Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study-Reference-Cited by-同舟云学术

Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study

Published:2023-02 Issue:2 Volume:220 Page:245-255
ISSN:0361-803X
Container-title:American Journal of Roentgenology
language:en
Short-container-title:American Journal of Roentgenology

Author:

Iseke Simon¹²,Zeevi Tal¹,Kucukkaya Ahmet S.¹³,Raju Rajiv¹,Gross Moritz¹³,Haider Stefan P.¹,Petukhova-Greenstein Alexandra¹³,Kuhn Tom N.¹⁴,Lin MingDe¹⁵,Nowak Michal¹,Cooper Kirsten¹,Thomas Elizabeth¹,Weber Marc-André²,Madoff David C.¹⁶,Staib Lawrence¹,Batra Ramesh⁷,Chapiro Julius¹

Affiliation:

1. Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520.

2. Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany.

3. Charité Center for Diagnostic and Interventional Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.

4. Department of Diagnostic and Interventional Radiology, Heinrich Heine University Duesseldorf, Duesseldorf, Germany.

5. Clinical Research North America, Visage Imaging, Inc., San Diego, CA.

6. Department of Internal Medicine, Yale University School of Medicine, New Haven, CT.

7. Department of Surgery, Transplantation and Immunology, Yale University School of Medicine, New Haven, CT.

Publisher

American Roentgen Ray Society

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://www.ajronline.org/doi/pdf/10.2214/AJR.22.28077

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