Cargando…

Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis

BACKGROUND: Leiomyosarcoma (LMS) is one of the most common soft tissue sarcomas. LMS is prone to distant metastasis (DM), and patients with DM have a poor prognosis. AIM: In this study, we investigated the risk factors of DM in LMS patients and the prognostic factors of LMS patients with DM. METHODS...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Zhehong, Wei, Junqiang, Cao, Haiying, Song, Mingze, Zhang, Yafang, Jin, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124496/
https://www.ncbi.nlm.nih.gov/pubmed/34859618
http://dx.doi.org/10.1002/cnr2.1594
_version_ 1784711752214118400
author Li, Zhehong
Wei, Junqiang
Cao, Haiying
Song, Mingze
Zhang, Yafang
Jin, Yu
author_facet Li, Zhehong
Wei, Junqiang
Cao, Haiying
Song, Mingze
Zhang, Yafang
Jin, Yu
author_sort Li, Zhehong
collection PubMed
description BACKGROUND: Leiomyosarcoma (LMS) is one of the most common soft tissue sarcomas. LMS is prone to distant metastasis (DM), and patients with DM have a poor prognosis. AIM: In this study, we investigated the risk factors of DM in LMS patients and the prognostic factors of LMS patients with DM. METHODS AND RESULTS: LMS patients diagnosed between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Result (SEER) database. Patients were randomly divided into the training set and validation set. Univariate and multivariate logistic regression analyses were performed, and a nomogram was established. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram. Based on the nomogram, a web‐based nomogram is established. The univariate and multivariate Cox regression analyses were used to assess the prognostic risk factors of LMS patients with DM. Eventually, 2184 patients diagnosed with LMS were enrolled, randomly divided into the training set (n = 1532, 70.14%) and validation set (n = 652, 29.86%). Race, primary site, grade, T stage, and tumor size were correlated with DM incidence in LMS patients. The AUC of the nomogram is 0.715 in training and 0.713 in the validation set. The calibration curve and DCA results showed that the nomogram performed well in predicting the DM risk. A web‐based nomogram was established to predict DM's risk in LMS patients (https://wenn23.shinyapps.io/riskoflmsdm/). Epithelioid LMS, in uterus, older age, giant tumor, multiple organ metastasis, without surgery, and chemotherapy had a poor prognosis. CONCLUSIONS: The established web‐based nomogram (https://wenn23.shinyapps.io/riskoflmsdm/) is an accurate and personalized tool to predict the risks of LMS developing DM. Advanced age, larger tumor, multiple organ metastasis, epithelioid type, uterine LMS, no surgery, and no chemotherapy were associated with poor prognosis in LMS patients with DM.
format Online
Article
Text
id pubmed-9124496
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-91244962022-05-25 Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis Li, Zhehong Wei, Junqiang Cao, Haiying Song, Mingze Zhang, Yafang Jin, Yu Cancer Rep (Hoboken) Clinical Research Articles BACKGROUND: Leiomyosarcoma (LMS) is one of the most common soft tissue sarcomas. LMS is prone to distant metastasis (DM), and patients with DM have a poor prognosis. AIM: In this study, we investigated the risk factors of DM in LMS patients and the prognostic factors of LMS patients with DM. METHODS AND RESULTS: LMS patients diagnosed between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Result (SEER) database. Patients were randomly divided into the training set and validation set. Univariate and multivariate logistic regression analyses were performed, and a nomogram was established. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram. Based on the nomogram, a web‐based nomogram is established. The univariate and multivariate Cox regression analyses were used to assess the prognostic risk factors of LMS patients with DM. Eventually, 2184 patients diagnosed with LMS were enrolled, randomly divided into the training set (n = 1532, 70.14%) and validation set (n = 652, 29.86%). Race, primary site, grade, T stage, and tumor size were correlated with DM incidence in LMS patients. The AUC of the nomogram is 0.715 in training and 0.713 in the validation set. The calibration curve and DCA results showed that the nomogram performed well in predicting the DM risk. A web‐based nomogram was established to predict DM's risk in LMS patients (https://wenn23.shinyapps.io/riskoflmsdm/). Epithelioid LMS, in uterus, older age, giant tumor, multiple organ metastasis, without surgery, and chemotherapy had a poor prognosis. CONCLUSIONS: The established web‐based nomogram (https://wenn23.shinyapps.io/riskoflmsdm/) is an accurate and personalized tool to predict the risks of LMS developing DM. Advanced age, larger tumor, multiple organ metastasis, epithelioid type, uterine LMS, no surgery, and no chemotherapy were associated with poor prognosis in LMS patients with DM. John Wiley and Sons Inc. 2021-12-03 /pmc/articles/PMC9124496/ /pubmed/34859618 http://dx.doi.org/10.1002/cnr2.1594 Text en © 2021 The Authors. Cancer Reports published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Research Articles
Li, Zhehong
Wei, Junqiang
Cao, Haiying
Song, Mingze
Zhang, Yafang
Jin, Yu
Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
title Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
title_full Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
title_fullStr Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
title_full_unstemmed Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
title_short Development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
title_sort development, validation, and visualization of a web‐based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis
topic Clinical Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124496/
https://www.ncbi.nlm.nih.gov/pubmed/34859618
http://dx.doi.org/10.1002/cnr2.1594
work_keys_str_mv AT lizhehong developmentvalidationandvisualizationofawebbasednomogramforpredictingtheincidenceofleiomyosarcomapatientswithdistantmetastasis
AT weijunqiang developmentvalidationandvisualizationofawebbasednomogramforpredictingtheincidenceofleiomyosarcomapatientswithdistantmetastasis
AT caohaiying developmentvalidationandvisualizationofawebbasednomogramforpredictingtheincidenceofleiomyosarcomapatientswithdistantmetastasis
AT songmingze developmentvalidationandvisualizationofawebbasednomogramforpredictingtheincidenceofleiomyosarcomapatientswithdistantmetastasis
AT zhangyafang developmentvalidationandvisualizationofawebbasednomogramforpredictingtheincidenceofleiomyosarcomapatientswithdistantmetastasis
AT jinyu developmentvalidationandvisualizationofawebbasednomogramforpredictingtheincidenceofleiomyosarcomapatientswithdistantmetastasis