Results 121 to 130 of about 48,979 (300)

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

Machine Learning Comparison and Combined Model Optimization Based on DCE-MRI Radiomics for Preoperative Assessment of Lymphovascular Invasion in Breast Cancer: A Multicenter Study. [PDF]

Cancer Rep (Hoboken)
ABSTRACT Purpose To explore the value of constructing a radiomics combined model based on preoperative dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) for predicting lymphovascular invasion (LVI) in breast cancer. Materials and Methods Retrospective data collection was performed from December 2022 to November 2025, involving 912 patients
Li H, Xiao Q, Zeng Y, Wang X, Zhang Y.
europepmc   +2 more sources

Heterogeneity Analyzed by CT‐Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review

The Kaohsiung Journal of Medical Sciences, EarlyView.
ABSTRACT Tumors are highly heterogeneous, and whole‐lesion radiomics analysis is a popular method for extracting texture features that reflect this heterogeneity, which can be used to build models for cancer diagnosis, staging, therapy response evaluation, and prognosis prediction.
Yang Zhang, Zhi‐Yan Jin, Min‐Ying Lydia Su   +2 more
wiley   +1 more source

The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis

, 2023
Parhat Yasin,1 Muradil Mardan,2 Dilxat Abliz,3 Tao Xu,1 Nuerbiyan Keyoumu,4 Abasi Aimaiti,4 Xiaoyu Cai,1 Weibin Sheng,1 Mardan Mamat1 1Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054 ...
Sheng W, Abliz D, Aimaiti A, Yasin P, Xu T, Keyoumu N, Mamat M, Mardan M, Cai X   +8 more
core  

Are Radiology Systematic Reviews Methodologically Sound? A Perspective on PRISMA, PROSPERO, and Database Selection Practices

iRADIOLOGY, EarlyView.
Arosh S. Perera Molligoda Arachchige
wiley   +1 more source

Early prediction of neoadjuvant chemotherapy efficacy among patients with triple-negative breast cancer using an ultrasound-based radiomics nomogram [PDF]

PURPOSE: To develop and validate a radiomics nomogram based on early ultrasound (US) imaging for predicting pathologic complete response (pCR) in patients with triple-negative breast cancer (TNBC) receiving neoadjuvant chemotherapy (NAC).
Min Zong, Cui-Ying Li, Hai-Ling Zha, Man-Qi Zhang, Min-Jia Lin, Yu Du   +5 more
core   +1 more source

Artificial intelligence demonstrates comparable diagnostic accuracy to radiologists for anterior cruciate ligament tears on MRI: A systematic review and meta‐analysis

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose Anterior cruciate ligament (ACL) tears are among the most common knee injuries, accounting for half of all knee ligament injuries. Magnetic resonance imaging (MRI) is the standard for diagnosing ACL tears, but its interpretation is experience‐dependent.
Gabriel Moraes de Oliveira, Serafina Zotter, Brian Tao, Ana Beatriz Nardelli da Silva, Maria Mascarenhas, Chilan B. G. Leite, Guilherme Almeida, Nacime Mansur, Giovanna Medina   +8 more
wiley   +1 more source

Editorial: Imaging assessment of response to immunotherapy

Frontiers in Oncology, 2023
Mariaelena Occhipinti, Andrea R. Filippi, Andrea R. Filippi, Sean Walsh, Jan P. van Meerbeeck   +4 more
doaj   +1 more source

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani, Rüdiger von Eisenhart‐Rothe, Klaus Woertler, Michael T. Hirschmann, Daniel Rueckert, Florian Hinterwimmer   +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