Background:Prostate cancer (PC) is a leading cause of cancer-related deaths in men worldwide. PSMA-directed positron emission tomography (PET) has shown promising results in detecting recurrent PC and metastasis, improving the accuracy of diagnosis and treatment planning. To evaluate an artificial intelligence (AI) model based on [18F]-prostate specific membrane antigen (PSMA)-1007 PET datasets for the detection of local recurrence in patients with prostate cancer.Methods:We retrospectively analyzed 1404 [18F]-PSMA-1007 PET/CTs from patients with histologically confirmed prostate cancer. Artificial neural networks were trained to recognize the presence of local recurrence based on the PET data. First, the hyperparameters were optimized for an initial model (model A). Subsequently, the bladder was localized using an already published model and a model (model B) was trained only on a 20 cm cube around the bladder. Finally, two separate models were trained on the same section depending on the prostatectomy status (model C (post-prostatectomy) and model D (non-operated)).Results:Model A achieved an accuracy of 56% on the validation data. By restricting the region to the area around the bladder, Model B achieved a validation accuracy of 71%. When validating the specialized models according to prostatectomy status, model C achieved an accuracy of 77% and model D an accuracy of 77%. All models achieved accuracies of almost 100% on the training data, indicating overfitting.Conclusions:For the presented task, 1404 examinations were insufficient to reach an accuracy of over 90% even when employing data augmentation, including additional metadata and performing automated hyperparameter optimization. The low F1-score and AUC values indicate that none of the presented models produce reliable results. However, we will facilitate future research and the development of better models by openly sharing our source code and all pre-trained models for transfer learning.
背景：前列腺癌是全球男性癌症相关死亡的主要原因。前列腺特异性膜抗原导向的正电子发射断层扫描在检测前列腺癌复发和转移方面显示出良好前景，提高了诊断和治疗计划的准确性。本研究旨在评估基于[18F]-前列腺特异性膜抗原-1007 PET数据集的人工智能模型在前列腺癌患者局部复发检测中的应用价值。 方法：我们回顾性分析了1404例经组织学确诊的前列腺癌患者的[18F]-PSMA-1007 PET/CT影像数据。基于PET数据训练人工神经网络识别局部复发病灶。首先对初始模型（模型A）进行超参数优化；随后采用已发表模型定位膀胱区域，并仅针对膀胱周围20立方厘米区域训练新模型（模型B）；最后根据前列腺切除状态，在同一区域分别训练两个独立模型（模型C（术后）和模型D（未手术））。 结果：模型A在验证数据上的准确率为56%。通过将分析区域限制在膀胱周围，模型B的验证准确率提升至71%。根据前列腺切除状态验证专用模型时，模型C和模型D的准确率均达到77%。所有模型在训练数据上的准确率均接近100%，表明存在过拟合现象。 结论：对于本研究所涉及的任务，即使采用数据增强、添加元数据和自动超参数优化等技术，1404例检查数据仍不足以使模型准确率达到90%以上。较低的F1分数和AUC值表明现有模型均无法产生可靠结果。但我们将通过公开共享源代码和所有预训练模型（用于迁移学习），为未来研究和开发更优模型提供支持。
Detection of Local Prostate Cancer Recurrence from PET/CT Scans Using Deep Learning
肿瘤(癌症)患者之家