机器学习利用前列腺特异性膜抗原（PSMA）PET 放射组学预测寡转移去势敏感型前列腺癌（omCSPC）的常规成像无转移生存（MFS）。
Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.
机构信息
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
出版信息
Radiother Oncol. 2024 Oct;199:110443. doi: 10.1016/j.radonc.2024.110443. Epub 2024 Jul 31.
PURPOSE
This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient outcomes.
MATERIALS/METHODS: An international cohort of 117 oligometastatic castration-sensitive prostate cancer (omCSPC) patients, comprising 34 from John Hopkins Hospital (JHH) and 83 from Baskent University (BU), were treated with stereotactic ablative radiation therapy (SABR) MDT with both pre- and post-MDT PSMA-PET/CT scans acquired. PET radiomic features were analyzed from a CT-PET fusion defined gross tumor volume ((GTV) or zone 1), and a 5 mm expansion ring area outside the GTV (zone 2). A total of 1748 PET radiomic features were extracted from these zones. The six most significant features selected using the Chi2 method, along with five clinical parameters (age, Gleason score, number of total lesions, untreated lesions, and pre-MDT prostate-specific antigen (PSA)) were extracted as inputs to the models. Various machine learning models, including Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayesian, were employed for 2-year MFS prediction and tested using leave-one-out and cross-institution validation.
RESULTS
Six radiomic features, including Total Energy, Entropy, and Standard Deviation from pre-PSMA-PET zone 1, Total Energy and Contrast from post-PSMA-PET zone 1, and Entropy from pre-PSMA-PET zone 2, along with five clinical parameters were selected for predicting 2-year MFS. In a leave-one-out test with all the patients, random forest achieved an accuracy of 80 % and an AUC of 0.82 in predicting 2-year MFS. In cross-institution validation, the model correctly predicted 2-year MFS events with an accuracy of 75 % and an AUC of 0.77 for patients from JHH, and an accuracy of 78 % and an AUC of 0.80 for BU patients, respectively.
CONCLUSION
Our study demonstrated the promise of using pre- and post-MDT PSMA-PET-based imaging biomarkers for MFS prediction for omCSPC patients.
目的
本研究旨在探讨 PSMA-PET 成像生物标志物在转移定向治疗（MDT）前后的变化，以预测 2 年无转移生存率（MFS），为改善患者预后提供有价值的早期反应评估。
材料/方法：本研究纳入了来自约翰霍普金斯医院（JHH）的 34 例和巴肯特大学（BU）的 83 例寡转移性去势敏感前列腺癌（omCSPC）患者，所有患者均接受了立体定向消融放疗（SABR）MDT，治疗前后均行 PSMA-PET/CT 扫描。对 CT-PET 融合定义的大体肿瘤体积（GTV）或区域 1 进行 PET 放射组学特征分析，并在 GTV 外 5mm 扩张环区域（区域 2）进行分析。共提取了来自这两个区域的 1748 个 PET 放射组学特征。采用 Chi2 法选择 6 个最显著的特征，以及 5 个临床参数（年龄、Gleason 评分、总病变数、未治疗病变数和治疗前前列腺特异性抗原（PSA））作为模型的输入。使用随机森林、决策树、支持向量机和朴素贝叶斯等多种机器学习模型进行 2 年 MFS 预测，并采用留一法和跨机构验证进行测试。
结果
来自治疗前 PSMA-PET 区域 1 的总能量、熵和标准差、治疗后 PSMA-PET 区域 1 的总能量和对比度、治疗前 PSMA-PET 区域 2 的熵等 6 个放射组学特征，以及 5 个临床参数被选择用于预测 2 年 MFS。在对所有患者进行的留一法测试中，随机森林在预测 2 年 MFS 方面的准确率为 80%，AUC 为 0.82。在跨机构验证中，该模型对 JHH 患者的 2 年 MFS 事件的预测准确率为 75%，AUC 为 0.77；对 BU 患者的预测准确率为 78%，AUC 为 0.80。
结论
本研究表明，使用 MDT 前后 PSMA-PET 成像生物标志物预测 omCSPC 患者的 MFS 具有一定的应用前景。
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