Department of Radiology, Seoul National College of Medicine, Seoul National University Hospital, Seoul, Korea.
Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Thorac Cancer. 2022 Oct;13(19):2823-2828. doi: 10.1111/1759-7714.14637. Epub 2022 Sep 2.
Shared decision-making is imperative for patient-and family-centered care. However, gathering individuals in a single place was challenged by modern life and pandemic restrictions. This study conducted a 1:1 randomized trial to examine the feasibility of a CT-derived 3D virtual explanation module for lung cancer to improve the understanding of patients and third parties in physically separate locations. We prospectively enrolled adults in whom elective surgical resection for lung cancer was planned at a single tertiary hospital in 2020. From presurgical CT scans, deep neural networks automatically segmented lung cancer, airway, pulmonary lobes, skin, and bony thorax. The segmented structures were subsequently transformed into an anonymized interactive 3D module which comprised a standardized scenario with explanatory texts. The intervention group received a link to the module on their smartphone before admission and could repeatedly access the link or transfer it to patients' third parties. A total of 33 and 29 patients were enrolled in the intervention and control arms. The understanding score did not statistically differ between the arms (mean difference, 0.7 [95% CI: -0.2, 1.5]; p = 0.13). However, 76% of patients in the intervention arm accessed the link, and patient median access count was 14. The link recipients of third parties had comparable understanding scores to the patients (mean difference, -0.2 [95% CI: -1.9, 1.5]; p = 1.00), indicating that the understanding could be shared remotely with patients and patients' third parties. In conclusion, it was feasible that people physically separated from patients obtained a comparable understanding of lung cancer surgery using the patient's CT-derived 3D virtual explanation module.
共同决策对于以患者和家庭为中心的护理至关重要。然而，现代生活和大流行限制使聚集个人在一个地方面临挑战。本研究进行了一项 1:1 随机试验，以检查用于肺癌的 CT 衍生 3D 虚拟解释模块是否可以改善身体上分开的患者和第三方的理解。我们前瞻性地招募了 2020 年在一家三级医院计划接受肺癌择期手术的成年人。从术前 CT 扫描中，深度神经网络自动分割肺癌、气道、肺叶、皮肤和骨性胸廓。分割结构随后转换为匿名交互式 3D 模块，其中包含具有说明性文本的标准化场景。干预组在入院前在智能手机上收到模块的链接，并且可以重复访问该链接或将其转发给患者的第三方。共有 33 名和 29 名患者分别入组干预组和对照组。手臂之间的理解评分没有统计学差异（平均差异，0.7 [95%CI：-0.2, 1.5]；p=0.13）。然而，干预组的 76%的患者访问了该链接，并且患者的中位数访问次数为 14 次。第三方的链接接收者与患者具有可比的理解分数（平均差异，-0.2 [95%CI：-1.9, 1.5]；p=1.00），表明可以与患者和患者的第三方远程共享理解。总之，使用患者的 CT 衍生 3D 虚拟解释模块，身体上与患者分开的人可以获得对肺癌手术的可比理解，这是可行的。
