Wang Junqing, Chen Bingqian, Zhu Jing, Zhang Junfeng, Jiang Rui
Department of Radiodiagnosis, General Hospital of Western Warfare Zone, Chengdu, Sichuan, China.
Front Genet. 2023 Feb 16;14:1119990. doi: 10.3389/fgene.2023.1119990. eCollection 2023.
Colorectal cancer is a common malignant tumor in clinic. With the change of people's diet, living environment and living habits, the incidence of colorectal cancer has risen sharply in recent years, which poses a great threat to people's health and quality of life. This paper aims to investigate the pathogenesis of colorectal cancer and improve the efficiency of clinical diagnosis and treatment. This paper firstly introduces MR Medical imaging technology and related theories of colorectal cancer through literature survey, and then applies MR technology to preoperative T staging of colorectal cancer. 150 patients with colorectal cancer admitted to our hospital every month from January 2019 to January 2020 were used as research objects to carry out the application experiment of MR Medical imaging in the intelligent diagnosis of preoperative T staging of colorectal cancer, and to explore the diagnostic sensitivity, specificity and histopathological T staging diagnosis coincidence rate of MR Staging. The final study results showed that there was no statistical significance in the general data of stage T1-2, T3 and T4 patients (p > 0.05); for patients with preoperative T stage of colorectal cancer, the overall diagnosis coincidence rate of MR Was 89.73%, indicating that it was highly consistent with pathological T stage; compared with MR Staging, the overall diagnosis coincidence rate of CT for preoperative T staging of colorectal cancer patients was 86.73%, which was basically consistent with the diagnosis of pathological T staging. At the same time, three different dictionary learning depth techniques are proposed in this study to solve the shortcomings of long MR Scanning time and slow imaging speed. Through performance testing and comparison, it is found that the structural similarity of MR Image reconstructed by depth dictionary method based on convolutional neural network is up to 99.67%, higher than that of analytic dictionary and synthetic dictionary, which proves that it has the best optimization effect on MR Technology. The study indicated the importance of MR Medical imaging in preoperative T staging diagnosis of colorectal cancer and the necessity of its popularization.
结直肠癌是临床上常见的恶性肿瘤。随着人们饮食、生活环境和生活习惯的改变,近年来结直肠癌的发病率急剧上升,对人们的健康和生活质量构成了巨大威胁。本文旨在探讨结直肠癌的发病机制,提高临床诊断和治疗效率。本文首先通过文献调研介绍了结直肠癌的磁共振(MR)医学成像技术及相关理论,然后将MR技术应用于结直肠癌术前T分期。选取2019年1月至2020年1月我院每月收治的150例结直肠癌患者作为研究对象,开展MR医学成像在结直肠癌术前T分期智能诊断中的应用实验,探讨MR分期的诊断敏感性、特异性及组织病理学T分期诊断符合率。最终研究结果显示,T1-2期、T3期和T4期患者的一般资料无统计学意义(p>0.05);对于结直肠癌术前T分期患者,MR的总体诊断符合率为89.73%,表明与病理T分期高度一致;与MR分期相比,CT对结直肠癌患者术前T分期的总体诊断符合率为86.73%,与病理T分期诊断基本一致。同时,本研究提出了三种不同的字典学习深度技术,以解决MR扫描时间长、成像速度慢的缺点。通过性能测试和比较发现,基于卷积神经网络的深度字典法重建的MR图像结构相似度高达99.67%,高于解析字典和合成字典,证明其对MR技术具有最佳的优化效果。该研究表明了MR医学成像在结直肠癌术前T分期诊断中的重要性及其推广的必要性。