Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study
BackgroundPathological complete response (pCR) is considered a surrogate for favorable survival in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NACT), which is the goal of NACT. This study aimed to develop and validate a nomogram for predicting the pCR probability of BC patient...
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.955250/full |
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| author | Bei Qian Jing Yang Jun Zhou Longqing Hu Shoupeng Zhang Min Ren Xincai Qu |
| author_facet | Bei Qian Jing Yang Jun Zhou Longqing Hu Shoupeng Zhang Min Ren Xincai Qu |
| author_sort | Bei Qian |
| collection | DOAJ |
| description | BackgroundPathological complete response (pCR) is considered a surrogate for favorable survival in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NACT), which is the goal of NACT. This study aimed to develop and validate a nomogram for predicting the pCR probability of BC patients after NACT based on the clinicopathological features.MethodsA retrospective analysis of 527 BC patients treated with NACT between January 2018 and December 2021 from two institutions was conducted. Univariate and multivariate logistic regression analyses were performed to select the most useful predictors from the training cohort (n = 225), and then a nomogram model was developed. The performance of the nomogram was evaluated with respect to its discrimination, calibration, and clinical usefulness. Internal validation and external validation were performed in an independent validation cohort of 96 and 205 consecutive BC patients, respectively.ResultsAmong the 18 clinicopathological features, five variables were selected to develop the prediction model, including age, American Joint Committee on Cancer (AJCC) T stage, Ki67 index before NACT, human epidermal growth factor receptor 2 (HER2), and hormone receptor (HR) status. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.825 (95% CI, 0.772 to 0.878) in the training cohort, and 0.755 (95% CI, 0.658 to 0.851) and 0.79 (95% CI, 0.724 to 0.856) in the internal and external validation cohorts, respectively. The calibration curve presented good agreement between prediction by nomogram and actual observation, and decision curve analysis (DCA) indicated that the nomogram had good net benefits in clinical scenarios.ConclusionThis study constructed a validated nomogram based on age, AJCC T stage, Ki67 index before NACT, HER2, and HR status, which could be non-invasively applied to personalize the prediction of pCR in BC patients treated with NACT. |
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| language | English |
| last_indexed | 2024-04-13T13:12:45Z |
| publishDate | 2022-08-01 |
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| series | Frontiers in Endocrinology |
| spelling | doaj.art-52ad4cf1ce1a438b867e19e06bc14a5e2022-12-22T02:45:33ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-08-011310.3389/fendo.2022.955250955250Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter studyBei Qian0Jing Yang1Jun Zhou2Longqing Hu3Shoupeng Zhang4Min Ren5Xincai Qu6Department of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Breast Surgery, Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Breast Surgery, Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaBackgroundPathological complete response (pCR) is considered a surrogate for favorable survival in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NACT), which is the goal of NACT. This study aimed to develop and validate a nomogram for predicting the pCR probability of BC patients after NACT based on the clinicopathological features.MethodsA retrospective analysis of 527 BC patients treated with NACT between January 2018 and December 2021 from two institutions was conducted. Univariate and multivariate logistic regression analyses were performed to select the most useful predictors from the training cohort (n = 225), and then a nomogram model was developed. The performance of the nomogram was evaluated with respect to its discrimination, calibration, and clinical usefulness. Internal validation and external validation were performed in an independent validation cohort of 96 and 205 consecutive BC patients, respectively.ResultsAmong the 18 clinicopathological features, five variables were selected to develop the prediction model, including age, American Joint Committee on Cancer (AJCC) T stage, Ki67 index before NACT, human epidermal growth factor receptor 2 (HER2), and hormone receptor (HR) status. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.825 (95% CI, 0.772 to 0.878) in the training cohort, and 0.755 (95% CI, 0.658 to 0.851) and 0.79 (95% CI, 0.724 to 0.856) in the internal and external validation cohorts, respectively. The calibration curve presented good agreement between prediction by nomogram and actual observation, and decision curve analysis (DCA) indicated that the nomogram had good net benefits in clinical scenarios.ConclusionThis study constructed a validated nomogram based on age, AJCC T stage, Ki67 index before NACT, HER2, and HR status, which could be non-invasively applied to personalize the prediction of pCR in BC patients treated with NACT.https://www.frontiersin.org/articles/10.3389/fendo.2022.955250/fullbreast cancerBCneoadjuvant chemotherapyNACTpathological complete responsepCR |
| spellingShingle | Bei Qian Jing Yang Jun Zhou Longqing Hu Shoupeng Zhang Min Ren Xincai Qu Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study Frontiers in Endocrinology breast cancer BC neoadjuvant chemotherapy NACT pathological complete response pCR |
| title | Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study |
| title_full | Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study |
| title_fullStr | Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study |
| title_full_unstemmed | Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study |
| title_short | Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study |
| title_sort | individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer a multicenter study |
| topic | breast cancer BC neoadjuvant chemotherapy NACT pathological complete response pCR |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.955250/full |
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