Geady Caryn, Patel Hemangini, Peoples Jacob, Simpson Amber, Haibe-Kains Benjamin
Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
medRxiv. 2024 Jul 5:2024.07.04.24309964. doi: 10.1101/2024.07.04.24309964.
Radiomics traditionally focuses on analyzing a single lesion within a patient to extract tumor characteristics, yet this process may overlook inter-lesion heterogeneity, particularly in the multi-metastatic setting. There is currently no established method for combining radiomic features in such settings, leading to diverse approaches with varying strengths and limitations. Our quantitative review aims to illuminate these methodologies, assess their replicability, and guide future research toward establishing best practices, offering insights into the challenges of multi-lesion radiomic analysis across diverse datasets.
We conducted a comprehensive literature search to identify methods for integrating data from multiple lesions in radiomic analyses. We replicated these methods using either the author's code or by reconstructing them based on the information provided in the papers. Subsequently, we applied these identified methods to three distinct datasets, each depicting a different metastatic scenario.
We compared ten mathematical methods for combining radiomic features across three distinct datasets, encompassing a total of 16,850 lesions in 3,930 patients. Performance of these methods was evaluated using the Cox proportional hazards model and benchmarked against univariable analysis of total tumor volume. We observed variable performance in methods across datasets. However, no single method consistently outperformed others across all datasets. Notably, while some methods surpassed total tumor volume analysis in certain datasets, others did not. Averaging methods showed higher median performance in patients with colorectal liver metastases, and in soft tissue sarcoma, concatenation of radiomic features from different lesions exhibited the highest median performance among tested methods.
Radiomic features can be effectively selected or combined to estimate patient-level outcomes in multi-metastatic patients, though the approach varies by metastatic setting. Our study fills a critical gap in radiomics research by examining the challenges of radiomic-based analysis in this setting. Through a comprehensive review and rigorous testing of different methods across diverse datasets representing unique metastatic scenarios, we provide valuable insights into effective radiomic analysis strategies.
传统上，放射组学专注于分析患者体内的单个病灶以提取肿瘤特征，但这一过程可能会忽略病灶间的异质性，尤其是在多转移灶的情况下。目前在这种情况下尚无既定的方法来组合放射组学特征，导致出现了各种优缺点各异的方法。我们的定量综述旨在阐明这些方法，评估其可重复性，并指导未来研究建立最佳实践，为跨不同数据集进行多病灶放射组学分析的挑战提供见解。
我们进行了全面的文献检索，以确定在放射组学分析中整合来自多个病灶数据的方法。我们使用作者的代码或根据论文中提供的信息进行重构来复制这些方法。随后，我们将这些确定的方法应用于三个不同的数据集，每个数据集描绘了不同的转移情况。
我们比较了十种用于跨三个不同数据集组合放射组学特征的数学方法，这些数据集总共包含3930例患者的16850个病灶。使用Cox比例风险模型评估这些方法的性能，并与总肿瘤体积的单变量分析进行基准比较。我们观察到各数据集的方法性能存在差异。然而，没有一种方法在所有数据集中始终优于其他方法。值得注意的是，虽然某些方法在某些数据集中超过了总肿瘤体积分析，但其他方法则没有。平均方法在结直肠癌肝转移患者中表现出较高的中位数性能，而在软组织肉瘤中，来自不同病灶的放射组学特征串联在测试方法中表现出最高的中位数性能。
尽管方法因转移情况而异，但放射组学特征可有效选择或组合以估计多转移患者的个体水平结局。我们的研究通过研究这种情况下基于放射组学分析的挑战，填补了放射组学研究中的关键空白。通过对代表独特转移情况的不同数据集的不同方法进行全面综述和严格测试，我们为有效的放射组学分析策略提供了有价值的见解。
