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Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics

Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of (18)F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric ne...

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Autores principales: Feng, Lijuan, Qian, Luodan, Yang, Shen, Ren, Qinghua, Zhang, Shuxin, Qin, Hong, Wang, Wei, Wang, Chao, Zhang, Hui, Yang, Jigang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871335/
https://www.ncbi.nlm.nih.gov/pubmed/35204353
http://dx.doi.org/10.3390/diagnostics12020262
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author Feng, Lijuan
Qian, Luodan
Yang, Shen
Ren, Qinghua
Zhang, Shuxin
Qin, Hong
Wang, Wei
Wang, Chao
Zhang, Hui
Yang, Jigang
author_facet Feng, Lijuan
Qian, Luodan
Yang, Shen
Ren, Qinghua
Zhang, Shuxin
Qin, Hong
Wang, Wei
Wang, Chao
Zhang, Hui
Yang, Jigang
author_sort Feng, Lijuan
collection PubMed
description Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of (18)F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy.
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spelling pubmed-88713352022-02-25 Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics Feng, Lijuan Qian, Luodan Yang, Shen Ren, Qinghua Zhang, Shuxin Qin, Hong Wang, Wei Wang, Chao Zhang, Hui Yang, Jigang Diagnostics (Basel) Article Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of (18)F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy. MDPI 2022-01-20 /pmc/articles/PMC8871335/ /pubmed/35204353 http://dx.doi.org/10.3390/diagnostics12020262 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Feng, Lijuan
Qian, Luodan
Yang, Shen
Ren, Qinghua
Zhang, Shuxin
Qin, Hong
Wang, Wei
Wang, Chao
Zhang, Hui
Yang, Jigang
Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics
title Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics
title_full Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics
title_fullStr Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics
title_full_unstemmed Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics
title_short Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based (18)F-FDG PET/CT Radiomics
title_sort prediction for mitosis-karyorrhexis index status of pediatric neuroblastoma via machine learning based (18)f-fdg pet/ct radiomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871335/
https://www.ncbi.nlm.nih.gov/pubmed/35204353
http://dx.doi.org/10.3390/diagnostics12020262
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