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.

Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher’s linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.

Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions. QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. 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. Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively. QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

Https://doi.org/10.18632/oncotarget.28002 Christopher Kolios, Lakshmanan Sannachi, Archya Dasgupta, Harini Suraweera, Daniel DiCenzo, Gregory Stanisz, Arjun Sahgal, Frances Wright, Nicole Look-Hong,...

Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral... Lay abstract Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this s...

Second harmonic generation (SHG) polarization microscopy was used to investigate the organization of myosin nanomotors in myofilaments of muscle cells. The distribution of the second-order nonlinear susceptibility component ratio $χzzz(2)/χzxx(2)$ along anisotropic bands of sarcomeres revealed differences between the headless and head-containing regions of myofilaments. The polarization-in polarization-out SHG measurements of headless myosin mutants of indirect flight muscle in Drosophila melanogaster confirmed a lower susceptibility component ratio compared to the head-containing myocytes with wild-type myosins. The increase in the ratio is assigned to the change in the deflection angle of the myosin S2 domain and possible contribution of myosin heads. The nonlinear susceptibility component ratio is a sensitive indicator of the myosin structure, and therefore, it can be used for conformational studies of myosin nanomotors. The measured ratio values can also be used as the reference for ab initio calculations of nonlinear optical properties of different parts of myosins.