Remotely shared CT-derived presurgical understanding of lung cancer: A randomized trial

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2823-2828
Number of pages6
JournalThoracic Cancer
Volume13
Issue number19
DOIs
StatePublished - Oct 2022
Externally publishedYes

Keywords

  • 3D understanding
  • computed tomography
  • lung cancer

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