Results 191 to 200 of about 43,566 (284)

Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis. [PDF]

J Med Internet Res
He Z, Liu Y, Jiang Q, Hou F, Xu B.
europepmc   +1 more source

Applications of Artificial Intelligence in Cancer Diagnosis and Treatment

Cancer Medicine, Volume 15, Issue 8, August 2026.
This review explores how AI and multimodal data fusion transform oncology through enhanced screening, precision diagnosis, and personalized therapy. By dissecting core challenges—including data bias and explainability—it provides a strategic framework for the digital transformation and ethical implementation of AI in clinical cancer care.
Yifeng Xie, Zixuan Wang, Zhiwen Zeng, Tongyao Xin, Shaofei Yuan, Feiwei Qin, Zhonglin Chen   +6 more
wiley   +1 more source

Decoding Treatment Response in Hepatocellular Carcinoma: The Era of Radiomics and Radiogenomics. [PDF]

J Hepatocell Carcinoma
Wang Z, Gan W, Gong J, Li X.
europepmc   +1 more source

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods

Head &Neck, Volume 48, Issue 8, Page 2190-2198, August 2026.
ABSTRACT Background Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. Methods Clinical data and postoperative flap photos of head and neck cancer patients (2018–2024) at our center were retrospectively collected, categorized into normal and venous ...
Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang, Qi Zhong   +10 more
wiley   +1 more source

Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review

Health Science Reports, Volume 9, Issue 8, August 2026.
ABSTRACT Background and Aims Solid tumors emerge from uncontrolled cell division and interactions with neighboring cells that influence their growth and resistance to therapy. Anti‐cancer drugs are developed to disrupt these processes, but they often damage healthy cells, making treatment difficult to manage safely and effectively. As the global cancer
Mohsin Kamran, Abdul Majeed, A S M Rafiul Haque, Johari Yap Abdullah   +3 more
wiley   +1 more source

Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation. [PDF]

Diagnostics (Basel)
Mezher M, Elgamal M, Schoeman S, Al-Hasani M, Otero HJ, Sultan LR.   +5 more
europepmc   +1 more source

Time Is Aorta: Can Artificial Intelligence Improve Surgical Timelines in Acute Type A Aortic Dissection? A Comprehensive Review

Health Science Reports, Volume 9, Issue 8, August 2026.
ABSTRACT Background and Aims Acute Type A aortic dissection (ATAAD) is a surgical emergency in which delays in diagnosis, transfer, and operative activation increase mortality and organ damage. Artificial intelligence (AI) may support earlier recognition, faster imaging interpretation, and more efficient multidisciplinary communication.
Ibrahim Antoun, Georgia R. Layton, Riyaz Somani, Mokhtar Ibrahim, G. André Ng, Giovanni Mariscalco, Mustafa Zakkar   +6 more
wiley   +1 more source

Radiomics-enhanced ¹⁸F-AV45 PET/MRI for integrative assessment and centiloid estimation of amyloid-β burden in Alzheimer's disease. [PDF]

Eur Radiol Exp
Yuan Z, Zhang J, Zhou Z, Chen X, Qi N, Wang W, Cheng X, Lin Y, Zhao J.   +8 more
europepmc   +1 more source

Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation

Journal of Magnetic Resonance Imaging, Volume 64, Issue 2, Page 468-479, August 2026.
ABSTRACT Background The impact of deep learning (DL) reconstruction and segmentation on MRI radiomics stability has not been fully assessed. Purpose To investigate the effects of acquisition, reconstruction, and segmentation on the reproducibility and variability of radiomic features in abdominal MRI. Study Type Prospective.
Jingyu Zhong, Yue Xing, Yangfan Hu, Xianwei Liu, Shun Dai, Defang Ding, Junjie Lu, Jiarui Yang, Yue Li, Yang Song, Minda Lu, Dominik Nickel, Wenjie Lu, Huan Zhang, Weiwu Yao   +14 more
wiley   +1 more source

Radiomics in Medical Imaging: Methods, Applications, and Challenges. [PDF]

J Imaging
Neha F, Shukla DK.
europepmc   +1 more source