Pulmonary lobectomy, which consists of the partial or complete resection of a lung lobe, is the gold standard intervention for lung cancer removal. The removal of functional tissue during the surgery and the re-adaptation of the remaining thoracic structures decrease the patient's post-operative pulmonary function. Residual functionality is evaluated through pulmonary function tests, which account for the number of resected segments without considering local structural alterations and provide an average at-the-mouth estimation. Computational Fluid Dynamics (CFD) has been demonstrated to provide patient-specific, quantitative, and local information about airways airflow dynamics. A CFD investigation was performed on image-based airway trees reconstructed before and after the surgery for twelve patients who underwent lobectomy at different lobes. The geometrical alterations and the variations in fluid dynamics parameters and in lobar ventilation between the pre and post-operative conditions were evaluated. The post-operative function was estimated and compared with current clinical algorithms and with actual clinical data. The post-operative configuration revealed a high intersubject variability: regardless of the lobectomy site, an increment of global velocity, wall pressure, and wall shear stress was observed. Local flow disturbances also emerged at, and downstream of, the resection site. The analysis of lobar ventilation showed severe variations in the volume flow rate distribution, highlighting the compensatory effects in the contralateral lung with an increment of inflow. The estimation of post-operative function through CFD was comparable with the current clinical algorithm and the actual spirometric measurements. The results confirmed that CFD could provide additional information to support the current clinical approaches both in the operability assessment and in the prescription of personalized respiratory rehabilitation.

Functional analysis of the airways after pulmonary lobectomy through computational fluid dynamics

Aliboni L.;Tullio M.;Pennati F.;Lo Mauro A.;Aliverti A.
2022-01-01

Abstract

Pulmonary lobectomy, which consists of the partial or complete resection of a lung lobe, is the gold standard intervention for lung cancer removal. The removal of functional tissue during the surgery and the re-adaptation of the remaining thoracic structures decrease the patient's post-operative pulmonary function. Residual functionality is evaluated through pulmonary function tests, which account for the number of resected segments without considering local structural alterations and provide an average at-the-mouth estimation. Computational Fluid Dynamics (CFD) has been demonstrated to provide patient-specific, quantitative, and local information about airways airflow dynamics. A CFD investigation was performed on image-based airway trees reconstructed before and after the surgery for twelve patients who underwent lobectomy at different lobes. The geometrical alterations and the variations in fluid dynamics parameters and in lobar ventilation between the pre and post-operative conditions were evaluated. The post-operative function was estimated and compared with current clinical algorithms and with actual clinical data. The post-operative configuration revealed a high intersubject variability: regardless of the lobectomy site, an increment of global velocity, wall pressure, and wall shear stress was observed. Local flow disturbances also emerged at, and downstream of, the resection site. The analysis of lobar ventilation showed severe variations in the volume flow rate distribution, highlighting the compensatory effects in the contralateral lung with an increment of inflow. The estimation of post-operative function through CFD was comparable with the current clinical algorithm and the actual spirometric measurements. The results confirmed that CFD could provide additional information to support the current clinical approaches both in the operability assessment and in the prescription of personalized respiratory rehabilitation.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1206801
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