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Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study
OBJECTIVE: gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. METHODS: Overall, 126 patients with GISTs who underwent preoperative enhanced CT we...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461453/ https://www.ncbi.nlm.nih.gov/pubmed/37645430 http://dx.doi.org/10.3389/fonc.2023.1193010 |
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author | Zhang, Yu Yue, Xiaofei Zhang, Peng Zhang, Yuying Wu, Linxia Diao, Nan Ma, Guina Lu, Yuting Ma, Ling Tao, Kaixiong Li, Qian Han, Ping |
author_facet | Zhang, Yu Yue, Xiaofei Zhang, Peng Zhang, Yuying Wu, Linxia Diao, Nan Ma, Guina Lu, Yuting Ma, Ling Tao, Kaixiong Li, Qian Han, Ping |
author_sort | Zhang, Yu |
collection | PubMed |
description | OBJECTIVE: gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. METHODS: Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1. RESULTS: The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621–0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore. CONCLUSION: The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance. |
format | Online Article Text |
id | pubmed-10461453 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104614532023-08-29 Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study Zhang, Yu Yue, Xiaofei Zhang, Peng Zhang, Yuying Wu, Linxia Diao, Nan Ma, Guina Lu, Yuting Ma, Ling Tao, Kaixiong Li, Qian Han, Ping Front Oncol Oncology OBJECTIVE: gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. METHODS: Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1. RESULTS: The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621–0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore. CONCLUSION: The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance. Frontiers Media S.A. 2023-08-14 /pmc/articles/PMC10461453/ /pubmed/37645430 http://dx.doi.org/10.3389/fonc.2023.1193010 Text en Copyright © 2023 Zhang, Yue, Zhang, Zhang, Wu, Diao, Ma, Lu, Ma, Tao, Li and Han https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Zhang, Yu Yue, Xiaofei Zhang, Peng Zhang, Yuying Wu, Linxia Diao, Nan Ma, Guina Lu, Yuting Ma, Ling Tao, Kaixiong Li, Qian Han, Ping Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
title | Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
title_full | Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
title_fullStr | Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
title_full_unstemmed | Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
title_short | Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
title_sort | clinical-radiomics-based treatment decision support for kit exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461453/ https://www.ncbi.nlm.nih.gov/pubmed/37645430 http://dx.doi.org/10.3389/fonc.2023.1193010 |
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