Department of Medicine, Surgery and Pharmacy, Universitá degli Studi di Sassari, Sassari, Italy.
Department of Urology, Fundació Puigvert, Barcelona, Spain.
World J Urol. 2024 Jan 13;42(1):37. doi: 10.1007/s00345-023-04720-5.
To identify the predictive factors of prostate cancer extracapsular extension (ECE) in an institutional cohort of patients who underwent multiparametric MRI of the prostate prior to radical prostatectomy (RP).
Overall, 126 patients met the selection criteria, and their medical records were retrospectively collected and analysed; 2 experienced radiologists reviewed the imaging studies. Logistic regression analysis was conducted to identify the variables associated to ECE at whole-mount histology of RP specimens; according to the statistically significant variables associated, a predictive model was developed and calibrated with the Hosmer-Lomeshow test.
The predictive ability to detect ECE with the generated model was 81.4% by including the length of capsular involvement (LCI) and intraprostatic perineural invasion (IPNI). The predictive accuracy of the model at the ROC curve analysis showed an area under the curve (AUC) of 0.83 [95% CI (0.76-0.90)], p < 0.001. Concordance between radiologists was substantial in all parameters examined (p < 0.001). Limitations include the retrospective design, limited number of cases, and MRI images reassessment according to PI-RADS v2.0.
The LCI is the most robust MRI factor associated to ECE; in our series, we found a strong predictive accuracy when combined in a model with the IPNI presence. This outcome may prompt a change in the definition of PI-RADS score 5.
在接受根治性前列腺切除术（RP）前进行前列腺多参数 MRI 的机构队列中，确定前列腺癌包膜外扩展（ECE）的预测因素。
共有 126 名患者符合入选标准，回顾性收集并分析了他们的病历；2 名有经验的放射科医生对影像学研究进行了回顾。进行逻辑回归分析，以确定与 RP 标本全距组织学上 ECE 相关的变量；根据统计学上显著的相关变量，开发了一个预测模型，并通过 Hosmer-Lomeshow 检验进行校准。
通过纳入包膜受累长度（LCI）和前列腺内神经周围侵犯（IPNI），生成的模型对 ECE 的预测能力为 81.4%。在 ROC 曲线分析中，模型的预测准确性显示曲线下面积（AUC）为 0.83[95%CI（0.76-0.90）]，p<0.001。在所有检查的参数中，放射科医生之间的一致性都很高（p<0.001）。局限性包括回顾性设计、病例数量有限以及根据 PI-RADS v2.0 对 MRI 图像进行重新评估。
LCI 是与 ECE 最相关的最强 MRI 因素；在我们的系列中，当与 IPNI 存在结合在一个模型中时，我们发现了很强的预测准确性。这一结果可能会促使 PI-RADS 评分 5 的定义发生变化。
