Crossval | RF | 109 | 14 | 57 | 230 | 0.886 (0.817-0.931) |
0.801 (0.751-0.844) |
| Reda, 2017 [47] | LB | 18 | 53 (26, 27) | Crossval | SNCSAE | 26 | 1 | 1 | 27 | 0.963 (0.779-0.995) |
0.964 (0.786-0.995) |
| Starobinets, 2017 [48] | LB | 169 | 509 (291, 218) | Crossval | LR | 264 | 27 | 24 | 194 | 0.907 (0.868-0.936) |
0.890 (0.841-0.925) |
| Wang, 2017 [36] | PB | 172 | 172 (79, 93) | Crossval | DCNN | 55 | 24 | 15 | 78 | 0.696 (0.587-0.787) |
0.839 (0.750-0.900) |
| Le, 2017 [52] | LB | 364 | 913 (463, 450) | 275 (139, 135) | multimodal CNN | 125 | 14 | 6 | 129 | 0.899 (0.837-0.939) |
0.956 (0.905-0.980) |
| Kwon, 2018 [49] | LB | 204 | 191 (36, 155) | Crossval | LASSO LR | 35 | 5 | 9 | 90 | 0.875 (0.733-0.947) |
0.909 (0.834-0.952) |
| Song, 2018 [50] | LB | 195 | 547 (261, 286) | 55 (23, 32) | DNN | 20 | 3 | 3 | 29 | 0.870 (0.665-0.957) |
0.906 (0.746-0.969) |
| Chen, 2019 [56] | PB | 381 | 381 (182, 199) | 155 (55, 60) | LR | 55 | 1 | 1 | 59 | 0.982 (0.884-0.997) |
0.983 (0.891-0.998) |
| Devine, 2019 [51] | LB | 65 | 97 (81, 16) | Crossval | LR | 61 | 20 | 2 | 14 | 0.753 (0.648-0.835) |
0.875 (0.614-0.969) |
| Gholizadeh, 2019 [54] | LB | 11 | 297 (161, 136) | Crossval | SVM | 161 | 1 | 9 | 127 | 0.994 (0.958-0.999) |
0.934 (0.878-0.965) |
| Ma, 2019 [58] | PB | 81 | 81 (44, 37) | None | LR | 42 | 2 | 5 | 32 | 0.955 (0.836-0.989) |
0.865 (0.714-0.943) |
| Mazaheri, 2019 [53] | LB | 67 | 170 (102, 68) | 91 (52, 39) | CART | 51 | 1 | 19 | 20 | 0.981 (0.876-0.997) |
0.513 (0.360-0.664) |
| Qi, 2019 [57] | PB | 199 | 199 (85, 114) | 66 (28, 38) | LR | 23 | 5 | 3 | 35 | 0.821 (0.636-0.924) |
0.921 (0.782-0.974) |
| Zhang, 2019 [59] | PB | 140 | 140 (60, 80) | Crossval | RF | 14 | 6 | 5 | 22 | 0.700 (0.473-0.859) |
0.815 (0.625-0.921) |
aLB: lesion-based model; PB: patient-based model.
bPCa: prostate cancer.
cCrossval: cross-validation techniques.
dML: machine learning.
eANN: artificial neural networks; LDA: linear discriminant analysis; RF: random forest; SNCSAE: stacked nonnegativity constraint sparse autoencoders; LR: logistic regression; DCNN: deep convolutional neural networks; LASSO: least absolute shrinkage and selection operator; DNN: deep neural networks; SVM: support vector machine; CART: classification and regression tree.
fTP: true-positive.
gFN: false-negative.
hFP: false-positive.
iTN: true-negative.
jSen: sensitivity.
kSpe: specificity.