Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results

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.

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Publication Details

Journal
npj Precision Oncology
Published
2025-12-04
DOI
https://doi.org/10.1038/s41698-025-01134-x
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results

Mia Skarpathiotakis, Belinda Curpen, Lakshmanan Sannachi, Carlos González et al.
npj Precision Oncology
Breast Cancer Treatment Studies
article

Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results

Mia Skarpathiotakis, Belinda Curpen, Lakshmanan Sannachi, Carlos González, Nicole Look-Hong, Carrie Betel, Ali Sadeghi‐Naini, Daniel Moore-Palhares, Maria Lourdes Anzola Pena, Michael C. Kolios, Sonal Gandhi, Adrian Wai Chan, Archya Dasgupta, Joanne Y. W. Yip, Frances C. Wright, Maureen Trudeau, Amanda Roberts, Ellen Warner, Andrea Eisen, Gregory J. Czarnota, Daniel DiCenzo, David Alberico, Katarzyna J. Jerzak, Rossanna C. Pezo
article en

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.

npj Precision OncologyVol. 9(1)
Sunnybrook Health Science Centre (CA), University of Toronto (CA), York University (CA), Health Sciences Centre (CA), Sunnybrook Hospital (CA), Sunnybrook Research Institute, Toronto Metropolitan University (CA)
Sunnybrook Research Institute, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 53%
Breast Cancer Treatment Studies
DOIOpen Access PDFOpenAlex
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