Artificial intelligence-based radiomics models in endometrial cancer: A systematic review. Issue 11 (November 2021)

• Record Type: Journal Article
• Authors: Lecointre, Lise; Dana, Jérémy; Lodi, Massimo; Akladios, Chérif; Gallix, Benoît
• Is Part Of: European journal of surgical oncology. Volume 47:Issue 11(2021)
• Page Start: 2734
• Page End: 2741
• Publication Date: 2021-11
• Subjects: Endometrial carcinoma -- Imaging -- Radiomics -- Machine learning -- Deep learning -- Artificial intelligence; Oncology -- Periodicals; Cancer -- Surgery -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- surgery -- Periodicals; Cancer -- Chirurgie -- Périodiques; Cancérologie -- Périodiques; Oncologie; Chirurgie (geneeskunde); Electronic journals; Electronic journals -- Sciences; Electronic journals -- Medicine; Electronic journals; 616.994059005
• DOI: 10.1016/j.ejso.2021.06.023
• Languages: English
• ISSNs: 0748-7983

Abstract

Abstract: Background: 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. Aims: 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. Methods: 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. Results: 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]). Conclusion: 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. Highlights: Current preoperative staging may be inaccurate and underestimate disease extension. Radiomics may improve preoperative radiological assessment of endometrial carcinoma. Radiomics models should follow high-quality standards to ensure generalizability. Evidence of benefit of these model remains insufficient to be part of clinical practice.