Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Radiological Sciences, Emory University School of Medicine, Atlanta, GA.
Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD.
Semin Nucl Med. 2022 Nov;52(6):662-672. doi: 10.1053/j.semnuclmed.2022.04.001. Epub 2022 May 29.
Treatment response assessment in lung cancer is crucial in the management strategy and outcome of patients. Accurate treatment response assessment can guide the treating physicians and improve patient survival. Anatomic and metabolic tumor response assessments have been evaluated extensively, showing a positive impact in the management of these patients. F-FDG PET/CT provides early and more specific treatment response assessments, preceding anatomic changes in these tumors. Familiarity with the different treatment response assessment algorithms, criteria, time intervals, imaging pitfalls is essential for treating physicians and nuclear radiologists to provide accurate response assessments. Artificial Intelligence is being more frequently explored for this purpose and can assist physicians in providing prompt and accurate treatment response assessments.
肺癌的治疗反应评估在患者的治疗策略和预后中至关重要。准确的治疗反应评估可以指导治疗医生并提高患者的生存率。已经广泛评估了解剖学和代谢肿瘤反应评估，显示出对这些患者管理的积极影响。F-FDG PET/CT 提供了更早和更具体的治疗反应评估，先于这些肿瘤的解剖变化。熟悉不同的治疗反应评估算法、标准、时间间隔和成像陷阱对于治疗医生和核放射科医生提供准确的反应评估至关重要。人工智能正被更频繁地用于此目的，并可以帮助医生提供及时和准确的治疗反应评估。
