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Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning
BACKGROUND: Neuroendocrine cervical carcinoma (NECC) is a rare but aggressive malignancy with younger patients compared to other common histology types. This study aimed to evaluate the impacts of ovarian preservation (OP) on the prognosis of NECC through machine learning. METHODS: Between 2013 and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176922/ https://www.ncbi.nlm.nih.gov/pubmed/37173713 http://dx.doi.org/10.1186/s12957-023-03014-9 |
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author | Xiang, Xuesong Zhang, Yunqiang Hua, Keqin Ding, Jingxin |
author_facet | Xiang, Xuesong Zhang, Yunqiang Hua, Keqin Ding, Jingxin |
author_sort | Xiang, Xuesong |
collection | PubMed |
description | BACKGROUND: Neuroendocrine cervical carcinoma (NECC) is a rare but aggressive malignancy with younger patients compared to other common histology types. This study aimed to evaluate the impacts of ovarian preservation (OP) on the prognosis of NECC through machine learning. METHODS: Between 2013 and 2021, 116 NECC patients with a median age of 46 years received OP or bilateral salpingo-oophorectomy (BSO) and were enrolled in a retrospective analysis with a median follow-up of 41 months. The prognosis was estimated using Kaplan–Meier analysis. Random forest, LASSO, stepwise, and optimum subset prognostic models were constructed in training cohort (randomly selected 70 patients) and tested in 46 patients through receiver operator curves. Risk factors for ovarian metastasis were identified through univariate and multivariate regression analyses. All data processing was carried out in R 4.2.0 software. RESULTS: Among 116 patients, 30 (25.9%) received OP and showed no significantly different OS compared with BSO group (p = 0.072) and got better DFS (p = 0.038). After construction of machine learning models, the safety of OP was validated in lower prognostic risk group (p > 0.05). In patients ≤ 46 years, no impacts of OP were shown for DFS (p = 0.58) or OS (p = 0.67), and OP had no impact on DFS in different relapse risk population (p > 0.05). In BSO group, regression analyses showed that later stage, para-aortic LNM, and parametrial involvement were associated with ovarian metastasis (p < 0.05). CONCLUSIONS: Preserving ovaries had no significant impact on prognosis in patients with NECC. OP should be considered cautiously in patients with ovarian metastasis risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-023-03014-9. |
format | Online Article Text |
id | pubmed-10176922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101769222023-05-13 Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning Xiang, Xuesong Zhang, Yunqiang Hua, Keqin Ding, Jingxin World J Surg Oncol Research BACKGROUND: Neuroendocrine cervical carcinoma (NECC) is a rare but aggressive malignancy with younger patients compared to other common histology types. This study aimed to evaluate the impacts of ovarian preservation (OP) on the prognosis of NECC through machine learning. METHODS: Between 2013 and 2021, 116 NECC patients with a median age of 46 years received OP or bilateral salpingo-oophorectomy (BSO) and were enrolled in a retrospective analysis with a median follow-up of 41 months. The prognosis was estimated using Kaplan–Meier analysis. Random forest, LASSO, stepwise, and optimum subset prognostic models were constructed in training cohort (randomly selected 70 patients) and tested in 46 patients through receiver operator curves. Risk factors for ovarian metastasis were identified through univariate and multivariate regression analyses. All data processing was carried out in R 4.2.0 software. RESULTS: Among 116 patients, 30 (25.9%) received OP and showed no significantly different OS compared with BSO group (p = 0.072) and got better DFS (p = 0.038). After construction of machine learning models, the safety of OP was validated in lower prognostic risk group (p > 0.05). In patients ≤ 46 years, no impacts of OP were shown for DFS (p = 0.58) or OS (p = 0.67), and OP had no impact on DFS in different relapse risk population (p > 0.05). In BSO group, regression analyses showed that later stage, para-aortic LNM, and parametrial involvement were associated with ovarian metastasis (p < 0.05). CONCLUSIONS: Preserving ovaries had no significant impact on prognosis in patients with NECC. OP should be considered cautiously in patients with ovarian metastasis risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-023-03014-9. BioMed Central 2023-05-12 /pmc/articles/PMC10176922/ /pubmed/37173713 http://dx.doi.org/10.1186/s12957-023-03014-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xiang, Xuesong Zhang, Yunqiang Hua, Keqin Ding, Jingxin Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
title | Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
title_full | Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
title_fullStr | Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
title_full_unstemmed | Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
title_short | Impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
title_sort | impacts of ovarian preservation on the prognosis of neuroendocrine cervical carcinoma: a retrospective analysis based on machine learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176922/ https://www.ncbi.nlm.nih.gov/pubmed/37173713 http://dx.doi.org/10.1186/s12957-023-03014-9 |
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