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Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics

BACKGROUND: Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC. METHODS: Histologically confirmed...

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Autores principales: Lei, Xiyao, Cao, Zhuo, Wu, Yibo, Lin, Jie, Zhang, Zhenhua, Jin, Juebin, Ai, Yao, Zhang, Ji, Du, Dexi, Tian, Zhifeng, Xie, Congying, Yin, Weiwei, Jin, Xiance
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577114/
https://www.ncbi.nlm.nih.gov/pubmed/37840068
http://dx.doi.org/10.1186/s13244-023-01528-0
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author Lei, Xiyao
Cao, Zhuo
Wu, Yibo
Lin, Jie
Zhang, Zhenhua
Jin, Juebin
Ai, Yao
Zhang, Ji
Du, Dexi
Tian, Zhifeng
Xie, Congying
Yin, Weiwei
Jin, Xiance
author_facet Lei, Xiyao
Cao, Zhuo
Wu, Yibo
Lin, Jie
Zhang, Zhenhua
Jin, Juebin
Ai, Yao
Zhang, Ji
Du, Dexi
Tian, Zhifeng
Xie, Congying
Yin, Weiwei
Jin, Xiance
author_sort Lei, Xiyao
collection PubMed
description BACKGROUND: Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC. METHODS: Histologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) was applied to select optimal radiomics features from PET, CT, and fused PET-CT images, respectively. Logistic regression (LR) was applied to classify the T stage (T(1,2) vs. T(3,4)), lymph node metastasis (LNM) (LNM((−)) vs. LNM((+))), and pathological state (pstage) (I–II vs. III–IV) with features from CT (CT_LR_Score), PET (PET_LR_Score), fused PET/CT (Fused_LR_Score), and combined CT and PET features (CT + PET_LR_Score), respectively. RESULTS: Seven, 10, and 7 CT features; 7, 8, and 7 PET features; and 3, 6, and 3 fused PET/CT features were selected using mRMR for the prediction of T stage, LNM, and pstage, respectively. The area under curves (AUCs) for T stage, LNM, and pstage prediction in the validation cohorts were 0.846, 0.756, 0.665, and 0.815; 0.769, 0.760, 0.665, and 0.824; and 0.727, 0.785, 0.689, and 0.837 for models of CT_LR_Score, PET_ LR_Score, Fused_ LR_Score, and CT + PET_ LR_Score, respectively. CONCLUSIONS: Accurate prediction ability was observed with combined PET and CT radiomics in the prediction of T stage, LNM, and pstage for EC patients. CRITICAL RELEVANCE STATEMENT: PET/CT radiomics is feasible and promising to stratify stages for esophageal cancer preoperatively. KEY POINTS: • PET-CT radiomics achieved the best performance for Node and pathological stage prediction. • CT radiomics achieved the best AUC for T stage prediction. • PET-CT radiomics is feasible and promising to stratify stages for EC preoperatively. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01528-0.
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spelling pubmed-105771142023-10-17 Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics Lei, Xiyao Cao, Zhuo Wu, Yibo Lin, Jie Zhang, Zhenhua Jin, Juebin Ai, Yao Zhang, Ji Du, Dexi Tian, Zhifeng Xie, Congying Yin, Weiwei Jin, Xiance Insights Imaging Original Article BACKGROUND: Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC. METHODS: Histologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) was applied to select optimal radiomics features from PET, CT, and fused PET-CT images, respectively. Logistic regression (LR) was applied to classify the T stage (T(1,2) vs. T(3,4)), lymph node metastasis (LNM) (LNM((−)) vs. LNM((+))), and pathological state (pstage) (I–II vs. III–IV) with features from CT (CT_LR_Score), PET (PET_LR_Score), fused PET/CT (Fused_LR_Score), and combined CT and PET features (CT + PET_LR_Score), respectively. RESULTS: Seven, 10, and 7 CT features; 7, 8, and 7 PET features; and 3, 6, and 3 fused PET/CT features were selected using mRMR for the prediction of T stage, LNM, and pstage, respectively. The area under curves (AUCs) for T stage, LNM, and pstage prediction in the validation cohorts were 0.846, 0.756, 0.665, and 0.815; 0.769, 0.760, 0.665, and 0.824; and 0.727, 0.785, 0.689, and 0.837 for models of CT_LR_Score, PET_ LR_Score, Fused_ LR_Score, and CT + PET_ LR_Score, respectively. CONCLUSIONS: Accurate prediction ability was observed with combined PET and CT radiomics in the prediction of T stage, LNM, and pstage for EC patients. CRITICAL RELEVANCE STATEMENT: PET/CT radiomics is feasible and promising to stratify stages for esophageal cancer preoperatively. KEY POINTS: • PET-CT radiomics achieved the best performance for Node and pathological stage prediction. • CT radiomics achieved the best AUC for T stage prediction. • PET-CT radiomics is feasible and promising to stratify stages for EC preoperatively. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01528-0. Springer Vienna 2023-10-15 /pmc/articles/PMC10577114/ /pubmed/37840068 http://dx.doi.org/10.1186/s13244-023-01528-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Lei, Xiyao
Cao, Zhuo
Wu, Yibo
Lin, Jie
Zhang, Zhenhua
Jin, Juebin
Ai, Yao
Zhang, Ji
Du, Dexi
Tian, Zhifeng
Xie, Congying
Yin, Weiwei
Jin, Xiance
Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
title Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
title_full Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
title_fullStr Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
title_full_unstemmed Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
title_short Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
title_sort preoperative prediction of clinical and pathological stages for patients with esophageal cancer using pet/ct radiomics
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577114/
https://www.ncbi.nlm.nih.gov/pubmed/37840068
http://dx.doi.org/10.1186/s13244-023-01528-0
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