Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma-Reference-Cited by-同舟云学术

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma

Published:2019-03 Issue:2 Volume:1 Page:180012
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Khorrami Mohammadhadi^ORCID,Khunger Monica,Zagouras Alexia,Patil Pradnya,Thawani Rajat^ORCID,Bera Kaustav,Rajiah Prabhakar^ORCID,Fu Pingfu^ORCID,Velcheti Vamsidhar,Madabhushi Anant

Funder

U.S. Department of Defense

the National Cancer Institute

the National Institute of Diabetes and Digestive and Kidney Diseases

the National Center for Research Resources

the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering

Publisher

Radiological Society of North America (RSNA)

Subject

General Medicine

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2019180012

Reference45 articles.

1. Lung Cancer: Epidemiology, Etiology, and Prevention

2. PointBreak: A Randomized Phase III Study of Pemetrexed Plus Carboplatin and Bevacizumab Followed by Maintenance Pemetrexed and Bevacizumab Versus Paclitaxel Plus Carboplatin and Bevacizumab Followed by Maintenance Bevacizumab in Patients With Stage IIIB or IV Nonsquamous Non–Small-Cell Lung Cancer

3. Randomized Phase III Trial of Single-Agent Pemetrexed Versus Carboplatin and Pemetrexed in Patients With Advanced Non–Small-Cell Lung Cancer and Eastern Cooperative Oncology Group Performance Status of 2

4. ERCC1 Isoform Expression and DNA Repair in Non–Small-Cell Lung Cancer

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