总生存期 (OS) 和无进展生存期 (PFS) 分析是评估治疗效果和影响的关键指标。本研究评估了临床生物标志物和剂量测定参数对接受 90Y 选择性体内放射治疗 (SIRT) 的患者生存结果的作用。这项初步和回顾性分析包括 17 名接受 90Y SIRT 治疗的肝细胞癌 (HCC) 患者。患者接受了个性化治疗计划和体素剂量测定。手术后，评估 OS 和 PFS。描绘了三种结构，包括肿瘤肝脏（TL）、正常灌注肝脏（NPL）和整个正常肝脏（WNL）。从 99mTc-MAA 和 90Y SPECT/CT 图像计算的物理和生物有效剂量 (BED) 图的剂量体积直方图中提取了 289 个剂量体积约束 (DVC)。随后，DVC 和 16 种临床生物标志物被用作单变量和多变量分析的特征。采用 Cox 比例风险比 (HR) 进行单变量分析。计算每个特征的 HR 和一致性指数 (C-Index)。使用八种不同的策略，将各种模型和特征选择（FS）方法的交叉组合应用于多变量分析。每个模型的性能均使用三重嵌套交叉验证框架上的平均 C 指数进行评估。 Kaplan-Meier (KM) 曲线用于单变量和机器学习 (ML) 模型性能评估。中位 OS 为 11 个月 [95% CI: 8.5, 13.09]，而 PFS 为 7 个月 [95% CI: 5.6] ，10.98]。单变量分析显示腹水的存在（HR：9.2[1.8,47]）和 SIRT 的目标（肺段切除术、肺叶切除术、姑息治疗）（HR：0.066 [0.0057，0.78]）、天冬氨酸转氨酶（AST）水平（HR：0.1） [0.012-0.86]）和 MAA-Dose-V205(%)-TL (HR:8.5[1,72]) 作为 OS 的预测因子。 90Y 衍生参数与 PFS 相关，但与 OS 无关。 MAA-Dose-V205(%)-WNL、MAA-BED-V400(%)-WNL 与 (HR:13 [1.5-120]) 和 90Y-Dose-mean-TL、90Y-D50-TL-Gy、90Y剂量测定参数中，-Dose-V205(%)-TL、90Y-Dose-D50-TL-Gy 和 90Y-BED-V400(%)-TL (HR:15 [1.8-120]) 与 PFS 高度相关。使用 ML 的多变量分析中观察到的最高 C 指数为 0.94±0.13，该值是根据临床特征的变量狩猎-变量重要性 (VH.VIMP) FS 和预测 OS 的 Cox 比例风险模型获得的。然而，VH的组合。 VIMP FS 方法采用广义线性模型网络模型，使用治疗策略功能预测 OS，在 C 指数和 KM 曲线分层方面均优于其他模型（C 指数：0.93 ± 0.14，对数秩 p 值为 0.023） KM曲线分层）。这项初步研究证实了基线临床生物标志物和剂量测定参数在预测治疗结果中的作用，为建立剂量效应关系铺平了道路。此外，在 90Y-SIRT 之前和之后，使用 ML 和这些功能的可行性已被证明是临床管理患者的有用工具。© 2024。作者。

Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing 90Y selective internal radiation therapy (SIRT).This preliminary and retrospective analysis included 17 patients with hepatocellular carcinoma (HCC) treated with 90Y SIRT. The patients underwent personalized treatment planning and voxel-wise dosimetry. After the procedure, the OS and PFS were evaluated. Three structures were delineated including tumoral liver (TL), normal perfused liver (NPL), and whole normal liver (WNL). 289 dose-volume constraints (DVCs) were extracted from dose-volume histograms of physical and biological effective dose (BED) maps calculated on 99mTc-MAA and 90Y SPECT/CT images. Subsequently, the DVCs and 16 clinical biomarkers were used as features for univariate and multivariate analysis. Cox proportional hazard ratio (HR) was employed for univariate analysis. HR and the concordance index (C-Index) were calculated for each feature. Using eight different strategies, a cross-combination of various models and feature selection (FS) methods was applied for multivariate analysis. The performance of each model was assessed using an averaged C-Index on a three-fold nested cross-validation framework. The Kaplan-Meier (KM) curve was employed for univariate and machine learning (ML) model performance assessment.The median OS was 11 months [95% CI: 8.5, 13.09], whereas the PFS was seven months [95% CI: 5.6, 10.98]. Univariate analysis demonstrated the presence of Ascites (HR: 9.2[1.8,47]) and the aim of SIRT (segmentectomy, lobectomy, palliative) (HR: 0.066 [0.0057, 0.78]), Aspartate aminotransferase (AST) level (HR:0.1 [0.012-0.86]), and MAA-Dose-V205(%)-TL (HR:8.5[1,72]) as predictors for OS. 90Y-derived parameters were associated with PFS but not with OS. MAA-Dose-V205(%)-WNL, MAA-BED-V400(%)-WNL with (HR:13 [1.5-120]) and 90Y-Dose-mean-TL, 90Y-D50-TL-Gy, 90Y-Dose-V205(%)-TL, 90Y-Dose- D50-TL-Gy, and 90Y-BED-V400(%)-TL (HR:15 [1.8-120]) were highly associated with PFS among dosimetry parameters. The highest C-index observed in multivariate analysis using ML was 0.94 ± 0.13 obtained from Variable Hunting-variable-importance (VH.VIMP) FS and Cox Proportional Hazard model predicting OS, using clinical features. However, the combination of VH. VIMP FS method with a Generalized Linear Model Network model predicting OS using Therapy strategy features outperformed the other models in terms of both C-index and stratification of KM curves (C-Index: 0.93 ± 0.14 and log-rank p-value of 0.023 for KM curve stratification).This preliminary study confirmed the role played by baseline clinical biomarkers and dosimetry parameters in predicting the treatment outcome, paving the way for the establishment of a dose-effect relationship. In addition, the feasibility of using ML along with these features was demonstrated as a helpful tool in the clinical management of patients, both prior to and following 90Y-SIRT.© 2024. The Author(s).