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Maimaitiaili A, Li Y, Chai N, Liu Z, Ling R, Zhao Y, Yang H, Liu Y, Liu K, Zhang J, Mao D, Yu Z, Liu Y, Fu P, Wang J, Jiang H, Zhao Z, Tian X, Cao Z, Wu K, Song A, Jin F, Wu P, He J, Fan Z, Zhang H. A nomogram for predicting pathologic node negativity after neoadjuvant chemotherapy in breast cancer patients: a nationwide, multicenter retrospective cohort study (CSBrS-012). Front Oncol 2024; 14:1326385. [PMID: 38800388 PMCID: PMC11116706 DOI: 10.3389/fonc.2024.1326385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 04/24/2024] [Indexed: 05/29/2024] Open
Abstract
Purpose This study aimed to investigate the factors associated with pathologic node-negativity (ypN0) in patients who received neoadjuvant chemotherapy (NAC) to develop and validate an accurate prediction nomogram. Methods The CSBrS-012 study (2010-2020) included female patients with primary breast cancer treated with NAC followed by breast and axillary surgery in 20 hospitals across China. In the present study, 7,711 eligible patients were included, comprising 6,428 patients in the primary cohort from 15 hospitals and 1,283 patients in the external validation cohort from five hospitals. The hospitals were randomly assigned. The primary cohort was randomized at a 3:1 ratio and divided into a training set and an internal validation set. Univariate and multivariate logistic regression analyses were performed on the training set, after which a nomogram was constructed and validated both internally and externally. Results In total, 3,560 patients (46.2%) achieved ypN0, and 1,558 patients (20.3%) achieved pathologic complete response in the breast (bpCR). A nomogram was constructed based on the clinical nodal stage before NAC (cN), ER, PR, HER2, Ki67, NAC treatment cycle, and bpCR, which were independently associated with ypN0. The area under the receiver operating characteristic curve (AUC) for the training set was 0.80. The internal and external validation demonstrated good discrimination, with AUCs of 0.79 and 0.76, respectively. Conclusion We present a real-world study based on nationwide large-sample data that can be used to effectively screen for ypN0 to provide better advice for the management of residual axillary disease in breast cancer patients undergoing NAC.
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Affiliation(s)
- Amina Maimaitiaili
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yijun Li
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Na Chai
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Zhenzhen Liu
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Rui Ling
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Yi Zhao
- Surgical Oncology Department, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hongjian Yang
- Department of Breast Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yunjiang Liu
- Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ke Liu
- Fourth Department of Breast Surgery, Jilin Cancer Hospital, Changchun, China
| | - Jianguo Zhang
- Department of Breast Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dahua Mao
- Department of Breast Surgery, Affiliated Wudang Hospital of Guizhou Medical University, Guiyang, China
| | - Zhigang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yinhua Liu
- Breast Disease Center, Peking University First Hospital, Beijing, China
| | - Peifen Fu
- Department of Breast Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiandong Wang
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hongchuan Jiang
- Department of Breast Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Zuowei Zhao
- Department of Breast Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xingsong Tian
- Department of Breast and Thyroid Surgery , Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Zhongwei Cao
- Department of Thyroid, Breast, Hernia Surgery, The Inner Mongolia Autonomous Region People’s Hospital, Hohhot, China
| | - Kejin Wu
- Department of Breast Surgery, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Ailin Song
- Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, China
| | - Feng Jin
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Puzhao Wu
- Department of Vascular Surgery/Interventional Medicine, Xiang yang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jianjun He
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Zhimin Fan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
| | - Huimin Zhang
- Department of Breast Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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Wang J, Tian C, Zheng BJ, Zhang J, Jiao DC, Qu JR, Liu ZZ. The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer. BMC Cancer 2024; 24:549. [PMID: 38693523 PMCID: PMC11062000 DOI: 10.1186/s12885-024-12257-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/12/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). METHODS We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. RESULTS A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. CONCLUSIONS The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.
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Affiliation(s)
- Jia Wang
- Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China
| | - Cong Tian
- Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China
| | - Bing-Jie Zheng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China
| | - Jiao Zhang
- Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China
| | - De-Chuang Jiao
- Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China
| | - Jin-Rong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China.
| | - Zhen-Zhen Liu
- Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China.
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Maimaitiaili A, Chen H, Xie P, Liu Z, Ling R, Zhao Y, Yang H, Liu Y, Liu K, Zhang J, Mao D, Yu Z, Liu Y, Fu P, Wang J, Jiang H, Zhao Z, Tian X, Cao Z, Wu K, Song A, Jin F, He J, Fan Z, Zhang H. Nomogram for predicting axillary upstaging in clinical node-negative breast cancer patients receiving neoadjuvant chemotherapy. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04817-9. [PMID: 37129606 DOI: 10.1007/s00432-023-04817-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 04/24/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE The prediction of axillary lymph node status after neoadjuvant chemotherapy (NAC) becoming critical because of the advocation of the de-escalation of axillary management. We investigate associated factors of axillary upstaging in clinical node-negative (cN0) breast cancer patients receiving NAC to develop and validate an accurate prediction nomogram. METHODS We retrospectively analyzed 1892 breast cancer patients with stage of cT1-3N0 treated by NAC and subsequent surgery between 2010 and 2020 in twenty hospitals across China. Patients randomly divided into a training set and validation set (3:1). Univariate and multivariate logistic regression analysis were performed, after which a nomogram was constructed and validated. RESULTS In total, pathologic node negativity (ypN0) achieved in 1406 (74.3%) patients and another 486 (25.7%) patients upstaged to pathologic node positive (ypN+). Breast pathologic complete response (bpCR) was achieved in 445 (23.5%) patients and non-bpCR in 1447 (76.5%) patients. A nomogram was established by ER, tumor histology, HER2 status, cycle of NAC treatment, and the bpCR, which were confirmed by multivariate logistic analysis as independent predictors of nodal upstaging in the training cohort (n = 1419). The area under the receiver operating characteristic curve (AUC) of the training cohort and validation cohort (n = 473) were 0.73 (95% CI 0.693-0.751) and 0.77 (95% CI 0.723-0.812) respectively. CONCLUSION We present a nomogram with a nationwide large sample data which can effectively predict axillary upstaging after neoadjuvant chemotherapy to give better advice for individualized axillary lymph node management of breast cancer.
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Affiliation(s)
- Amina Maimaitiaili
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi Province, China
| | - Heyan Chen
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi Province, China
| | - Peiling Xie
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi Province, China
| | - Zhenzhen Liu
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Dongming Road, Zhengzhou, 450008, Henan Province, China
| | - Rui Ling
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Yi Zhao
- Surgical Oncology Department, Shengjing Hospital of China Medical University, Shenyang, 110022, Liaoning Province, China
| | - Hongjian Yang
- Department of Breast Surgery, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang Province, China
| | - Yunjiang Liu
- Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 052360, Hebei Province, China
| | - Ke Liu
- Fourth Department of Breast Surgery, Jilin Cancer Hospital, Changchun, 130012, Jilin Province, China
| | - Jianguo Zhang
- Department of Breast Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, Heilongjiang Province, China
| | - Dahua Mao
- Department of Breast Surgery, Affiliated Wudang Hospital of Guizhou Medical University, Guiyang, 550009, Guizhou Province, China
| | - Zhigang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong Province, China
| | - Yinhua Liu
- Breast Disease Center, Peking University First Hospital, Beijing, 100034, China
| | - Peifen Fu
- Department of Breast Surgery, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Jiandong Wang
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, 100852, China
| | - Hongchuan Jiang
- Department of Breast Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China
| | - Zuowei Zhao
- Department of Breast Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, Liaoning Province, China
| | - Xingsong Tian
- Department of Breast and Thyroid Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, 250021, Shandong Province, China
| | - Zhongwei Cao
- Department of Thyroid, Breast, Hernia Surgery, The Inner Mongolia Autonomous Region People's Hospital, Hohhot, 010017, Inner Mongolia Autonomous Region, China
| | - Kejin Wu
- Department of Breast Surgery, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200433, China
| | - Ailin Song
- Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, 730000, Gansu Province, China
| | - Feng Jin
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, 110002, Liaoning Province, China
| | - Jianjun He
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi Province, China.
| | - Zhimin Fan
- Department of Breast Surgery, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, 130021, Jilin Province, China.
| | - Huimin Zhang
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi Province, China.
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Zhou T, Yang M, Wang M, Han L, Chen H, Wu N, Wang S, Wang X, Zhang Y, Cui D, Jin F, Qin P, Wang J. Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods. Front Oncol 2022; 12:1046039. [PMID: 36353547 PMCID: PMC9637839 DOI: 10.3389/fonc.2022.1046039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/10/2022] [Indexed: 11/28/2022] Open
Abstract
Purpose To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods. Methods A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion – post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram’s performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis. Results The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002–1.024; p=0.018), LNS changes (pre-NAT LNS – post-NAT LNS; OR, 2.790; 95% CI, 1.190–6.544; p=0.018), N stage (OR, 0.496; 95% CI, 0.269–0.915; p=0.025), and HER2 status (OR, 2.244; 95% CI, 1.147–4.392; p=0.018) were independent predictors of apCR. The AUCs of the nomogram were 0.74 (95% CI, 0.68–0.81) and 0.76 (95% CI, 0.63–0.90) for training and validation sets, respectively. In RF model, the maximum diameter of the primary lesion, axillary lymph node, and LNS in each cycle, estrogen receptor status, progesterone receptor status, HER2, Ki67, and T and N stages were included in the training set. The final validation set had an AUC value of 0.85 (95% CI, 0.74–0.87). Conclusion Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient’s clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.
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Affiliation(s)
- Tianyang Zhou
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Mengting Yang
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Mijia Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Linlin Han
- Health Management Center, The Second Hospital of Dalian Medical University, Dalian, China
| | - Hong Chen
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Nan Wu
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Shan Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Xinyi Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Yuting Zhang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Di Cui
- Information Center, The Second Hospital of Dalian Medical University, Dalian, China
| | - Feng Jin
- Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Pan Qin
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Jia Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Jia Wang,
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Huang X, Shi Z, Mai J, Liu C, Liu C, Chen S, Lu H, Li Y, He B, Li J, Cun H, Han C, Chen X, Liang C, Liu Z. An MRI-based Scoring System for Preoperative Prediction of Axillary Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer: A Multicenter Retrospective Study. Acad Radiol 2022:S1076-6332(22)00513-X. [DOI: 10.1016/j.acra.2022.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/17/2022] [Accepted: 09/26/2022] [Indexed: 11/29/2022]
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Li Z, Tong Y, Chen X, Shen K. Accuracy of ultrasonographic changes during neoadjuvant chemotherapy to predict axillary lymph node response in clinical node-positive breast cancer patients. Front Oncol 2022; 12:845823. [PMID: 35936729 PMCID: PMC9352991 DOI: 10.3389/fonc.2022.845823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/27/2022] [Indexed: 12/11/2022] Open
Abstract
Purpose To evaluate whether changes in ultrasound features during neoadjuvant chemotherapy (NAC) could predict axillary node response in clinically node-positive breast cancer patients. Methods Patients with biopsy-proven node-positive disease receiving NAC between February 2009 and March 2021 were included. Ultrasound (US) images were obtained using a 5-12-MHz linear array transducer before NAC, after two cycles, and at the completion of NAC. Long and short diameter, cortical thickness, vascularity, and hilum status of the metastatic node were retrospectively reviewed according to breast imaging-reporting and data system (BI-RADS). The included population was randomly divided into a training set and a validation set at a 2:1 ratio using a simple random sampling method. Factors associated with node response were identified through univariate and multivariate analyses. A nomogram combining clinical and changes in ultrasonographic (US) features was developed and validated. The receiver operating characteristic (ROC) and calibration plots were applied to evaluate nomogram performance and discrimination. Results A total of 296 breast cancer patients were included, 108 (36.5%) of whom achieved axillary pathologic complete response (pCR) and 188 (63.5%) had residual nodal disease. Multivariate regression indicated that independent predictors of node pCR contain ultrasound features in addition to clinical features, clinical features including neoadjuvant HER2-targeted therapy and clinical response, ultrasound features after NAC including cortical thickness, hilum status, and reduction in short diameter ≥50%. The nomogram combining clinical features and US features showed better diagnostic performance compared to clinical-only model in the training cohort (AUC: 0.799 vs. 0.699, P=0.001) and the validation cohort (AUC: 0.764 vs. 0.638, P=0.027). Conclusions Ultrasound changes during NAC could improve the accuracy to predict node response after NAC in clinically node-positive breast cancer patients.
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Affiliation(s)
| | | | | | - Kunwei Shen
- *Correspondence: Xiaosong Chen, ; Kunwei Shen,
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Li Y, Zhang J, Wang B, Zhang H, He J, Wang K. Development and Validation of a Nomogram to Predict the Probability of Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy: A Retrospective Cohort Study. Front Surg 2022; 9:878255. [PMID: 35756481 PMCID: PMC9218360 DOI: 10.3389/fsurg.2022.878255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/20/2022] [Indexed: 12/02/2022] Open
Abstract
Background The methods used to predict the pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have some limitations. In this study, we aimed to develop a nomogram to predict breast cancer pCR after NAC based on convenient and economical multi-system hematological indicators and clinical characteristics. Materials and Methods Patients diagnosed from July 2017 to July 2019 served as the training group (N = 114), and patients diagnosed in from July 2019 to July 2021 served as the validation group (N = 102). A nomogram was developed according to eight indices, including body mass index, platelet distribution width, monocyte count, albumin, cystatin C, phosphorus, hemoglobin, and D-dimer, which were determined by multivariate logistic regression. Internal and external validation curves are used to calibrate the nomogram. Results The area under the receiver operating characteristic curve was 0.942 (95% confidence interval 0.892–0.992), and the concordance index indicated that the nomogram had good discrimination. The Hosmer–Lemeshow test and calibration curve showed that the model was well-calibrated. Conclusion The nomogram developed in this study can help clinicians accurately predict the possibility of patients achieving the pCR after NAC. This information can be used to decide the most effective treatment strategies for patients.
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Affiliation(s)
| | | | | | | | | | - Ke Wang
- Correspondence: Jianjun He Ke Wang
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Shi W, Huang X, Wang Y, Wan X, He J, Xu Y, Zhang W, Chen R, Xu L, Zha X, Wang J. A novel nomogram containing efficacy indicators to predict axillary pathologic complete response after neoadjuvant systemic therapy in breast cancer. Front Endocrinol (Lausanne) 2022; 13:1042394. [PMID: 36506067 PMCID: PMC9732273 DOI: 10.3389/fendo.2022.1042394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/08/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Neoadjuvant systemic therapy (NST) could make some clinically node-positive (cN+) breast cancer patients achieve axillary pathologic complete response (pCR). This study aimed to identify the patients who are likely to achieve axillary pCR and help surgeons make surgical decisions on the axilla. METHODS The cN+ breast cancer patients who received NST from 2015 to 2021 at The First Affiliated Hospital of Nanjing Medical University were enrolled. Univariate and multivariate logistic regression analyses were performed, and a nomogram was constructed based on the results of multivariate logistic regression analysis to predict the probability of axillary pCR and validated. RESULTS The axillary pCR was achieved in 208 (38.7%) patients. Patients who had a higher radiological response rate of breast tumor (P = 0.039), smaller longest diameter of positive node after NST (P = 0.028), ER-negative status (P = 0.006), HER2-positive status (P = 0.048) and breast pCR (P < 0.001) were more likely to achieve axillary pCR. The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.795 (95% CI: 0.747-0.843), and the calibration curve showed good agreement. CONCLUSION A nomogram was constructed to predict the axillary pCR of cN+ patients receiving NST based on baseline and efficacy indicators to assist surgeons in making surgical decisions on the axilla.
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Affiliation(s)
- Wenjie Shi
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaofeng Huang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ye Wang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyu Wan
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jinzhi He
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yinggang Xu
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weiwei Zhang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Rui Chen
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lu Xu
- Department of Clinical Nutrition, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoming Zha
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Jue Wang, ; Xiaoming Zha,
| | - Jue Wang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Jue Wang, ; Xiaoming Zha,
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Bi Z, Qiu PF, Zhang Y, Song XG, Chen P, Xie L, Wang YS, Song XR. A Three lncRNA Set: AC009975.1, POTEH-AS1 and AL390243.1 as Nodal Efficacy Biomarker of Neoadjuvant Therapy for HER-2 Positive Breast Cancer. Front Oncol 2021; 11:779140. [PMID: 34938660 PMCID: PMC8685269 DOI: 10.3389/fonc.2021.779140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/11/2021] [Indexed: 12/04/2022] Open
Abstract
Purpose The study aimed to explore whether the expression of lncRNAs in primary tumors could predict nodal efficacy after neoadjuvant therapy (NAT) for HER2+ breast cancer. Methods Total RNA was extracted from HER2+ breast cancer tissues before NAT (n=103) and from 48 pairs of cancers and para-cancers tissues that did not receive NAT. Different lncRNAs were selected by microarray, validated by qPCR, and analyzed to illuminate their potential as nodal efficacy biomarkers after NAT. Results Our results demonstrated that three lncRNA sets, lncRNA-AL390243.1, POTEH-AS1, and lncRNA-AC009975.1, were up-regulated in non-apCR tissues. The AUC value was 0.789 (95%CI: 0.703-0.876). The multivariate logistic regression analysis identified the expression of lncRNA-AL390243.1 (OR 5.143; 95% CI: 1.570-16.847), tumor type (OR 0.144; 95% CI: 0.024-0.855), and nodal stage (OR 0.507; 95% CI: 0.289-0.888) as independent predictors for apCR after NAT in HER2+ patients (all p<0.05). Then the three predictors were used to create a predictive nomogram. The AUC value was 0.859 (95%CI: 0.790-0.929). The calibration curve showed a satisfactory fit between predictive and actual observation based on internal validation with a bootstrap resampling frequency of 1000. Patients with higher expression of lncRNA-AL390243.1 had worse survival. LncRNA-AL390243.1 was up-regulated more in the nodal positive subgroup than in the nodal negative subgroup (p=0.0271). Conclusion The lncRNA-AL390243.1, POTEH-AS1, and lncRNA-AC009975.1 were upregulated in non-apCR breast cancer tissues. These three lncRNAs might have the potential to be used as predictive biomarkers of nodal efficacy of HER2+ breast cancer. Further studies are required to illuminate the underlying molecular mechanisms further.
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Affiliation(s)
- Zhao Bi
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Peng-Fei Qiu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yue Zhang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xing-Guo Song
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Peng Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Li Xie
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yong-Sheng Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xian-Rang Song
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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10
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Gu W, Hu M, Xu L, Ren Y, Mei J, Wang W, Wang C. The Ki-67 Proliferation Index-Related Nomogram to Predict the Response of First-Line Tyrosine Kinase Inhibitors or Chemotherapy in Non-small Cell Lung Cancer Patients With Epidermal Growth Factor Receptor-Mutant Status. Front Med (Lausanne) 2021; 8:728575. [PMID: 34805200 PMCID: PMC8602562 DOI: 10.3389/fmed.2021.728575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/28/2021] [Indexed: 12/24/2022] Open
Abstract
Background: The correlation between Ki-67 and epidermal growth factor receptor (EGFR)- or Kristen rat sarcoma viral oncogene homolog (KRAS)-mutant status in advanced or postoperative-recurrent non-small cell lung cancer (NSCLC) has fewer studies reported, and the prognostic role of Ki-67 with first-line EGFR-tyrosine kinase inhibitors (TKIs) or chemotherapy remains controversial. Methods: A total of 295 patients were tested for EGFR-mutant status in advanced or postoperative-recurrent NSCLC and received first-line EGFR-TKIs or chemotherapy for treatment. Ki-67 expression was retrospectively analyzed by immunohistochemistry. The Kaplan-Meier method was used to calculate survival rates. The multivariate Cox proportional hazards model was used to generate a nomogram. The established nomogram was validated using the calibration plots. Results: The expression levels of Ki-67 were divided into low (<60%, n = 186) and high (≥60%, n = 109) groups, based on the receiver operating characteristic curve. The expression levels of Ki-67 were found to be higher in patients with KRAS mutations when compared to KRAS wildtype, and EGFR wildtype was higher than EGFR mutations. The median overall survival (OS) of the low Ki-67 expression group was significantly longer than that of the high Ki-67 group, no matter in all NSCLC, EGFR mutations, EGFR wildtype, KRAS-mutant status, EGFR-TKIs, or chemotherapy of patients (P < 0.05). Subgroup analysis showed that the KRAS wildtype or EGFR mutations combine with low Ki-67 expression group had the longest median OS than KRAS mutations or EGFR wildtype combine with Ki-67 high expression group (P < 0.05). In the training cohort, the multivariate Cox analysis identified age, serum lactate dehydrogenase (LDH), serum Cyfra211, EGFR mutations, and Ki-67 as independent prognostic factors, and a nomogram was developed based on these covariates. The calibration curve for predicting the 12-, 24-, and 30-month OS showed an optimal agreement between the predicted and actual observed outcomes. Conclusions: The Ki-67 expression-based nomogram can well predict the efficacy of first-line therapy in NSCLC patients with EGFR- or KRAS-mutant status, high expression levels of Ki-67 correlated with a poor prognosis.
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Affiliation(s)
- Weiguo Gu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Mingbin Hu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Linlin Xu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Institute of Molecular Pathology, Nanchang University, Nanchang, China
| | - Yuanhui Ren
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jinhong Mei
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Institute of Molecular Pathology, Nanchang University, Nanchang, China
| | - Weijia Wang
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chunliang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
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11
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Ma Y, Su X, Li X, Zhi X, Jiang K, Xia J, Li H, Yan C, Zhou L. Combined detection of peripheral blood VEGF and inflammation biomarkers to evaluate the clinical response and prognostic prediction of non-operative ESCC. Sci Rep 2021; 11:15305. [PMID: 34315926 PMCID: PMC8316563 DOI: 10.1038/s41598-021-94329-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/03/2021] [Indexed: 12/24/2022] Open
Abstract
An association between angiogenesis/inflammation status and tumor has been reported in various types of cancer. This study sought to assess the role of peripheral blood VEGF and some inflammation biomarkers in evaluating clinical response and prognosis in patients with non-operative esophageal squamous cell carcinoma (ESCC). Peripheral blood of 143 patients with non-operative ESCC at our institute was dynamically collected at 5 time points including 1 day before radiotherapy, during radiotherapy (15f), at the end of radiotherapy, 1 month after radiotherapy, and 3 months after radiotherapy. VEGF expression in the peripheral blood was detected and related inflammation biomarkers such as GPS, CAR and CLR were counted. Logistic regression and Cox regression were implemented respectively to analyze the correlation of each predictor with clinical response and prognosis. The performance of combined testing was estimated using AUCs. Based on independent predictors, a nomogram prediction model was established to predict the probabilities of 1- and 2-year PFS of patients. The effectiveness of the nomogram model was characterized by C-index, AUC, calibration curves and DCA. VEGF and CLR levels at the end of radiotherapy were independent predictors of clinical response, while VEGF and GPS levels at 3 months after radiotherapy were independent prognostic predictors. The efficacy of combined detection of VEGF and CLR is superior to the single detection in evaluating clinical response and prognosis. The nomogram showed excellent accuracy in predicting PFS. The combined detection of VEGF and CLR at the end of radiotherapy can be used to evaluate the clinical response of patients with non-operative ESCC, and the combined detection of VEGF and GPS 3 months after radiotherapy can be used to predict the prognosis. Implemented by nomogram model, it is expected to provide practical and reliable method to evaluate the clinical response and prognosis of patients with non-operative ESCC tool.
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Affiliation(s)
- Yuanyuan Ma
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Xinyu Su
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Xin Li
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Xiaohui Zhi
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Kan Jiang
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Jianhong Xia
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Hongliang Li
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Chen Yan
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China
| | - Liqing Zhou
- Department of Radiation Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China.
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12
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Zhu J, Jiao D, Yan M, Chen X, Wang C, Lu Z, Li L, Sun X, Qin L, Guo X, Zhang C, Qiao J, Li J, Fan Z, Wang H, Zhang J, Yin Y, Fu P, Geng C, Jin F, Jiang Z, Cui S, Liu Z. Establishment and Verification of a Predictive Model for Node Pathological Complete Response After Neoadjuvant Chemotherapy for Initial Node Positive Early Breast Cancer. Front Oncol 2021; 11:675070. [PMID: 33996607 PMCID: PMC8117332 DOI: 10.3389/fonc.2021.675070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/06/2021] [Indexed: 02/06/2023] Open
Abstract
Objective Axillary node status after neoadjuvant chemotherapy (NCT) in early breast cancer patients influences the axillary surgical staging procedure. This study was conducted for the identification of the likelihood of patients being node pathological complete response (pCR) post NCT. We aimed to recognize patients most likely to benefit from sentinel lymph node biopsy (SLNB) following NCT and to reduce the risk of missed detection of positive lymph nodes through the construction and validation of a clinical preoperative scoring prediction model. Methods The existing data (from March 2010 to December 2018) of the Chinese Society of Clinical Oncology Breast Cancer Database (CSCO-BC) was used to evaluate the independent related factors of node pCR after NCT by Binary Logistic Regression analysis. A predictive model was established according to the score of considerable factors to identify ypN0. Model performance was confirmed in a cohort of NCT patients treated between January 2019 and December 2019 in Henan Cancer Hospital, and model discrimination was evaluated via assessing the area under the receiver operating characteristic (ROC) curve (AUC). Results Multivariate regression analysis showed that the node stage before chemotherapy, the expression level of Ki-67, biologic subtype, and breast pCR were all independent related factors of ypN0 after chemotherapy. According to the transformation and summation of odds ratio (OR) values of each variable, the scoring system model was constructed with a total score of 1–5. The AUC for the ROC curves was 0.715 and 0.770 for the training and the validation set accordingly. Conclusions A model was established and verified for predicting ypN0 after chemotherapy in newly diagnosed cN+ patients and the model had good accuracy and efficacy. The underlined effective model can suggest axillary surgical planning, and reduce the risk of missing positive lymph nodes by SLNB after NCT. It has great value for identifying initial cN+ patients who are more appropriate for SLNB post-chemotherapy.
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Affiliation(s)
- Jiujun Zhu
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Dechuang Jiao
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Min Yan
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Xiuchun Chen
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Chengzheng Wang
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Zhenduo Lu
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Lianfang Li
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Xianfu Sun
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Li Qin
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Xuhui Guo
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Chongjian Zhang
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Jianghua Qiao
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Jianbin Li
- Department of Breast Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zhimin Fan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
| | - Haibo Wang
- Department of Breast Cancer Center, Affiliated Hospital of Medical College Qingdao University, Qingdao, China
| | - Jianguo Zhang
- Department of Breast Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yongmei Yin
- Department of Breast Cancer, Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital, Nanjing, China
| | - Peifen Fu
- Department of Breast Center, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cuizhi Geng
- Department of Breast Cancer Center, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China
| | - Feng Jin
- Department of Breast Surgery, The First affiliated Hospital of China Medical University, Shenyang, China
| | - Zefei Jiang
- Department of Breast Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shude Cui
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Zhenzhen Liu
- Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
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13
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Chen P, Zhao T, Bi Z, Zhang ZP, Xie L, Liu YB, Song XG, Song XR, Wang CJ, Wang YS. Laboratory indicators predict axillary nodal pathologic complete response after neoadjuvant therapy in breast cancer. Future Oncol 2021; 17:2449-2460. [PMID: 33878939 DOI: 10.2217/fon-2020-1231] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The purpose was to integrate clinicopathological and laboratory indicators to predict axillary nodal pathologic complete response (apCR) after neoadjuvant therapy (NAT). The pretreatment clinicopathological and laboratory indicators of 416 clinical nodal-positive breast cancer patients who underwent surgery after NAT were analyzed from April 2015 to 2020. Predictive factors of apCR were examined by logistic analysis. A nomogram was built according to logistic analysis. Among the 416 patients, 37.3% achieved apCR. Multivariate analysis showed that age, pathological grading, molecular subtype and neutrophil-to-lymphocyte ratio were independent predictors of apCR. A nomogram was established based on these four factors. The area under the curve (AUC) was 0.758 in the training set. The validation set showed good discrimination, with AUC of 0.732. In subtype analysis, apCR was 23.8, 47.1 and 50.8% in hormone receptor-positive/HER2-, HER2+ and triple-negative subgroups, respectively. According to the results of the multivariate analysis, pathological grade and fibrinogen level were independent predictors of apCR after NAT in HER2+ patients. Except for traditional clinicopathological factors, laboratory indicators could also be identified as predictive factors of apCR after NAT. The nomogram integrating pretreatment indicators demonstrated its distinguishing capability, with a high AUC, and could help to guide individualized treatment options.
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Affiliation(s)
- Peng Chen
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250000, PR China.,Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Tong Zhao
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Zhao Bi
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Zhao-Peng Zhang
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Li Xie
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Yan-Bing Liu
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Xing-Guo Song
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Xian-Rang Song
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Chun-Jian Wang
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
| | - Yong-Sheng Wang
- Shandong Cancer Hospital & Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, 250000, PR China
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