The application of machine learning and deep learning radiomics in the treatment of esophageal cancer-Reference-Cited by-同舟云学术

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The application of machine learning and deep learning radiomics in the treatment of esophageal cancer

Published:2023-12 Issue:4 Volume:4 Page:182-189
ISSN:2666-5557
Container-title:Radiation Medicine and Protection
language:en
Short-container-title:Radiation Medicine and Protection

Author:

Yi Jinling,Wu Yibo,Ning Boda,Zhang Ji,Pleshkov Maksim^ORCID,Tolmachev Ivan^ORCID,Jin Xiance^ORCID

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Emergency Medical Services

Reference89 articles.

1. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Sung;CA Cancer J Clin,2021

2. Global cancer statistics, 2012;Torre;CA Cancer J Clin,2015

3. Epidemiology of esophageal cancer;Wheeler;Surg Clin North Am,2012

4. Only pathologic complete response to neoadjuvant chemotherapy improves significantly the long term survival of patients with resectable esophageal squamous cell carcinoma

5. Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting;Liu;Nat Commun,2021

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