Abstract
A balance between preserving urinary continence as well as sexual potency and achieving negative surgical margins is of clinical relevance while implementary difficulty. Accurate detection of extracapsular extension (ECE) of prostate cancer (PCa) is thus crucial for determining appropriate treatment options. We aimed to develop and validate an artificial intelligence (AI)-based tool for detecting ECE of PCa using multiparametric magnetic resonance imaging (mpMRI). Eight hundred andfortynine consecutive PCa patients who underwent mpMRI and prostatectomy without previous radio- or hormonal therapy from two medical centers were retrospectively included. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts' prior knowledge (PAGNet) from 596 training patients. Model validation was performed in 150 internal and 103 external patients. Performance comparison was made between AI, two experts using a criteria-based ECE grading system, and expert-AI interaction. An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867), and 0.728 (95% CI, 0.631-0.811) in training, internal, and external validation data, respectively. The performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When experts' interpretations were adjusted by AI assessments, the performance of two experts was improved. Our AI tool, showing improved accuracy, offers a promising alternative to human experts for ECE staging using mpMRI.
Highlights
Preoperative staging of prostate cancer (PCa) is critical for guiding the treatment selection of patients and preventing both under and over treatment[1]
The balance between preserving urinary continence and the achievement of negative margins for radical prostatectomy remains a challenge, preoperatively accurate detection of extracapsular extension (ECE) would have a significant impact on treatment planning and prediction of outcomes in patients with PCa
The heterogeneity of MRI in PCa T3a-staging may be caused by the fact that there are no standard criteria for evaluation[20]. , the high level of expertise required for radiologists with the aim of accurate interpretation and interobserver variability limit its consistency and availability[21]
Summary
Preoperative staging of prostate cancer (PCa) is critical for guiding the treatment selection of patients and preventing both under and over treatment[1]. The presence of extracapsular extension (ECE), that is, T3a stage, accounting for one-third of all PCa patients primarily diagnosed[2, 3], is associated with higher rates of positive surgical margins and early biochemical recurrence after radical prostatectomy[4]. The balance between preserving urinary continence and the achievement of negative margins for radical prostatectomy remains a challenge, preoperatively accurate detection of ECE would have a significant impact on treatment planning and prediction of outcomes in patients with PCa. Historically, digital rectal examination (DRE) has been the principal approach for clinical T-staging of PCa[7]. The use of MRI instead of DRE leads to a significant upstaging of clinical T-stage and risk grouping[12,13,14,15]. The heterogeneity of MRI in PCa T3a-staging may be caused by the fact that there are no standard criteria for evaluation[20]. , the high level of expertise required for radiologists with the aim of accurate interpretation and interobserver variability limit its consistency and availability[21]
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