Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study

Abstract

Background This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics. Methods This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty‐two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co‐occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical‐pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross‐validation was performed using a leave‐one‐out cross‐validation method. Results Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K ‐nearest neighbors ( K‐ NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%. Conclusion QUS‐based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.

A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods

“…In addition, we also implemented stricter data inclusion criteria that result in the exclusion of US RF data from a single site. Although, the best selected features in this study do not demonstrate p < 0.05 statistical significant differences when assessed individually as in [ 54 ], a combination of some of discriminating features still resulted in a multi-feature classification model that predict a priori response to NAC with 88% sensitivity, 78% specificity, 84% accuracy, and 0.86 AUC. In contrast to the results in [ 54 ] where the best classification algorithm was the instance-based nearest neighbours, here the SVM-RBF results in the best response prediction model by combining features from both tumour core and tumour margin.…”

“…This permits for the development of a priori response predictive model based on those features, as demonstrated herein. Recently, DiCenzo et al also reported a multi-institution response predictive model utilizing mean-values and texture features of QUS spectral parametric images from the tumour core [ 54 ]. In that study, the best response predictive results of 91% sensitivity, 83% specificity, 87% accuracy, and 0.73 AUC were attained [ 54 ].…”

“…Recently, DiCenzo et al also reported a multi-institution response predictive model utilizing mean-values and texture features of QUS spectral parametric images from the tumour core [ 54 ]. In that study, the best response predictive results of 91% sensitivity, 83% specificity, 87% accuracy, and 0.73 AUC were attained [ 54 ]. The work presented herein expanded this previous analysis through the addition of tumour margin analysis and the inclusion of texture-derivate features.…”

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

“…The critical premise of quantitative ultrasound (QUS) is the processing of raw RF data from tissue backscatters, which can be used to characterize and distinguish phenotypic changes within a region of interest at a cellular level [12]. In preclinical studies, QUS had demonstrated efficacy in detecting treatment effects of various forms of cancer therapies [13][14][15][16][17]. QUS is sensitive to cell death-related structural changes even within 24 h, or clinically week 1 of treatment, arising from the changes in ultrasound scatterer elastic properties with phenomena like nuclear condensation, fragmentation, and the formation of apoptotic bodies.…”

Radiomics in Predicting Recurrence for Patients with Locally Advanced Breast Cancer using Quantitative Ultrasound

“…Texture analysis from spectral images using gray level co-occurrence matrix (GLCM) can extract second-order imaging features like contrast (CON), correlation (COR), energy (ENE), and homogeneity (HOM), which can provide insights into different aspects of tumour heterogeneity. Studies have demonstrated the clinical e cacy of QUS in predicting response to neoadjuvant chemotherapy (NAC) in LABC [16][17][18][19][20] , and in patients with head-neck malignancies treated with radiotherapy 21 .…”