Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results.
Authors
Affiliations (16)
Affiliations (16)
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.
- Department of Radiation Oncology, University of Toronto, Toronto, Canada.
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada.
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.
- Department of Medicine, University of Toronto, Toronto, Canada.
- Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada.
- Department of Surgery, University of Toronto, Toronto, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada.
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada.
- Department of Medical Imaging, University of Toronto, Toronto, Canada.
- Department of Physics, Toronto Metropolitan University, Toronto, Canada.
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada. [email protected].
- Department of Radiation Oncology, University of Toronto, Toronto, Canada. [email protected].
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada. [email protected].
- Department of Medical Biophysics, University of Toronto, Toronto, Canada. [email protected].
Abstract
Quantitative ultrasound (QUS) detects early tumor microstructural changes during neoadjuvant chemotherapy (NAC), enabling personalized treatment adaptation. This study assessed the accuracy of machine learning models using serial QUS data to predict treatment response and evaluated their feasibility for guiding treatment personalization. This single-center, phase 2 randomized controlled trial (clinicaltrials.gov NCT04050228, Dec/2019) enrolled stage II-III breast cancer patients planned for standard NAC. QUS imaging was performed at baseline and week 4, with the latter used for response prediction. Patients were randomized 1:1 to standard or experimental arms, stratified by hormone receptor status. In the standard arm, oncologists were blinded to QUS results. In the experimental arm, predictions were disclosed to allow treatment modification at week 4. Final response was determined histopathologically (>30% tumor reduction or <5% cellularity). Between June 2018 and September 2023, 146 patients were enrolled, and 120 randomized (standard: 57, experimental: 63). Response rates were 93.0% (standard) and 96.8% (experimental). The model achieved 92% accuracy, 83% sensitivity, 93% specificity, and 99% positive predictive value. In the experimental arm, 8/63 patients were predicted non-responders, with 4 undergoing treatment modification. QUS-based machine learning enables accurate early response prediction and supports adaptive treatment strategies in future trials.