Xiangdi Meng1, Hongyu Ma1, Hang Yin1, Huizi Yin1, Lili Yu2, Li Liu1, Tingting Li1, Siqi Wang1, Qingyong Xu3. 1. Department of Breast Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China. 2. Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China. 3. Department of Breast Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China. Electronic address: xuqingyonghmu@163.com.
Abstract
BACKGROUND: The risk of locoregional recurrence (LRR) after mastectomy for breast invasive micropapillary carcinoma (IMPC) remains poorly defined. We aimed to construct an effective prognostic nomogram to estimate the individualized risk of LRR for providing accurate information for long-term follow-up. PATIENTS AND METHODS: A total of 388 patients with breast IMPC were included in the current study. Based on the Cox regression and clinical significance, a nomogram with an online prediction version was created. This model was evaluated and internally validated by concordance index and calibration plot. Receiver operating characteristic curve and decision curve analysis were used to assess the discrimination and clinical utility, and Kaplan-Meier curves estimated the probability of LRR. RESULTS: The variables (age, lymph node metastasis, hormone receptor status, lymphovascular invasion, histologic grade, and adjuvant radiotherapy) were included in the nomogram. This model was well-calibrated to predict the possibility of LRR and displayed favorable clinical utility; the concordance index was 0.86 (95% confidence interval, 0.81-0.91), which was higher than any single predictor. The area under the curve of the nomogram was 0.89, whereas that of the conventional staging system was 0.72. An online prognostic nomogram was built for convenient use. Kaplan-Meier curves showed that the nomogram had a better risk stratification than the conventional staging system. CONCLUSIONS: The nomogram could accurately predict the individualized risk of LRR after mastectomy for breast IMPC. By identifying the risk stratification, this model is expected to assist clinicians and patients in improving long-term follow-up strategies.
BACKGROUND: The risk of locoregional recurrence (LRR) after mastectomy for breast invasive micropapillary carcinoma (IMPC) remains poorly defined. We aimed to construct an effective prognostic nomogram to estimate the individualized risk of LRR for providing accurate information for long-term follow-up. PATIENTS AND METHODS: A total of 388 patients with breast IMPC were included in the current study. Based on the Cox regression and clinical significance, a nomogram with an online prediction version was created. This model was evaluated and internally validated by concordance index and calibration plot. Receiver operating characteristic curve and decision curve analysis were used to assess the discrimination and clinical utility, and Kaplan-Meier curves estimated the probability of LRR. RESULTS: The variables (age, lymph node metastasis, hormone receptor status, lymphovascular invasion, histologic grade, and adjuvant radiotherapy) were included in the nomogram. This model was well-calibrated to predict the possibility of LRR and displayed favorable clinical utility; the concordance index was 0.86 (95% confidence interval, 0.81-0.91), which was higher than any single predictor. The area under the curve of the nomogram was 0.89, whereas that of the conventional staging system was 0.72. An online prognostic nomogram was built for convenient use. Kaplan-Meier curves showed that the nomogram had a better risk stratification than the conventional staging system. CONCLUSIONS: The nomogram could accurately predict the individualized risk of LRR after mastectomy for breast IMPC. By identifying the risk stratification, this model is expected to assist clinicians and patients in improving long-term follow-up strategies.
Authors: Georgios-Ioannis Verras; Levan Tchabashvili; Francesk Mulita; Ioanna Maria Grypari; Sofia Sourouni; Evangelia Panagodimou; Maria-Ioanna Argentou Journal: Breast Cancer (Dove Med Press) Date: 2022-03-12
Authors: Xiangdi Meng; Nan Wang; Meng Yu; Dechen Kong; Zhengtao Zhang; Xiaolong Chang; Yinghua Guo; Yang Li Journal: Front Public Health Date: 2022-09-09