MRI Using Artificial Intelligence Algorithm to Evaluate Concurrent Chemoradiotherapy for Local Recurrence and Distant Metastasis of Cervical Squamous Cell Carcinoma

• Published in: Computational and mathematical methods in medicine Vol. 2022; pp. 1 - 11
• Main Authors: Wu, Youyi, Chen, Tingting, Huang, Yiwei, Li, Yongchou, Wang, Xiaoyan
• Format: Journal Article
• Language: English
• Published: United States Hindawi 28.07.2022
• Subjects: Artificial Intelligence; Carcinoma, Squamous Cell - diagnostic imaging; Carcinoma, Squamous Cell - therapy; Chemoradiotherapy - methods; Female; Humans; Magnetic Resonance Imaging; Uterine Cervical Neoplasms - diagnostic imaging; Uterine Cervical Neoplasms - therapy
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2022/4449696

The aim of this study was to investigate the magnetic resonance imaging (MRI) features of patients with local recurrence and distant metastasis of cervical squamous cell carcinoma before and after concurrent chemoradiotherapy based on artificial intelligence algorithm. In this study, 100 patients with cervical squamous cell carcinoma with local recurrence and distant metastasis who underwent concurrent chemoradiotherapy were collected as the research subjects, and all underwent MRI multisequence imaging scans. At the same time, according to the evaluation criteria of solid tumor efficacy, patients with complete remission were classified into the effective group, and patients with partial remission, progressive disease, and stable disease were classified into the ineffective group. In addition, an image segmentation algorithm based on Balloon Snake model was proposed for MRI image processing, and simulation experiments were carried out. The results showed that the Dice coefficient of the proposed model segmentation of the reconstructed image was significantly higher than that of the level set model and the greedy algorithm, while the running time was the opposite (P<0.05). The lesion volume (38.76±5.34 cm3) in the effective group after treatment was significantly smaller than that in the noneffective group (46.33±4.64 cm3), and the rate of lesion volume shrinkage (28.71%) was significantly larger than that in the noneffective group (12.49%) (P<0.05). The relative apparent diffusion coefficient (rADC) value and rADC value change rate of the lesion after treatment in the effective group were significantly greater than those in the noneffective group (P<0.05). In summary, the image segmentation and reconstruction algorithm based on Balloon Snake model can not only improve the quality of MRI images but also shorten the processing time and improve the diagnostic efficiency. The volume regression rate and rADC value change rate of cervical squamous cell carcinoma lesion can reflect the early efficacy of concurrent chemoradiotherapy for cervical squamous cell carcinoma and have predictive value.