Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature-Reference-Cited by-同舟云学术

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Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature

Published:2024-01-11 Issue:8 Volume:34 Page:5349-5359
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Liu Qianhan,Liu Shengdan,Mao Yu,Kang Xuefeng,Yu Mingling,Chen Guangxiang^ORCID

Funder

Sichuan Province Science and Technology Support Program

Luzhou Science & Technology Department

Sichuan University & Luzhou Collaborative Foundation

Project for Doctors of Affiliated Hospital of Southwest Medical University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00330-023-10557-8.pdf

Reference42 articles.

1. Preda L, Conte G, Bonello L et al (2017) Diagnostic accuracy of surface coil MRI in assessing cartilaginous invasion in laryngeal tumours: do we need contrast-agent administration? Eur Radiol 27(11):4690–4698

2. Steuer CE, El-Deiry M, Parks JR, Higgins KA, Saba NF (2017) An update on larynx cancer. CA Cancer J Clin 67(1):31–50

3. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249

4. Corry J, Peters L, Kleid S, Rischin D (2013) Larynx preservation for patients with locally advanced laryngeal cancer. J Clin Oncol 31(7):840–844

5. Amin MB, Greene FL, Edge SB et al (2017) The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin 67(2):93–99

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