Iniciar sesión

Julius Chapiro

Department of Radiology and Biomedical Imaging

Yale School of Medicine

BIOGRAPHY

Julius Chapiro has not added Biography.

If you are Julius Chapiro and would like to personalize this page please email our Author Liaison for assistance.

PUBLICATIONS

3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival.

European radiology Sep, 2016 | Pubmed ID: 26762942

Sorafenib in combination with transarterial chemoembolization for the treatment of hepatocellular carcinoma.

Clinical advances in hematology & oncology : H&O Aug, 2016 | Pubmed ID: 27487101

Intra-arterial therapies for liver cancer: assessing tumor response.

Expert review of anticancer therapy Feb, 2017 | Pubmed ID: 27983883

The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma.

Clinical imaging Nov - Dec, 2017 | Pubmed ID: 28668723

C-Arm Cone Beam CT for Intraprocedural Image Fusion and 3D Guidance in Portal Vein Embolization (PVE).

Cardiovascular and interventional radiology Mar, 2018 | Pubmed ID: 28875339

Clinical Experience with Real-Time 3-D Guidance Based on C-Arm-Acquired Cone-Beam CT (CBCT) in Transjugular Intrahepatic Portosystemic Stent Shunt (TIPSS) Placement.

Cardiovascular and interventional radiology Jul, 2018 | Pubmed ID: 29541837

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Journal of vascular and interventional radiology : JVIR 06, 2018 | Pubmed ID: 29548875

INSTITUTIONS

Yale School of Medicine

JOVE ARTICLES

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Aaron Abajian¹,

Nikitha Murali¹,

Lynn Jeanette Savic^1,2,

Fabian Max Laage-Gaupp¹,

Nariman Nezami¹,

James S. Duncan³,

Todd Schlachter¹,

MingDe Lin⁴,

Jean-François Geschwind⁵,

Julius Chapiro¹

¹Department of Radiology and Biomedical Imaging, Yale School of Medicine,

²Department of Diagnostic and Interventional Radiology, Universitätsmedizin Charité Berlin,

³Department of Biomedical Engineering, Yale School of Engineering and Applied Science,

⁴, Philips Research North America,

⁵, Prescience Labs

Medicine

Privacidad

Condiciones de uso

Políticas

Contáctenos

RECOMIENDE A LA BIBLIOTECA

BOLETINES de JoVE

Investigación

JoVE Journal
Colecciones de métodos
JoVE Encyclopedia of Experiments
Archivo

Educación

JoVE Core
JoVE Business
JoVE Science Education
JoVE Lab Manual
Centro de recursos académicos
Faculty Site

Autores

Descripción
Proceso de publicación
Comité editorial
Ámbito y políticas
Revisión por pares
Preguntas frecuentes
Enviar

Bibliotecarios

Descripción
Testimonios
Suscripciones
Acceso
Recursos
Junta Asesora de Bibliotecarios
Preguntas frecuentes

ACERCA DE JoVE

Descripción
Liderazgo
Blog
JoVE Help Center

Julius Chapiro

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Radiology and Biomedical Imaging

Yale School of Medicine

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Department of Radiology and Biomedical Imaging