Gitto Salvatore, Cuocolo Renato, Albano Domenico, Morelli Francesco, Pescatori Lorenzo Carlo, Messina Carmelo, Imbriaco Massimo, Sconfienza Luca Maria
Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy.
Insights Imaging. 2021 Jun 2;12(1):68. doi: 10.1186/s13244-021-01008-3.
Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability.
Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies.
The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.
特征可重复性和模型验证是放射组学的两个主要挑战。本研究旨在系统回顾在骨与软组织肉瘤的CT和MRI放射组学研究中放射组学特征的可重复性及预测模型验证策略。最终目标是促进在放射组学工作流程的这些方面达成共识，并推动临床可转移性。
在278篇检索到的论文中，纳入了2008年至2020年发表的49篇论文。这些论文涉及骨肿瘤（n = 12）或软组织肿瘤（n = 37）的放射组学。18项（37%）研究进行了特征可重复性分析。16项（33%）研究中，阅片者间/阅片者内分割变异性是可重复性分析的主题，其数量超过了聚焦于图像采集或后处理的分析（n = 2，4%）。组内相关系数是评估可重复性最常用的统计方法，范围在0.6至0.9之间。25篇（51%）论文在模型开发中至少使用了一种机器学习验证技术，其中K折交叉验证是最常用的。19篇（39%）论文报告了模型的临床验证。14项（29%）研究使用来自原机构的单独数据集进行临床验证（即内部验证），5项（10%）研究使用与不同扫描仪相关的独立数据集或来自另一机构的独立数据集进行临床验证（即独立验证）。
在肌肉骨骼肉瘤的研究中，放射组学特征可重复性和模型验证问题差异很大，未来的研究应解决这些问题，以使放射组学领域从临床前研究阶段迈向临床阶段。
