Diagnostic Imaging Unit, Department of Medico-Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, ICOT Hospital, Via Franco Faggiana, 166804100, Latina, Italy.
Department of Radiology, Fondazione I.R.C.C.S. Policlinico San Matteo, Viale Camillo Golgi, 19, 27100, Pavia, Italy.
Radiol Med. 2022 Aug;127(8):819-836. doi: 10.1007/s11547-022-01512-6. Epub 2022 Jun 30.
The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.
人工智能 (AI) 和放射组学在医疗保健领域的应用，旨在推进疾病诊断和管理，并促进新疗法的研发，正日益受到关注。鉴于在癌症治疗过程中收集了大量的数据，人们非常关注利用现有的算法和技术，以改善肿瘤学护理为根本目标。随着 AI 技术的出现，放射科医生将获得更高的精度和效果，使机器辅助医疗服务成为未来肿瘤学医疗保健的有价值和重要选择。因此，至关重要的是要确定哪些特定的放射学活动最有潜力从 AI 和放射组学模型以及肿瘤成像方法中受益，同时还要考虑算法的能力和限制。我们的目的是综述 AI 和放射组学算法在肿瘤成像研究中的现有证据和未来前景，重点关注全球最常见的三种癌症，即肺癌、乳腺癌和结直肠癌。我们讨论了 AI 和放射组学如何用于检测和特征化癌症，并评估治疗反应。
