Small cell lung cancer(SCLC) represents a group of highly malignant tumors that give rise to early and widespread metastases at the time of diagnosis.The preferential metastatic sites are the brain,liver,adrenal gland...Small cell lung cancer(SCLC) represents a group of highly malignant tumors that give rise to early and widespread metastases at the time of diagnosis.The preferential metastatic sites are the brain,liver,adrenal glands,bone,and bone marrow.However,metastases of the gastrointestinal system,especially the stomach,are rare; most cases of stomach metastasis are asymptomatic and,as a result,are usually only discovered at autopsy.We report a case of gastric metastasis originating from SCLC.The patient was a 66-year-old man admitted to our hospital due to abdominal pain.He underwent gastroscopy,with the pathological report of the tissue biopsy proving it to be a small cell cancer.Immunohistochemistry was positive for CD56,synaptophysin,and pan-cytokeratin.These results confirmed the diagnosis of gastric metastasis of a neuroendocrine small cell carcinoma from the lung.展开更多
AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN ...AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.展开更多
基金Supported by Shandong Cancer Hospital and Institute,Jinan,Shandong Province,China
文摘Small cell lung cancer(SCLC) represents a group of highly malignant tumors that give rise to early and widespread metastases at the time of diagnosis.The preferential metastatic sites are the brain,liver,adrenal glands,bone,and bone marrow.However,metastases of the gastrointestinal system,especially the stomach,are rare; most cases of stomach metastasis are asymptomatic and,as a result,are usually only discovered at autopsy.We report a case of gastric metastasis originating from SCLC.The patient was a 66-year-old man admitted to our hospital due to abdominal pain.He underwent gastroscopy,with the pathological report of the tissue biopsy proving it to be a small cell cancer.Immunohistochemistry was positive for CD56,synaptophysin,and pan-cytokeratin.These results confirmed the diagnosis of gastric metastasis of a neuroendocrine small cell carcinoma from the lung.
基金Supported by Hunan Provincial Science and Technology Department Clinical Medical Technology Innovation Guidance Project(No.2021SK50103)。
文摘AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.
