The cover for Issue 42 of Oncotarget features Figure 4, "Generation of parametric and texture maps from radiofrequency data," recently published in "Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer" by Dasgupta, et al. which reported that to investigate quantitative ultrasound based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Three machine learning algorithms based on linear discriminant, k-nearest-neighbors, and support vector machine were used for developing radiomic models of response prediction.

The most helpful features in separating the two response groups were QUS-Tex1-Tex2 features.

The 5-year recurrence-free survival calculated for KNN predicted responders and non-responders using QUS-Tex1-Tex2 model was comparable to RFS for the actual response groups.

The Oncotarget authors report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.

Dr. Gregory J. Czarnota from The Sunnybrook Health Sciences Centre, The University of Toronto, The Sunnybrook Research Institute, as well as York University said, "Breast cancer is the second most common cancer globally in terms of incidence, comprising 11.6% of all new cancers and is the 5th leading cause of mortality attributed to 6.6% of all cancer deaths."

Imaging modalities like ultrasonography, mammography, magnetic resonance imaging, and computed tomography are commonly used in response monitoring of NAC, which primarily considers long-term size related changes of the disease.

Radiofrequency data from QUS provides valuable information compared to conventional B-mode ultrasound imaging, where there is a loss of crucial details involved with instrument-based signal processing. The analysis of the power spectra from ultrasound RF data can be used to determine quantitative parameters, which include average scatterer diameter, average acoustic concentration, mid-band fit, spectral slope, spectral 0-MHz intercept.

The spatial distribution of features within QUS parametric images can be further studied using grey-level co-occurrence matrix analyses, which represent the angular relationship and distance between neighboring pixels.

In the study here, higher-order imaging features in the form of QUS texture-derivatives have been determined from pretreatment QUS data for patients with LABC undergoing NAC to predict treatment response.

The Czarnota Research Team concluded in their Oncotarget Research paper that QUS is a simple, easily accessible imaging modality, with similar scanning techniques akin to the B-mode US, which is widely used in clinical practice.

With appropriate data processing, it is possible to use the information normally processed to generate B-mode images to obtain additional information from tumors. These are related to tumor acoustic properties, which can be linked with biological features and clinical behavior. In this research, we established the role of higher-order imaging analysis (radiomics) of QUS in predicting treatment response to NAC involving 100 patients with LABC.

The work provides a framework for using QUS-radiomics in clinical practice to choose appropriate chemotherapy regimens or other treatment modalities like upfront surgery (in predicted chemo-resistant tumors), leading the way towards personalized oncology.

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DOI - https://doi.org/10.18632/oncotarget.27742

Full text - https://www.oncotarget.com/article/27742/text/

Correspondence to - Gregory J. Czarnota - gregory.czarnota@sunnybrook.ca

Keywords - radiomics, breast cancer, texture derivatives, quantitative ultrasound, neoadjuvant chemotherapy

Oncotarget - Generation Of Parametric And Texture Maps From Radiofrequency Data

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Oncotarget - Generation Of Parametric And Texture Maps From Radiofrequency Data

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