Lung cancer is the most common cancer after breast and colon cancer, with high rates of mortality, worldwide. There are two main types of lung cancer, small cell lung carcinoma (SCLC), which accounts for approximately 20% of all lung cancer cases and non-SCLC, which accounts for almost 80% of lung cancer cases. Although lung cancer is one of the most aggressive types of cancer, progress in achieving better clinical outcomes has been gradual. Even though a number of markers have been suggested for the diagnosis of lung cancer and monitoring of disease progression, there is no clear way of assessing the invasion, epithelial-mesenchymal transition (EMT) and metastasizing capability of the primary tumor cells. We investigated the incidence of cytokeratin 19 (CK19)-negative expressers in different types of lung cancer from 111 lung cancer patients, their serum and pleural effusion CYFRA21-1 levels and whether induction of EMT in the primary focus cells influences the expression of CK19. In addition, we examined whether CK19-negative lung cancers were more invasive and metastatic. We also examined the propensity of primary focus cells to undergo EMT in the presence of transforming growth factor-β1 (TGF-β1). The results obtained suggested that the invasion and metastasis of lung tumor cells can be assessed by having a complete picture of serum CYFRA21-1 together with the CK19 expression status of primary focus cells and pleural effusion. This assessment may be further improved by examining the propensity of the isolated primary focus cells to undergo TGF-β1 induced EMT in cell culture.
Background: To those patients with advanced lung cancer, the ultimate objective is to improve the quality of life, and lung function is an important factor affecting quality of life. We detect lung function of patients with lung cancer and study the correlation between lung function and the patients' survival time, to provide reference for evaluation of disease progression and prognosis. Methods: Lung function was detected on 59 cases of lung cancer and 63 normal controls. The relationship between lung function indexes and survival time was analyzed. Results: There was significant difference in ventilation function and diffusing capacity between lung cancer group and control group. Vital capacity (VC), Forced expiratory volume in one second (FEV1), Forced vital capacity (FVC), peak expiratory flow (PEF), peak expiratory flow% (PEF%), Maximal ventilatory volume (MVV) were positively correlated to survival time in patients with advanced lung cancer (r = 0.28522064, 0.28053851, 0.28289252, 0.26908133, 0.26335034, 0.28409036, P < 0.05), residual volume/total lung capacity was negatively correlated to survival time (r = −0.30760097, P < 0.05). Conclusions: The lung function decrease in the patients with lung cancer. Vital capacity (VC), Forced expiratory volume in one second (FEV1), Forced vital capacity (FVC), peak expiratory flow (PEF), peak expiratory flow% (PEF%), Maximal ventilatory volume (MVV), and residual volume/total lung capacity are correlated to survival time in patients with advanced lung cancer. The lung function indexes are important marker of prognosis of patients with lung cancer.
Aimed to automate the segmentation of organs at risk (OARs) in head and neck (H&N) cancer radiotherapy, we develop a novel Prior Attention enhanced convolutional neural Network (PANet) based Stepwise Refinement Segmentation Framework (SRSF) on full-size computed tomography (CT) images. The SRSF is built with a multiscale segmentation concept, in which OARs are segmented from coarse to fine. PANet is a pyramidal architecture with elements of inception block and prior attention. In this study, the developed PANet based SRSF is applied for OARs segmentation in H&N radiotherapy. 139 CT series and manually delineated contours of twenty-two OARs by experienced oncologists are collected from 139 H&N patients for training and evaluating the proposed PANet based SRSF. The mean testing Dice similarity coefficients (DSC) on 39 CT series range from 76.1±8.3% (left middle ear) to 91.9±1.4% (right mandible) for large volume OARs(mean volume>1cc) while the corresponding ranges are 63.4±12.3%(chiasm) to 81.0±14.1% (right lens) for small and challenging OARs(mean volume1cc). Furthermore, the proposed method also achieved superior segmentations over reference methods on the MICCAI 2015 H&N dataset with mean DSC of 95.6±0.7%, 81.3±4.0%, 77.6±4.5%, 77.5±4.6%, and 69.2±7.6%, on the mandible, left submandibular, left and right optical nerve, and chiasm, respectively. The accurate segmentation of OARs is obtained on both the self-collected testing data and public testing dataset, which implies that the proposed method can be used as a practicable and efficient tool for automated OARs contouring in the H&N cancer radiotherapy.
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