Prospective clinical research of radiomics and deep learning in oncology: A translational review-Reference-Cited by-同舟云学术

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Prospective clinical research of radiomics and deep learning in oncology: A translational review

Published:2022-11 Issue: Volume:179 Page:103823
ISSN:1040-8428
Container-title:Critical Reviews in Oncology/Hematology
language:en
Short-container-title:Critical Reviews in Oncology/Hematology

Author:

Zhang Xingping,Zhang Yanchun,Zhang Guijuan,Qiu Xingting,Tan Wenjun,Yin Xiaoxia,Liao Liefa

Funder

National Natural Science Foundation of China

Medical Research Council

Guangzhou Science and Technology Program key projects

Australian Research Council

Marsden Fund

National Health and Medical Research Council

Publisher

Elsevier BV

Subject

Oncology,Hematology

Reference59 articles.

1. Radiomics-based detection of radionecrosis using harmonized multiparametric MRI;Acquitter;Cancers,2022

2. Differentiation between vasogenic edema and infiltrative tumor in patients with high-grade gliomas using texture patch-based analysis;Artzi;J. Magn. Reson Imaging,2018

3. Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer;Beer;Eur. Radio.,2021

4. F-18-FMISO-PET hypoxia monitoring for head-and-neck cancer patients: radiomics analyses predict the outcome of chemo-radiotherapy;Carles;Cancers,2021

5. FDG-PET radiomics for response monitoring in non-small-cell lung cancer treated with radiation therapy;Carles;Cancers,2021

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2. Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning;Health Information Science and Systems;2024-04-18

3. Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach;Radiotherapy and Oncology;2024-02

4. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes;Biomarker Research;2024-01-25

5. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer;Scientific Reports;2023-09-29

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