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Published on: December 15, 2014
A nomogram integrating DCE-MRI imaging features and clinicopathological parameters for predicting pathological
Jianlong Wu1, Bo Gao1, Jingna Wu1
1Department of Breast Oncology, Center for Cancer Prevention and Treatment, Meizhou People's Hospital (Huangtang Hospital), Meizhou Academy of Medical Sciences, Meizhou, Guangdong, China.
Background:
HER2-positive breast cancer represents approximately 15-20% of all breast cancers and is characterized by aggressive biological behavior. Neoadjuvant chemotherapy (NAC) has become standard treatment, with pathological complete response (pCR) serving as a crucial prognostic indicator. This study aimed to develop and validate a nomogram integrating dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and clinicopathological parameters for predicting pCR in HER2-positive breast cancer patients receiving NAC.
Methods:
We retrospectively analyzed 183 HER2-positive breast cancer patients who received NAC followed by surgery between January 2015 and December 2024. After excluding 17 patients with incomplete data, 166 patients were randomly divided into training (n=111) and validation (n=55) cohorts. DCE-MRI imaging features including tumor size, lymph node status, apparent diffusion coefficient (ADC) values, and tumor location parameters were extracted. Clinicopathological variables including age, BMI, hormone receptor status, Ki-67 index, clinical staging, and treatment regimen were analyzed. Molecular characterization and multi-omics feature analysis were performed to identify independent predictors of pCR. A nomogram was constructed and validated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
Results:
The overall pCR rate was 44.6% (74/166 evaluable patients). Molecular subtype analysis revealed significantly higher pCR rates in HR-/HER2+ patients (52.2%) versus HR+/HER2+ patients (38.5%, P = 0.048). Gene set enrichment analysis identified HER2 signaling and cell proliferation pathways as the most significantly enriched pathways in pCR responders. Multi-omics feature integration identified ADCmin value, Ki-67 index, tumor size, clinical N stage, and HR status as independent predictors. The nomogram demonstrated excellent discrimination with AUC of 0.823 (95%CI: 0.754-0.892) in the training cohort and 0.795 (95%CI: 0.691-0.899) in the validation cohort. Calibration plots showed good agreement (Hosmer-Lemeshow P = 0.412). DCA confirmed clinical utility across threshold probabilities of 0.15-0.75.
Conclusions:
We successfully developed and validated a nomogram integrating DCE-MRI features and clinicopathological parameters for predicting pCR in HER2-positive breast cancer. This practical tool may facilitate individualized treatment decision-making.
