Zobrazeno 1 - 10
of 4 200
pro vyhledávání: '"Low-dose ct"'
Autor:
Louis Gros, Rowena Yip, Yeqing Zhu, Pengfei Li, Natela Paksashvili, Qi Sun, David F. Yankelevitz, Claudia I. Henschke
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract Lung cancer, the leading cause of cancer deaths globally, has better survival rates with early detection. Annual low-dose CT (LDCT) screenings are recommended for high-risk individuals due to age and smoking. These individuals are also at ri
Externí odkaz:
https://doaj.org/article/ad7e69a8f8004eef9f4391bfec7e7841
Publikováno v:
Tomography, Vol 10, Iss 9, Pp 1513-1526 (2024)
Background: The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients’ nutritional status. With the increasi
Externí odkaz:
https://doaj.org/article/a6b3ac4c0d744e1bb4ad9feee8d144be
Autor:
Xuanzhuang LU, Qiuxia QIU, Chunyu YANG, Caichen LI, Jianfu LI, Shan XIONG, Bo CHENG, Chujing ZHOU, Xiaoqin DU, Yi ZHANG, Jianxing HE, Wenhua LIANG, Nanshan ZHONG
Publikováno v:
Chinese Journal of Lung Cancer, Vol 27, Iss 5, Pp 345-358 (2024)
Background and objective Both of lung cancer incidence and mortality rank first among all cancers in China. Previous lung cancer screening trials were mostly selective screening for high-risk groups such as smokers. Non-smoking women accounted for a
Externí odkaz:
https://doaj.org/article/2a037850d3994348985f814306dd346e
Autor:
Wong, Lye-Yeng 1, ⁎, Kapula, Ntemena 1, Kang, Augustine 1, Phadke, Anuradha J. 2, Schechtman, Andrew D. 2, Elliott, Irmina A. 1, Guenthart, Brandon A. 1, Liou, Douglas Z. 1, Backhus, Leah M. 1, 3, Berry, Mark F. 1, Shrager, Joseph B. 1, 3, Lui, Natalie S. 1
Publikováno v:
In Clinical Lung Cancer January 2025 26(1):39-44
Publikováno v:
In Digital Signal Processing January 2025 156 Part B
Autor:
Hilde ten Berge, Katerina Togka, Xuanqi Pan, Marina Borges, Fernando Palma Martelo, Fernando Guedes, Daniel Cabral, Encarnacao Teixeira, Gabriela Fernandes, Lurdes Ferreira, Sara Figueiredo, Rita Sousa, Lourdes Barradas, Fernanda Estevinho, Antonio Araujo, Venceslau Hespanhol, Rui Medeiros
Publikováno v:
Journal of Comparative Effectiveness Research, Vol 13, Iss 11 (2024)
Aim: Lung cancer is the most common cause of cancer death in Portugal. The Dutch–Belgian lung cancer screening (LCS) study (NELSON), the biggest European LCS study, showed a lung cancer mortality reduction in a high-risk population when being scr
Externí odkaz:
https://doaj.org/article/116666e18c3a4d5e8d541d248e144709
Autor:
Mehdi Hemmati, Sayaka Ishizawa, Rafael Meza, Edwin Ostrin, Samir M. Hanash, Mara Antonoff, Andrew J. Schaefer, Martin C. Tammemägi, Iakovos Toumazis
Publikováno v:
EClinicalMedicine, Vol 74, Iss , Pp 102743- (2024)
Summary: Background: Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The E
Externí odkaz:
https://doaj.org/article/9f8ed164955f488d98792a93bf2cdb73
Autor:
Lye-Yeng Wong, MD, Tiffany Yue, BS, Ghazal Aghagoli, BS, Ioana Baiu, MD, Laura Shula, PA-C, Angela Lee, NP, Natalie S. Lui, MD, Leah M. Backhus, MD, MPH
Publikováno v:
JTO Clinical and Research Reports, Vol 5, Iss 6, Pp 100671- (2024)
Introduction: The screening mammogram could be a “teachable moment” to improve lung cancer screening (LCS) uptake. The aim of our project was to combine patient self-referral with eligibility identification by providers as a two-pronged approach
Externí odkaz:
https://doaj.org/article/0ffdcf68069f4c24bb244528a4b99d7b
Publikováno v:
IEEE Access, Vol 12, Pp 180992-181008 (2024)
Reconstructing low-dose CT imaging deals with handling the inherent noise within the data, which makes it a complex mathematical problem known as an ill-posed inverse problem. Recent attention has shifted towards deep learning-based techniques in CT
Externí odkaz:
https://doaj.org/article/078b054878514edab5eaaa814829e59c
Publikováno v:
IEEE Access, Vol 12, Pp 98693-98706 (2024)
Given the escalating potential risk associated with X-ray radiation exposure to patients, scholars have been dedicated to investigating advanced algorithms for low-dose CT (LDCT) imaging. The utilization of Generative Adversarial Networks (GANs) has
Externí odkaz:
https://doaj.org/article/087dcf3cfd7a47e18d069ec68db99a0c