Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

Abstract

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy

“…A board-certified pathologist who remained blinded to the study results examined all pathology samples. A modified response (MR) grading system based on response evaluation criteria in solid tumors (RECIST) 48 and histopathological criteria 38 , 49 was used to categorize the patients into two groups of responders and non-responders, as before 50 . In the MR grading system, the MR score is defined as follows: MR 1: no reduction in tumor size; MR 2: less than 30% reduction in tumor size; MR 3: between 30 and 90% reduction in tumor size or a very low residual tumor cellularity determined histopathologically; MR 4: more than 90% reduction in tumor; MR 5: no evident tumor and no malignant cells identifiable in sections from the site of the tumor (ductal carcinoma in situ may be present).…”

Quantitative ultrasonography as a tool for the evaluation of breast tumor response to neoadjuvant chemotherapy

“…To evaluate the effectiveness of NAC, the above-mentioned pilot studies used the mean values of biomarkers determined for the entire mass of the tumor. The analysis of tumor changes due to chemotherapy may suggest that this is not always the best method (21) . Mean values represent global changes that may mask subtle remodeling of the tissue microstructure, especially at the beginning of therapy.…”

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

“…This technique can detect tumor cell death in response to chemotherapy and, in addition, could predict response to NAC after one-to-four weeks of chemotherapy [ 109 , 110 , 111 , 112 , 113 , 114 ]. Recently, Taleghamar et al acquired multiple parametric images with QUS from 181 locally advanced breast cancer patients before NAC [ 115 ]. After creating a QUS signature composed of four specific features from intratumoral regions, they could predict response to NAC with a sensitivity of 87% and a specificity of 85%.…”