Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer
BackgroundEarly identification of response to neoadjuvant chemotherapy (NAC) is instrumental in predicting patients prognosis. However, since a fixed criterion with high accuracy cannot be generalized to molecular subtypes, our study first aimed to redefine grades of clinical response to NAC in inva...
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1117538/full |
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| author | Ke-Nie Wang Ke-Nie Wang Ke-Nie Wang Ke-Nie Wang Ya-Jiao Meng Yue Yu Yue Yu Yue Yu Yue Yu Wen-Run Cai Wen-Run Cai Wen-Run Cai Wen-Run Cai Xin Wang Xin Wang Xin Wang Xin Wang Xu-Chen Cao Xu-Chen Cao Xu-Chen Cao Xu-Chen Cao Jie Ge Jie Ge Jie Ge Jie Ge |
| author_facet | Ke-Nie Wang Ke-Nie Wang Ke-Nie Wang Ke-Nie Wang Ya-Jiao Meng Yue Yu Yue Yu Yue Yu Yue Yu Wen-Run Cai Wen-Run Cai Wen-Run Cai Wen-Run Cai Xin Wang Xin Wang Xin Wang Xin Wang Xu-Chen Cao Xu-Chen Cao Xu-Chen Cao Xu-Chen Cao Jie Ge Jie Ge Jie Ge Jie Ge |
| author_sort | Ke-Nie Wang |
| collection | DOAJ |
| description | BackgroundEarly identification of response to neoadjuvant chemotherapy (NAC) is instrumental in predicting patients prognosis. However, since a fixed criterion with high accuracy cannot be generalized to molecular subtypes, our study first aimed to redefine grades of clinical response to NAC in invasive breast cancer patients (IBC). And then developed a prognostic model based on clinical features and ultrasound semantics.MethodsA total of 480 IBC patients were enrolled who underwent anthracycline and taxane-based NAC between 2018 and 2020. The decrease rate of the largest diameter was calculated by ultrasound after NAC and their cut-off points were determined among subtypes. Thereafter, a nomogram was constructed based on clinicopathological and ultrasound-related data, and validated using the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC).ResultsThe optimal cut-off points for predicting pCR were 53.23%, 51.56%, 41.89%, and 53.52% in luminal B-like (HER2 negative), luminal B-like (HER2 positive), HER2 positive, and triple-negative, respectively. In addition, time interval, tumor size, molecular subtypes, largest diameter decrease rate, and change of blood perfusion were significantly associated with pCR (all p < 0.05). The prediction model based on the above variables has great predictive power and clinical value.ConclusionTaken together, our data demonstrated that calculated cut-off points of tumor reduction rates could be reliable in predicting pathological response to NAC and developed nomogram predicting prognosis would help tailor systematic regimens with high precision. |
| first_indexed | 2024-04-09T22:21:35Z |
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| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T22:21:35Z |
| publishDate | 2023-03-01 |
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| series | Frontiers in Oncology |
| spelling | doaj.art-4c5f6308334b474297e8dbe2bb325ce72023-03-22T14:07:40ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.11175381117538Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancerKe-Nie Wang0Ke-Nie Wang1Ke-Nie Wang2Ke-Nie Wang3Ya-Jiao Meng4Yue Yu5Yue Yu6Yue Yu7Yue Yu8Wen-Run Cai9Wen-Run Cai10Wen-Run Cai11Wen-Run Cai12Xin Wang13Xin Wang14Xin Wang15Xin Wang16Xu-Chen Cao17Xu-Chen Cao18Xu-Chen Cao19Xu-Chen Cao20Jie Ge21Jie Ge22Jie Ge23Jie Ge24The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, ChinaDepartment of Obstetrics & Gynecology, Tianjin 4th Centre Hospital, Tianjin, ChinaThe First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, ChinaThe First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, ChinaThe First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, ChinaThe First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, ChinaThe First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Cancer Prevention and Therapy, Tianjin, ChinaTianjin’s Clinical Research Center for Cancer, Tianjin, ChinaKey Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, ChinaBackgroundEarly identification of response to neoadjuvant chemotherapy (NAC) is instrumental in predicting patients prognosis. However, since a fixed criterion with high accuracy cannot be generalized to molecular subtypes, our study first aimed to redefine grades of clinical response to NAC in invasive breast cancer patients (IBC). And then developed a prognostic model based on clinical features and ultrasound semantics.MethodsA total of 480 IBC patients were enrolled who underwent anthracycline and taxane-based NAC between 2018 and 2020. The decrease rate of the largest diameter was calculated by ultrasound after NAC and their cut-off points were determined among subtypes. Thereafter, a nomogram was constructed based on clinicopathological and ultrasound-related data, and validated using the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC).ResultsThe optimal cut-off points for predicting pCR were 53.23%, 51.56%, 41.89%, and 53.52% in luminal B-like (HER2 negative), luminal B-like (HER2 positive), HER2 positive, and triple-negative, respectively. In addition, time interval, tumor size, molecular subtypes, largest diameter decrease rate, and change of blood perfusion were significantly associated with pCR (all p < 0.05). The prediction model based on the above variables has great predictive power and clinical value.ConclusionTaken together, our data demonstrated that calculated cut-off points of tumor reduction rates could be reliable in predicting pathological response to NAC and developed nomogram predicting prognosis would help tailor systematic regimens with high precision.https://www.frontiersin.org/articles/10.3389/fonc.2023.1117538/fulldiagnostic imaginginvasive breast cancernomogramtreatment outcomecutoffs |
| spellingShingle | Ke-Nie Wang Ke-Nie Wang Ke-Nie Wang Ke-Nie Wang Ya-Jiao Meng Yue Yu Yue Yu Yue Yu Yue Yu Wen-Run Cai Wen-Run Cai Wen-Run Cai Wen-Run Cai Xin Wang Xin Wang Xin Wang Xin Wang Xu-Chen Cao Xu-Chen Cao Xu-Chen Cao Xu-Chen Cao Jie Ge Jie Ge Jie Ge Jie Ge Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer Frontiers in Oncology diagnostic imaging invasive breast cancer nomogram treatment outcome cutoffs |
| title | Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer |
| title_full | Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer |
| title_fullStr | Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer |
| title_full_unstemmed | Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer |
| title_short | Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer |
| title_sort | predicting pathological complete response after neoadjuvant chemotherapy a nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer |
| topic | diagnostic imaging invasive breast cancer nomogram treatment outcome cutoffs |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1117538/full |
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