Life Sciences Commons^™

Comparison Of Deep Learning Schemes In Grading Non-Alcoholic Fatty Liver Disease Using B-Mode Ultrasound Hepatorenal Window Images With Liver Biopsy As The Gold Standard, Petros Drazinos, Ilias Gatos, Paraskevi F Katsakiori, Stavros Tsantis, Efstratios Syrmas, Stavros Spiliopoulos, Dimitris Karnabatidis, Ioannis Theotokas, Pavlos Zoumpoulis, John D Hazle, George C Kagadis

Faculty, Staff and Student Publications

Background/introduction: To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cortex and liver parenchyma (LP) areas indicated by an expert radiologist.

Methods: A total of 112 consecutively enrolled, biopsy-validated NAFLD patients underwent a regular abdominal B-mode US examination. For each patient, a radiologist obtained a B-mode US image containing RK cortex and LP and marked a point between the RK and LP, around which a window was automatically cropped. The cropped image dataset was augmented using up-sampling, …

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model To Predict Late Xerostomia In Patients With Head And Neck Cancer, Hung Chu, Suzanne P M De Vette, Hendrike Neh, Nanna M Sijtsema, Roel J H M Steenbakkers, Amy Moreno, Johannes A Langendijk, Peter M A Van Ooijen, Clifton D Fuller, Lisanne V Van Dijk

Faculty, Staff and Student Publications

Purpose: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value variables, which, for radiation-induced xerostomia, are baseline xerostomia and mean salivary gland doses. This study aimed to improve the prediction of late xerostomia by using 3-dimensional information from radiation dose distributions, computed tomography imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL).

Methods and materials: An international cohort of 1208 patients with head and neck cancer from 2 institutes was used to train and twice validate DL models (deep convolutional neural network, EfficientNet-v2, and ResNet) with 3-dimensional dose distribution, …

Survival Analysis Of Clear Cell Renal Cell Carcinoma Based On Radiomics And Deep Learning Features From Ct Images, Zhennan Lu, Sijia Wu, Dan Ni, Meng Zhou, Tao Wang, Xiaobo Zhou, Liyu Huang, Yu Yan

Faculty, Staff and Student Publications

Purpose: To create a nomogram for accurate prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on computed tomography images.

Methods: Eight hundred twenty-two ccRCC patients with contrast-enhanced computed tomography images involved in this study were collected. A rectangular region of interest surrounding the tumor was used to extract quantitative radiomics and deep-learning features, which were filtered by Cox proportional hazard regression model and least absolute shrinkage and selection operator. Then the selected features formed a fusion signature, which was assessed by Cox proportional hazard regression model method, Kaplan-Meier analysis, receiver operating characteristic curves, and concordance index (C-index) …

Automated Segmentation Of Mri White Matter Hyperintensities In 8421 Patients With Acute Ischemic Stroke, Hosung Kim, Wi-Sun Ryu, Dawid Schellingerhout, Jonghyeok Park, Jinyong Chung, Sang-Wuk Jeong, Dong-Seok Gwak, Beom Joon Kim, Joon-Tae Kim, Keun-Sik Hong, Kyung Bok Lee, Tai Hwan Park, Jong-Moo Park, Kyusik Kang, Yong-Jin Cho, Byung-Chul Lee, Kyung-Ho Yu, Mi Sun Oh, Soo Joo Lee, Jae-Kwan Cha, Dae-Hyun Kim, Jun Lee, Man Seok Park, Hee-Joon Bae, Dong-Eog Kim

Faculty, Staff and Student Publications

Background and purpose: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction; this issue is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction using multisite data sets involving 8421 patients with acute ischemic stroke.

Materials and methods: We included 8421 patients with stroke from 9 centers in Korea. 2D UNet and squeeze-and-excitation (SE)-UNet models were trained using 2408 FLAIR MRIs from 3 hospitals and validated using 6013 FLAIR …

Cmos-Based Rotational Spectroscopy: Massive Spectral Fingerprint Generation And Molecular Detection With Deep Learning, Yasamin Fozouni

Computer Science and Engineering Theses and Dissertations

Rotational Spectroscopy is a powerful spectral fingerprinting approach that can be used for identifying different gas molecules in a sample. Gas molecules are free to rotate, with inertia, in fixed states of quantized energy. In Rotational Spectroscopy, radiative beams are shown onto a sample to cause an energy-based transition between quantized rotational states. By sweeping the frequency of the radiative beams and monitoring the absorption with a sensor, one can profile the different rotational states, monitoring for energy based transitions. These transitions are dependent on unique properties of the molecules, thus presenting a unique molecular identification fingerprint (in the form …

Prediction Of Pathological Complete Response To Chemotherapy For Breast Cancer Using Deep Neural Network With Uncertainty Quantification, Bowen Jing, Kai Wang, Erich Schmitz, Shanshan Tang, Yunxiang Li, You Zhang, Jing Wang

Faculty, Staff and Student Publications

Background: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early …

Explainable Artificial Intelligence And Domain Adaptation For Predicting Hiv Infection With Graph Neural Networks, Evan Yu, Jingcheng Du, Yang Xiang, Xinyue Hu, Jingna Feng, Xi Luo, John A Schneider, Degui Zhi, Kayo Fujimoto, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.

METHODS: Network data from two cohorts of younger sexual minority men (SMM) from two U.S. cities (Chicago, IL, and Houston, TX) were collected between 2014 and 2016. Feature importance from graph attention network (GAT) models were determined using GNNExplainer. Domain adaptation was performed to examine model transferability from one city dataset to the other dataset, training with 100% of the source dataset with 30% of the target dataset and prediction …

Analyzing And Extending Machine Learning Frameworks On High Risk Domains, Chengbin Hu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) has become a transformative force in high-risk domains such as genomics and cybersecurity, where accurate predictions and robust defenses are essential. This dissertation advances ML frameworks in these areas by developing methods to enhance predictive power in health applications and assess vulnerabilities in machine learning systems.

In the genomics field, the work addresses challenges in Non-Invasive Prenatal Testing (NIPT) of monogenic disorders by proposing a deep learning model that reconstructs the fetal genome using maternal plasma cell-free DNA (cfDNA) and parental whole-genome sequencing (WGS) data. This model achieves high accuracy in single nucleotide variation (SNV) prediction, surpassing …

Immuneapp For Hla-I Epitope Prediction And Immunopeptidome Analysis, Haodong Xu, Ruifeng Hu, Xianjun Dong, Lan Kuang, Wenchao Zhang, Chao Tu, Zhihong Li, Zhongming Zhao

Faculty, Staff and Student Publications

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- …

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Multi-Modal Segmentation With Missing Image Data For Automatic Delineation Of Gross Tumor Volumes In Head And Neck Cancers, Yao Zhao, Xin Wang, Jack Phan, Xinru Chen, Anna Lee, Cenji Yu, Kai Huang, Laurence E Court, Tinsu Pan, He Wang, Kareem Abdul Wahid, Abdalah S R Mohamed, Mohamed Naser, Clifton D Fuller, Jinzhong Yang

Faculty, Staff and Student Publications

Background: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed.

Purpose: To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data.

Methods: Two datasets were included for this study: Dataset I: 524 (training) …

Metadegron: Multimodal Feature-Integrated Protein Language Model For Predicting E3 Ligase Targeted Degrons, Mengqiu Zheng, Shaofeng Lin, Kunqi Chen, Ruifeng Hu, Liming Wang, Zhongming Zhao, Haodong Xu

Faculty, Staff and Student Publications

Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive …

Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles Of Datasets With Different Contrast Protocols And Liver Disease Etiologies, Nihil Patel, Adrian Celaya, Mohamed Eltaher, Rachel Glenn, Kari Brewer Savannah, Kristy K Brock, Jessica I Sanchez, Tiffany L Calderone, Darrel Cleere, Ahmed Elsaiey, Matthew Cagley, Nakul Gupta, David Victor, Laura Beretta, Eugene J Koay, Tucker J Netherton, David T Fuentes

Faculty, Staff and Student Publications

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture's testing performance when trained both …

Hybridizing Mechanistic Modeling And Deep Learning For Personalized Survival Prediction After Immune Checkpoint Inhibitor Immunotherapy, Joseph D Butner, Prashant Dogra, Caroline Chung, Eugene J Koay, James W Welsh, David S Hong, Vittorio Cristini, Zhihui Wang

Faculty, Staff and Student Publications

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, …

Artificial Intelligence In Fusion Protein Three-Dimensional Structure Prediction: Review And Perspective, Himansu Kumar, Pora Kim

Faculty, Staff and Student Publications

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action …

Auto-Segmentation Of Adult-Type Diffuse Gliomas: Comparison Of Transfer Learning-Based Convolutional Neural Network Model Vs Radiologists, Qi Wan, Jisoo Kim, Clifford Lindsay, Xin Chen, Jing Li, J Bryan Iorgulescu, Raymond Y Huang, Chenxi Zhang, David Reardon, Geoffrey S Young, Lei Qin

Faculty, Staff and Student Publications

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consuming. Fully automated tools for the segmentation of multi-sequence MRI are needed. We developed and pretrained a deep learning (DL) model using publicly available datasets A (n = 210) and B (n = 369) containing FLAIR, T2WI, and contrast-enhanced (CE)-T1WI. This was then fine-tuned with our institutional dataset (n = 197) containing ADC, T2WI, and CE-T1WI, manually annotated by radiologists, and split into training (n = 100) and testing (n = 97) sets. The Dice similarity coefficient (DSC) was used to …

Interpretable And Evidential Deep Learning For Medical Image Analysis, Sai Chandra Kosaraju

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automatic histopathological Whole Slide Image (WSI) analysis has been highlighted along with the advancements in microscopic imaging techniques, but manual examination and diagnosis of WSIs are time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. Especially, Convolutional Neural Networks CNN models such as Inception and DenseNet have achieved effective performance. However, automatic histopathological WSI analysis still has significant drawbacks such as considering deep learning as black-box models, predicting disease independently on a small part of images (patch images) extracted from WSIs, limitations in predicting a single slide-based score for a patient, and capturing disease-specific morphology …

Detection Of Diffusely Abnormal White Matter In Multiple Sclerosis On Multiparametric Brain Mri Using Semi-Supervised Deep Learning, Benjamin C Musall, Refaat E Gabr, Yanyu Yang, Arash Kamali, John A Lincoln, Michael A Jacobs, Vi Ly, Xi Luo, Jerry S Wolinsky, Ponnada A Narayana, Khader M Hasan

Faculty, Staff and Student Publications

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to …

Deepface: Deep-Learning-Based Framework To Contextualize Orofacial-Cleft-Related Variants During Human Embryonic Craniofacial Development, Yulin Dai, Toshiyuki Itai, Guangsheng Pei, Fangfang Yan, Yan Chu, Xiaoqian Jiang, Seth M Weinberg, Nandita Mukhopadhyay, Mary L Marazita, Lukas M Simon, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between …

Evaluating The Relationship Between Magnetic Resonance Image Quality Metrics And Deep Learning-Based Segmentation Accuracy Of Brain Tumors, Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P Yung, James P Long, Satish E Viswanath, Daniel S Marcus, Caroline Chung, David Fuentes

Faculty, Staff and Student Publications

Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy.

Purpose: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models …

Evaluating Automatically Generated Normal Tissue Contours For Safe Use In Head And Neck And Cervical Cancer Treatment Planning, Raphael Douglas, Adenike Olanrewaju, Raymond Mumme, Lifei Zhang, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.

Methods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan …

Effects Of Vitamin D Supplementation On A Deep Learning-Based Mammographic Evaluation In Swog S0812, Julia E Mcguinness, Garnet L Anderson, Simukayi Mutasa, Dawn L Hershman, Mary Beth Terry, Parisa Tehranifar, Danika L Lew, Monica Yee, Eric A Brown, Sebastien S Kairouz, Nafisa Kuwajerwala, Therese B Bevers, John E Doster, Corrine Zarwan, Laura Kruper, Lori M Minasian, Leslie Ford, Banu Arun, Marian L Neuhouser, Gary E Goodman, Powel H Brown, Richard Ha, Katherine D Crew

Faculty, Staff and Student Publications

Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was …

An Improved Method For Diagnosis Of Parkinson’S Disease Using Deep Learning Models Enhanced With Metaheuristic Algorithm, Babita Majhi, Aarti Kashyap, Siddhartha Suprasad Mohanty, Sujata Dash, Saurav Mallik, Aimin Li, Zhongming Zhao

Faculty, Staff and Student Publications

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of …

Automated Spontaneous Echo Contrast Detection Using A Multisequence Attention Convolutional Neural Network, Ouwen Huang, Zewei Shi, Naveen Garg, Corey Jensen, Mark L Palmeri

Faculty, Staff and Student Publications

OBJECTIVE: Spontaneous echo contrast (SEC) is a vascular ultrasound finding associated with increased thromboembolism risk. However, identification requires expert determination and clinician time to report. We developed a deep learning model that can automatically identify SEC. Our model can be applied retrospectively without deviating from routine clinical practice. The retrospective nature of our model means future works could scan archival data to opportunistically correlate SEC findings with documented clinical outcomes.

METHODS: We curated a data set of 801 archival acquisitions along the femoral vein from 201 patients. We used a multisequence convolutional neural network (CNN) with ResNetv2 backbone and visualized …

Ver-Net: A Hybrid Transfer Learning Model For Lung Cancer Detection Using Ct Scan Images, Anindita Saha, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Rakesh Kumar Yadav, Saurav Mallik, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data.

METHODS: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. …

Application Of Deep Learning On Mammographies To Discriminate Between Low And High-Risk Dcis For Patient Participation In Active Surveillance Trials, Sena Alaeikhanehshir, Madelon M Voets, Frederieke H Van Duijnhoven, Esther H Lips, Emma J Groen, Marja C J Van Oirsouw, Shelley E Hwang, Joseph Y Lo, Jelle Wesseling, Ritse M Mann, Jonas Teuwen

Faculty, Staff and Student Publications

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because …

Developing Deep Learning-Based Strategies To Predict The Risk Of Hepatocellular Carcinoma Among Patients With Nonalcoholic Fatty Liver Disease From Electronic Health Records, Zhao Li, Lan Lan, Yujia Zhou, Ruoxing Li, Kenneth D Chavin, Hua Xu, Liang Li, David J H Shih, W Jim Zheng

Faculty, Staff and Student Publications

OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, …

Deep Learning Model For Personalized Prediction Of Positive Mrsa Culture Using Time-Series Electronic Health Records, Masayuki Nigo, Laila Rasmy, Bingyu Mao, Bijun Sai Kannadath, Ziqian Xie, Degui Zhi

Faculty, Staff and Student Publications

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC

Fully Automated Deep Learning Based Auto-Contouring Of Liver Segments And Spleen On Contrast-Enhanced Ct Images, Aashish C Gupta, Guillaume Cazoulat, Mais Al Taie, Sireesha Yedururi, Bastien Rigaud, Austin Castelo, John Wood, Cenji Yu, Caleb O'Connor, Usama Salem, Jessica Albuquerque Marques Silva, Aaron Kyle Jones, Molly Mcculloch, Bruno C Odisio, Eugene J Koay, Kristy K Brock

Faculty, Staff and Student Publications

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), …

The Hsp90-Myc-Cdk9 Network Drives Therapeutic Resistance In Mantle Cell Lymphoma, Fangfang Yan, Vivian Jiang, Alexa Jordan, Yuxuan Che, Yang Liu, Qingsong Cai, Yu Xue, Yijing Li, Joseph Mcintosh, Zhihong Chen, Jovanny Vargas, Lei Nie, Yixin Yao, Heng-Huan Lee, Wei Wang, Johnnelson R Bigcal, Maria Badillo, Jitendra Meena, Christopher Flowers, Jia Zhou, Zhongming Zhao, Lukas M Simon, Michael Wang

Faculty, Staff and Student Publications

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton's tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 …