CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study-Reference-Cited by-同舟云学术

CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study

Published:2024-01 Issue: Volume:39 Page:101804
ISSN:1936-5233
Container-title:Translational Oncology
language:en
Short-container-title:Translational Oncology

Author:

Zhang Meng,Lu Yukun,Sun Hongfu,Hou Chuanke,Zhou Zichun,Liu Xiao^ORCID,Zhou Qichao,Li Zhenjiang,Yin Yong^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Cancer Research,Oncology

Reference47 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. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the Global Burden of Disease Study 2019;Kocarnik;JAMA Oncol.,2022

3. Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS): long-term results of a randomised controlled trial;Shapiro;Lancet Oncol.,2015

4. Ten-year outcome of neoadjuvant chemoradiotherapy plus surgery for esophageal cancer: the randomized controlled CROSS trial;Eyck;J. Clin. Oncol.,2021

5. Complete clinical response after neoadjuvant chemoradiotherapy for squamous cell cancer of the thoracic oesophagus: is surgery always necessary?;Castoro;J. Gastrointest. Surg.,2013

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1. Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study;Translational Oncology;2024-09

2. Integrating MR radiomics and dynamic hematological factors predicts pathological response to neoadjuvant chemoradiotherapy in esophageal cancer;Heliyon;2024-07

3. Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma;Journal of Translational Medicine;2024-05-18

4. A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors;Frontiers in Immunology;2024-01-30