Nuclear Medicine Unit, Oncological Medical and Specialists Department, University Hospital of Ferrara, Ferrara, Italy.
Medical Physics Unit, Azienda USL of Ferrara, Ferrara, Italy.
Expert Rev Med Devices. 2023 Jul-Dec;20(12):1183-1191. doi: 10.1080/17434440.2023.2280685. Epub 2023 Nov 24.
To evaluate the relevance of incidental prostate [F]FDG uptake (IPU) and to explore the potential of radiomics and machine learning (ML) to predict prostate cancer (PCa).
We retrieved [F]FDG PET/CT scans with evidence of IPU performed in two institutions between 2015 and 2021. Patients were divided into PCa and non-PCa, according to the biopsy. Clinical and PET/CT-derived information (comprehensive of radiomic analysis) were acquired. Five ML models were developed and their performance in discriminating PCa vs non-PCa IPU was evaluated. Radiomic analysis was investigated to predict ISUP Grade.
Overall, 56 IPU were identified and 31 patients performed prostate biopsy. Eighteen of those were diagnosed as PCa. Only PSA and radiomic features (eight from CT and nine from PET images, respectively) showed statistically significant difference between PCa and non-PCa patients. Eight features were found to be robust between the two institutions. CT-based ML models showed good performance, especially in terms of negative predictive value (NPV 0.733-0.867). PET-derived ML models results were less accurate except the Random Forest model (NPV = 0.933). Radiomics could not accurately predict ISUP grade.
Paired with PSA, radiomic analysis seems to be promising to discriminate PCa/non-PCa IPU. ML could be a useful tool to identify non-PCa IPU, avoiding further investigations.
评估偶然前列腺[F]FDG 摄取（IPU）的相关性，并探讨放射组学和机器学习（ML）预测前列腺癌（PCa）的潜力。
我们检索了 2015 年至 2021 年在两个机构进行的具有 IPU 证据的[F]FDG PET/CT 扫描。根据活检结果，将患者分为 PCa 和非 PCa。采集临床和 PET/CT 衍生信息（包括放射组学分析）。开发了五个 ML 模型，并评估了它们在区分 PCa 与非 PCa IPU 中的性能。研究了放射组学分析以预测 ISUP 分级。
总体上，确定了 56 个 IPU，31 名患者进行了前列腺活检。其中 18 例被诊断为 PCa。仅 PSA 和放射组学特征（分别来自 CT 和 PET 图像的 8 个和 9 个）在 PCa 和非 PCa 患者之间显示出统计学上的显著差异。在两个机构之间发现了 8 个稳健的特征。基于 CT 的 ML 模型表现出良好的性能，尤其是在阴性预测值（NPV 0.733-0.867）方面。除了随机森林模型（NPV=0.933）外，基于 PET 的 ML 模型的结果准确性较低。放射组学不能准确预测 ISUP 分级。
与 PSA 相结合，放射组学分析似乎有望区分 PCa/非 PCa IPU。ML 可以成为识别非 PCa IPU 的有用工具，避免进一步的检查。
Zotero 插件