Artificial intelligence in tumor subregion analysis based on medical imaging: A review-Reference-Cited by-同舟云学术

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Artificial intelligence in tumor subregion analysis based on medical imaging: A review

Published:2021-06-24 Issue:7 Volume:22 Page:10-26
ISSN:1526-9914
Container-title:Journal of Applied Clinical Medical Physics
language:en
Short-container-title:J Appl Clin Med Phys

Author:

Lin Mingquan¹,Wynne Jacob F.¹,Zhou Boran¹,Wang Tonghe¹,Lei Yang¹,Curran Walter J.¹,Liu Tian¹,Yang Xiaofeng¹^ORCID

Affiliation:

1. Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta Georgia USA

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.13321

Reference188 articles.

1. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach;Aerts HJ;Nat Commun,2014

2. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

3. 18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient Cohort

4. Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

5. Early-Stage Non–Small Cell Lung Cancer: Quantitative Imaging Characteristics of18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis

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