KEYWORDS: Histograms, Tumor growth modeling, Image classification, Breast cancer, Feature extraction, Breast, Data modeling, Image restoration, Deep learning, Education and training
SignificanceUltrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.AimWe aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions.ApproachWe propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis.ResultsThe first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features.ConclusionsThe proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated success in breast cancer diagnosis. However, DOT data pre-processing and reconstruction still require some level of manual operation, for example, contralateral reference selection and data cleaning. In this study, we introduce an automated data pre-processing and reconstruction pipeline to accelerate the DOT clinical translation. The pipeline has integrated several data pre-processing modules and reconstruction methods that are adapted to data. The pipeline is implemented using a graphical user interface. Initial testing has shown that it can automate DOT right after the data acquisition and provides an accurate diagnostic score on cancer vs. benign probability.
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis. Previous diagnostic strategies all require image reconstruction, which hindered real-time diagnosis. In this study, we propose a deep learning approach to combine DOT frequency-domain measurement data and co-registered US images to classify breast lesions. The combined deep learning model achieved an AUC of 0.886 in distinguishing between benign and malignant breast lesions in patient data without reconstructing images.
Significance: “Difference imaging,” which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction.Aim: We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only.Approach: We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions.Results: The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data.Conclusions: The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.
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