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Zhu XD, Yu JH, Ai FL, Wang Y, Lv W, Yu GL, Cao XK, Lin J. Construction and Validation of a Novel Nomogram for Predicting the Risk of Metastasis in a Luminal B Type Invasive Ductal Carcinoma Population. World J Oncol 2023; 14:476-487. [PMID: 38022397 PMCID: PMC10681780 DOI: 10.14740/wjon1553] [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: 02/04/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Postoperative distant metastasis is the main cause of death in breast cancer patients. We aimed to construct a nomogram to predict the risk of metastasis of luminal B type invasive ductal carcinoma. Methods We applied the data of 364 luminal B type breast cancer patients between 2008 and 2013. Patients were categorized into modeling group and validation group randomly (1:1). The breast cancer metastasis nomogram was developed from the logistic regression model using clinicopathological variables. The area under the receiver-operating characteristic curve (AUC) was calculated in modeling group and validation group to evaluate the predictive accuracy of the nomogram. Results The multivariate logistic regression analysis showed that tumor size, No. of the positive level 1 axillary lymph nodes, human epidermal growth factor receptor 2 (HER2) status and Ki67 index were the independent predictors of the breast cancer metastasis. The AUC values of the modeling group and the validation group were 0.855 and 0.818, respectively. The nomogram had a well-fitted calibration curve. The positive and negative predictive values were 49.3% and 92.7% in the modeling group, and 47.9% and 91.0% in the validation group. Patients who had a score of 60 or more were thought to have a high risk of breast cancer metastasis. Conclusions The nomogram has a great predictive accuracy of predicting the risk of breast cancer metastasis. If patients had a score of 60 or more, necessary measures, like more standard treatment methods and higher treatment adherence of patients, are needed to take to lower the risk of metastasis and improve the prognosis.
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Affiliation(s)
- Xu Dong Zhu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Jia Hui Yu
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Fu Lu Ai
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
| | - Yue Wang
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
| | - Wu Lv
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
| | - Gui Lin Yu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
| | - Xian Kui Cao
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
| | - Jie Lin
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, Liaoning Province, China
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Wang X, Yi X, Zhang Q, Wang X, Zhang H, Peng S, Wang K, Liao L. Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer. BIOMOLECULES & BIOMEDICINE 2023; 23:680-688. [PMID: 36724018 PMCID: PMC10351098 DOI: 10.17305/bb.2022.8564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/03/2023] [Accepted: 01/03/2023] [Indexed: 01/15/2023]
Abstract
Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGradesignatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM.
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Affiliation(s)
- Xiaomin Wang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, Hunan, China
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, Hunan, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Changsha, Hunan, China
| | - Xiaoping Yi
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, Hunan, China
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, Hunan, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Changsha, Hunan, China
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qian Zhang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiaoxiao Wang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hanghao Zhang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shuai Peng
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Liqiu Liao
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Zhang J, Xiao L, Pu S, Liu Y, He J, Wang K. Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors. Ann Surg Oncol 2021; 28:2632-2645. [PMID: 33095360 PMCID: PMC8043913 DOI: 10.1245/s10434-020-09214-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice. OBJECTIVE The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables. METHODS Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value. RESULTS The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646. CONCLUSION We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.
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Affiliation(s)
- Jian Zhang
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an, 710061, China
| | - Linhai Xiao
- School of Public Health, Fudan University, No. 130 Dong'an Road, Shanghai, 200032, China
| | - Shengyu Pu
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an, 710061, China
| | - Yang Liu
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an, 710061, China
| | - Jianjun He
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an, 710061, China.
| | - Ke Wang
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an, 710061, China.
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Liu C, Zhao Z, Gu X, Sun L, Chen G, Zhang H, Jiang Y, Zhang Y, Cui X, Liu C. Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients. Front Oncol 2019; 9:282. [PMID: 31041192 PMCID: PMC6476951 DOI: 10.3389/fonc.2019.00282] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Accepted: 03/27/2019] [Indexed: 11/16/2022] Open
Abstract
Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application. Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC). Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%). Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.
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Affiliation(s)
- Chao Liu
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zeyin Zhao
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China
| | - Xi Gu
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lisha Sun
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Guanglei Chen
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hao Zhang
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yanlin Jiang
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yixiao Zhang
- Department of Urology Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoyu Cui
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China
| | - Caigang Liu
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
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