The aim of this study was to compare diagnostic performance for extra-prostatic extension (EPE) and image quality among three image datasets: conventional T2-weighted images (T2WIconv, slice thickness, 3 mm) and high-resolution thin-slice T2WI (T2WIHR, 2 mm), with and without deep learning reconstruction (DLR) in patients with prostatic cancer (PCa). A total of 88 consecutive patients (28 EPE-positive and 60 negative) diagnosed with PCa via radical prostatectomy who had undergone 3T-MRI were included. Two independent reviewers performed a crossover review in three sessions, in which each reviewer recorded five-point confidence scores for the presence of EPE and image quality using a five-point Likert scale. Pathologic topographic maps served as the reference standard. For both reviewers, T2WIconvshowed better diagnostic performance than T2WIHRwith and without DLR (AUCs, in order, for reviewer 1, 0.883, 0.806, and 0.772,p= 0.0006; for reviewer 2, 0.803, 0.762, and 0.745,p= 0.022). The image quality was also the best in T2WIconv, followed by T2WIHR with DLRand T2WIHR without DLRfor both reviewers (median, in order, 3, 4, and 5,p< 0.0001). In conclusion, T2WIconvwas optimal in regard to image quality and diagnostic performance for the evaluation of EPE in patients with PCa.
本研究旨在比较前列腺癌（PCa）患者中三种影像数据集对前列腺外侵犯（EPE）的诊断效能及图像质量：常规T2加权成像（T2WIconv，层厚3 mm）与高分辨率薄层T2加权成像（T2WIHR，层厚2 mm），并分别评估其在使用与不使用深度学习重建（DLR）技术后的表现。研究纳入了88例经根治性前列腺切除术确诊为前列腺癌并接受3T-MRI检查的连续患者（其中28例EPE阳性，60例阴性）。两位独立评审员分三次进行交叉审阅，采用五分制李克特量表分别记录对EPE存在的置信度评分及图像质量评分。病理拓扑图作为参考标准。两位评审员的结果均显示，无论是否应用DLR技术，T2WIconv的诊断效能均优于T2WIHR（评审员1的AUC值依次为0.883、0.806和0.772，p=0.0006；评审员2的AUC值依次为0.803、0.762和0.745，p=0.022）。图像质量方面，T2WIconv同样表现最佳，其次是应用DLR的T2WIHR及未应用DLR的T2WIHR（两位评审员评分中位数依次为3、4和5，p<0.0001）。综上所述，在前列腺癌患者EPE评估中，常规T2加权成像在图像质量与诊断效能方面均表现最优。
肿瘤(癌症)患者之家