Lise Lecointre1, Jérémy Dana2, Massimo Lodi3, Chérif Akladios3, Benoît Gallix4. 1. 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. Electronic address: lise.lecointre@chru-strasbourg.fr. 2. 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. 3. Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. 4. 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; Department of Diagnostic Radiology, McGill University, Montreal, Canada.
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.
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.
Authors: Jérémy Dana; Aïna Venkatasamy; Antonio Saviano; Joachim Lupberger; Yujin Hoshida; Valérie Vilgrain; Pierre Nahon; Caroline Reinhold; Benoit Gallix; Thomas F Baumert Journal: Hepatol Int Date: 2022-02-09 Impact factor: 9.029