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.

翻译：PI-CAI（前列腺影像：癌症人工智能）挑战赛催生了用于临床显著性前列腺癌检测的专家级诊断算法。这些算法以双参数磁共振扫描作为输入，包括T2加权和扩散加权扫描。由于扫描过程中的多种因素，这些图像可能存在错位问题。图像配准技术可通过预测序列间的形变来缓解此问题。本研究探讨了图像配准对基于人工智能的前列腺癌诊断性能的影响。首先，使用包含配对病灶标注的数据集分析了在MeVisLab平台上开发的图像配准算法。其次，通过比较使用原始数据集、刚性对齐的扩散加权扫描以及可变形对齐的扩散加权扫描在病例层面癌症诊断的性能，评估了配准对诊断的影响。刚性配准未显示任何改善效果。可变形配准在病灶重叠度方面表现出显著提升（中位Dice评分提高+10%），在诊断性能方面呈现积极但未达统计学意义的改善（AUROC提升+0.3%，p=0.18）。研究表明，病灶对齐程度的显著改善并不能直接转化为诊断性能的统计学显著提升。定性分析表明，联合开发图像配准方法与诊断性人工智能算法，有望提升诊断准确性和患者预后。