Prediction of axillary lymph node metastasis in T1 breast cancer using diffuse optical tomography, strain elastography and molecular markers.

Background: Early-stage breast cancer (BC) presents a certain risk of axillary lymph node (ALN) metastasis (ALNM), leading to different individualized treatment. Preoperative non-invasive prediction to determine ALN status is of great significance for avoiding ineffective axillary surgery. Tumor total hemoglobin concentration (TTHC), strain ratio (SR), and Ki-67 expression are associated with ALNM in BC, but few studies have focused on T1 BC. This study aimed to explore the usefulness of these factors individually and in combination for the preoperative prediction of ALNM in T1 BC. Methods: This was a cross-sectional study. A total of 122 patients with T1 BC were enrolled. TTHC and SR were assessed preoperatively using diffuse optical tomography and strain elastography, respectively. All patients were pathologically evaluated to determine ALN status. Univariate analysis and logistic regression trend test were performed to identify independent predictors. A combined model of imaging-pathological parameters for ALNM was developed. Results: Histopathological analysis indicated that 56 patients (45.9%) exhibited ALNM. The fully adjusted model demonstrated a significant trend correlating increased TTHC (P<0.01 for trend), SR (P<0.001 for trend), and Ki-67 expression (P=0.004 for trend) with ALNM. The area under the receiver operating characteristic curves (AUCs) of TTHC, SR, and Ki-67 expression for predicting ALNM were 0.707 [95% confidence interval (CI): 0.61-0.80], 0.718 (95% CI: 0.63-0.81) and 0.642 (95% CI: 0.56-0.73), respectively. The integration of imaging parameters (TTHC and SR) and Ki-67 expression yielded superior predictive performance for ALN status compared to each parameter individually, as evidenced by an AUC of 0.837 (95% CI: 0.76-0.91). The Hosmer-Lemeshow test P value was 0.880, demonstrating good calibration. In the subgroup analysis, the model exhibited positive predictive capabilities (AUC >0.75) across various subgroups, including molecular subtype, pathological type, and grade. Notably, the predictive performance was particularly enhanced in patients with invasive lobular carcinomas and triple-negative BC, with AUC values exceeding 0.90 for both subgroups. Conclusions: The study found that TTHC ≥185.75 µmol/L, SR ≥3.93 and Ki-67 expression ≥20% were strongly associated with ALNM in patients with T1 BC. The combined imaging-pathological model might provide a convenient preoperative method for predicting ALN status, which can assist clinicians in individualizing management for patients with early-stage BC.