Bulletin of the American Physical Society
2021 Joint Spring Meeting of the Texas Sections of APS, AAPT and Zone 13 of the SPS
Volume 66, Number 2
Thursday–Sunday, April 8–11, 2021; Virtual
Session C20: APS: Biophysics and Medical Physics |
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Saturday, April 10, 2021 3:00PM - 3:12PM |
C20.00001: Detecting Attomolar DNA-Damaging Anticancer Drug Activity in Cell Lysates with Electrochemical DNA Devices Ashan Wettasinghe, Jason Slinker This work, we utilize DNA-based electrochemical devices to quantitatively follow drug-induced DNA damage from the catalytic biochemical reaction of an experimental drug under clinical trials, isobutyl-deoxynyboquinone (IB-DNQ). These measurements are performed directly in lysates of cancer and control cells, and the cancer-selective DNA-damaging nature of these drugs is confirmed. Under cancerous conditions, we observed a limit of detection of IB-DNQ of a mere 380 aM (57000 molecules) and high selectivity over the control. While this speaks to the high sensitivity of these biosensing devices, this low limit was surprising given the lethal concentration (LC$_{\mathrm{50}})$ of 110 nM in cell survival assays and similar activity thresholds in other cell experiments. This discrepancy led to a key observation of the biotechnology: our measurement technique does not require cellular uptake of the drug, permitting us to see its inherent potential for DNA damage. The rate of DNA damage on our chip surface matched that found in the cell survival assay (Hill coefficients, NQO1$+)$. Ultimately, these results speak to the noteworthy potency and selectivity of IB-DNQ and the high sensitivity and precision of electrochemical DNA devices to analyze agents/drugs involved in DNA-damaging immunotherapies. [Preview Abstract] |
Saturday, April 10, 2021 3:12PM - 3:24PM |
C20.00002: Deep Learning Model for Mass Classification in Mammograms Using Artificial Multi-Channel Inputs To Enhance Accuracy Ivan Vazquez, Nathaniel R. Fredette Breast cancer is responsible for approximately 30,000 deaths annually in the US and remains a leading cause of cancer-related death among women. Early and accurate diagnosis increases the chances of survival, with mammography as the primary screening method. Some automated detection tools, like computer-aided diagnosis (CAD) systems, have proven invaluable to find early traces of the disease. However, detection and characterization of masses in mammograms remain tedious and error- prone tasks. Recently, some deep learning (DL) models trained to detect cancerous growth in mammograms have shown performances exceeding that of traditional CAD systems and even radiologists. Yet, most DL models used were designed for multi-channeled color inputs, and typically a single mammogram is repeated for each of the input channels. We propose a method that uses extracted image features as artificial input channels to train the model. We investigate 12 attributes to reveal the best performing set of three. We trained the Xception model using a large open- source mammography dataset. Since this model relies on cross-channel correlations to make predictions, differences in performance will help us understand the benefits of our approach. [Preview Abstract] |
Saturday, April 10, 2021 3:24PM - 3:36PM |
C20.00003: Using the nnU-net Framework for Pectoral Muscle Segmentation and Posterior Nipple Line Quantification Nathaniel Fredette, Ivan Vazquez The length of the posterior nipple line (PNL) is essential for positioning the breast to achieve high quality mammograms. Therefore, a method to determine this parameter quickly while setting up mammographic exams can be valuable. The calculation of PNLs can be described as a three-step process: (1) segmenting and drawing the boundary of the pectoralis muscle (PM), (2) locating the nipple, and (3) calculating the orthogonal Euclidean distance between the nipple and PM. Traditionally, this workflow is performed manually by a radiologist. More recent methods involve hand-engineered image processing techniques. We propose to approach chest wall identification through a nnU-net, which is state of the art in biomedical image segmentation. Additionally, a hand- engineered method using a geometrical approach followed by region growing will be compared with the nnU-net performance. An automatic nipple locator technique will be used in combination with both PM segmentation methods. We will estimate PNLs for the two automatic methods and compare with manual picks. The results from manual picks will be used as the ground truth in our experiments. We expect with enough training data, the nnU-net method will produce distances closer to the ground truths than the existing technique. [Preview Abstract] |
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