Grassi Francesca, Granata Vincenza, Fusco Roberta, De Muzio Federica, Cutolo Carmen, Gabelloni Michela, Borgheresi Alessandra, Danti Ginevra, Picone Carmine, Giovagnoni Andrea, Miele Vittorio, Gandolfo Nicoletta, Barile Antonio, Nardone Valerio, Grassi Roberta
Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy.
Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy.
J Clin Med. 2023 Feb 10;12(4):1442. doi: 10.3390/jcm12041442.
The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.
原发性和继发性肺部肿瘤的治疗如今凸显了放射治疗的基础作用，它与手术及全身治疗相结合。生存结果的改善也使人们更加关注生活质量、治疗依从性以及副作用的管理。影像学的作用不仅限于识别治疗效果，还在于尽早发现罕见的效应，尤其是在联合使用化疗、免疫治疗和放疗等多种治疗方法时。放射性肺炎是一种罕见的治疗并发症，应正确加以识别，认识放射性肺炎发病机制及诊断特征对于及时识别并采取最佳治疗策略至关重要，同时要尽可能缩短当前肿瘤药物的停用时间。在这种情况下，人工智能可能发挥关键作用，尽管需要更大的患者数据集。
