Trägårdh Elin, Ulén Johannes, Enqvist Olof, Larsson Måns, Valind Kristian, Minarik David, Edenbrandt Lars
Department of Translational Medicine, Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden.
Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Inga Marie Nilssons G 47, 205 02, Malmö, Sweden.
EJNMMI Phys. 2025 Aug 20;12(1):78. doi: 10.1186/s40658-025-00786-9.
In this study, we further developed an artificial intelligence (AI)-based method for the detection and quantification of tumours in the prostate, lymph nodes and bone in prostate-specific membrane antigen (PSMA)-targeting positron emission tomography with computed tomography (PET-CT) images.
A total of 1064 [F]PSMA-1007 PET-CT scans were used (approximately twice as many compared to our previous AI model), of which 120 were used as test set. Suspected lesions were manually annotated and used as ground truth. A convolutional neural network was developed and trained. The sensitivity and positive predictive value (PPV) were calculated using two sets of manual segmentations as reference. Results were also compared to our previously developed AI method. The correlation between manually and AI-based calculations of total lesion volume (TLV) and total lesion uptake (TLU) were calculated.
The sensitivities of the AI method were 85% for prostate tumour/recurrence, 91% for lymph node metastases and 61% for bone metastases (82%, 86% and 70% for manual readings and 66%, 88% and 71% for the old AI method). The PPVs of the AI method were 85%, 83% and 58%, respectively (63%, 86% and 39% for manual readings, and 69%, 70% and 39% for the old AI method). The correlations between manual and AI-based calculations of TLV and TLU ranged from r = 0.62 to r = 0.96.
The performance of the newly developed and fully automated AI-based method for detecting and quantifying prostate tumour and suspected lymph node and bone metastases increased significantly, especially the PPV. The AI method is freely available to other researchers ( www.recomia.org ).
在本研究中，我们进一步开发了一种基于人工智能（AI）的方法，用于在前列腺特异性膜抗原（PSMA）靶向正电子发射断层扫描与计算机断层扫描（PET-CT）图像中检测和量化前列腺、淋巴结及骨骼中的肿瘤。
共使用了1064例[F]PSMA-1007 PET-CT扫描（约为我们之前AI模型使用数量的两倍），其中120例用作测试集。对疑似病变进行手动标注并用作真实标准。开发并训练了一个卷积神经网络。以两组手动分割作为参考计算敏感性和阳性预测值（PPV）。还将结果与我们之前开发的AI方法进行比较。计算了手动与基于AI的总病变体积（TLV）和总病变摄取（TLU）计算之间的相关性。
AI方法对前列腺肿瘤/复发的敏感性为85%，对淋巴结转移为91%，对骨转移为61%（手动读数分别为82%、86%和70%，旧AI方法分别为66%、88%和71%）。AI方法的PPV分别为85%、83%和58%（手动读数分别为63%、86%和39%，旧AI方法分别为69%、70%和39%）。手动与基于AI的TLV和TLU计算之间的相关性范围为r = 0.62至r = 0.96。
新开发的基于AI的全自动检测和量化前列腺肿瘤及疑似淋巴结和骨转移的方法性能显著提高，尤其是PPV。该AI方法可供其他研究人员免费使用（www.recomia.org）。
