Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients.
<h4>Background</h4>A second primary malignant tumor is one of the most important factors affecting the long-term survival of young women with breast cancer (YWBC). As one of the main treatments for breast cancer YWBC patients, postoperative radiotherapy (PORT) may increase the risk of se...
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Public Library of Science (PLoS)
2025-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0316722 |
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| author | Yulin Lai Peiyuan Huang |
| author_facet | Yulin Lai Peiyuan Huang |
| author_sort | Yulin Lai |
| collection | DOAJ |
| description | <h4>Background</h4>A second primary malignant tumor is one of the most important factors affecting the long-term survival of young women with breast cancer (YWBC). As one of the main treatments for breast cancer YWBC patients, postoperative radiotherapy (PORT) may increase the risk of second primary malignancy (SPM).<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. Multivariate logistic regression analysis was used to assess the risk of SPM associated with PORT. The cumulative incidence of SPMs was determined by competing risk regression analysis.<h4>Results</h4>Among the 44223 YWBC patients included in our study, 3017 developed SPMs. Among all the clinical characteristics, PORT was the most common SPM. YWBC patients receiving PORT had significantly greater risks of second primary solid malignancies (SPSMs, RR = 1.61), including breast cancer (RR = 1.89), lung cancer (RR = 2.12) and thyroid cancer (RR = 1.48), but not second primary hematologic malignancies (RR = 1.32; 0.94-1.88). SPSMs were more common in YWBC individuals who were black, had a lower median household income and had fewer lymph nodes examined. Additionally, we developed a prediction nomogram with an area under the curve of 0.75 to assess the likelihood of developing SPMs.<h4>Conclusion</h4>YWBC patients receiving PORT had a greater risk of developing SPSMs (thyroid, lung, and breast cancer), indicating the necessity of long-term surveillance of these patients. Standard adjuvant PORT should not be recommended for breast cancer patients with favorable histology and a low risk of relapse. |
| format | Article |
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| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj-art-0f5d27c545724f4aa4fc42b473187f312025-02-12T05:30:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031672210.1371/journal.pone.0316722Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients.Yulin LaiPeiyuan Huang<h4>Background</h4>A second primary malignant tumor is one of the most important factors affecting the long-term survival of young women with breast cancer (YWBC). As one of the main treatments for breast cancer YWBC patients, postoperative radiotherapy (PORT) may increase the risk of second primary malignancy (SPM).<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. Multivariate logistic regression analysis was used to assess the risk of SPM associated with PORT. The cumulative incidence of SPMs was determined by competing risk regression analysis.<h4>Results</h4>Among the 44223 YWBC patients included in our study, 3017 developed SPMs. Among all the clinical characteristics, PORT was the most common SPM. YWBC patients receiving PORT had significantly greater risks of second primary solid malignancies (SPSMs, RR = 1.61), including breast cancer (RR = 1.89), lung cancer (RR = 2.12) and thyroid cancer (RR = 1.48), but not second primary hematologic malignancies (RR = 1.32; 0.94-1.88). SPSMs were more common in YWBC individuals who were black, had a lower median household income and had fewer lymph nodes examined. Additionally, we developed a prediction nomogram with an area under the curve of 0.75 to assess the likelihood of developing SPMs.<h4>Conclusion</h4>YWBC patients receiving PORT had a greater risk of developing SPSMs (thyroid, lung, and breast cancer), indicating the necessity of long-term surveillance of these patients. Standard adjuvant PORT should not be recommended for breast cancer patients with favorable histology and a low risk of relapse.https://doi.org/10.1371/journal.pone.0316722 |
| spellingShingle | Yulin Lai Peiyuan Huang Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. PLoS ONE |
| title | Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. |
| title_full | Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. |
| title_fullStr | Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. |
| title_full_unstemmed | Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. |
| title_short | Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. |
| title_sort | machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young onset breast cancer patients |
| url | https://doi.org/10.1371/journal.pone.0316722 |
| work_keys_str_mv | AT yulinlai machinelearningidentifiestheassociationbetweensecondprimarymalignanciesandpostoperativeradiotherapyinyoungonsetbreastcancerpatients AT peiyuanhuang machinelearningidentifiestheassociationbetweensecondprimarymalignanciesandpostoperativeradiotherapyinyoungonsetbreastcancerpatients |