Objective: To evaluate the lung CT scan as a possible predictive diagnostic method for COVID-19 in the Cameroonian context. Methods: We designed a cross sectional study. Suspected cases of COVID-19 during the first wa...Objective: To evaluate the lung CT scan as a possible predictive diagnostic method for COVID-19 in the Cameroonian context. Methods: We designed a cross sectional study. Suspected cases of COVID-19 during the first wave at the national social insurance fund (NSIF) hospital were screened with both COVID-19 with lung CT scan and a PCR test. Univariate analysis was performed for sample description and multivariate analysis to assess the correlation between positive results for the PCR and other parameters. We estimated the optimum threshold of sensitivity/specificity, and area under curve using the empirical method and package. Results: A total of 62 suspected COVID-19 cases were recorded, predominantly males (Sex Ratio = 2.2) with a median age of 58.5 (IQR = 19.7). Among our 62 patients, 29 (46.8%) were confirmed COVID-19 cases with positive PCR results. All the patients had a thorax CT scan with a median impairment of 40% (IQR = 20%). The optimum threshold estimate for CT scan for COVID-19 infection diagnosis was 60% (95% CI = 25% - 80%). Overall, the sensitivity and specificity estimates were 0.30 (95% CI = 0.15 - 0.49) and 0.87 (95% CI = 0.70 - 0.96), respectively, leading to an Area Under Curve (AUC) estimate of 0.59 (95% CI = 0.46, 0.71). Conclusion: In this setting, lung CT scan was neither sensitive nor specific to predict COVID-19 disease.展开更多
Background: Computed tomography (CT) and bronchoscopy have been shown to improve the detection rates of peripheral and central lung cancers (LC), respectively. However, the performance of the combination of CT and bro...Background: Computed tomography (CT) and bronchoscopy have been shown to improve the detection rates of peripheral and central lung cancers (LC), respectively. However, the performance of the combination of CT and bronchoscopy in detecting LC, in high-risk patients, is not clear. Patients & Methods: This prospective study included 205 high-risk patients with a history of at least 2 of the following risk factors: (1) heavy smoking;(2) aero-digestive cancer;(3) pulmonary asbestosis or;(4) chronic obstructive pulmonary disease. Patients were offered chest X-ray, sputum cytology, conventional white-light followed by autofluorescence beonchoscopy (WL/AFB) and low-dose spiral CT both at baseline and follow-up visits. Results: Seven patients (3.4%) were diagnosed with LC or carcinoma in-situ (CIS) at baseline: CT evaluation detected 5 LC/CIS, while WL/AFB evaluation also identified 5 LC/CIS, 2 of which were not detected on CT. Six (85%) of these baseline lesions were early stage (0/IA). The relative-sensitivity of CT with WL/ AFB was 40% better than CT alone. On four year follow-up, 20 patients (9.8%) were diagnosed with an LC/CIS. CT with WL/AFB detected 19 cases (95%), whereas CT alone detected 15 cases (75%). Conclusion: Bimodality surveillance with spiral CT and WL/AFB can improve the detection of early stage LCs among high-risk展开更多
Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system,...Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system, pattern recognition technique, backpropagation algorithm, etc. Recently, convolutional neural network (CNN) finds promising applications in many areas. In this research, we investigated 3D CNN to detect early lung cancer using LUNA 16 dataset. At first, we preprocessed raw image using thresholding technique. Then we used Vanilla 3D CNN classifier to determine whether the image is cancerous or non-cancerous. The experimental results show that the proposed method can achieve a detection accuracy of about 80% and it is a satisfactory performance compared to the existing technique.展开更多
文摘Objective: To evaluate the lung CT scan as a possible predictive diagnostic method for COVID-19 in the Cameroonian context. Methods: We designed a cross sectional study. Suspected cases of COVID-19 during the first wave at the national social insurance fund (NSIF) hospital were screened with both COVID-19 with lung CT scan and a PCR test. Univariate analysis was performed for sample description and multivariate analysis to assess the correlation between positive results for the PCR and other parameters. We estimated the optimum threshold of sensitivity/specificity, and area under curve using the empirical method and package. Results: A total of 62 suspected COVID-19 cases were recorded, predominantly males (Sex Ratio = 2.2) with a median age of 58.5 (IQR = 19.7). Among our 62 patients, 29 (46.8%) were confirmed COVID-19 cases with positive PCR results. All the patients had a thorax CT scan with a median impairment of 40% (IQR = 20%). The optimum threshold estimate for CT scan for COVID-19 infection diagnosis was 60% (95% CI = 25% - 80%). Overall, the sensitivity and specificity estimates were 0.30 (95% CI = 0.15 - 0.49) and 0.87 (95% CI = 0.70 - 0.96), respectively, leading to an Area Under Curve (AUC) estimate of 0.59 (95% CI = 0.46, 0.71). Conclusion: In this setting, lung CT scan was neither sensitive nor specific to predict COVID-19 disease.
文摘Background: Computed tomography (CT) and bronchoscopy have been shown to improve the detection rates of peripheral and central lung cancers (LC), respectively. However, the performance of the combination of CT and bronchoscopy in detecting LC, in high-risk patients, is not clear. Patients & Methods: This prospective study included 205 high-risk patients with a history of at least 2 of the following risk factors: (1) heavy smoking;(2) aero-digestive cancer;(3) pulmonary asbestosis or;(4) chronic obstructive pulmonary disease. Patients were offered chest X-ray, sputum cytology, conventional white-light followed by autofluorescence beonchoscopy (WL/AFB) and low-dose spiral CT both at baseline and follow-up visits. Results: Seven patients (3.4%) were diagnosed with LC or carcinoma in-situ (CIS) at baseline: CT evaluation detected 5 LC/CIS, while WL/AFB evaluation also identified 5 LC/CIS, 2 of which were not detected on CT. Six (85%) of these baseline lesions were early stage (0/IA). The relative-sensitivity of CT with WL/ AFB was 40% better than CT alone. On four year follow-up, 20 patients (9.8%) were diagnosed with an LC/CIS. CT with WL/AFB detected 19 cases (95%), whereas CT alone detected 15 cases (75%). Conclusion: Bimodality surveillance with spiral CT and WL/AFB can improve the detection of early stage LCs among high-risk
文摘Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system, pattern recognition technique, backpropagation algorithm, etc. Recently, convolutional neural network (CNN) finds promising applications in many areas. In this research, we investigated 3D CNN to detect early lung cancer using LUNA 16 dataset. At first, we preprocessed raw image using thresholding technique. Then we used Vanilla 3D CNN classifier to determine whether the image is cancerous or non-cancerous. The experimental results show that the proposed method can achieve a detection accuracy of about 80% and it is a satisfactory performance compared to the existing technique.
