A5113237209- Radiomics and Machine Learning in Medical Imaging
- Brain Tumor Detection and Classification
- Medical Imaging Techniques and Applications
- Head and Neck Cancer Studies
- Medical Image Segmentation Techniques
- Lung Cancer Diagnosis and Treatment
- AI and HR Technologies
- Advanced Technologies in Various Fields
- Glioma Diagnosis and Treatment
- Brain Metastases and Treatment
Sun Yat-sen University Cancer Center
2024-2025
Sun Yat-sen University
2024-2025
Current reporting methods for nasopharyngeal carcinoma (NPC) imaging are typically limited to binary descriptions of whether tumors invade specific anatomical structures. This approach is coarse and fails capture precise invasion details necessary improved clinical decision-making. Research on structured NPC that fine-grained limited. In this study, we analyzed voxelwise rate (VIR) based the coordinate system registration proposed a new preliminary method automatic generation with...
Motivation: There is currently no reliable tool to predict postoperative recurrence for patients who undergo surgery brain metastases (BrMs). Goal(s): This study aimed develop and externally validate a prognostic model intracranial recurrence-free survival (RFS) lung cancer receiving BrM surgery. Approach: A combined model-based nomogram was developed by incorporating clinical structural MRI predictors, radiomics deep signatures extracted from MR images. Results: The predicted accurately RFS...