Department of Urology Surgery, 215 Hospital of Shaanxi Nuclear Industry, Xianyang 712000, Shaanxi, China.
Department of Urology, Affiliated Hospital of Yan'an University, Yan'an 716000, Shaanxi, China.
J Healthc Eng. 2021 Nov 27;2021:1034661. doi: 10.1155/2021/1034661. eCollection 2021.
This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk grading. A total of 89 patients with prostate cancer and benign prostatic hyperplasia diagnosed by MRI examination and pathological examination in hospital were selected as the research objects in this study (they passed the exclusion criteria). The MRI images of these patients were collected in two groups and divided into two groups before and after treatment according to whether the CNN algorithm was used to process them. The number of diagnosed diseases and the number of cases of risk level inferred based on the tumor grading were compared to observe which group was closer to the diagnosis of pathological biopsy. Through comparative analysis, compared with the positive rate of pathological diagnosis (44%), the positive rate after the treatment of the CNN algorithm (42%) was more similar to that before the treatment (34%), and the comparison was statistically marked ( < 0.05). In terms of risk stratification, the grading results after treatment (37 cases) were closer to the results of pathological grading (39 cases) than those before treatment (30 cases), and the comparison was statistically obvious ( < 0.05). In addition, it was obvious that the MRT images would be clearer after treatment through the observation of the MRT images before and after treatment. In conclusion, MRI image segmentation algorithm based on CNN was more accurate in the diagnosis and risk stratification of prostate cancer than routine MRI. According to the evaluation of Dice similarity coefficient (DSC) and Hausdorff I distance (HD), the CNN segmentation method used in this study was more perfect than other segmentation methods.
本研究旨在探讨基于卷积神经网络（CNN）算法的磁共振成像（MRI）图像在前列腺癌患者诊断和肿瘤风险分级中的准确性。选取我院经 MRI 检查及病理检查诊断为前列腺癌和前列腺增生的 89 例患者作为研究对象（均通过排除标准），收集这些患者的 MRI 图像，按是否使用 CNN 算法处理分为处理前组和处理后组，比较基于肿瘤分级推断出的诊断疾病数量和风险级别数量，观察哪一组更接近病理活检诊断。通过对比分析，CNN 算法处理后的阳性率（42%）与治疗前的阳性率（34%）相比更接近病理诊断的阳性率（44%），且比较有统计学意义（<0.05）。在风险分层方面，治疗后的分级结果（37 例）与病理分级结果（39 例）更接近，与治疗前的分级结果（30 例）相比，比较有统计学意义（<0.05）。此外，通过观察治疗前后的 MRT 图像，发现治疗后 MRT 图像更清晰。综上所述，基于 CNN 的 MRI 图像分割算法在前列腺癌的诊断和风险分层方面比常规 MRI 更准确。根据 Dice 相似系数（DSC）和 Hausdorff I 距离（HD）的评估，本研究中使用的 CNN 分割方法比其他分割方法更完善。
