External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study
BackgroundThis study aims to externally validate the performance of the Oncotype DX (ODX) breast cancer (BC) recurrence score nomogram in predicting adjuvant chemotherapy (ACT) for BC after surgery and subsequently develop a machine learning-based model to predict postoperative overall survival (OS)...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1586262/full |
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| author | Dongdong Wang Xinfeng Wang Xin Yang |
| author_facet | Dongdong Wang Xinfeng Wang Xin Yang |
| author_sort | Dongdong Wang |
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| description | BackgroundThis study aims to externally validate the performance of the Oncotype DX (ODX) breast cancer (BC) recurrence score nomogram in predicting adjuvant chemotherapy (ACT) for BC after surgery and subsequently develop a machine learning-based model to predict postoperative overall survival (OS) and guide ACT, demonstrating superior comprehensive performance.MethodsThis analysis leveraged data from the SEER database spanning 2010-2020, alongside a BC cohort from the Beijing Hospital (BJH). Machine learning methods were applied for predictor selection by wrapper methods and the development of the predictive model. The optimal model was determined using the concordance index (C-index), time-dependent calibration curves, time dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The benefit analysis of ACT was primarily conducted using Kaplan-Meier survival analysis.ResultsThe ODX nomogram performed poorly in predicting ACT benefit in both the SEER cohort and the BJH cohort. Subsequently, we employed ten machine learning methods to develop ten prognostic models. The Accelerated oblique random survival forest model (AORSFM), exhibiting the highest prediction performance, was selected. The C-index for AORSFM is 0.799 (95% CI 0.779-0.823) in the SEER cohort and 0.793 (95% CI 0.687-0.934) in the BJH cohort. Furthermore, time-dependent calibration curves, time-dependent ROC analysis, and DCA indicate that the AORSFM demonstrates good calibration, predictive accuracy, and clinical net benefit. A publicly accessible web tool was developed for the AORSFM. Notably, the new staging system based on AORSFM can provide guidance for postoperative ACT in such patients.ConclusionsThe AORSF has the potential to identify postoperative OS and guide ACT in patients with BC. This can assist clinicians in assessing the severity of the disease, facilitating patient follow-up, and aiding in the formulation of adjuvant treatment strategies. |
| format | Article |
| id | doaj-art-ea3259fc94614d7c893ea8423d218e52 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-ea3259fc94614d7c893ea8423d218e522025-08-20T02:33:19ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-05-011510.3389/fonc.2025.15862621586262External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort studyDongdong Wang0Xinfeng Wang1Xin Yang2Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, ChinaBackgroundThis study aims to externally validate the performance of the Oncotype DX (ODX) breast cancer (BC) recurrence score nomogram in predicting adjuvant chemotherapy (ACT) for BC after surgery and subsequently develop a machine learning-based model to predict postoperative overall survival (OS) and guide ACT, demonstrating superior comprehensive performance.MethodsThis analysis leveraged data from the SEER database spanning 2010-2020, alongside a BC cohort from the Beijing Hospital (BJH). Machine learning methods were applied for predictor selection by wrapper methods and the development of the predictive model. The optimal model was determined using the concordance index (C-index), time-dependent calibration curves, time dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The benefit analysis of ACT was primarily conducted using Kaplan-Meier survival analysis.ResultsThe ODX nomogram performed poorly in predicting ACT benefit in both the SEER cohort and the BJH cohort. Subsequently, we employed ten machine learning methods to develop ten prognostic models. The Accelerated oblique random survival forest model (AORSFM), exhibiting the highest prediction performance, was selected. The C-index for AORSFM is 0.799 (95% CI 0.779-0.823) in the SEER cohort and 0.793 (95% CI 0.687-0.934) in the BJH cohort. Furthermore, time-dependent calibration curves, time-dependent ROC analysis, and DCA indicate that the AORSFM demonstrates good calibration, predictive accuracy, and clinical net benefit. A publicly accessible web tool was developed for the AORSFM. Notably, the new staging system based on AORSFM can provide guidance for postoperative ACT in such patients.ConclusionsThe AORSF has the potential to identify postoperative OS and guide ACT in patients with BC. This can assist clinicians in assessing the severity of the disease, facilitating patient follow-up, and aiding in the formulation of adjuvant treatment strategies.https://www.frontiersin.org/articles/10.3389/fonc.2025.1586262/fullbreast cancermachine learningprognostic modelguidance for adjuvant chemotherapyweb calculator |
| spellingShingle | Dongdong Wang Xinfeng Wang Xin Yang External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study Frontiers in Oncology breast cancer machine learning prognostic model guidance for adjuvant chemotherapy web calculator |
| title | External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study |
| title_full | External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study |
| title_fullStr | External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study |
| title_full_unstemmed | External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study |
| title_short | External validation of the Oncotype DX breast cancer recurrence score nomogram and development and validation of a novel machine learning-based model to predict postoperative overall survival and guide adjuvant chemotherapy in ER positive, Her-2 negative breast cancer patients: a retrospective cohort study |
| title_sort | external validation of the oncotype dx breast cancer recurrence score nomogram and development and validation of a novel machine learning based model to predict postoperative overall survival and guide adjuvant chemotherapy in er positive her 2 negative breast cancer patients a retrospective cohort study |
| topic | breast cancer machine learning prognostic model guidance for adjuvant chemotherapy web calculator |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1586262/full |
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