Second affiliated hospital of Medical school, Zhejiang university
Hangzhou, Zhejiang, China
Location status: Recruiting
NCT Number: NCT07304934
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.
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Female
Observational
Hangzhou, Zhejiang, China
Location status: Recruiting
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Cohort 1-Specific Inclusion Criteria
Participants in Cohort 1 must meet all of the following criteria:
Cohort 2-Specific Inclusion Criteria
Participants in Cohort 2 must meet all of the following criteria:
Exclusion criteria
Participants meeting any of the following criteria will be excluded:
No Intervention: Observational Cohort
Time frame: up to 2 weeks
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: 2 weeks
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
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
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
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
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
Assessment of model calibration and clinical utility using Brier score, calibration intercept, calibration slope, calibration curve, and decision curve analysis.
Time frame: 2 weeks
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
Using final surgical pathology as the reference standard, the predictive performance of the TuFEst-LN model for post-neoadjuvant pathological axillary lymph node status (ypN) will be evaluated, including area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and calibration performance.
Time frame: 2 weeks
Evaluation of the ability of preoperative magnetic resonance imaging (MRI) clinical and quantitative imaging features after neoadjuvant therapy to predict residual pathological axillary lymph node status (ypN0 versus ypN-positive).
Time frame: 2 weeks
Evaluation of the incremental value of integrating TuFEst-LN cfDNA fragmentomic features with MRI-based prediction models by comparing changes in ROC-AUC, Brier score, calibration performance, and decision curve analysis.
Time frame: 2 weeks
Exploration of the association between longitudinal changes in cfDNA fragmentomic scores or features before and after neoadjuvant therapy and pathological outcomes, including ypN status, breast pathological complete response (pCR), residual cancer burden (RCB), and residual tumor burden.
Time frame: 2 weeks
Exploration of the predictive performance of the TuFEst-LN model across clinically relevant subgroups, including molecular subtype, treatment regimen, and baseline lymph node burden.
Contact information is provided by the study sponsor or research team.
Second Affiliated Hospital, Zhejiang University, School of Medicine
Other
A Prospective Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for Axillary Lymph Node Status Assessment in Breast Cancer Patients Undergoing Primary Surgery or Neoadjuvant Therapy
Acronym: PRO-TuFEst-LN
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