Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models-Reference-Cited by-同舟云学术

Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models

Published:2023-04-01 Issue:1 Volume:307 Page:
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Gidwani Mishka^ORCID,Chang Ken^ORCID,Patel Jay Biren,Hoebel Katharina Viktoria^ORCID,Ahmed Syed Rakin^ORCID,Singh Praveer^ORCID,Fuller Clifton David^ORCID,Kalpathy-Cramer Jayashree^ORCID

Funder

National Institute of Biomedical Imaging and Bioengineering

National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award

Academic Industrial Partnership

NCI Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program

UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program

Developmental Research Program Award

Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award

Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data

Division of Civil, Mechanical, and Manufacturing Innovation

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.220715

Reference34 articles.

1. Radiomics: Images Are More than Pictures, They Are Data

2. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

3. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer

4. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

5. Leakage in data mining

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