Results 101 to 110 of about 73,359 (292)

Integrating Clinical Features, Laboratory Biomarkers and Computed Tomography for the Discrimination of Non‐Small Cell Lung Cancer and Benign Pulmonary Diseases: A Clinical Prediction Model

Journal of Clinical Laboratory Analysis, EarlyView.
This study developed and validated a clinical prediction model to differentiate non‐small cell lung cancer (NSCLC) from benign pulmonary diseases using clinical features, laboratory biomarkers, and CT features. The model achieved an AUC‐ROC of 0.95 in the training set and 0.82 in the validation set, demonstrating reliable diagnostic performance and ...
Jie Zhang, Shui Liu, Mingjian Bai, Fucun Ma, Guowei Liang, Bing Wang   +5 more
wiley   +1 more source

Distinguishing lung cancer from atypical tuberculosis: Deep transfer learning and imaging omics features

Journal of Intelligent Medicine, EarlyView.
The 3D‐ResNet model demonstrates superior discriminative power in differentiating lung cancer from atypical tuberculosis by leveraging deep omics features derived from volumetric lung cancer imaging, outperforming conventional clinical and radiomic analyses.
Yi Wu, Siyi Li, Zhiping Long, Yanjie Jia, Yue Yu, Bing Pei, Heli Yuan, Yuhua Hao, Zhiyun Jiang, Lei Cao, Kezheng Wang, Fan Wang   +11 more
wiley   +1 more source

Computed tomography based radiomics signature for predicting the expression of vascular endothelial growth factor in pediatric patients with nephroblastoma

Scientific Reports
To construct a computed tomography (CT) based radiomics signature and assess its performance in predicting vascular endothelial growth factor (VEGF) expression in pediatric patients with nephroblastoma.
Ma Fu, Han Qi, Suyue Zhu, Yan Gao, Yanlin Li, Jian Wu, Dongsheng Zhu   +6 more
doaj   +1 more source

Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features

Frontiers in Oncology
PurposeTo develop and validate computed tomography (CT)-based intratumoral and peritumoral radiomics signatures for preoperative prediction of lymph node metastasis (LNM) in patients with ovarian cancer (OC).MethodsPatients with pathological diagnosis of
Jing Zhang, Qiyuan Li, Haoyu Liang, Yao Wang, Li Sun, Qingyuan Zhang, Chuanping Gao   +6 more
doaj   +1 more source

Radiomics May Be a New Opportunity for Bariatric Surgery [PDF]

, 2022
Yancheng Song, Yu Li
openalex   +1 more source

Artificial intelligence strategies for predicting kinase inhibitor resistance: A comprehensive review of methods, challenges, and future perspectives

Journal of Intelligent Medicine, EarlyView.
Abstract Kinase inhibitors are essential in targeted cancer therapy, yet resistance often emerges through secondary mutations, activation of compensatory signaling pathways, or drug‐efflux mechanisms. Artificial intelligence (AI) provides a workflow‐based strategy rather than a list of unrelated tools for predicting and addressing kinase‐inhibitor ...
Faris Hassan, Mohanad Ali Deifallah, Alaa Zaghloul, Rania Elgohary   +3 more
wiley   +1 more source

Differentiation of Small (≤3 Cm) Hepatocellular Carcinomas From Benign Nodules in Cirrhotic Liver: Additive Value of MRI-Based Radiomics Analysis to LI-RADS Version 2018 Algorithm [PDF]

, 2021
Xi Zhong, Tianpei Guan, D R Tang, Jiansheng Li, Bingui Lu, Shuzhong Cui, Hongsheng Tang   +6 more
openalex   +1 more source

Enhancing Study Design and Analysis of MR Imaging Markers Through Measurement Error Modeling

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Measurement error in imaging reduces statistical power and potentially biases parameter estimation, compromising study reliability. Purpose To introduce a dual data collection design (reliability and main datasets) to quantify measurement error and apply regression calibration to correct error‐prone imaging markers, thereby ...
Xiaofeng Wang, Walter Zhao, Yifan Wang, Deborah H. Kwon, Ting‐Yu Su, Nancy A. Obuchowski, Mark A. Griswold, Zhong Irene Wang, Dan Ma   +8 more
wiley   +1 more source

Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans

, 2021
Mohammadhadi Khorrami, Kaustav Bera, Rajat Thawani, Prabhakar Rajiah, Amit Gupta, Pingfu Fu, Philip A. Linden, Nathan A. Pennell, Frank J. Jacono, Robert Gilkeson, Vamsidhar Velcheti, Anant Madabhushi   +11 more
openalex   +2 more sources

Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications

Journal of Medical Radiation Sciences, EarlyView.
This scoping review examined models estimating long‐term radiation risks from repeated mammography screening. While modelling indicates the risks are low, approaches vary by protocol and vendor. Emerging AI tools show promise for improving personalised risk estimation, though further development is needed for cross‐platform compatibility.
Nazli A. Moda, Mo'ayyad E. Suleiman, Sahand Hooshmand, Warren M. Reed   +3 more
wiley   +1 more source