在可变层厚增强CT中寻找结直肠肝转移的可重复且具有预后价值的影像组学特征
Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases
AI总结：
本研究在结直肠肝转移CT中评估影像组学特征的可重复性与预后价值，发现数据驱动的多设置特征提取结合可重复性筛选可构建等效预测模型，支持广泛特征纳入策略。
摘要：
建立影像组学特征的可重复性是定量影像生物标志物走向临床采用的关键步骤；然而，影像组学特征还必须与具有临床重要性的结局有意义地关联，才能对个性化医疗有价值。在本研究中，我们分析了从结直肠肝转移（CRLM）患者的增强CT扫描中提取的肝实质和最大肝转移灶的影像组学特征的可重复性和预后价值。使用来自美国两个主要癌症中心的前瞻性队列（81名患者）来建立从不同层厚重建图像中提取的影像组学特征的可重复性。使用一个公开可用的单中心队列（197例术前扫描，来自接受肝切除术治疗CRLM的患者）来评估特征和模型预测总生存期的预后价值。从所有图像中提取了标准93个特征，使用八种不同的提取器设置。产生最可重复以及最具预后判别力的特征值的特征提取设置高度依赖于感兴趣区域和具体特征。虽然最佳整体预测模型是使用特定设置提取的特征构建的，未考虑可重复性（C指数=0.630（0.603--0.649）），但通过汇集所有提取设置的特征，并在特征选择前对低可重复性特征进行阈值化（$\mathrm{CCC} \geq 0.85$），产生了等效性能的模型（C指数=0.629（0.605--0.645））。我们的研究结果支持数据驱动的特征提取和选择方法，倾向于包含许多特征，并在有相关数据时基于可重复性缩小特征选择。
英文摘要：
Establishing the reproducibility of radiomic signatures is a critical step in the path to clinical adoption of quantitative imaging biomarkers; however, radiomic signatures must also be meaningfully related to an outcome of clinical importance to be of value for personalized medicine. In this study, we analyze both the reproducibility and prognostic value of radiomic features extracted from the liver parenchyma and largest liver metastases in contrast enhanced CT scans of patients with colorectal liver metastases (CRLM). A prospective cohort of 81 patients from two major US cancer centers was used to establish the reproducibility of radiomic features extracted from images reconstructed with different slice thicknesses. A publicly available, single-center cohort of 197 preoperative scans from patients who underwent hepatic resection for treatment of CRLM was used to evaluate the prognostic value of features and models to predict overall survival. A standard set of 93 features was extracted from all images, with a set of eight different extractor settings. The feature extraction settings producing the most reproducible, as well as the most prognostically discriminative feature values were highly dependent on both the region of interest and the specific feature in question. While the best overall predictive model was produced using features extracted with a particular setting, without accounting for reproducibility, (C-index = 0.630 (0.603--0.649)) an equivalent-performing model (C-index = 0.629 (0.605--0.645)) was produced by pooling features from all extraction settings, and thresholding features with low reproducibility ($\mathrm{CCC} \geq 0.85$), prior to feature selection. Our findings support a data-driven approach to feature extraction and selection, preferring the inclusion of many features, and narrowing feature selection based on reproducibility when relevant data is available.
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