Russo Tommaso, Quarta Leonardo, Pellegrino Francesco, Cosenza Michele, Camisassa Enrico, Lavalle Salvatore, Apostolo Giovanni, Zaurito Paolo, Scuderi Simone, Barletta Francesco, Marzorati Clara, Stabile Armando, Montorsi Francesco, De Cobelli Francesco, Brembilla Giorgio, Gandaglia Giorgio, Briganti Alberto
Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Unit of Urology/Division of Oncology, Gianfranco Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Via Olgettina 58, 20132, Milan, Italy.
Radiol Med. 2025 May 7. doi: 10.1007/s11547-025-02017-8.
Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the prostate. We evaluate the diagnostic performance of radiologists with different levels of experience when reporting mpMRI with the support of available AI-based software (Quantib Prostate).
This is a single-center study (NCT06298305) involving 110 patients. Those with a positive mpMRI (PI-RADS ≥ 3) underwent targeted plus systematic biopsy (TBx plus SBx), while those with a negative mpMRI but a high clinical suspicion of prostate cancer (PCa) underwent SBx. Three readers with different levels of experience, identified as R1, R2, and R3 reviewed all mpMRI. Inter-reader agreement among the three readers with or without the assistance of Quantib Prostate as well as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for the detection of clinically significant PCa (csPCa) were assessed.
102 patients underwent prostate biopsy and the csPCa detection rate was 47%. Using Quantib Prostate resulted in an increased number of lesions identified for R3 (101 vs. 127). Inter-reader agreement slightly increased when using Quantib Prostate from 0.37 to 0.41 without vs. with Quantib Prostate, respectively. PPV, NPV and diagnostic accuracy (measured by the area under the curve [AUC]) of R3 improved (0.51 vs. 0.55, 0.65 vs.0.82 and 0.56 vs. 0.62, respectively). Conversely, no changes were observed for R1 and R2.
Using Quantib Prostate did not enhance the detection rate of csPCa for readers with some experience in prostate imaging. However, for an inexperienced reader, this AI-based software is demonstrated to improve the performance.
Name of registry: clinicaltrials.gov.
NCT06298305. Date of registration: 2022-09.
有人提出利用人工智能（AI）辅助放射科医生进行前列腺多参数磁共振成像（mpMRI）报告。我们评估了在基于人工智能的现有软件（Quantib Prostate）支持下，不同经验水平的放射科医生在报告mpMRI时的诊断性能。
这是一项单中心研究（NCT06298305），涉及110名患者。mpMRI阳性（前列腺影像报告和数据系统[PI-RADS]≥3）的患者接受靶向加系统性活检（TBx加SBx），而mpMRI阴性但临床高度怀疑前列腺癌（PCa）的患者接受SBx。三名经验水平不同的阅片者，分别为R1、R2和R3，对所有mpMRI进行了评估。评估了三名阅片者在有无Quantib Prostate辅助下的阅片者间一致性，以及检测临床显著性前列腺癌（csPCa）的敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和诊断准确性。
102名患者接受了前列腺活检，csPCa检出率为47%。使用Quantib Prostate后，R³识别出的病变数量增加（分别为101个和127个）。使用Quantib Prostate时，阅片者间一致性略有提高，无Quantib Prostate和有Quantib Prostate时分别为0.37和0.41。R³的PPV、NPV和诊断准确性（通过曲线下面积[AUC]衡量）有所提高（分别为0.51对0.55、0.65对0.82和0.56对0.62）。相反，R1和R2未观察到变化。
对于有前列腺成像经验的阅片者，使用Quantib Prostate并未提高csPCa的检出率。然而，对于经验不足的阅片者，该基于人工智能的软件被证明可提高其性能。
注册机构名称：clinicaltrials.gov。
NCT06298305。注册日期：2022年9月。
