Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach

Abstract

PurposeThis study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).MethodsUsing a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival.ResultsMulti-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively).ConclusionThis study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.

Photoacoustic signal characterization of cancer treatment response: Correlation with changes in tumor oxygenation

“…To compute the US and PA spectral parameters as a function of time post-treatment, the average, normalized power spectrum (PS norm ) was computed by removing the system dependencies through the use of the reference phantom technique developed for QUS [9]:$P{S}_{normUS}(f,z)=\frac{1}{S}{{\displaystyle \sum _{i=1}^S}{\log }_{10}\left(\frac{U{false(f,z_{i}false)}_{tumor}}{U{false(f,z_{i}false)}_{ref}}\right)}^2$…”

“…Quantitative ultrasound (QUS) [6], [7], [8], [9], [10] and Diffuse Optical Spectroscopy Imaging (DOSI) [11], [12], [13], [14], [15] are two promising modalities in early cancer treatment monitoring. QUS utilizes the frequency content of the ultrasound radiofrequency (US RF) signals to extract relevant information about the changes in the structural properties of cancer tumor tissue during treatment [8].…”

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

“…demonstrated that the mean of intensity of ASD and AAC images derived from US backscatter are effective in a similar clinical application. Furthermore, Tadayyon et al 23. demonstrated that a QUS multi-parametric imaging approach utilizing MBF, SS, and mean scatterer spacing parameters submitted to a k-nearest neighbor (k-NN) classifier is an effective method of predicting breast tumour responses to chemotherapy early during the course of the treatment.…”

“…Sannachi et al 22,. and Tadayyon et al 23. with the following key contributions:

Baseline QUS imaging features were evaluated in terms of their ability to predict breast tumour response in advance of any treatment.

In addition to examining textural features within the tumor core as done previously, QUS features in the tumor’s surrounding tissue, referred to here as the margin, extending 3–10 mm from the core were analyzed.

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“…with the following key contributions:

Baseline QUS imaging features were evaluated in terms of their ability to predict breast tumour response in advance of any treatment.

In addition to examining textural features within the tumor core as done previously, QUS features in the tumor’s surrounding tissue, referred to here as the margin, extending 3–10 mm from the core were analyzed. Quantitative features such as core-to-margin ratio (CMR) and core-to-margin contrast ratio (CMCR) QUS features were examined here, for the first time, and were considered as potential ultrasound-based biomarkers of tumour aggressiveness that determine the likelihood of tumour response.

Molecular biomarkers including estrogen receptor (ER), progesterone receptor (PR), and HER2 were investigated as stand-alone and as additional biomarkers for response prediction.In addition to good versus poor response classification done previously in a different therapy monitoring study23, complete versus incomplete response classification was also investigated but for data obtained a priori to patient treatment.…”