Results 111 to 120 of about 73,359 (292)

Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?

Cancer Imaging
Background To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell ...
Yang Li, Xiaolong Gu, Li Yang, Xiangming Wang, Qi Wang, Xiaosheng Xu, Andu Zhang, Meng Yue, Mingbo Wang, Mengdi Cong, Jialiang Ren, Wei Ren, Gaofeng Shi   +12 more
doaj   +1 more source

Molecular theranostics: principles, challenges and controversies

Journal of Medical Radiation Sciences, Volume 72, Issue 1, Page 156-164, March 2025.
Molecular theranostics offers a powerful tool to drive precision medicine in nuclear oncology. While theranostics is not a new principle in nuclear medicine, recent advances in instrumentation and radiopharmacy have driven a reinvigoration and a broader suite of applications.
Geoffrey Currie
wiley   +1 more source

Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder

, 2021
Zichun Yan, Huan Liu, Xiaoya Chen, Qiao Zheng, Chun Zeng, Yineng Zheng, Shuang Ding, Yuling Peng, Yongmei Li   +8 more
openalex   +1 more source

A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach

Journal of Medical Radiation Sciences, Volume 72, Issue 1, Page 93-105, March 2025.
This study developed a two‐stage model using radiomics‐based multiparametric MRI and clinical indicators to help identify and grade clinically significant prostate cancer. The model showed promising levels of diagnostic accuracy and predictive performance.
Yuyan Zou, Xuechun Wang, Fen Ma, Xulun Liu, Chunyue Jiao, Zhen Kang, Jingjing Cui, Yang Zhang, Yan Xie, Lei Chen, Ronghua Tian   +10 more
wiley   +1 more source

Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis

The Laryngoscope, EarlyView.
Machine learning algorithms (MLAs) demonstrated significantly higher diagnostic performance than radiologists in detecting extranodal extension (ENE) in head and neck squamous cell carcinoma using CT scans. This meta‐analysis of six studies found that MLAs had a pooled AUC of 0.91, whereas radiologists achieved only 0.65.
Arshbir Aulakh, Masih Sarafan, Amardeep S. Sekhon, Khanh Linh Tran, Ameen Amanian, Farahna Sabiq, Cornelius Kürten, Eitan Prisman   +7 more
wiley   +1 more source

Novel Nomogram for Preoperative Prediction of Early Recurrence in Intrahepatic Cholangiocarcinoma

Frontiers in Oncology, 2018
Introduction: The emerging field of “radiomics” has considerable potential in disease diagnosis, pathologic grading, prognosis evaluation, and prediction of treatment response.
Wenjie Liang, Wenjie Liang, Lei Xu, Lei Xu, Pengfei Yang, Pengfei Yang, Lele Zhang, Lele Zhang, Lele Zhang, Dalong Wan, Qiang Huang, Tianye Niu, Feng Chen   +12 more
doaj   +1 more source

Advances and Prospects of Stereotactic Radiosurgery and Stereotactic Ablative Body Radiotherapy: Evolving Paradigms in Precision Oncology

Med Research, EarlyView.
The graphical abstract outlines the progressive development and impact of stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT). Technological Evolution illustrates the transition from brachytherapy with single‐dose, LDR/HDR schedules to fractionated radiotherapy, three‐dimensional conformal radiotherapy (3DCRT) and Gamma Knife ...
Jing Zhang, Wenhao Xu, Weiqing Lu, Yingxuan Lin, Luoxi He, Zonglin Lv, Yiliyaer Nuerrula, Zouting, Zhen Zhang, Hailiang Zhang, Xuejun Ma   +10 more
wiley   +1 more source

A Semi-Unsupervised Segmentation Methodology Based on Texture Recognition for Radiomics: A Preliminary Study on Brain Tumours [PDF]

, 2022
Massimo Donelli, Giuseppe Espa, Paola Feraco   +2 more
openalex   +1 more source

Classification of Salivary Gland Tumors on Ultrasound Using Artificial Intelligence: A Systematic Review and Meta‐Analysis

Otolaryngology–Head and Neck Surgery, EarlyView.
Abstract Objective Accurate classification of salivary gland tumors is critical to guiding appropriate management. This study evaluates the diagnostic performance of artificial intelligence models in classifying salivary gland tumors on ultrasound. Data Sources A comprehensive search of CINAHL, PubMed, and Scopus was conducted through January 28, 2025.
Isabelle J. Chau, Cory Hyun‐su Kim, Shaun A. Nguyen, Lauren R. McCray, Jesse D. Murdaugh, Jason G. Newman   +5 more
wiley   +1 more source

Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy

Insights into Imaging
Objectives To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses.
Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong   +6 more
doaj   +1 more source