Abstract
Purpose: We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.Materials and Methods: QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.Results: A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.Conclusions: A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.
Highlights
Advanced breast cancer (LABC) is an aggressive type of breast cancer with a wide range of clinical presentations [1, 2]
This study demonstrates that variations in ultrasound imaging systems, acquisition system settings, and different operators collecting the ultrasound RF data from different sites do not influence the ability of Quantitative ultrasound (QUS) spectral parametric imaging and novel derivative texture methods framework in developing an accurate response predictive model
Core needle biopsy specimens were collected from all patients prior to the start of Neoadjuvant chemotherapy (NAC) in order to obtain histological confirmation, determine tumour subtype and hormone receptor status consisting of estrogen receptor (ER), progesterone receptor (PR), and Human epidermal growth factor receptor 2 (HER2) expression
Summary
Advanced breast cancer (LABC) is an aggressive type of breast cancer with a wide range of clinical presentations [1, 2]. Neoadjuvant chemotherapy (NAC) facilitates tumour shrinkage, allowing inoperable tumours to be resected in selected patients. This is followed by surgery and adjuvant radiotherapy and targeted therapy or endocrinal therapy as indicated [4]. Treatment response of LABC to NAC is conventionally evaluated at the conclusion of treatment, several months after treatment initiation. This evaluation is based on pathology assessments commonly using a Miller-Payne (MP) grading system that assesses tumour cellularity between pre-treatment core needle biopsies and post-treatment surgical specimens [6, 7]. Imaging biomarkers that can predict tumour responses at early stages NAC could guide individualized treatments
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