Digital Commons Network^™

Automatically Generating Natural Language Descriptions Of Images By A Deep Hierarchical Framework, Lin Huo, Lin Bai, Shang Ming Zhou

School of Nursing and Midwifery

Automatically generating an accurate and meaningful description of an image is very challenging. However, the recent scheme of generating an image caption by maximizing the likelihood of target sentences lacks the capacity of recognizing the human–object interaction (HOI) and semantic relationship between HOIs and scenes, which are the essential parts of an image caption. This article proposes a novel two-phase framework to generate an image caption by addressing the above challenges: 1) a hybrid deep learning and 2) an image description generation. In the hybrid deep-learning phase, a novel factored three-way interaction machine was proposed to learn the relational features …

Implantable Doppler Probe As A Vascular Monitoring Device In Kidney Transplant Patients: Investigation Of Use At A Single Center, M. Shahzar Malik, Jacob Akoh, Somaiah Aroori, Jos M. Latour

School of Nursing and Midwifery

Objectives: Vascular complications account for 30% to 35% of total kidney grafts lost during the first 3 months posttransplant. Early detection of vascular complications allows an opportunity for prompt intervention, which is critical to reducing graft loss. In this study, we evaluated the usefulness of an implantable Doppler probe as a vascular monitoring device in kidney transplant patients. Materials and Methods: An implantable Doppler probe is used intermittently for postoperative monitoring of kidney transplant patients at our center. In this retrospective study, we analyzed prospectively maintained medical data in which we compared clinical outcomes of kidney transplant recipients who had …

Corrigendum To Evidence-Based Clinical Practice Guidelines On The Management Of Pain In Older People – A Summary Report (British Journal Of Pain, (2020), 10.1177/2049463720976155), Patricia Schofield

School of Nursing and Midwifery

In the referenced article, the authorship has been updated and should now read as: Patricia Schofield¹, Margaret Dunham², Denis Martin³, Gary Bellamy⁴, Sally-Anne Francis⁵, Dave Sookhoo⁶, Antonio Bonacaro⁷, Eshtar Hamid⁸, Rebecca Chandler⁹, Aza Abdulla¹⁰, Mike Cumberbatch¹¹, and Roger Knaggs¹² The current affiliation to the authors is as follows: ¹University of Plymouth, UK ²Edinburgh Napier University, UK ³Teesside University, UK ⁴Hammersmith and Fulham Local Authority, UK ⁵Anglia Ruskin University, UK ⁶University of …

Self-Perceived Loneliness And Depression During The Covid-19 Pandemic: A Two-Wave Replication Study, Alessandro Carollo, Andrea Bizzego, Giulio Gabrieli, Keri Ka Yee Wong, Adrian Raine, Gianluca Esposito

Research Outputs: 2025-Present

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual’s health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. We tested a) the impact of the chosen model on the identification of the …

Dataset Of Parent-Child Hyperscanning Functional Near-Infrared Spectroscopy Recordings, Andrea Bizzego, Giulio Gabrieli, Atiqah Azhari, Mengyu Lim, Gianluca Esposito

Research Outputs: 2025-Present

The term “hyperscanning” refers to the simultaneous recording of multiple individuals’ brain activity. As a methodology, hyperscanning allows the investigation of brain-to-brain synchrony. Despite being a promising technique, there is a limited number of publicly available functional Near-infrared Spectroscopy (fNIRS) hyperscanning recordings. In this paper, we report a dataset of fNIRS recordings from the prefrontal cortical (PFC) activity of 33 mother-child dyads and 29 father-child dyads. Data was recorded while the parent-child dyads participated in an experiment with two sessions: a passive video attention task and a free play session. Dyadic metadata, parental psychological traits, behavioural annotations of the play …

Levy Enhanced Cross Entropy-Based Optimized Training Of Feedforward Neural Networks, Kartik Pandya, Pratik Mochi, Dharmesh Dabhi, Vipul Rajput

Research Outputs: 2025-Present

An Artificial Neural Network (ANN) is one of the most powerful tools to predict the behavior of a system with unforeseen data. The feedforward neural network is the simplest, yet most efficient topology that is widely used in computer industries. Training of feedforward ANNs is an integral part of an ANN-based system. Typically an ANN system has inherent non-linearity with multiple parameters like weights and biases that must be optimized simultaneously. To solve such a complex optimization problem, this paper proposes the Levy Enhanced Cross Entropy (LE-CE) method. It is a population-based meta-heuristic method. In each iteration, this method produces …

Executive Functions, Math Anxiety And Math Performance In Middle School Students, Marija Živković, Sandra Pellizzoni, Irene Cristina Mammarella, Maria Chiara Passolunghi

Research Outputs: 2025-Present

Previous studies mainly investigated working memory (WM) and math anxiety (MA) leaving almost unexplored other aspects of executive functions (EFs) in middle school period. Filling the gap in the literature, the aims of this study were: (1) to better examine the relationship between MA and math performance, (2) to better examine the relationship between EFs and math performance and (3) to investigate the interplay between EFs and MA on math performances. This study confirmed a significant and negative relationship between MA and math performance, indicates a significant and positive relationship between visuospatial WM and math performance, shifting and math performance …

Hybrid Approach Integrated With Gaussian Process Regression For Condition Monitoring Strategies At The Rotor Side Of A Doubly-Fed Induction Generator, Shuo Zhang, Emma Robinson, Malabika Basu

Research Outputs: 2025-Present

Regression-based Machine Learning (ML) approaches are mainly applied to fit the power curve for the performance evaluation of wind turbines (WTs). Although a fitted power curve is prevalent and straightforward for anomaly detection, it is difficult to identify the fault types at the rotor side of a WT, particularly, because the operation can be dependent on multiple parameters. The present paper suggests an interesting approach towards condition monitoring (CM) and fault diagnosis of a DFIG by only processing rotor currents through several signal processing techniques to recognize and localize miscellaneous electrical disturbances. A non-parametric regression approach, Gaussian process regression (GPR), …

Alterations In Cortisol Profiles Among Mothers Of Children With Asd Related To Poor Child Sleep Quality, Wasmiah Bin Eid, Mengyu Lim, Giulio Gabrieli, Melanie Kölbel, Elizabeth Halstead, Gianluca Esposito, Dagmara Dimitriou

Research Outputs: 2025-Present

Caregivers of children with autism spectrum disorder (ASD) experience poorer sleep, but studies have not yet used objective measures to investigate how child and caregiver sleep affect each other. In this study, 29 mothers and their child with ASD aged between 6 and 16 years were recruited. Questionnaires measuring child autism, maternal depression, and maternal and child sleep quality were administered. Cortisol salivary samples were also obtained from the mothers over the course of a day. Results revealed that maternal depression is significantly correlated with their subjective sleep quality, sleep latency and daytime dysfunction. Child sleep quality was also found …

Univariate And Multivariate Lstm Models For One Step And Multistep Pv Power Forecasting, Tariq Limouni, Reda Yaagoubi, Khalid Bouziane, Khalid Guissi, El Houssain Baali

Research Outputs: 2025-Present

The energy demand is increasing due to population growth and economic development. To satisfy this energy demand, the use of renewable energy is essential to face global warming and the depletion of fossil fuels. Photovoltaic energy is one of the renewable energy sources, widely used by several countries over the world. The integration of PV energy into the grid brings significant benefits to the economy and environment, however, high penetration of this energy also brings some challenges to the stability of the electrical grid, due to the intermittency of solar energy. To overcome this issue, the use of a forecasting …

Global Responsibility Of Engineering - The Extent To Which Global Responsibility Is Embedded Into Engineering Practice: An Exploratory Study, Jonathan Truslove, Shannon M. Chance, Katie Cresswell-Maynard, Emma Crichton, Inês Direito, John E Mitchell

Research Outputs: 2025-Present

No abstract provided.

Elastic Shape Analysis Of Brain Structures For Predictive Modeling Of Ptsd, Yuexuan Wu, Suprateek Kundu, Jennifer S Stevens, Negar Fani, Anuj Srivastava

Faculty, Staff and Student Publications

It is well-known that morphological features in the brain undergo changes due to traumatic events and associated disorders such as post-traumatic stress disorder (PTSD). However, existing approaches typically offer group-level comparisons, and there are limited predictive approaches for modeling behavioral outcomes based on brain shape features that can account for heterogeneity in PTSD, which is of paramount interest. We propose a comprehensive shape analysis framework representing brain sub-structures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of brain …

Automated Segmentation Of Colorectal Liver Metastasis And Liver Ablation On Contrast-Enhanced Ct Images, Brian M Anderson, Bastien Rigaud, Yuan-Mao Lin, A Kyle Jones, Hynseon Christine Kang, Bruno C Odisio, Kristy K Brock

Faculty, Staff and Student Publications

Objectives: Colorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones.

Methods: Four FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from …

Recent Advances On Dna Base Flipping: A General Mechanism For Writing, Reading, And Erasing Dna Modifications, Ren Ren, John R Horton, Samuel Hong, Xiaodong Cheng

Faculty, Staff and Student Publications

The modification of DNA bases is a classic hallmark of epigenetics. Four forms of modified cytosine-5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine-have been discovered in eukaryotic DNA. In addition to cytosine carbon-5 modifications, cytosine and adenine methylated in the exocyclic amine-N4-methylcytosine and N6-methyladenine-are other modified DNA bases discovered even earlier. Each modified base can be considered a distinct epigenetic signal with broader biological implications beyond simple chemical changes. Since 1994, several crystal structures of proteins and enzymes involved in writing, reading, and erasing modified bases have become available. Here, we present a structural synopsis of writers, readers, and erasers of the modified …

Use Of The Fully Spectroscopic Pixelated Cadmium Telluride Detector For Benchtop X-Ray Fluorescence Computed Tomography, Sandun Jayarathna, Hem Moktan, Sang Hyun Cho

Faculty, Staff and Student Publications

In this work, we integrated a commercially-available fully-spectroscopic pixelated cadmium telluride (CdTe) detector system as a two-dimensional (2D) array detector into our existing benchtop cone-beam x-ray fluorescence computed tomography (XFCT) system. After integrating this detector, known as High-Energy X-ray Imaging Technology (HEXITEC), we performed quantitative imaging of gold nanoparticle (GNP) distribution in a small animal-sized phantom using our benchtop XFCT system. Owing to the upgraded detector component within our benchtop XFCT system, we were able to conduct this phantom imaging in an unprecedented manner by volumetric XFCT scans followed by XFCT image reconstruction in 3D. The current results showed that …

Cell-Directed Aptamer Therapeutic Targeting For Cancers Including Those Within The Central Nervous System, Jun Wei, Renduo Song, Aria Sabbagh, Anantha Marisetty, Neal Shukla, Dexing Fang, Hinda Najem, Martina Ott, James Long, Lijie Zhai, Maciej S Lesniak, Charles David James, Leonidas Platanias, Michael Curran, Amy B Heimberger

Faculty, Staff and Student Publications

Osteopontin (OPN) is produced by tumor cells as well as by myeloid cells and is enriched in the tumor microenvironment (TME) of many cancers. Given the roles of OPN in tumor progression and immune suppression, we hypothesized that targeting OPN with aptamers that have high affinity and specificity could be a promising therapeutic strategy. Bi-specific aptamers targeting ligands for cellular internalization were conjugated to siRNAs to suppress OPN were created, and therapeutic leads were selected based on target engagement and

Deep Learning Auto-Segmentation Of Cervical Skeletal Muscle For Sarcopenia Analysis In Patients With Head And Neck Cancer, Mohamed A Naser, Kareem A Wahid, Aaron J Grossberg, Brennan Olson, Rishab Jain, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S R Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D Fuller

Faculty, Staff and Student Publications

Background/purpose: Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.

Materials/methods: 390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the …

Evaluation Of Deep Learning-Based Multiparametric Mri Oropharyngeal Primary Tumor Auto-Segmentation And Investigation Of Input Channel Effects: Results From A Prospective Imaging Registry, Kareem A Wahid, Sara Ahmed, Renjie He, Lisanne V Van Dijk, Jonas Teuwen, Brigid A Mcdonald, Vivian Salama, Abdallah S R Mohamed, Travis Salzillo, Cem Dede, Nicolette Taku, Stephen Y Lai, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

Background/purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance.

Materials/methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, …

Screening Of Hydrocarbon-Stapled Peptides For Inhibition Of Calcium-Triggered Exocytosis, Ying Lai, Michael J Tuvim, Jeremy Leitz, John Peters, Richard A Pfuetzner, Luis Esquivies, Qiangjun Zhou, Barbara Czako, Jason B Cross, Philip Jones, Burton F Dickey, Axel T Brunger

Faculty, Staff and Student Publications

The so-called primary interface between the SNARE complex and synaptotagmin-1 (Syt1) is essential for Ca2+-triggered neurotransmitter release in neuronal synapses. The interacting residues of the primary interface are conserved across different species for synaptotagmins (Syt1, Syt2, Syt9), SNAP-25, and syntaxin-1A homologs involved in fast synchronous release. This Ca2+-independent interface forms prior to Ca2+-triggering and plays a role in synaptic vesicle priming. This primary interface is also conserved in the fusion machinery that is responsible for mucin granule membrane fusion. Ca2+-stimulated mucin secretion is mediated by the SNAREs syntaxin-3, SNAP-23, VAMP8, Syt2, and other proteins. Here, we designed and screened a …

Exploiting Induced Vulnerability To Overcome Parpi Resistance And Clonal Heterogeneity In Brca Mutant Triple-Negative Inflammatory Breast Cancer, David J H Shih, Mei-Kuang Chen, Jun Yin, Daniel J Mcgrail, Hui Dai, Rongbin Wei, Jing Zhang, Wenjin Jim Zheng, Kim-Anh Do, Liuqing Yang, Mien-Chie Hung, Shiaw-Yih Lin

Faculty, Staff and Student Publications

Acquired resistance and clonal heterogeneity are critical challenges in cancer treatment, and the lack of effective computational tools hampers the discovery of new treatments to overcome resistance. Using high-throughput transcriptomic databases of compound perturbation profiles, we have developed a bioinformatic strategy for identifying candidate drugs to overcome resistance with combinatorial therapy. We devised this strategy during an investigation into the acquired resistance against PARP inhibitors (PARPi) in a triple-negative inflammatory breast cancer cell line. In this study, we derived multiple PARPi-resistant clones and characterized their transcriptomic adaptations compared to the parental clone. The transcriptomes of the resistant clones showed substantial …

Surgical Management Of Brain Metastasis: Challenges And Nuances, Chibawanye I Ene, Sherise D Ferguson

Faculty, Staff and Student Publications

Brain metastasis is the most common type of intracranial tumor. The contemporary management of brain metastasis is a challenging issue and traditionally has carried a poor prognosis as these lesions typically occur in the setting of advanced cancer. However, improvement in systemic therapy, advances in radiation techniques and multimodal therapy tailored to the individual patient, has given hope to this patient population. Surgical resection has a well-established role in the management of brain metastasis. Here we discuss the evolving role of surgery in the treatment of this diverse patient population.

High-Resolution Metabolomics Of Exposure To Tobacco Smoke During Pregnancy And Adverse Birth Outcomes In The Atlanta African American Maternal-Child Cohort, Youran Tan, Dana Boyd Barr, P Barry Ryan, Veronika Fedirko, Jeremy A Sarnat, Audrey J Gaskins, Che-Jung Chang, Ziyin Tang, Carmen J Marsit, Elizabeth J Corwin, Dean P Jones, Anne L Dunlop, Donghai Liang

Faculty, Staff and Student Publications

Exposure to tobacco smoke during pregnancy has been associated with a series of adverse reproductive outcomes; however, the underlying molecular mechanisms are not well-established. We conducted an untargeted metabolome-wide association study to identify the metabolic perturbations and molecular mechanisms underlying the association between cotinine, a widely used biomarker of tobacco exposure, and adverse birth outcomes. We collected early and late pregnancy urine samples for cotinine measurement and serum samples for high-resolution metabolomics (HRM) profiling from 105 pregnant women from the Atlanta African American Maternal-Child cohort (2014-2016). Maternal metabolome perturbations mediating prenatal tobacco smoke exposure and adverse birth outcomes were assessed …

Progression Free Survival Prediction For Head And Neck Cancer Using Deep Learning Based On Clinical And Pet/Ct Imaging Data, Mohamed A Naser, Kareem A Wahid, Abdallah S R Mohamed, Moamen Abobakr Abdelaal, Renjie He, Cem Dede, Lisanne V Van Dijk, Clifton D Fuller

Faculty, Staff and Student Publications

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT …

Deep-Learning-Based Generation Of Synthetic 6-Minute Mri From 2-Minute Mri For Use In Head And Neck Cancer Radiotherapy, Kareem A Wahid, Jiaofeng Xu, Dina El-Habashy, Yomna Khamis, Moamen Abobakr, Brigid Mcdonald, Nicolette O' Connell, Daniel Thill, Sara Ahmed, Christina Setareh Sharafi, Kathryn Preston, Travis C Salzillo, Abdallah S R Mohamed, Renjie He, Nathan Cho, John Christodouleas, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

Background: Quick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.

Methods: We used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and …

Head And Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling Of Deep Learning In Pet/Ct Images, Mohamed A Naser, Kareem A Wahid, Lisanne V Van Dijk, Renjie He, Moamen Abobakr Abdelaal, Cem Dede, Abdallah S R Mohamed, Clifton D Fuller

Faculty, Staff and Student Publications

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient …

Cancer Treatment-Induced Nad+ Depletion In Premature Senescence And Late Cardiovascular Complications, Priyanka Banerjee, Elizabeth A Olmsted-Davis, Anita Deswal, Minh Th Nguyen, Efstratios Koutroumpakis, Nicholas L Palaskas, Steven H Lin, Sivareddy Kotla, Cielito Reyes-Gibby, Sai-Ching J Yeung, Syed Wamique Yusuf, Momoko Yoshimoto, Michihiro Kobayashi, Bing Yu, Keri Schadler, Joerg Herrmann, John P Cooke, Abhishek Jain, Eduardo Chini, Nhat-Tu Le, Jun-Ichi Abe

Faculty, Staff and Student Publications

Numerous studies have revealed the critical role of premature senescence induced by various cancer treatment modalities in the pathogenesis of aging-related diseases. Senescence-associated secretory phenotype (SASP) can be induced by telomere dysfunction. Telomeric DNA damage response induced by some cancer treatments can persist for months, possibly accounting for long-term sequelae of cancer treatments. Telomeric DNA damage-induced mitochondrial dysfunction and increased reactive oxygen species production are hallmarks of premature senescence. Recently, we reported that the nucleus-mitochondria positive feedback loop formed by p90 ribosomal S6 kinase (p90RSK) and phosphorylation of S496 on ERK5 (a unique member of the mitogen-activated protein kinase family …

Comparison Of Three Exercise Interventions With And Without Gemcitabine Treatment On Pancreatic Tumor Growth In Mice: No Impact On Tumor Infiltrating Lymphocytes, Priti Gupta, Charles F Hodgman, Claudia Alvarez-Florez, Keri L Schadler, Melissa M Markofski, Daniel P O'Connor, Emily C Lavoy

Faculty, Staff and Student Publications

Exercise has been shown to slow pancreatic tumor growth, but whether exercise interventions of differing volume or intensity yield differential effects on tumor outcomes is unknown. In this study, we compared three exercise training interventions implemented with and without chemotherapy on pancreatic tumor growth in mice. Methods: Male C57BL/6 mice (6-8 weeks old) were subcutaneously inoculated with pancreatic ductal adenocarcinoma tumor cells (PDAC 4662). Upon tumor detection, mice received gemcitabine 15 mg/kg intraperitoneally 3 days/week and were assigned to exercise: high volume continuous exercise (HVCE), low volume continuous exercise (LVCE), high intensity interval training (HIIT), or sedentary (SED). HVCE ran …

Impact Of Psychosocial, Behavioral And Lifestyle Factors On Subjective Cognitive Complaints And Perceived Quality Of Life In A Large Cohort Of Italian Breast Cancer Patients, Thomas West, Corrado Cavallero, Rita Ceccherini, Silva Foladore, Daniele Generali, Francesco Versace, Bruna Scaggiante

Faculty, Staff and Student Publications

The impact of psychosocial and behavioral factors on Cancer Related Cognitive Impairment manifestations is still under debate. Study's purpose is to determine the prevalence rate of cancer related cognitive impairment in a cohort of Italian breast cancer patients and to evaluate the implication of specific behavioral factors. For these purposes, a total of 233 women (106 breast cancer patients and 127 age-matched controls without oncological diagnosis) completed a questionnaire investigating cognitive functionality (FACT-Cog v3.0), sociodemographic characteristics, clinical information, psychosocial and behavioral factors (cognitive reserve, sleep quality, dietary habits, physical activity). The results indicated a higher prevalence rate of subjective cognitive …

Dose Accumulation For Mr-Guided Adaptive Radiotherapy: From Practical Considerations To State-Of-The-Art Clinical Implementation, Brigid A Mcdonald, Cornel Zachiu, John Christodouleas, Mohamed A Naser, Mark Ruschin, Jan-Jakob Sonke, Daniela Thorwarth, Daniel Létourneau, Neelam Tyagi, Tony Tadic, Jinzhong Yang, X Allen Li, Uffe Bernchou, Daniel E Hyer, Jeffrey E Snyder, Edyta Bubula-Rehm, Clifton D Fuller, Kristy K Brock

Faculty, Staff and Student Publications

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI …

Application Of Artificial Intelligence To Plasma Metabolomics Profiles To Predict Response To Neoadjuvant Chemotherapy In Triple-Negative Breast Cancer, Ehsan Irajizad, Ranran Wu, Jody Vykoukal, Eunice Murage, Rachelle Spencer, Jennifer B Dennison, Stacy Moulder, Elizabeth Ravenberg, Bora Lim, Jennifer Litton, Debu Tripathym, Vicente Valero, Senthil Damodaran, Gaiane M Rauch, Beatriz Adrada, Rosalind Candelaria, Jason B White, Abenaa Brewster, Banu Arun, James P Long, Kim Anh Do, Sam Hanash, Johannes F Fahrmann

Faculty, Staff and Student Publications

There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied …