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CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children

OBJECTIVE: To assess the computed tomography (CT) and magnetic resonance (MR) imaging characteristics of soft tissue rhabdoid tumors (RT) and compare them with those of rhabdomyosarcoma (RMS). METHODS: We conducted a retrospective analysis of 49 pediatric patients from 2011 to 2022, comprising 16 pa...

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Autores principales: Sheng, Jing, Li, Ting-Ting, Zhang, Huan-Huan, Xu, Hua-Feng, Cai, Xue-Mei, Xu, Rong, Ji, Qiong-Qiong, Wu, Yu-Meng, Huang, Ting, Yang, Xiu-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401262/
https://www.ncbi.nlm.nih.gov/pubmed/37547104
http://dx.doi.org/10.3389/fped.2023.1199444
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author Sheng, Jing
Li, Ting-Ting
Zhang, Huan-Huan
Xu, Hua-Feng
Cai, Xue-Mei
Xu, Rong
Ji, Qiong-Qiong
Wu, Yu-Meng
Huang, Ting
Yang, Xiu-Jun
author_facet Sheng, Jing
Li, Ting-Ting
Zhang, Huan-Huan
Xu, Hua-Feng
Cai, Xue-Mei
Xu, Rong
Ji, Qiong-Qiong
Wu, Yu-Meng
Huang, Ting
Yang, Xiu-Jun
author_sort Sheng, Jing
collection PubMed
description OBJECTIVE: To assess the computed tomography (CT) and magnetic resonance (MR) imaging characteristics of soft tissue rhabdoid tumors (RT) and compare them with those of rhabdomyosarcoma (RMS). METHODS: We conducted a retrospective analysis of 49 pediatric patients from 2011 to 2022, comprising 16 patients with soft tissue RT and 33 patients with RMS who underwent CT or MRI scans. Key imaging features, as well as clinical and pathological data, were compared between the two groups. The multivariate logistic regression analysis was used to determine independent differential factors for distinguishing soft tissue RT from RMS, and the model was established. The final prediction model was visualized by nomograms and verified internally by using a bootstrapped resample 1,000 times. The diagnostic accuracy of the combined model was assessed in terms of discrimination, calibration, and clinical utility. RESULTS: Age, sex, number of lesions, and primary locations were similar in both groups. The imaging characteristics, including margin, calcification, surrounding blood vessels, and rim enhancement, were associated with the two groups of soft tissue tumors, as determined by univariate analysis (all p < 0.05). On multivariate logistic regression analysis, the presence of unclear margin (p-value, adjusted odds ratio [95% confidence interval]: 0.03, 7.96 [1.23, 51.67]) and calcification (0.012, 30.37 [2.09, 440.70]) were independent differential factors for predicting soft tissue RT over RMS. The presence of rim enhancement (0.007, 0.05 [0.01, 0.43]) was an independent differential factor for predicting RMS over soft tissue RT. The comprehensive model established by logistic regression analysis showed an AUC of 0.872 with 81.8% specificity and 81.3% sensitivity. The decision curve analysis (DCA) curve displayed that the model achieved a better net clinical benefit. CONCLUSION: Our study revealed that the image features of calcification, indistinct margins, and a lack of rim enhancement on CT and MRI might be reliable to distinguish soft tissue RT from RMS.
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spelling pubmed-104012622023-08-05 CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children Sheng, Jing Li, Ting-Ting Zhang, Huan-Huan Xu, Hua-Feng Cai, Xue-Mei Xu, Rong Ji, Qiong-Qiong Wu, Yu-Meng Huang, Ting Yang, Xiu-Jun Front Pediatr Pediatrics OBJECTIVE: To assess the computed tomography (CT) and magnetic resonance (MR) imaging characteristics of soft tissue rhabdoid tumors (RT) and compare them with those of rhabdomyosarcoma (RMS). METHODS: We conducted a retrospective analysis of 49 pediatric patients from 2011 to 2022, comprising 16 patients with soft tissue RT and 33 patients with RMS who underwent CT or MRI scans. Key imaging features, as well as clinical and pathological data, were compared between the two groups. The multivariate logistic regression analysis was used to determine independent differential factors for distinguishing soft tissue RT from RMS, and the model was established. The final prediction model was visualized by nomograms and verified internally by using a bootstrapped resample 1,000 times. The diagnostic accuracy of the combined model was assessed in terms of discrimination, calibration, and clinical utility. RESULTS: Age, sex, number of lesions, and primary locations were similar in both groups. The imaging characteristics, including margin, calcification, surrounding blood vessels, and rim enhancement, were associated with the two groups of soft tissue tumors, as determined by univariate analysis (all p < 0.05). On multivariate logistic regression analysis, the presence of unclear margin (p-value, adjusted odds ratio [95% confidence interval]: 0.03, 7.96 [1.23, 51.67]) and calcification (0.012, 30.37 [2.09, 440.70]) were independent differential factors for predicting soft tissue RT over RMS. The presence of rim enhancement (0.007, 0.05 [0.01, 0.43]) was an independent differential factor for predicting RMS over soft tissue RT. The comprehensive model established by logistic regression analysis showed an AUC of 0.872 with 81.8% specificity and 81.3% sensitivity. The decision curve analysis (DCA) curve displayed that the model achieved a better net clinical benefit. CONCLUSION: Our study revealed that the image features of calcification, indistinct margins, and a lack of rim enhancement on CT and MRI might be reliable to distinguish soft tissue RT from RMS. Frontiers Media S.A. 2023-07-21 /pmc/articles/PMC10401262/ /pubmed/37547104 http://dx.doi.org/10.3389/fped.2023.1199444 Text en © 2023 Sheng, Li, Zhang, Xu, Cai, Xu, Ji, Wu, Huang and Yang. 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) (https://creativecommons.org/licenses/by/4.0/) . 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 Pediatrics
Sheng, Jing
Li, Ting-Ting
Zhang, Huan-Huan
Xu, Hua-Feng
Cai, Xue-Mei
Xu, Rong
Ji, Qiong-Qiong
Wu, Yu-Meng
Huang, Ting
Yang, Xiu-Jun
CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
title CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
title_full CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
title_fullStr CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
title_full_unstemmed CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
title_short CT and MR imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
title_sort ct and mr imaging features of soft tissue rhabdoid tumor: compared with rhabdomyosarcoma in children
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401262/
https://www.ncbi.nlm.nih.gov/pubmed/37547104
http://dx.doi.org/10.3389/fped.2023.1199444
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