CT R&D Image Analytics, Siemens Healthineers, Forchheim, Germany.
Comprehensive Cancer Center Munich, University Hospital, LMU Munich, Munich, Germany.
Eur Radiol. 2021 Feb;31(2):834-846. doi: 10.1007/s00330-020-07192-y. Epub 2020 Aug 27.
To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model.
A total of 103 patients (mean age: 61.0 ± 11.2 years) with colorectal liver metastases were analyzed in this retrospective study. Automatic segmentations of WLTB from baseline contrast-enhanced CT images were used. Established biomarkers as well as a standard radiomics model building were used to derive 3 prognostic models. The benefits of a geometric metastatic spread (GMS) model, the Aerts radiomics prior model of the WLTB, and the performance of TBS and WLTB volume alone were assessed. All models were analyzed in both statistical and predictive machine learning settings in terms of AUC.
TBS showed the best discriminative performance in a statistical setting to discriminate 1-YS (AUC = 0.70, CI: [0.56, 0.90]). For the machine learning-based prediction for unseen patients, both a model of the GMS of WLTB (0.73, CI: [0.60, 0.84]) and the Aerts radiomics prior model (0.76, CI: [0.65, 0.86]) applied on the WLTB showed a numerically higher predictive performance than TBS (0.68, CI: [0.54, 0.79]), radiomics (0.65, CI: [0.55, 0.78]), WLTB volume alone (0.53, CI: [0.40. 0.66]), or the clinical model (0.56, CI: [0.43, 0.67]).
The imaging-based GMS model may be a first step towards a more fine-grained machine learning extension of the TBS concept for risk stratification in mCRC patients without the vulnerability to technical variance of radiomics.
• CT-based geometric distribution and radiomics analysis of whole liver tumor burden in metastatic colorectal cancer patients yield prognostic information. • Differences in survival are possibly attributable to the spatial distribution of metastatic lesions and the geometric metastatic spread analysis of all liver metastases may serve as robust imaging biomarker invariant to technical variation. • Imaging-based prediction models outperform clinical models for 1-year survival prediction in metastatic colorectal cancer patients with liver metastases.
通过对全肝肿瘤负荷（WLTB）的几何和放射组学分析，基于定量成像生物标志物（QIB）进行系统比较分析，预测转移性结直肠癌患者的 1 年生存率（1-YS），并与基于肿瘤负荷评分（TBS）、WLTB 体积和临床模型的预测进行比较。
本回顾性研究共分析了 103 例（平均年龄：61.0±11.2 岁）结直肠癌肝转移患者。使用基线增强 CT 图像的自动分割方法对 WLTB 进行了分割。利用已建立的生物标志物和标准放射组学模型构建方法，得出了 3 个预后模型。评估了基于几何转移分布（GMS）的模型、基于 WLTB 的 Aerts 放射组学先验模型和 TBS 以及 WLTB 体积的性能。在统计和预测机器学习环境中，所有模型均基于 AUC 进行分析。
TBS 在统计学上对 1-YS 进行区分的判别性能最佳（AUC=0.70，CI：[0.56, 0.90]）。对于基于机器学习的对未知患者的预测，WLTB 的 GMS 模型（0.73，CI：[0.60, 0.84]）和 Aerts 放射组学先验模型（0.76，CI：[0.65, 0.86]）在 WLTB 上的应用都比 TBS（0.68，CI：[0.54, 0.79]）、放射组学（0.65，CI：[0.55, 0.78]）、WLTB 体积单独应用（0.53，CI：[0.40. 0.66]）或临床模型（0.56，CI：[0.43, 0.67]）具有更高的预测性能。
基于影像学的 GMS 模型可能是 TBS 概念的一个重要步骤，为无辐射组学技术易变性的结直肠癌患者提供更精细的基于机器学习的风险分层。
· 基于 CT 的转移性结直肠癌患者全肝肿瘤负荷的几何分布和放射组学分析可提供预后信息。
· 生存差异可能归因于转移病灶的空间分布，对所有肝转移灶的 GMS 分析可作为对技术变化具有稳健性的成像生物标志物。
· 基于影像学的预测模型在预测转移性结直肠癌肝转移患者的 1 年生存率方面优于临床模型。
