Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests

作者：Caibiao Wei, Yihua Liang, Dan Mo, Qiumei Lin, Zhimin Liu, Meiqin Li, Yuling Qin, Min Fang · 发表于：Frontiers in Endocrinology · 年份：2024 · DOI：10.3389/fendo.2024.1324617 · 被引用次数：3 · 研究领域：Breast Cancer Treatment Studies、Cancer Genomics and Diagnostics、Biomarkers in Disease Mechanisms

Background: Breast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management. Methods: We examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies). Results: The STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient's total sco...