Work

Evaluation of quantitative modeling methods in whole-body, dynamic [11C]-erlotinib PET.

Year: 2021

Type: article

Abstract: BACKGROUND: C]-erlotinib. Data were analyzed using a variety of quantitative techniques common in PET (graphical methods, kinetic models, and uptake value-based endpoints). Our primary goal was to determine the most reliable imaging endpoint given the need for maintaining minimal patient burden and recognizing the advantage of simple calculations in future trials. RESULTS: C]-erlotinib.

Source: PubMed Central

Authors: Joseph Ryan Petrulli, Mingqiang Zheng, Yiyun Huang, Nabeel B Nabulsi, Sarah B Goldberg, Joseph N Contessa, Evan D Morris

Institution: Yale University

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Center for Research Resources

Awards: Validation of Quantitative 11C-Erlotinib PET for Imaging EGFR-Mutant Lung Cancer, Yale University Clinical and Translational Science Award Program, Yale University Clinical and Translational Science Award Program, Yale Clinical and Translational Science Award

OpenAlex ID: W3164814731

FWCI: 0.871

Cites: 22

Cited by: 9

Related to: 10