Radiothérapie et dégénérescence maculaire liée à l'âge: revue de la littérature

Autor: T Schmitt, C Faivre, M Benchaboun, F Koenig, D Chauvaud, C Français, G Caputo, J Balosso, LH Schwartz

Publikováno v: Cancer/Radiothérapie. 1:208-212

Macular degeneration is a major health problem. Less than 10% of the cases can be successfully treated by laser therapy. Low dose radiation therapy (in the range of 20 Gy) appears to decrease neovascularisation. These early results need to be confirm

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bfea9a5f5913dbba96cecd1b2c205e8b
https://doi.org/10.1016/s1278-3218(97)89766-1

Lack of correlation between the location of choroidal melanoma and ultraviolet-radiation dose distribution

Autor: Lh, Schwartz, REGIS FERRAND, Py, Boelle, Maylin C, D'Hermies F, Virmont J

Publikováno v: Europe PubMed Central

Ocular melanomas arise from the choroid. The results of our study of a total of 92 ocular melanomas would indicate that there is no preferential location for tumors on the eye. We estimated the ultraviolet (UV) radiation dose distribution using data

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::3a8cd77a810a2620e0727876b678cf87
http://europepmc.org/abstract/med/9092925

Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver.

Autor: Ma J; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Yang H; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Chou Y; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.; Fu Jen Catholic University Hospital, Department of Medical Imaging and Fu Jen Catholic University, School of Medicine, New Taipei City, Taiwan., Yoon J; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA., Allison T; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Komandur R; Project Data Sphere, Cary, North Carolina, USA., McDunn J; Project Data Sphere, Cary, North Carolina, USA., Taneem A; Project Data Sphere, Cary, North Carolina, USA., Do RK; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Schwartz LH; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Zhao B; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Publikováno v: Medical physics [Med Phys] 2024 Nov 22. Date of Electronic Publication: 2024 Nov 22.

The reproducibility and predictivity of radiomic features extracted from dynamic contrast-enhanced computed tomography of hepatocellular carcinoma.

Autor: Ibrahim A; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., Guha S; Department of Radiology, Columbia University Irving Medical Center, New York, New York, United States of America., Lu L; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., Geng P; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., Wu Q; First Affiliated Hospital of Nanjing Medical University, Jiangsu, China., Chou Y; Department of Medical Imaging, Fu Jen Catholic University Hospital, New Taipei City, Taiwan., Yang H; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., Wang D; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China., Schwartz LH; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., Xie CM; Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China., Zhao B; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.

Publikováno v: PloS one [PLoS One] 2024 Sep 13; Vol. 19 (9), pp. e0310486. Date of Electronic Publication: 2024 Sep 13 (Print Publication: 2024).

A quantitative analysis of the improvement provided by comprehensive annotation on CT lesion detection using deep learning.

Autor: Ma J; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Yoon JH; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA., Lu L; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Yang H; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Guo P; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Yang D; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China., Li J; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China., Shen J; Medical Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China., Schwartz LH; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA., Zhao B; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Publikováno v: Journal of applied clinical medical physics [J Appl Clin Med Phys] 2024 Sep; Vol. 25 (9), pp. e14434. Date of Electronic Publication: 2024 Jul 30.