Assessment of the impact of residual tumors at different sites post-neoadjuvant chemotherapy on prognosis in breast cancer patients and development of a disease-free survival prediction model

Author:

Yang Hanzhao1ORCID,Ruan Yuxia1,Sun Yadong1,Wang Peili1,Qiao Jianghua1,Wang Chengzheng1,Liu Zhenzhen2ORCID

Affiliation:

1. Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China

2. Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, No. 127, Dongming Road, Zhengzhou 450008, China

Abstract

Background: Residual disease after neoadjuvant chemotherapy (NAC) in breast cancer patients predicts worse outcomes than pathological complete response. Differing prognostic impacts based on the anatomical site of residual tumors are not well studied. Objectives: The study aims to assess disease-free survival (DFS) in breast cancer patients with different residual tumor sites following NAC and to develop a nomogram for predicting 1- to 3-year DFS in these patients. Design: A retrospective cohort study. Methods: Retrospective analysis of 953 lymph node-positive breast cancer patients with residual disease post-NAC. Patients were categorized into three groups: residual disease in breast (RDB), residual disease in lymph nodes (RDN), and residual disease in both (RDBN). DFS compared among groups. Patients were divided into a training set and a validation set in a 7:3 ratio. Prognostic factors for DFS were analyzed to develop a nomogram prediction model. Results: RDB patients had superior 3-year DFS of 94.6% versus 85.2% for RDN and 81.8% for RDBN ( p < 0.0001). Clinical T stage, N stage, molecular subtype, and postoperative pN stage were independently associated with DFS on both univariate and multivariate analyses. Nomogram integrating clinical tumor-node-metastasis (TNM) stage, molecular subtype, pathological response demonstrated good discrimination (C-index 0.748 training, 0.796 validation cohort), and calibration. Conclusion: The location of residual disease has prognostic implications, with nodal residuals predicting poorer DFS. The validated nomogram enables personalized DFS prediction to guide treatment decisions.

Publisher

SAGE Publications

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