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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2023
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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. |
format | Online Article Text |
id | pubmed-10577114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
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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