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.
Highlights
Conventional methods of clinical tumor response assessment involve tracking changes in tumor size, using the guidelines provided by Response Evaluation Criteria in Solid Tumors (RECIST) [1]
This study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid quantitative ultrasound (QUS) biomarker including midband fit (MBF), spectral slope (SS), and SAS could, with relatively high sensitivity and specificity, detect the response of locally advanced breast cancer (LABC) tumors to neoadjuvant chemotherapy (NAC) as early as after 4 weeks of therapy
In terms of statistical analysis, the ∆MBF, ∆spectral intercept (SI), and ∆attenuation coefficient estimate (ACE) parameters in our study demonstrated a significant change in responders at week 1 (p < 0.05), just as tracer uptake change did after one cycle in the PET study (p < 0.05) [4], just as total diffusion change did after one cycle of NAC in the DW-magnetic resonance imaging (MRI) study (p < 0.05) [5], and just as changes in deoxygenated hemoglobin, oxygenated hemoglobin, total hemoglobin concentration, water percentage, and tissue optical index did at week 1 in the diffuse optical spectroscopy (DOS) study (p < 0.05) [3]
Summary
Conventional methods of clinical tumor response assessment involve tracking changes in tumor size, using the guidelines provided by Response Evaluation Criteria in Solid Tumors (RECIST) [1]. Such measurements are ascertained using anatomical-based imaging modalities such as X-ray imaging, magnetic resonance imaging (MRI), or conventional diagnostic ultrasound. Several imaging methods have been developed in research to assess early therapeutic responses of breast tumors, including diffuse optical spectroscopy (DOS) [3], fluoro-deoxyglucose positron emission tomography (FDG-PET) [4], and diffusion-weighted magnetic resonance imaging (DW-MRI) [5]. Ultrasound is relatively inexpensive and safe and its imaging methods with respect to QUS rely on the inherent changes in tissue microstructure to generate tissue contrast, requiring no external contrast agents
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