Analysis of clinical features of large-cell neuroendocrine carcinoma patients guided by chest CT image under deep learning

Abstract

This work aimed to explore chest computed tomography (CT) image segmentation of patients with large-cell neuroendocrine carcinoma (LCNEC) based on deep learning, as well as the clinical manifestations and imaging and pathological features of LCNEC patients. Clinical data of 40 patients with LCNEC confirmed by pathological examination in the X Hospital from December 2015 to December 2017 were retrospectively selected. CT image data were segmented by TJ-1 model modified full convolutional neural network (FCNN) model. The accuracy and training time of TJ-1 FCNN model and classic deep learning segmentation network model AlexNet model were compared in terms of image segmentation. According to the image segmentation results by TJ-1 FCNN model, chest CT images of LCNEC patients, were reviewed, and the clinical manifestations, as well as the imaging and pathological features of the patients were reviewed, sorted, and summarized. The results showed that the image segmentation accuracy of TJ-1 network model (99.38%) was higher than that of AlexNet model. The iteration training time of TJ-1 network model for 30 times was 45 min, lower than that of AlexNet model (82 min). LCNEC was more likely to be found in elderly male with a long history of smoking. The clinical symptoms were cough, sputum, sputum blood, and chest pain with no significant specificity. CT imaging showed that peripheral mass was the most common manifestation (67.5%), both lungs were visible, the upper lobe was more likely with lesion (60%), the edge of the lesion was clear or smooth (57.5%), which was lobulated (70%). Under the light microscope, tumor cells were characterized by large volume, low nucleocytoplasmic ratio, high mitosis, and large area necrosis. The positive rates of immunohistochemical neuroendocrine markers were CD56 (62.5%), CgA (50%), and Syn (85%), among which Syn was with the highest positive rate. To sum up, LCNEC lacked clinical and radiological specificity manifestations, while chest CT image segmentation based on TJ-1 FCNN model can quickly mark the location of the lesion, providing technical support for the diagnosis and evaluation of LCNEC clinically.