Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging and directing prostate carcinoma treatment. Here, we summarize the available literature data on radiomics and machine learning using these imaging agents in prostate carcinoma. Gleason scores derived from biopsy and after resection are discordant in a large number of prostate carcinoma patients. Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma might be better performing than biopsy-based Gleason-scoring and could serve as an alternative for non-invasive GS characterization. Furthermore, it may allow for the prediction of biochemical recurrence with a net benefit for clinical utilization. Machine learning based on PET/CT radiomics features was also shown to be able to differentiate benign from malignant increased tracer uptake on PSMA-targeting radioligand PET/CT examinations, thus paving the way for a fully automated image reading in nuclear medicine. As for prediction to treatment outcome following177Lu-PSMA therapy and overall survival, a limited number of studies have reported promising results on radiomics and machine learning applied to PSMA-targeting radioligand PET/CT images for this purpose. Its added value to clinical parameters warrants further exploration in larger datasets of patients.
在过去十年中，利用放射性标记的前列腺特异性膜抗原靶向PET显像剂进行正电子发射断层扫描（PET）已越来越多地应用于前列腺癌的成像和治疗指导。本文总结了关于在前列腺癌中应用这些显像剂的影像组学和机器学习的现有文献数据。大量前列腺癌患者的活检结果与术后切除标本的格里森评分存在不一致。现有研究表明，对PSMA放射性配体高摄取的原发性前列腺癌应用影像组学和机器学习，其性能可能优于基于活检的格里森评分，并可作为无创格里森评分分级的替代方法。此外，该方法可能有助于预测生化复发，为临床应用带来净获益。基于PET/CT影像组学特征的机器学习也被证明能够区分PSMA靶向放射性配体PET/CT检查中示踪剂摄取的良恶性增高，从而为核医学领域的全自动图像判读铺平道路。关于预测¹⁷⁷Lu-PSMA治疗后的疗效及总生存期，目前有限的研究显示，对PSMA靶向放射性配体PET/CT图像应用影像组学和机器学习已取得有前景的结果。其在临床参数基础上的附加价值，需要在更大规模的患者数据集中进一步探索。
肿瘤(癌症)患者之家