Comparing clinicopathological factors and quantitative background parenchymal enhancement to predict pathological complete response after neoadjuvant chemotherapy in breast cancer.

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Abstract

To investigate the predictive value of deep learning (nnU-Net)-based fully automated quantitative background parenchymal enhancement (BPE) metrics and clinicopathological factors for pathological complete response (pCR) in patients with breast cancer receiving neoadjuvant chemotherapy (NACT). This retrospective study included 142 patients who underwent NACT and had pre- and post-treatment magnetic resonance imaging (MRI) scans. The breast parenchyma and tumors were automatically segmented using nnU-Net. Quantitative BPE metrics were calculated using multiple threshold values based on signal intensity and the signal enhancement ratio. Clinicopathological variables analyzed included age, menopausal status, body mass index (BMI), hormone receptor (HR) status (estrogen receptor, progesterone receptor), human epidermal growth factor receptor 2 (HER2) status, Ki-67 proliferation index, histologic grade, tumor and breast parenchymal volumes, and pre-/post-treatment pathological findings. The BPE parameters and clinicopathological data were evaluated using univariate and multivariable logistic regression models. Pre-specified subgroup analyses were performed using Mann-Whitney U tests, and the independence of subgroup BPE signals from menopausal status and BMI was tested in adjusted logistic models. None of the evaluated quantitative BPE metrics showed a statistically significant association with pCR in the overall cohort (<i>P</i> > 0.05 for all). In pre-specified exploratory subgroup analyses, however, baseline BPE was significantly lower among patients with pCR in the HER2-negative subgroup (median 49.8% vs. 59.3%, <i>P</i> = 0.014) and in HR-positive disease (median 44.6% vs. 58.3%, <i>P</i> = 0.003), and a relative increase in BPE during NACT was associated with pCR in HR-positive tumors (<i>P</i> = 0.017). In triple-negative disease, lower post-treatment BPE accompanied pCR (<i>P</i> = 0.010). Multivariable analysis revealed that a high Ki-67 proliferation index [adjusted odds ratio (OR) 1.03 per unit, 95% confidence interval (CI), 1.01-1.04; <i>P</i> = 0.0029] and HER2 positivity (adjusted OR: 2.99, 95% CI, 1.19-7.56; <i>P</i> = 0.020) were strong and independent predictors of pCR. The diagnostic performance [area under the curve (AUC)] of the multivariable model was 0.76 (95% CI: 0.69-0.83). Even when standardized and reproducible quantitative measurements are obtained via deep learning algorithms, BPE dynamics did not independently predict pCR in this single-center cohort; however, hypothesis-generating subgroup-specific signals in HER2-negative and HR-positive disease warrant prospective evaluation. Traditional clinicopathological features reflecting the tumor's intrinsic biology remain the most reliable determinants in this cohort for predicting NACT response. Although custom-trained deep learning models enable standardized and reproducible quantification of BPE, our findings show that BPE dynamics do not independently predict pCR across an unselected NACT cohort. Even a model combining BPE with clinicopathological variables achieved only modest cross-validated discrimination (AUC ≈ 0.69), and this discrimination was fully accounted for by Ki-67 and HER2 status; quantitative BPE added no incremental value. These results do not support the standalone clinical use of quantitative BPE for early response prediction. Clinicians should continue to prioritize intrinsic tumor markers (Ki-67 index, HER2 status), but the subgroup-specific BPE signals observed here are exploratory and require prospective validation before any clinical application.

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