Lecointre Lise, Dana Jérémy, Lodi Massimo, Akladios Chérif, Gallix Benoît
Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France.
Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Inserm U1110, Institut de Recherche sur Les Maladies Virales et Hépatiques, Strasbourg, France.
Eur J Surg Oncol. 2021 Nov;47(11):2734-2741. doi: 10.1016/j.ejso.2021.06.023. Epub 2021 Jun 24.
Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (AI) in radiology.
To investigate the contribution of radiomics on the radiological preoperative assessment of patients with EC; and to establish a simple and reproducible AI Quality Score applicable to Machine Learning and Deep Learning studies.
We conducted a systematic review of current literature including original articles that studied EC through imaging-based AI techniques. Then, we developed a novel Simplified and Reproducible AI Quality score (SRQS) based on 10 items which ranged to 0 to 20 points in total which focused on clinical relevance, data collection, model design and statistical analysis. SRQS cut-off was defined at 10/20.
We included 17 articles which studied different radiological parameters such as deep myometrial invasion, lympho-vascular space invasion, lymph nodes involvement, etc. One article was prospective, and the others were retrospective. The predominant technique was magnetic resonance imaging. Two studies developed Deep Learning models, while the others machine learning ones. We evaluated each article with SRQS by 2 independent readers. Finally, we kept only 7 high-quality articles with clinical impact. SRQS was highly reproducible (Kappa = 0.95 IC 95% [0.907-0.988]).
There is currently insufficient evidence on the benefit of radiomics in EC. Nevertheless, this field is promising for future clinical practice. Quality should be a priority when developing these new technologies.
子宫内膜癌（EC）的术前放射学评估在某些情况下不够精确，提高其性能可能带来临床益处。放射组学是人工智能（AI）在放射学中的一个新兴应用领域。
探讨放射组学对EC患者术前放射学评估的贡献；并建立一个适用于机器学习和深度学习研究的简单且可重复的AI质量评分。
我们对当前文献进行了系统综述，包括通过基于影像的AI技术研究EC的原创文章。然后，我们基于10个项目开发了一种新颖的简化且可重复的AI质量评分（SRQS），总分范围为0至20分，重点关注临床相关性、数据收集、模型设计和统计分析。SRQS的临界值设定为10/20。
我们纳入了17篇研究不同放射学参数的文章，如肌层深部浸润、淋巴血管间隙浸润、淋巴结受累等。1篇文章为前瞻性研究，其余为回顾性研究。主要技术是磁共振成像。2项研究开发了深度学习模型，其他研究则是机器学习模型。我们由2名独立读者使用SRQS对每篇文章进行评估。最后，我们仅保留了7篇具有临床影响的高质量文章。SRQS具有高度可重复性（Kappa = 0.95，95%置信区间[0.907 - 0.988]）。
目前关于放射组学在EC中的益处证据不足。然而，该领域对未来临床实践具有前景。在开发这些新技术时，质量应是首要考虑因素。
