Żak Klaudia, Zaremba Bartłomiej, Rajtak Alicja, Kotarski Jan, Amant Frédéric, Bobiński Marcin
I Chair and Department of Oncological Gynaecology and Gynaecology, Student Scientific Association, Medical University of Lublin, 20-059 Lublin, Poland.
I Chair and Department of Oncological Gynaecology and Gynaecology, Medical University of Lublin, 20-059 Lublin, Poland.
Cancers (Basel). 2022 Apr 13;14(8):1966. doi: 10.3390/cancers14081966.
The distinguishing of uterine leiomyosarcomas (ULMS) and uterine leiomyomas (ULM) before the operation and histopathological evaluation of tissue is one of the current challenges for clinicians and researchers. Recently, a few new and innovative methods have been developed. However, researchers are trying to create different scales analyzing available parameters and to combine them with imaging methods with the aim of ULMs and ULM preoperative differentiation ULMs and ULM. Moreover, it has been observed that the technology, meaning machine learning models and artificial intelligence (AI), is entering the world of medicine, including gynecology. Therefore, we can predict the diagnosis not only through symptoms, laboratory tests or imaging methods, but also, we can base it on AI. What is the best option to differentiate ULM and ULMS preoperatively? In our review, we focus on the possible methods to diagnose uterine lesions effectively, including clinical signs and symptoms, laboratory tests, imaging methods, molecular aspects, available scales, and AI. In addition, considering costs and availability, we list the most promising methods to be implemented and investigated on a larger scale.
在手术前以及对组织进行组织病理学评估之前区分子宫平滑肌肉瘤(ULMS)和子宫平滑肌瘤(ULM),是临床医生和研究人员当前面临的挑战之一。最近,已经开发出了一些新的创新方法。然而,研究人员正在尝试创建不同的量表来分析可用参数,并将它们与成像方法相结合,目的是对子宫平滑肌肉瘤和子宫平滑肌瘤进行术前鉴别。此外,人们已经注意到,机器学习模型和人工智能(AI)等技术正在进入医学领域,包括妇科。因此,我们不仅可以通过症状、实验室检查或成像方法来预测诊断,还可以基于人工智能进行诊断。术前区分子宫平滑肌瘤和子宫平滑肌肉瘤的最佳选择是什么?在我们的综述中,我们重点关注有效诊断子宫病变的可能方法,包括临床体征和症状、实验室检查、成像方法、分子层面、可用量表以及人工智能。此外,考虑到成本和可及性,我们列出了最有前景的方法,以便在更大规模上实施和研究。