Breast Tumor Genes Subtype Profiling Using MR Image Radiomic Features and Machine Learning Algorithms-Reference-Cited by-同舟云学术

Breast Tumor Genes Subtype Profiling Using MR Image Radiomic Features and Machine Learning Algorithms

Published:2022-11-05 Issue: Volume: Page:1-3
ISSN:
Container-title:2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
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Author:

Akhavanallaf Aazadeh¹,Hoseininezhad Marziyeh²,Moradi Milad²,Hajianfar Ghasem²,Oveisi Mehrdad³,Shiri Isaac¹,Zaidi Habib¹

Affiliation:

1. Geneva University Hospital,Division of Nuclear Medicine and Molecular Imaging,Department of Medical Imaging,Geneva 4,Switzerland,CH-1211

2. Iran University of Medical Science,Rajaie Cardiovascular, Medical & Research Center,Tehran,Iran

3. King’s College London,Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine,London,United Kingdom

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10398888/10398889/10398913.pdf?arnumber=10398913

Reference18 articles.

1. Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images

2. MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas

3. Synergistic impact of motion and acquisition/reconstruction parameters on ¹⁸ F‐FDG PET radiomic features in non‐small cell lung cancer: Phantom and clinical studies

4. Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm

5. Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature