Table 3.
Predictive modelling characteristics of studies using deep learning-based fully-automated AI methods
| Study | No. of patients | Training set | Validation set | Test set | Algorithm | MRI input | Image registration | Image segmentation | Outcome | Zone | Analysis | Evaluation strategy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Wang [20] | 346 | 204 | fivefold CV | 142 | CNN (MISN) |
ADC, BVAL, DWI0, DWI1, DWI2, Ktrans, T2WI-Cor, T2WI-Sag, T2WI-Tra |
NR | Open data | csPCa vs iPCa or benign lesions | PZ or TZ | Per lesion | Internal hold-out |
| Fernandez-Quilez [21] | 200 | NRa | NRa | NRa | CNN (VGG16) | T2WI, ADC | NR | Open data | csPCa vs iPCa or benign lesions | WP | Per lesion | Internal hold-out |
| Schelb [22] | 312 | 250 | No | 62 | CNN (U-Net) | T2WI, DWI | SimpleITK, non-rigid Bspline with Mattes mutual information criterion | Automated (U-Net) | csPCa vs iPCa or benign lesions | WP | Per lesion, per patient | Internal hold-out |
| Deniffel [23] | 499 | 324 | 75 | 50b | CNN (3D) | T2WI, ADC, DWI | Static, affine | Manual bounding boxes | csPCa vs iPCa or benign lesions | WP | Per patient | Internal hold-out |
| Seetharaman [24] | 424 | 102 | fivefold CV | 322 | CNN (SPCNet) | T2WI, ADC | Manual | Registration from pathology images | csPCa vs iPCa or benign lesions | WP | Per pixel, per lesion | Internal hold-out |
ADC, apparent diffusion coefficient; CNN, convolutional neural networks; csPCa, clinically significant prostate cancer; CV, cross-validation; DWI, diffusion-weighted imaging; iPCa, indolent prostate cancer; MISN, multi-input selection network; MRI, magnetic resonance imaging; NR, not reported; PZ, peripheral zone; T2WI, T2-weighted imaging; TZ, transition zone; WP, whole prostate
aThe study included 200 patients and 299 lesions, of which 70% were used to train train, 20% to test, 10% to fine-tune the models
bDescribes the calibration cohort