scholarly journals P8‐30: Usefulness of pulmonary rehabilitation in non‐small cell lung cancer patients based on pulmonary function tests and muscle index analysis using computed tomography images

Respirology ◽  
2021 ◽  
Vol 26 (S3) ◽  
pp. 298-299
2012 ◽  
Vol 30 (15_suppl) ◽  
pp. TPS9151-TPS9151
Author(s):  
Narongwit Nakwan ◽  
Sarayut Lucien Geater

TPS9151 Background: Although chronic obstructive pulmonary disease (COPD) and lung cancer share a common risk factor, namely smoking, health care providers usually overlook the symptom of COPD in the management of lung cancer. Should, then, lung cancer patients undergo pulmonary function tests (PFT) to identify the presence of COPD? Our study was performed to describe the results of pulmonary function tests and define risk factors for COPD in non-small cell lung cancer (NSCLC) patients. Methods: A total of 31 eligible patients with NSCLC but no obvious symptoms of COPD participated. We collected baseline characteristics and conducted a detailed assessment of pulmonary function, particularly spirometry, lung volume measurement, and diffusing capacity of carbon monoxide (DLCO). Dyspnea was assessed using modified Borg (mBorg) and modified Medical Research Council (mMRC) scores. Results: Twelve patients had airflow limitation (FEV1/FVC<0.7). These patients had mean percent predicted FEV1, RV and DLCO of 52%, 143% and 66% respectively, and a mean RV/TLC of 0.55. Being male, and having a smoking history, low body mass index and squamous cell carcinoma were significantly associated with obstruction in univariate analysis. However, obstruction was not more common in advanced stage than in locally advanced NSCLC. Neither mBorg nor mMRC differed between obstructive and non-obstructive groups. Discussion: COPD was found in patients with NSCLC who had never been diagnosed as, or showed symptoms of, COPD. Male, smoking history, low BMI and squamous cell carcinoma were significantly associated with obstruction.


2021 ◽  
Vol 59 (2) ◽  
pp. 240-246
Author(s):  
Hirohisa Kano ◽  
Toshio Kubo ◽  
Kiichiro Ninomiya ◽  
Eiki Ichihara ◽  
Kadoaki Ohashi ◽  
...  

CHEST Journal ◽  
2009 ◽  
Vol 135 (6) ◽  
pp. 1588-1595 ◽  
Author(s):  
M. Patricia Rivera ◽  
Frank C. Detterbeck ◽  
Mark A. Socinski ◽  
Dominic T. Moore ◽  
Martin J. Edelman ◽  
...  

2020 ◽  
Vol 19 ◽  
pp. 153303382094748
Author(s):  
Fuli Zhang ◽  
Qiusheng Wang ◽  
Haipeng Li

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.


Sign in / Sign up

Export Citation Format

Share Document