Abstract
This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) (P < 0.05). The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) (P < 0.05). Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time (P < 0.05). The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.
Highlights
Lung cancer, a malignant tumor, mainly originated from the mucosal epithelium of the bronchus
The traditional boundary tracking algorithm needed to estimate the number of repeated tracking in advance
After obtaining the image contour, it needed to carefully judge whether there were omissions in some concave-convex parts manually, and the segmentation effect was not ideal. e target boundary tracking algorithm in this study can effectively avoid the omission of concave-convex parts and can accurately locate the contour of the target image at one time
Summary
A malignant tumor, mainly originated from the mucosal epithelium of the bronchus. With the serious air pollution and the gradual deterioration of the environment, the mortality rate of global lung cancer is on the rise, which seriously threatens the health and life of the people. According to the National Cancer Center, the number of new lung cancer cases in China reached 816,000 in 2020, accounting for 17.9% of all cancers. According to the degree of tumor resection and residual degree, lung cancer surgery can be divided into uncertain resection, incomplete resection, and complete resection. Uncertain resection is the naked eye can see the tumor remains. Incomplete resection is complete tumor resection under the naked eye, but there are still residues under the light microscope. Complete resection is the complete tumor resection under the naked eye, and there is no residue under the light microscope [5]. There is a lack of the fast and accurate lung cancer
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