Department of Otorhinolaryngology-Head and Neck Surgery, Malatya Training Research Hospital, Malatya, Turkey.
Department of Electric and Electronics Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University.
Curr Opin Otolaryngol Head Neck Surg. 2022 Apr 1;30(2):107-113. doi: 10.1097/MOO.0000000000000782.
Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed.
The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy.
All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.
计算机技术的进步和人们对计算机辅助系统的期望不断提高，推动人工智能发展成为深度学习和放射组学等子集，这些系统的应用正在彻底改变现代放射诊断。本文综述了基于放射组学和深度学习方法在腮腺肿瘤（PGT）鉴别诊断中开发的人工智能应用。
人工智能模型的发展开辟了新的场景，因为它有可能评估通常不受医师评估的医学图像特征。放射组学和深度学习模型在医学图像的计算机辅助诊断中处于前沿地位，尽管由于与这些罕见肿瘤相关的数据集中缺乏数据，其在 PGT 鉴别诊断中的应用受到限制。然而，最近的研究表明，人工智能工具可以合理准确地对常见的 PGT 进行分类。
本文确定了所有旨在鉴别诊断良性与恶性 PGT 或识别最常见 PGT 亚型的研究，发现了 5 项专注于基于深度学习的 PGT 鉴别诊断的研究。其中 3 项研究使用 MRI 创建了数据集，2 项研究使用 CT。另外 7 项研究与放射组学有关。其中，4 项是基于 MRI 的放射组学，2 项是基于 CT 的放射组学，1 项是在同一患者中比较基于 MRI 和 CT 的放射组学。
