A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer
PurposeThis study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.MethodsThis retrospective study analyzed data from 279 breast cancer patients wh...
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| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1323226/full |
| _version_ | 1797311581895262208 |
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| author | Shan Fang Wenjie Xia Haibo Zhang Chao Ni Jun Wu Qiuping Mo Mengjie Jiang Dandan Guan Hongjun Yuan Wuzhen Chen |
| author_facet | Shan Fang Wenjie Xia Haibo Zhang Chao Ni Jun Wu Qiuping Mo Mengjie Jiang Dandan Guan Hongjun Yuan Wuzhen Chen |
| author_sort | Shan Fang |
| collection | DOAJ |
| description | PurposeThis study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.MethodsThis retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People’s Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People’s Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.ResultsIn the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.ConclusionThe developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer. |
| first_indexed | 2024-03-08T02:02:42Z |
| format | Article |
| id | doaj.art-64275c0e49284f3c87af52270d3367a1 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-08T02:02:42Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-64275c0e49284f3c87af52270d3367a12024-02-14T05:02:02ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-02-011410.3389/fonc.2024.13232261323226A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancerShan Fang0Wenjie Xia1Haibo Zhang2Chao Ni3Jun Wu4Qiuping Mo5Mengjie Jiang6Dandan Guan7Hongjun Yuan8Wuzhen Chen9Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaGeneral Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaCancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaDepartment of Breast Surgery (Surgical Oncology), Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaGeneral Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaGeneral Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaDepartment of Radiotherapy, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, ChinaGeneral Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaGeneral Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, ChinaDepartment of Oncology, Lanxi People’s Hospital, Jinhua, ChinaPurposeThis study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.MethodsThis retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People’s Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People’s Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.ResultsIn the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.ConclusionThe developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.https://www.frontiersin.org/articles/10.3389/fonc.2024.1323226/fullbreast cancerpredictive modelneoadjuvant chemotherapypathological complete responseprognosis |
| spellingShingle | Shan Fang Wenjie Xia Haibo Zhang Chao Ni Jun Wu Qiuping Mo Mengjie Jiang Dandan Guan Hongjun Yuan Wuzhen Chen A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer Frontiers in Oncology breast cancer predictive model neoadjuvant chemotherapy pathological complete response prognosis |
| title | A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_full | A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_fullStr | A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_full_unstemmed | A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_short | A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer |
| title_sort | real world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer |
| topic | breast cancer predictive model neoadjuvant chemotherapy pathological complete response prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1323226/full |
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