Cancer Management and Research (Sep 2020)
Construction of Nomograms for Predicting Pathological Complete Response and Tumor Shrinkage Size in Breast Cancer
Abstract
Shuai Yan,1,* Wenjie Wang,2,* Bifa Zhu,3 Xixi Pan,1 Xiaoyan Wu,2 Weiyang Tao1,4 1Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, People’s Republic of China; 2Department of Nutrition and Food Hygiene, The National Key Discipline, School of Public Health, Harbin Medical University, Harbin 150081, People’s Republic of China; 3Department of Oncology, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Xianning 437000, People’s Republic of China; 4Department of Thyroid and Breast Surgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Weiyang TaoDepartment of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin 150081, People’s Republic of ChinaTel/Fax + 86 451 8629 8059Email [email protected]: Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NAC) for the HER2-positive and triple-negative subtypes of breast cancer and is related to survival benefit; however, luminal breast cancer is not sensitive to NAC, and the size of tumor shrinkage is a more meaningful clinical indicator for the luminal breast cancer subtype. We wanted to use a nomogram or formula to develop and implement a series of prediction models for pCR or tumor shrinkage size.Patients and Methods: We developed a prediction model in a primary cohort consisting of 498 patients with invasive breast cancer, and the data were gathered from July 2016 to September 2018. The endpoint was pCR and tumor shrinkage size. In the primary cohort, the HER2-positive cohort, and the triple-negative cohort, multivariate logistic regression analysis was used to screen the significant clinical features and clinicopathological features to develop nomograms. In the luminal group, multivariate linear regression analysis was used to test the risk factors that affect tumor shrinkage size. The area under the receiver operating characteristic curve (AUC) and calibration curves were adopted to evaluate and analyze the discrimination and calibration ability of nomograms. Furthermore, we also performed internal validation and independent validation in the primary cohort.Results: ER status, KI67 status, HER2 status, number of NAC cycles, and tumor size were independent predictive factors of pCR in the primary cohort. These indicators had good discrimination and calibration in the primary and validation cohorts (AUC: 0.873, 0.820). The nomogram for HER2-positive and triple-negative breast cancer (TNBC) had an AUC of 0.820 and 0.785, respectively. Both the HER2 positive and TNBC nomogram calibration curves indicated significant agreement. Moreover, the luminal subtype prediction model was Y (tumor shrinkage size) = − 0.576 × (age at diagnosis) + 2.158 × (number of NAC cycles) + 0.233 × (pre-NAC tumor size) + 51.662.Conclusion: Utilizing this predictive model will enable us to identify patients at high probability for pCR after NAC. Clinicians can stratify these patients and make individualized and personalized recommendations for therapy.Keywords: breast cancer, neoadjuvant chemotherapy, pathologic complete response, nomogram
