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Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients

Background: Occult breast cancer (OBC) is an uncommon malignant tumor and the prognosis and treatment of OBC remain controversial. Currently, there exists no accurate prognostic clinical model for OBC, and the treatment outcomes of chemotherapy and surgery in its different molecular subtypes are sti...

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Autores principales: Qu, Jingkun, Li, Chaofan, Liu, Mengjie, Wang, Yusheng, Feng, Zeyao, Li, Jia, Wang, Weiwei, Wu, Fei, Zhang, Shuqun, Zhao, Xixi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179501/
https://www.ncbi.nlm.nih.gov/pubmed/37176539
http://dx.doi.org/10.3390/jcm12093097
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author Qu, Jingkun
Li, Chaofan
Liu, Mengjie
Wang, Yusheng
Feng, Zeyao
Li, Jia
Wang, Weiwei
Wu, Fei
Zhang, Shuqun
Zhao, Xixi
author_facet Qu, Jingkun
Li, Chaofan
Liu, Mengjie
Wang, Yusheng
Feng, Zeyao
Li, Jia
Wang, Weiwei
Wu, Fei
Zhang, Shuqun
Zhao, Xixi
author_sort Qu, Jingkun
collection PubMed
description Background: Occult breast cancer (OBC) is an uncommon malignant tumor and the prognosis and treatment of OBC remain controversial. Currently, there exists no accurate prognostic clinical model for OBC, and the treatment outcomes of chemotherapy and surgery in its different molecular subtypes are still unknown. Methods: The SEER database provided the data used for this study’s analysis (2010–2019). To identify the prognostic variables for patients with ODC, we conducted Cox regression analysis and constructed prognostic models using six machine learning algorithms to predict overall survival (OS) of OBC patients. A series of validation methods, including calibration curve and area under the curve (AUC value) of receiver operating characteristic curve (ROC) were employed to validate the accuracy and reliability of the logistic regression (LR) models. The effectiveness of clinical application of the predictive models was validated using decision curve analysis (DCA). We also investigated the role of chemotherapy and surgery in OBC patients with different molecular subtypes, with the help of K-M survival analysis as well as propensity score matching, and these results were further validated by subgroup Cox analysis. Results: The LR models performed best, with high precision and applicability, and they were proved to predict the OS of OBC patients in the most accurate manner (test set: 1-year AUC = 0.851, 3-year AUC = 0.790 and 5-year survival AUC = 0.824). Interestingly, we found that the N1 and N2 stage OBC patients had more favorable prognosis than N0 stage patients, but the N3 stage was similar to the N0 stage (OS: N0 vs. N1, HR = 0.6602, 95%CI 0.4568–0.9542, p < 0.05; N0 vs. N2, HR = 0.4716, 95%CI 0.2351–0.9464, p < 0.05; N0 vs. N3, HR = 0.96, 95%CI 0.6176–1.5844, p = 0.96). Patients aged >80 and distant metastases were also independent prognostic factors for OBC. In terms of treatment, our multivariate Cox regression analysis discovered that surgery and radiotherapy were both independent protective variables for OBC patients, but chemotherapy was not. We also found that chemotherapy significantly improved both OS and breast cancer-specific survival (BCSS) only in the HR−/HER2+ molecular subtype (OS: HR = 0.15, 95%CI 0.037–0.57, p < 0.01; BCSS: HR = 0.027, 95%CI 0.027–0.81, p < 0.05). However, surgery could help only the HR−/HER2+ and HR+/HER2− subtypes improve prognosis. Conclusions: We analyzed the clinical features and prognostic factors of OBC patients; meanwhile, machine learning prognostic models with high precision and applicability were constructed to predict their overall survival. The treatment results in different molecular subtypes suggested that primary surgery might improve the survival of HR+/HER2− and HR−/HER2+ subtypes, however, only the HR−/HER2+ subtype could benefit from chemotherapy. The necessity of surgery and chemotherapy needs to be carefully considered for OBC patients with other subtypes.
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spelling pubmed-101795012023-05-13 Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients Qu, Jingkun Li, Chaofan Liu, Mengjie Wang, Yusheng Feng, Zeyao Li, Jia Wang, Weiwei Wu, Fei Zhang, Shuqun Zhao, Xixi J Clin Med Article Background: Occult breast cancer (OBC) is an uncommon malignant tumor and the prognosis and treatment of OBC remain controversial. Currently, there exists no accurate prognostic clinical model for OBC, and the treatment outcomes of chemotherapy and surgery in its different molecular subtypes are still unknown. Methods: The SEER database provided the data used for this study’s analysis (2010–2019). To identify the prognostic variables for patients with ODC, we conducted Cox regression analysis and constructed prognostic models using six machine learning algorithms to predict overall survival (OS) of OBC patients. A series of validation methods, including calibration curve and area under the curve (AUC value) of receiver operating characteristic curve (ROC) were employed to validate the accuracy and reliability of the logistic regression (LR) models. The effectiveness of clinical application of the predictive models was validated using decision curve analysis (DCA). We also investigated the role of chemotherapy and surgery in OBC patients with different molecular subtypes, with the help of K-M survival analysis as well as propensity score matching, and these results were further validated by subgroup Cox analysis. Results: The LR models performed best, with high precision and applicability, and they were proved to predict the OS of OBC patients in the most accurate manner (test set: 1-year AUC = 0.851, 3-year AUC = 0.790 and 5-year survival AUC = 0.824). Interestingly, we found that the N1 and N2 stage OBC patients had more favorable prognosis than N0 stage patients, but the N3 stage was similar to the N0 stage (OS: N0 vs. N1, HR = 0.6602, 95%CI 0.4568–0.9542, p < 0.05; N0 vs. N2, HR = 0.4716, 95%CI 0.2351–0.9464, p < 0.05; N0 vs. N3, HR = 0.96, 95%CI 0.6176–1.5844, p = 0.96). Patients aged >80 and distant metastases were also independent prognostic factors for OBC. In terms of treatment, our multivariate Cox regression analysis discovered that surgery and radiotherapy were both independent protective variables for OBC patients, but chemotherapy was not. We also found that chemotherapy significantly improved both OS and breast cancer-specific survival (BCSS) only in the HR−/HER2+ molecular subtype (OS: HR = 0.15, 95%CI 0.037–0.57, p < 0.01; BCSS: HR = 0.027, 95%CI 0.027–0.81, p < 0.05). However, surgery could help only the HR−/HER2+ and HR+/HER2− subtypes improve prognosis. Conclusions: We analyzed the clinical features and prognostic factors of OBC patients; meanwhile, machine learning prognostic models with high precision and applicability were constructed to predict their overall survival. The treatment results in different molecular subtypes suggested that primary surgery might improve the survival of HR+/HER2− and HR−/HER2+ subtypes, however, only the HR−/HER2+ subtype could benefit from chemotherapy. The necessity of surgery and chemotherapy needs to be carefully considered for OBC patients with other subtypes. MDPI 2023-04-24 /pmc/articles/PMC10179501/ /pubmed/37176539 http://dx.doi.org/10.3390/jcm12093097 Text en © 2023 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
Qu, Jingkun
Li, Chaofan
Liu, Mengjie
Wang, Yusheng
Feng, Zeyao
Li, Jia
Wang, Weiwei
Wu, Fei
Zhang, Shuqun
Zhao, Xixi
Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients
title Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients
title_full Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients
title_fullStr Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients
title_full_unstemmed Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients
title_short Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients
title_sort prognostic models using machine learning algorithms and treatment outcomes of occult breast cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179501/
https://www.ncbi.nlm.nih.gov/pubmed/37176539
http://dx.doi.org/10.3390/jcm12093097
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