English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This study was aimed to explore the adoption value of magnetic resonance imaging (MRI) under convolutional neural networks (CNN) in the therapeutic effect of nasopharyngeal carcinoma (NPC) radiotherapy. A total of 54 NPC patients were recruited. CNN was employed to perform 3D visualization processing on magnetic resonance (MR) images of NPC patients. MRI changes were analyzed before and after the patient received radiotherapy. The image segmentation and radiotherapy effects of CNN were evaluated by the Recall, intersection over union (IOU), postoperative apparent diffusion coefficient (ADC), and diagnostic coincidence rate. Moreover, gradient vector flow (GVF) algorithm, fuzzy c-means (FCM), and SegNet were adopted for comparative evaluation. Recall of CNN was 94.89% and the IOU was 84.16%, which was remarkably different from other algorithms (P &lt; 0.05). After analysis of the MRI images of patients receiving radiotherapy, ADC of local residual patients was 1.108 ± 0.097 measured by CNN, the ADC was 1.826 ± 0.115, and the missed diagnosis rate was only 7.14%. In summary, CNN had a good effect on the localization and segmentation of NPC patients, and can accurately evaluate the effect of patients receiving radiotherapy, which can assist clinical diagnosis and treatment of NPC.

Convolutional Neural Network in Evaluation of Radiotherapy Effect for Nasopharyngeal Carcinoma