Abstract
Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomarker for ovarian cancer progression and response to therapy, using contrast-enhanced in vivo imaging. This was done using a dual-modal (magnetic resonance and near infrared optical imaging) uMUC1-specific probe (termed MN-EPPT) consisted of iron-oxide magnetic nanoparticles (MN) conjugated to a uMUC1-specific peptide (EPPT) and labeled with a near-infrared fluorescent dye, Cy5.5. In vitro studies performed in uMUC1-expressing human ovarian cancer cell line SKOV3/Luc and control uMUC1low ES-2 cells showed preferential uptake on the probe by the high expressor (n = 3, p < .05). A decrease in MN-EPPT uptake by SKOV3/Luc cells in vitro due to uMUC1 downregulation after docetaxel therapy was paralleled by in vivo imaging studies that showed a reduction in probe accumulation in the docetaxel treated group (n = 5, p < .05). The imaging data were analyzed using deep learning-enabled segmentation and quantification of the tumor region of interest (ROI) from raw input MRI sequences by applying AI algorithms including a blend of Convolutional Neural Networks (CNN) and Fully Connected Neural Networks. We believe that the algorithms used in this study have the potential to improve studying and monitoring cancer progression, amongst other diseases.
Highlights
In several recent years Artificial Intelligence (AI) algorithms including such as deep learning have demonstrated remarkable progress in image-recognition tasks[1]
In case of uMUC1, it was shown that its downregulation following therapeutic intervention decreases the invasive potential of cancer cells, reduces metastatic burden, and improves s urvival[32,33,34]
In this study we investigated the response to chemotherapy in ovarian cancer by monitoring uMUC1 downregulation probed by MNEPPT imaging probe
Summary
In several recent years Artificial Intelligence (AI) algorithms including such as deep learning have demonstrated remarkable progress in image-recognition tasks[1]. In this study we aimed to utilize novel deep learning algorithms to render high throughput segmentation of tumor region of interest (ROI) as well as an in-depth analysis of tumor response to chemotherapy using an MN-EPPT contrast agent targeting the uMUC1 biomarker. We applied these algorithms in an orthotopic murine model of ovarian cancer but given a wide relevance of uMUC1 to human cancers, this approach can be applied for other human malignancies (Fig. 1)
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