发展 PSMA-PET 引导下 CT 基于影像组学特征预测 salvager 放疗后生化复发的方法。
Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.
发表日期：2023 Mar 16
作者：
Simon K B Spohn, Nina-Sophie Schmidt-Hegemann, Juri Ruf, Michael Mix, Matthias Benndorf, Fabian Bamberg, Marcus R Makowski, Simon Kirste, Alexander Rühle, Jerome Nouvel, Tanja Sprave, Marco M E Vogel, Polina Galitsnaya, Jürgen E Gschwend, Christian Gratzke, Christian Stief, Steffen Löck, Alex Zwanenburg, Christian Trapp, Denise Bernhardt, Stephan G Nekolla, Minglun Li, Claus Belka, Stephanie E Combs, Matthias Eiber, Lena Unterrainer, Marcus Unterrainer, Peter Bartenstein, Anca-L Grosu, Constantinos Zamboglou, Jan C Peeken
来源：
Eur J Nucl Med Mol I
摘要：
为了开发基于CT的放射性组学签名以预测阳性发生生化复发（BCR）的概率，胶体金标记前列腺特定膜抗原（PSMA-PET）引导下前列腺癌患者进行放疗后。本回顾性多中心研究纳入了来自德国三个高容量中心接受68Ga-PSMA11-PET/CT引导下的放疗治疗的连续患者，这些患者患有阳性病灶并接受调强放射治疗（IMRT）。放射性特征从CT的VIO上提取，这些VIO是由于局部PSMA-PET的吸收引导而成。预处理之后，采用嵌套交叉验证方法组合不同的特征缩减技术和Cox比例风险模型，开发了临床、放射和组合临床-放射模型。对于99名患者来说，BRC的中位时间为放射模型表现出了优于临床模型和组合临床-放射模型的性能，在测试集中的C指数分别为0.53和0.63，而放射机模型为0.71。与其他模型不同，放射性模型在Kaplan-Meier分析中实现了显着改善患者分层。放射学和临床-放射学模型在时间依赖的净重分类改进指数（分别为0.392和0.762）方面实现了显着的改进，而临床模型则没有。决策曲线分析显示两个模型都具有临床净益。平均密度是最具预测性的放射学特征。这是第一项开发PSMA-PET引导下基于CT的放射学模型预测放疗后BCR的研究。放射性模型优于临床模型，有助于指导个性化治疗决策。©2023. 作者。
To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET).Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach.Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature.This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.© 2023. The Author(s).