Zhang Haiying, Xie Cheng, Huang Changrong, Jiang Zhibing, Tang Qun
Guangzhou Huashang Vocational College, Guangzhou 511300, China (H.Z., C.X., C.H.).
Medical School, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China (Z.J., Q.T.).
Acad Radiol. 2025 Sep 20. doi: 10.1016/j.acra.2025.09.004.
We conducted a systematic review and meta-analysis to assess and compare the diagnostic performance of prostate-specific membrane antigen positron-emission tomography (PSMA PET) with conventional imaging modalities in detecting biochemical recurrence of prostate cancer, and to assess the role of artificial intelligence in this context.
A comprehensive search of PubMed, Embase, Web of Science, the Cochrane Library, and Scopus was conducted for studies, initially on May 7, 2025, and updated on July 28, 2025. Studies that compared PSMA PET with conventional imaging and assessed artificial intelligence for detecting biochemical recurrence of prostate cancer were considered. The QUADAS-2 technique was employed to evaluate study quality. Diagnosis accuracy and detection rates were aggregated utilizing a bivariate random-effects model.
A total of 7637 patients from 67 studies were included. PSMA PET demonstrated significantly higher overall diagnostic accuracy for biochemical recurrence of prostate cancer compared to mpMRI, CT, and AI test sets, with accuracy values of (0.89 vs. 0.71, 0.45, and 0.76, P<0.01). For local recurrence, mpMRI outperformed PSMA PET and CT (0.93 vs. 0.84 and 0.77, P<0.01). PSMA PET was superior in detecting lymph node metastasis than mpMRI and CT (0.89 vs. 0.79 and 0.72, P<0.05). For bone metastasis, PSMA PET outperformed mpMRI, CT, and Bone scan (0.95 vs. 0.85, 0.81, and 0.80, P<0.05). For visceral metastasis, PSMA PET outperformed mpMRI (0.96 vs. 0.89, P=0.23), and CT (0.96 vs. 0.78, P<0.05). 21 studies involving 3113 samples were included to evaluate the performance of artificial intelligence in detecting biochemical recurrence of prostate cancer. The pooled sensitivity, specificity, DOR, and AUC of AI test sets in detecting biochemical recurrence of prostate cancer were 0.77, 0.76, 10.39, and 0.79. Heterogeneity limits the generalizability of our findings.
PSMA PET outperformed mpMRI and CT in detecting overall, local recurrence, bone, and visceral metastasis, while mpMRI was more effective for local recurrence. While AI exhibits potential diagnostic efficacy. Despite promising results, heterogeneity and limited validation highlight the need for further research to support routine clinical use.
我们进行了一项系统评价和荟萃分析，以评估和比较前列腺特异性膜抗原正电子发射断层扫描（PSMA PET）与传统成像方式在检测前列腺癌生化复发方面的诊断性能，并评估人工智能在此背景下的作用。
于2025年5月7日对PubMed、Embase、Web of Science、Cochrane图书馆和Scopus进行全面检索以查找研究，并于2025年7月28日更新。纳入比较PSMA PET与传统成像并评估人工智能检测前列腺癌生化复发的研究。采用QUADAS - 2技术评估研究质量。利用双变量随机效应模型汇总诊断准确性和检测率。
共纳入67项研究中的7637例患者。与多参数磁共振成像（mpMRI）、计算机断层扫描（CT）和人工智能检测集相比，PSMA PET在检测前列腺癌生化复发方面总体诊断准确性显著更高，准确性值分别为（0.89对0.71、0.45和0.76，P<0.01）。对于局部复发，mpMRI优于PSMA PET和CT（0.93对0.84和0.77，P<0.01）。PSMA PET在检测淋巴结转移方面优于mpMRI和CT（0.89对0.79和0.72，P<0.05）。对于骨转移，PSMA PET优于mpMRI、CT和骨扫描（0.95对0.85、0.81和0.80，P<0.05）。对于内脏转移，PSMA PET优于mpMRI（0.96对0.89，P = 0.23）和CT（0.96对0.78，P<0.05）。纳入21项涉及3113个样本的研究以评估人工智能检测前列腺癌生化复发的性能。人工智能检测集在检测前列腺癌生化复发方面的合并灵敏度、特异度、诊断比值比（DOR）和曲线下面积（AUC）分别为0.77、0.76、10.39和0.79。异质性限制了我们研究结果的可推广性。
在检测总体、局部复发、骨和内脏转移方面，PSMA PET优于mpMRI和CT，而mpMRI在局部复发检测方面更有效。虽然人工智能显示出潜在的诊断效能。尽管结果令人鼓舞，但异质性和有限的验证突出表明需要进一步研究以支持其常规临床应用。
