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A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks.

ABSTRACT: We demonstrate the clinical utility of combining quantitative ultrasound (QUS) imaging of the breast with an artificial neural network (ANN) classifier to predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) administration prior to the start of treatment. Using a 6 MHz ultrasound system, radiofrequency (RF) ultrasound data were acquired from 100 patients with biopsy-confirmed locally advanced breast cancer prior to the start of NAC. Quantitative ultrasound mean parameter intensity and texture features were computed from the tumour core and margin, and were compared to the clinical/pathological response and 5-year recurrence-free survival (RFS) of patients. A multi-parametric QUS model in conjunction with an ANN classifier predicted patient response with 96 ± 6% accuracy, and a 0.96 ± 0.08 area under the receiver operating characteristic curve (AUC), compared to 65 ± 10 % accuracy and 0.67 ± 0.14 AUC achieved using a K-Nearest Neighbour (KNN) algorithm. A separate ANN model predicted patient RFS with 85 ± 7% accuracy, and a 0.89 ± 0.11 AUC, whereas the KNN methodology achieved a 58 ± 6 % accuracy and a 0.64 ± 0.09 AUC. The application of ANN for classifying patient response based on tumour QUS features performs well in terms of predicting response to chemotherapy. The findings here provide a framework for developing personalized a priori chemotherapy selection for patients that are candidates for NAC, potentially resulting in improved patient treatment outcomes and prognosis.

SUBMITTER: Tadayyon H

PROVIDER: S-EPMC6570472 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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<i>A priori</i> prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks.

Tadayyon Hadi H Gangeh Mehrdad M Sannachi Lakshmanan L Trudeau Maureen M Pritchard Kathleen K Ghandi Sonal S Eisen Andrea A Look-Hong Nicole N Holloway Claire C Wright Frances F Rakovitch Eileen E Vesprini Danny D Tran William Tyler WT Curpen Belinda B Czarnota Gregory G

Oncotarget 20190611 39

We demonstrate the clinical utility of combining quantitative ultrasound (QUS) imaging of the breast with an artificial neural network (ANN) classifier to predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) administration prior to the start of treatment. Using a 6 MHz ultrasound system, radiofrequency (RF) ultrasound data were acquired from 100 patients with biopsy-confirmed locally advanced breast cancer prior to the start of NAC. Quantitative ultrasound mean parame ...[more]

PMID: 31231468

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Project description:BackgroundAngiogenic activity of cancerous breast tumors can be impacted by neoadjuvant chemotherapy (NAC), thus potentially serving as a marker for response monitoring. While different imaging modalities can aid in evaluation of tumoral vascular changes, ultrasound-based approaches are particularly suitable for clinical use due to their availability and noninvasiveness. In this paper, we make use of quantitative high-definition microvasculature imaging (qHDMI) based on contrast-free ultrasound for assessment of NAC response in breast cancer patients.MethodsPatients with invasive breast cancer recommended treatment with NAC were included in the study and ultrafast ultrasound data were acquired at pre-NAC, mid-NAC, and post-NAC time points. Data acquisitions also took place at two additional timepoints - at two and four weeks after NAC initiation in a subset of patients. Ultrasound data frames were processed within the qHDMI framework to visualize the microvasculature in and around the breast tumors. Morphological analyses on the microvasculature structure were performed to obtain 12 qHDMI biomarkers. Pathology from surgery classified response using residual cancer burden (RCB) and was used to designate patients as responders (RCB 0/I) and non-responders (RCB II/III). Distributions of imaging biomarkers across the two groups were analyzed using Wilcoxon rank-sum test. The trajectories of biomarker values over time were investigated and linear mixed effects models were used to evaluate interactions between time and group for each biomarker.ResultsOf the 53 patients included in the study, 32 (60%) were responders based on their RCB status. The results of linear mixed effects model analysis showed statistically significant interactions between group and time in six out of the 12 qHDMI biomarkers, indicating differences in trends of microvascular morphological features by responder status. In particular, vessel density (p-value: 0.023), maximum tortuosity (p-value: 0.049), maximum diameter (p-value: 0.002), fractal dimension (p-value: 0.002), mean Murray's deviation (p-value: 0.034), and maximum Murray's deviation (p-value: 0.022) exhibited significantly different trends based on responder status.ConclusionsWe observed microvasculature changes in response to NAC in breast cancer patients using qHDMI as an objective and quantitative contrast-free ultrasound framework. These finding suggest qHDMI may be effective in identifying early response to NAC.

Dataset Information

A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks.

Publications

<i>A priori</i> prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks.

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