  The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level
diagnostic algorithms for clinically significant prostate cancer detection. The
algorithms receive biparametric MRI scans as input, which consist of
T2-weighted and diffusion-weighted scans. These scans can be misaligned due to
multiple factors in the scanning process. Image registration can alleviate this
issue by predicting the deformation between the sequences. We investigate the
effect of image registration on the diagnostic performance of AI-based prostate
cancer diagnosis. First, the image registration algorithm, developed in
MeVisLab, is analyzed using a dataset with paired lesion annotations. Second,
the effect on diagnosis is evaluated by comparing case-level cancer diagnosis
performance between using the original dataset, rigidly aligned
diffusion-weighted scans, or deformably aligned diffusion-weighted scans. Rigid
registration showed no improvement. Deformable registration demonstrated a
substantial improvement in lesion overlap (+10% median Dice score) and a
positive yet non-significant improvement in diagnostic performance (+0.3%
AUROC, p=0.18). Our investigation shows that a substantial improvement in
lesion alignment does not directly lead to a significant improvement in
diagnostic performance. Qualitative analysis indicated that jointly developing
image registration methods and diagnostic AI algorithms could enhance
diagnostic accuracy and patient outcomes.
