Wei Lise, Cui Can, Xu Jiarui, Kaza Ravi, El Naqa Issam, Dewaraja Yuni K
Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.
Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA.
EJNMMI Phys. 2020 Dec 9;7(1):74. doi: 10.1186/s40658-020-00340-9.
To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy Y PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies METHODS: Given the noisy nature of Y PET, first, a liver phantom study with repeated acquisitions and varying reconstruction parameters was used to identify a subset of robust radiomics features for the patient analysis. In 36 radioembolization procedures, Y PET/CT was performed within a couple of hours to extract 46 radiomics features and estimate absorbed dose in 105 primary and metastatic liver lesions. Robust radiomics modeling was based on bootstrapped multivariate logistic regression with shrinkage regularization (LASSO) and Cox regression with LASSO. Nested cross-validation and bootstrap resampling were used for optimal parameter/feature selection and for guarding against overfitting risks. Spearman rank correlation was used to analyze feature associations. Area under the receiver-operating characteristics curve (AUC) was used for lesion response (at first follow-up) analysis while Kaplan-Meier plots and c-index were used to assess progression model performance. Models with absorbed dose only, radiomics only, and combined models were developed to predict lesion outcome.
The phantom study identified 15/46 reproducible and robust radiomics features that were subsequently used in the patient models. A lesion response model with zone percentage (ZP) and mean absorbed dose achieved an AUC of 0.729 (95% CI 0.702-0.758), and a progression model with zone size nonuniformity (ZSN) and absorbed dose achieved a c-index of 0.803 (95% CI 0.790-0.815) on nested cross-validation (CV). Although the combined models outperformed the radiomics only and absorbed dose only models, statistical significance was not achieved with the current limited data set to establish expected superiority.
We have developed new lesion-level response and progression models using textural radiomics features, derived from Y PET combined with mean absorbed dose for predicting outcome in radioembolization. These encouraging, but limited results, will need further validation in independent and larger datasets prior to any clinical adoption.
评估从治疗后钇-90正电子发射断层扫描（Y PET）中提取的病变影像组学特征和吸收剂量指标能否整合，以更好地预测肝脏恶性肿瘤微球放射性栓塞治疗的结果。方法：鉴于Y PET的噪声特性，首先进行了一项肝脏体模研究，通过重复采集和改变重建参数，确定用于患者分析的一组稳健的影像组学特征。在36例放射性栓塞治疗过程中，在数小时内进行Y PET/CT检查，以提取46个影像组学特征，并估计105个原发性和转移性肝脏病变的吸收剂量。稳健的影像组学建模基于带有收缩正则化（LASSO）的自抽样多元逻辑回归和带有LASSO的Cox回归。采用嵌套交叉验证和自抽样重采样进行最佳参数/特征选择，并防范过度拟合风险。使用Spearman等级相关性分析特征关联。采用受试者操作特征曲线下面积（AUC）进行病变反应（首次随访时）分析，同时使用Kaplan-Meier曲线和c指数评估进展模型性能。开发了仅包含吸收剂量、仅包含影像组学以及两者结合的模型来预测病变结果。结果：体模研究确定了46个特征中的15个可重复且稳健的影像组学特征，随后将其用于患者模型。在嵌套交叉验证（CV）中，一个包含区域百分比（ZP）和平均吸收剂量的病变反应模型的AUC为0.729（95%可信区间0.702 - 0.758），一个包含区域大小不均匀性（ZSN）和吸收剂量的进展模型的c指数为0.803（95%可信区间0.790 - 0.815）。尽管结合模型优于仅包含影像组学和仅包含吸收剂量的模型，但在当前有限的数据集中未达到统计学显著性以确立预期的优越性。结论：我们利用从Y PET衍生的纹理影像组学特征结合平均吸收剂量，开发了新的病变水平反应和进展模型，用于预测放射性栓塞治疗结果。这些结果令人鼓舞，但有限，在临床应用之前，需要在独立的更大数据集中进一步验证。
