[Progress of Lung Cancer Screening with Low Dose Helical Computed Tomography]

Application effect of low-dose spiral CT on pulmonary nodules and its diagnostic value for benign and malignant nodules

OBJECTIVE

This study was designed to determine the application effect of low-dose computed tomography (LDCT) on detecting pulmonary nodules (PNs) and its diagnostic value for benign and malignant pulmonary nodules.

METHODS

Data of 432 patients with PNs admitted to Julu County Hospital between March 2018 and June 2021 in were collected and analysed retrospectively. All patients underwent LDCT and conventional-dose spiral computed tomography (CT). The detection rate and image characteristics of the two methods were compared, and the image quality and radiation dose of the two diagnostic methods were also compared.

RESULTS

No significant difference was found between LDCT and conventional-dose spiral CT in the detection rate of lung cancer (P>0.05). The area under the curve of conventional-dose CT was 0.932, with a specificity and sensitivity of 93.87% and 92.45%, and the area under the curve of LDCT was 0.902, with a specificity and sensitivity of 90.80% and 89.62%. The radiation dose consumed during LDCT was greatly less than that consumed by conventional-dose CT (P<0.05). Additionally, the two methods were not different in CT image quality and superior vena cava artifact (P>0.05). No notable difference was found between LDCT and conventional-dose CT in terms of the diagnosis rate of PNs in vascular aggregation sign, pleural indentation sign, lobulation sign and spiculation sign.

CONCLUSION

LDCT can clearly show the typical images of early lung cancer, with less effective radiation dose, and can thus contribute to a high detection rate, so it is worth popularizing.

[Low-dose Spiral Computed Tomography in Lung Cancer Screening]

Lung cancer is one of the malignant tumors with the highest morbidity and mortality in the world. The low early diagnosis rate and poor prognosis of patients have caused serious social burden. Regular screening of high-risk population by low-dose spiral computed tomography (LDCT) can significantly improve the early diagnosis rate of lung cancer and bring new opportunities for the diagnosis and treatment of lung cancer. In recent years, LDCT lung cancer screening programs have been carried out in many countries around the world and achieved good results, but there are still some controversies in the selection of screening subjects, screening frequency, cost effectiveness and other aspects. In this paper, the key factors of LDCT lung cancer screening, screening effect, pulmonary nodule management and artificial intelligence contribution to the development of LDCT will be reviewed, and the application progress of LDCT in lung cancer screening will be discussed.
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Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P < 0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P < 0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P < 0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P < 0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.