Imaging- and Tumor Biomarker-Based Multivariable Model for Early Prediction of Pathologic Complete Response to...

Area of Science:

• Oncology
• Radiology
• Biomarkers

Background:

• Triple-negative breast cancer (TNBC) exhibits variable response to neoadjuvant therapy (NAT).
• Accurate prediction of treatment response is essential for optimizing patient management.
• Pathologic complete response (pCR) is a key indicator of long-term outcomes.

Purpose of the Study:

• To evaluate clinicopathologic biomarkers and volumetric changes on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
• To determine the predictive performance of these factors for achieving pCR in TNBC patients undergoing NAT.
• To develop and validate a predictive model for pCR.

Main Methods:

• A prospective clinical trial included 264 patients with stage I-III TNBC.
• DCE-MRI was performed at baseline and after 2 and/or 4 cycles of NAT.
• Tumor volume (TV) was measured, and tumor volume reduction (TVR) was calculated. Clinicopathologic markers (Ki-67, sTILs) were analyzed.
• Patients were randomized into discovery and validation cohorts. Logistic regression and ROC analysis were used for model development and assessment.

Main Results:

• 47% of patients achieved pCR.
• Optimal thresholds for TVR were ≥60% after 2 cycles and ≥90% after 4 cycles.
• TVR, Ki-67, and sTILs were independently associated with pCR.
• Combined models incorporating TVR, Ki-67, and sTILs demonstrated high predictive performance (AUC 0.79-0.84) in both cohorts.

Conclusions:

• A predictive model combining DCE-MRI derived TVR with Ki-67 and sTILs shows robust performance.
• This model can aid in predicting pCR to NAT in TNBC patients.
• Integrating imaging and clinicopathologic data improves prediction accuracy.