Radiomics and deep learning in nasopharyngeal carcinoma: a review
Z Wang, M Fang, J Zhang, L Tang… - IEEE Reviews in …, 2023 - ieeexplore.ieee.org
Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical
management compared to other types of cancer. Precision risk stratification and tailored …
management compared to other types of cancer. Precision risk stratification and tailored …
Radiomics in nasopharyngeal carcinoma
W Duan, B Xiong, T Tian, X Zou… - Clinical Medicine …, 2022 - journals.sagepub.com
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies,
and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy …
and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy …
A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma
D Bao, Y Zhao, L Li, M Lin, Z Zhu, M Yuan, H Zhong… - European …, 2022 - Springer
Objectives To develop and validate a radiomics-based model for predicting radiation-
induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment …
induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment …
[HTML][HTML] Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients
Background and purpose To investigate the radiomic feature (RF) repeatability via
perturbation and its impact on cross-institutional prognostic model generalizability in …
perturbation and its impact on cross-institutional prognostic model generalizability in …
Radiomics-guided radiation therapy: opportunities and challenges
Radiomics is an advanced image-processing framework, which extracts image features and
considers them as biomarkers towards personalized medicine. Applications include disease …
considers them as biomarkers towards personalized medicine. Applications include disease …
Peritumoral and intratumoral texture features based on multiparametric MRI and multiple machine learning methods to preoperatively evaluate the pathological …
N Xie, X Fan, D Chen, J Chen, H Yu… - Journal of Magnetic …, 2023 - Wiley Online Library
Background Radiomics‐based preoperative evaluation of lymph node metastasis (LNM)
and histological grade (HG) might facilitate the decision‐making for pancreatic cancer and …
and histological grade (HG) might facilitate the decision‐making for pancreatic cancer and …
[HTML][HTML] Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma
Y Guo, G Dai, X Xiong, X Wang, H Chen, X Zhou… - Translational …, 2023 - Elsevier
Background Intravoxel incoherent motion (IVIM) plays an important role in predicting
treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study …
treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study …
Advances in MRI‐guided precision radiotherapy
Magnetic resonance imaging (MRI) is becoming increasingly important in precision
radiotherapy owing to its excellent soft‐tissue contrast and versatile scan options. Many …
radiotherapy owing to its excellent soft‐tissue contrast and versatile scan options. Many …
Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms
Y Peng, Y Wang, Z Wen, H Xiang, L Guo, L Su… - Frontiers in …, 2024 - frontiersin.org
Objective The objective of this study is to develop a model to predicts the postoperative Hunt-
Hess grade in patients with intracranial aneurysms by integrating radiomics and deep …
Hess grade in patients with intracranial aneurysms by integrating radiomics and deep …
Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer
M Cheng, R Lin, N Bai, Y Zhang, H Wang, M Guo… - Clinical Radiology, 2023 - Elsevier
AIM To develop and validate a nomogram model that combines computed tomography (CT)-
based radiological factors extracted from deep-learning and clinical factors for the early …
based radiological factors extracted from deep-learning and clinical factors for the early …