Your browser doesn't support javascript.
loading
Robust imaging approach for precise prediction of postoperative lung function in lung cancer patients prior to curative operation.
Kim, Suho; Kim, Jonghoon; Jeong, Uichan; Oh, You Jin; Park, Sung Goo; Lee, Ho Yun.
Afiliación
  • Kim S; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Kim J; Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
  • Jeong U; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Oh YJ; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Park SG; Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
  • Lee HY; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Thorac Cancer ; 15(1): 35-43, 2024 Jan.
Article en En | MEDLINE | ID: mdl-37967873
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 (FEV1 ; both 0.89, p < 0.05). The highest ICC value when using the combined variable method was for FEV1 /FVC (0.75, p < 0.05) and diffusing capacity of the lung for carbon monoxide (DLco; 0.80, p < 0.05) when using the perfusion map method. Overall, the perfusion map and ventilation map provided the best performance, followed by volume analysis, segment counting, perfusion scan, and the combined variable. 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 FEV1 .
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Thorac Cancer Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Singapur
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pulmonares Límite: Humans Idioma: En Revista: Thorac Cancer Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Singapur