Article ; Online: Robust imaging approach for precise prediction of postoperative lung function in lung cancer patients prior to curative operation.
2023 Volume 15, Issue 1, Page(s) 35–43
Abstract: Background: To create a combined variable integrating both ventilation and perfusion as measured by preoperative dual-energy computed tomography (DECT), compare the results with predicted postoperative (PPO) lung function as estimated using conventional ...
Abstract | Background: To create a combined variable integrating both ventilation and perfusion as measured by preoperative dual-energy computed tomography (DECT), compare the results with predicted postoperative (PPO) lung function as estimated using conventional methods, and assess agreement with actual postoperative lung function. Methods: A total of 33 patients with lung cancer who underwent curative surgery after DECT and perfusion scan were selected. Ventilation and perfusion values were generated from DECT data. In the "combined variable method," these two variables and clinical variables were linearly regressed to estimate PPO lung function. Six PPO lung function parameters (segment counting, perfusion scan, volume analysis, ventilation map, perfusion map, and combined variable) were compared with actual postoperative lung function using an intraclass correlation coefficient (ICC). Results: The segment counting method produced the highest ICC for forced vital capacity (FVC) at 0.93 (p < 0.05), while the segment counting and perfusion map methods produced the highest ICC for forced expiratory volume in 1 second (FEV Conclusions: Use of DECT image processing to predict postoperative lung function produced better agreement with actual postoperative lung function than conventional methods. The combined variable method produced ICC values of 0.8 or greater for FVC and FEV |
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MeSH term(s) | Humans ; Lung Neoplasms/diagnostic imaging ; Lung Neoplasms/surgery ; Lung/diagnostic imaging ; Lung/surgery ; Forced Expiratory Volume ; Respiratory Function Tests ; Vital Capacity ; Pneumonectomy |
Language | English |
Publishing date | 2023-11-15 |
Publishing country | Singapore |
Document type | Journal Article |
ZDB-ID | 2625856-0 |
ISSN | 1759-7714 ; 1759-7706 |
ISSN (online) | 1759-7714 |
ISSN | 1759-7706 |
DOI | 10.1111/1759-7714.15153 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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