Through the research of this project, we expect to validate the clinical utility of the TuFEst-LN model in assessing axillary lymph node status in breast cancer patients. Specifically, we aim to prospectively validate its ability to identify pathologically node-negative patients among clinically and radiologically assessed cN0 patients undergoing upfront surgery ,and explore the predictive value of the TuFEst-LN model combined with preoperative MRI for ypN status assessment in initially node-positive patients following neoadjuvant therapy.
Through the research of this project, we aim to prospectively validate the locked TuFEst-LN model, a cfDNA fragmentomics-based liquid biopsy model, for axillary lymph node status assessment in patients with breast cancer. This study will evaluate the ability of the TuFEst-LN model to identify pathologically node-negative patients among clinically and radiologically assessed cN0 patients with cT1-3 invasive breast cancer undergoing upfront surgery without neoadjuvant therapy. In addition, this study will explore the predictive value of the TuFEst-LN model combined with preoperative magnetic resonance imaging (MRI) for post-neoadjuvant pathological axillary lymph node status (ypN) in initially node-positive breast cancer patients. Peripheral blood samples and clinical data will be prospectively collected from multiple centers, and model predictions will be compared with final surgical pathology as the reference standard. This study aims to validate the clinical utility of cfDNA fragmentomics-based liquid biopsy for noninvasive axillary lymph node assessment and provide evidence for individualized axillary management in breast cancer.
Study Type
OBSERVATIONAL
Enrollment
400
No Intervention: Observational Cohort
Second affiliated hospital of Medical school, Zhejiang university
Hangzhou, Zhejiang, China
RECRUITINGPathological axillary lymph node positivity rate among TuFEst-LN-negative patients
Defined as the proportion of patients with pathological axillary lymph node positivity (pN1mi or higher) among patients predicted as negative by the locked TuFEst-LN model in Cohort 1. FOR = FN/(TN+FN) = 1-NPV.
Time frame: up to 2 weeks
Negative Predictive Value (NPV) of the TuFEst-LN Model
The proportion of patients with negative pathological axillary lymph node status (pN0 or pN0(i+)) among patients classified as negative by the locked TuFEst-LN model.
Time frame: 2 weeks
Sensitivity and False Negative Rate (FNR) for Detecting Pathological Axillary Lymph Node Positivity
The ability of the locked TuFEst-LN model to identify patients with pathological axillary lymph node metastasis (pN1mi or higher). FNR is defined as 1-sensitivity.
Time frame: 2 weeks
Specificity of the TuFEst-LN Model
The proportion of patients with true pathological node-negative status (pN0 or pN0(i+)) who are correctly classified as negative by the locked TuFEst-LN model.
Time frame: 2 weeks
Positive Predictive Value (PPV), Overall Accuracy, and Discrimination Performance
Evaluation of the predictive performance of the locked TuFEst-LN model, including PPV, overall accuracy, area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve (PR-AUC), and likelihood ratios.
Time frame: 2 weeks
Proportion of Patients Classified as Negative by the TuFEst-LN Model
The proportion of evaluable patients in Cohort 1 who are classified as negative by the locked TuFEst-LN model, used to estimate the potential rate of sentinel lymph node biopsy (SLNB) omission.
Time frame: 2 weeks
Calibration and Net Clinical Benefit of the TuFEst-LN Model
Assessment of model calibration and clinical utility using Brier score, calibration intercept, calibration slope, calibration curve, and decision curve analysis.
Time frame: 2 weeks
Pathological Burden of Missed Positive Cases
Evaluation of pathological characteristics among false-negative patients, including the number of micrometastatic and macrometastatic lymph node cases, extranodal extension, and other adverse pathological features.
Time frame: 2 weeks
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