TS-Net: A Deep Learning Framework for Automated Assessment of Longitudinal Tumor Volume Changes in an Orthotopic Breast Cancer Model Using MRI

Abstract

Monitoring tumor volume changes in response to therapeutic agents is a critical step in preclinical drug development. Here, an automated magnetic resonance imaging (MRI)-based approach is proposed using a deep learning framework for tracking longitudinal tumor volume changes in an orthotopic breast cancer model treated with chemotherapy. Longitudinal magnetic resonance images are employed to track changes in tumor volume over time, using an untreated group and a doxorubicin-treated group as the dataset to evaluate treatment effects. Our approach, called Tumor Segmentation-Net (TS-Net), involves replacing the encoder of U-Net with a pre-trained ResNet34 to improve performance. The model was trained using a sample size of n=19 from the untreated group and then subsequently assessed on both the untreated group (n=5) and treated group (n=6). The correlation between the tumor volume determined from the ground truth and that obtained from the trained output was strong ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{R}^{2}$ </tex-math></inline-formula> =0.984, slope=0.996). These results can lead to automated three-dimensional visualization of different longitudinal volume changes with and without treatment. Notably, for small tumors with volumes between 2 and 5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , the proposed TS-Net demonstrated an average Dice similarity coefficient score of 0.85, indicating the ability to reliably detect early tumors that may often be missed. Our approach offers a promising tool for preclinical evaluation of tumor volume changes and treatment efficacy in animal models.