Abstract
Tumor response to neoadjuvant chemotherapy in patients (n = 30) with locally advanced breast cancer (LABC) was examined using quantitative ultrasound. Three ultrasound backscatter parameters, the integrated backscatter coefficient (IBC), average scatterer diameter (ASD), and average acoustic concentration (AAC), were estimated from tumors prior to treatment and at four times during neoadjuvant chemotherapy treatment (weeks 0, 1, 4, 8, and prior to surgery) and compared to ultimate clinical and pathological tumor responses. Results demonstrated that among all parameters, AAC was the best indicator of tumor response early after starting treatment. The AAC parameter increased substantially in treatment-responding patients as early as one week after treatment initiation, further increased at week 4, and attained a maximum at week 8. In contrast, the backscatter parameters from non-responders did not show any changes after treatment initiation. The two patient populations exhibited a statistically significant difference in changes of AAC (p <. 0.001) and ASD (p = 0.023) over all treatment times examined. The best prediction of treatment response was achieved with the combination of AAC and ASD at week 4 (82% sensitivity, 100% specificity, and 86% accuracy) of 12-18. weeks of treatment. The survival of patients with responsive ultrasound parameters was higher than patients with non-responsive ultrasound parameters (35. ±. 11 versus 27. ±. 11. months, respectively, p = 0.043). This study demonstrates that ultrasound parameters derived from the ultrasound backscattered power spectrum can potentially serve as non-invasive early measures of clinical tumor response to chemotherapy treatments.
Original language | English (US) |
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Pages (from-to) | 224-236 |
Number of pages | 13 |
Journal | Medical Image Analysis |
Volume | 20 |
Issue number | 1 |
DOIs | |
State | Published - Feb 1 2015 |
Keywords
- Breast cancer
- Chemotherapy
- Clinical response
- Quantitative ultrasound
- Scattering property
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Radiology Nuclear Medicine and imaging
- Health Informatics
- Radiological and Ultrasound Technology