Results 151 to 160 of about 26,384 (312)

Multicenter development and external validation of clinical-radiomics models to predict surgically confirmed upstaging in biopsy-proven DCIS using DCE-MRI. [PDF]

J Appl Clin Med Phys
Hu X, Qian L, He J, Shou B, Hu Y, Chen Q, Xin E, Li F, Xie Z, Qian Y, Lou F, Liu N, Kuang Y, Hu H.   +13 more
europepmc   +1 more source

Level of awareness of selected junior and senior high school students on radiologic technology as an allied health profession

, 2011
This study used descriptive method employing the survey technique. A total of 542 junior and senior high school students were selected using stratified random sampling as respondents. The research instrument used was a self-made questionnaire. Frequency,
Taglinao, John Angelo M., Aliling, Jeffrey B., Cambe, Samuel John A.   +2 more
core  

PancDS in Real‐World Practice: A Prospective Multicenter Validation of a Clinical Decision‐Support System Bridging Experience Gaps in Pancreatic Lesion Diagnosis

Advanced Science, EarlyView.
A biomimetic artificial intelligence system, PancDS, has been developed to distinguish pancreatic ductal adenocarcinoma from mass‐forming pancreatitis by adaptively integrating clinical data, radiomics, and deep learning features. Validated across multicenter, reader‐study, and prospective settings, PancDS improves diagnostic accuracy, particularly for
Zhibo Wang, Weinuo Qu, Wenwen Cai, Chuhuai Wang, Chenxi Lyu, Qingguo Xie, Qian Chu, Yaqi Shen, Peng Xiao, Feng Li, Qingpeng Zhang, Jiali Li, Jeong Min Lee, Zhen Li   +13 more
wiley   +1 more source

Dural contact in a high cervical paraspinal spindle cell neoplasm. [PDF]

Surg Neurol Int
Durak FO, Güneş M.
europepmc   +1 more source

Prediction of Student Completion of an Associate Degree Radiologic Technology Program

, 1995
The primary purpose of this study was to investigate the admission/selection process of the Radiologic Technology Program at East Tennessee State University to ascertain predictive validity of the admission process and to identify specific indicators ...
Shehane, Donna R.
core  

Noninvasive Characterization of Tumor Heterogeneity in HNSCC: From Clinical Utility to Biological Correlates

Advanced Science, EarlyView.
This study establishes a CT‐based radiomics framework to quantify intratumoral heterogeneity (ITH) in HNSCC. Using unsupervised clustering, tumor ROIs and VOIs are analyzed to calculate 2D/3D ITH scores. The score shows strong predictive value for prognosis and immunotherapy response, and is associated with tumor metabolism and immune microenvironment,
Xinwei Chen, Rui Liu, Yuan Zhou, Yin Xiao, Chengmin Feng, Lin Chen, Yanshi Li, Mengna Wang, Zhaobo Cheng, Yuxi Luo, Long Liu, Lijing Niu, Yue Xiang, Juan Peng, Guohua Hu, Min Pan   +15 more
wiley   +1 more source

Morphologic variations of the sigmoid sinus on computed tomography: a classification-based study. [PDF]

Surg Radiol Anat
Medişoğlu MS, Öçbe M.
europepmc   +1 more source

Detection and Prevention of Domestic Violence in Radiologic Technology Education of Radiologic Technologists in Austria

Im gesellschaftlichen Diskurs wird häusliche Gewalt häufig mit sichtbaren körperlichen Verletzungen assoziiert. Besonders im medizinischen Kontext, beispielsweise in der Radiologietechnologie, stehen äußerlich erkennbare Verletzungsschemata, wie ...
Jakovljevic, Sabrina
core  

Artificial Intelligence‐Based Body Composition Analysis Reveals Sex‐Specific Prognostic Markers and Their Clinical Value in Gastric Cancer: A Multicenter Study

Advanced Science, EarlyView.
This study applied AI to quantify multidimensional body composition from CT images in gastric cancer and healthy controls. Distinct sex‐specific patterns and disease‐related alterations were identified and were associated with survival. Higher muscle and fat measures were linked to improved outcomes.
Tianxiang Li, Tingting Liu, Qian Yang, Zhiqing Zhang, Xiaofang Guo, Xiang Wang, Yihan Zhang, Limin Ge, Yu Su, Wenjun Wu, Weihua Guo, Zheng Wang, Lian Yang, Yanfeng Niu   +13 more
wiley   +1 more source

Erratum: Evaluating the Accuracy and Diagnostic Reasoning of Multimodal Large Language Models in Interpreting Neuroradiology Cases From <i>RadioGraphics</i>. [PDF]

Korean J Radiol
Suh PS, Ko JS, Shim WH, Heo H, Woo CY, Park H, Suh CH.   +6 more
europepmc   +1 more source