Keywords:
Artificial Intelligence, Genital / Reproductive system male, Oncology, MR, MR-Diffusion/Perfusion, Biopsy, Computer Applications-Detection, diagnosis, Cancer
Authors:
N. Debs, A. Routier, B. Lorenzi, L. Wood, F. Nicolas, M.-M. Rohé
DOI:
10.26044/ecr2022/C-22165
Results
1) Results on validation set:
- Detection results:
- At 0.5 FP/volume, 87% of significant lesions (GS>6) were detected with model trained on PIRADS and GS, versus 79% for the model trained on GS data only
- At 1FP/volume, 91% of significant lesions (GS>6) were detected with model trained on PIRADS and GS, versus 85% at 1FP/volume for the model trained on GS data only.
- Classification results:
- AUC = 0.723 for model trained on PIRADS and GS data, versus AUC=0.699 for the model trained on GS data only.
2) Results on test set:
- Detection results:
- At 0.5 FP/volume, 68% of significant lesions (GS>6) were detected with model trained on PIRADS and GS, versus 61% for the model trained on GS data only
- At 1 FP/volume, 72% of significant lesions (GS>6) were detected with model trained on PIRADS and GS, versus 63% for the model trained on GS data only
- Classification results:
- AUC = 0.612 for the model trained on PIRADS and GS data, versus AUC=0.558 for the model trained on GS data only.