Biomedical Informatics Commons^™

Dedifferentiated Liposarcomas Treated With Immune Checkpoint Blockade: The Md Anderson Experience, Madeline B Torres, Cheuk Hong Leung, Marianne Zoghbi, Rossana Lazcano, Davis Ingram, Khalida Wani, Emily Z Keung, M Alejandra Zarzour, Christopher P Scally, Kelly K Hunt, Anthony Conley, Andrew J Bishop, B Ashleigh Guadagnolo, Ahsan Farooqi, Devarati Mitra, Alison K Yoder, Michael S Nakazawa, Dejka Araujo, Andrew Livingston, Ravin Ratan, Shreyaskumar Patel, Vinod Ravi, Alexander J Lazar, Christina L Roland, Neeta Somaiah, Elise F Nassif Haddad

Faculty, Staff and Student Publications

Background: Dedifferentiated liposarcoma (DDLPS) is one of the most common types of soft tissue sarcoma (STS) characterized by liposarcomatous differentiation and a predilection for the retroperitoneum. Despite the growing number of histology-specific immune checkpoint blockade (ICB) trials in STS, it is still difficult to identify the radiographic objective response rate (ORR) for DDLPS in the real world setting. This study aimed to evaluate the ORR and survival of patients with DDLPS treated with ICB at a single center.

Methods: We conducted a retrospective study of 31 patients with pathologically confirmed DDLPS treated with ICB at MD Anderson Cancer Center between …

A Plain Language Summary Of The Final Analysis Of The Griffin Study Of Daratumumab Plus Lenalidomide, Bortezomib, And Dexamethasone For People With Newly Diagnosed Multiple Myeloma, Peter M Voorhees, Douglas W Sborov, Jacob Laubach, Jonathan L Kaufman, Brandi Reeves, Cesar Rodriguez, Rebecca Silbermann, Luciano J Costa, Larry D Anderson, Nitya Nathwani, Nina Shah, Naresh Bumma, Yvonne A Efebera, Sarah A Holstein, Caitlin Costello, Andrzej Jakubowiak, Tanya M Wildes, Robert Z Orlowski, Kenneth H Shain, Andrew J Cowan, Shira Dinner, Katharine S Gries, Huiling Pei, Annelore Cortoos, Sharmila Patel, Thomas S Lin, Saad Z Usmani, Paul G Richardson

Faculty, Staff and Student Publications

No abstract provided.

Grape-Pi: Graph-Based Neural Networks For Enhanced Protein Identification In Proteomics Pipelines, Chunhui Gu, Seyyed Mahmood Ghasemi, Yining Cai, Johannes F Fahrmann, James P Long, Hiroyuki Katayama, Chong Wu, Jody Vykoukal, Jennifer B Dennison, Samir Hanash, Kim-Anh Do, Ehsan Irajizad

Faculty, Staff and Student Publications

Motivation: Protein identification via mass spectrometry (MS) is the primary method for untargeted protein detection. However, the identification process is challenging due to data complexity and the need to control false discovery rates (FDR) of protein identification. To address these challenges, we developed a graph neural network (GNN)-based model, Graph Neural Network using Protein-Protein Interaction for Enhancing Protein Identification (Grape-Pi), which is applicable to all proteomics pipelines. This model leverages protein-protein interaction (PPI) data and employs two types of message-passing layers to integrate evidence from both the target protein and its interactors, thereby improving identification accuracy.

Results: Grape-Pi achieved significant …

A Critical Assessment Of Artificial Intelligence In Magnetic Resonance Imaging Of Cancer, Chengyue Wu, Meryem Abbad Andaloussi, David A Hormuth, Ernesto A B F Lima, Guillermo Lorenzo, Casey E Stowers, Sriram Ravula, Brett Levac, Alexandros G Dimakis, Jonathan I Tamir, Kristy K Brock, Caroline Chung, Thomas E Yankeelov

Faculty, Staff and Student Publications

Given the enormous output and pace of development of artificial intelligence (AI) methods in medical imaging, it can be challenging to identify the true success stories to determine the state-of-the-art of the field. This report seeks to provide the magnetic resonance imaging (MRI) community with an initial guide into the major areas in which the methods of AI are contributing to MRI in oncology. After a general introduction to artificial intelligence, we proceed to discuss the successes and current limitations of AI in MRI when used for image acquisition, reconstruction, registration, and segmentation, as well as its utility for assisting …

Inhibition Of Nitric Oxide Synthase Transforms Carotid Occlusion-Mediated Benign Oligemia Into De Novo Large Cerebral Infarction, Ha Kim, Jinyong Chung, Jeong Wook Kang, Dawid Schellingerhout, Soo Ji Lee, Hee Jeong Jang, Inyeong Park, Taesu Kim, Dong-Seok Gwak, Ji Sung Lee, Sung-Ha Hong, Kang-Hoon Je, Hee-Joon Bae, Joohon Sung, Eng H Lo, James Faber, Cenk Ayata, Dong-Eog Kim

Faculty, Staff and Student Publications

No abstract provided.

Upregulation Of Delta Opioid Receptor By Meningeal Interleukin-10 Prevents Relapsing Pain, Kufreobong E Inyang, Jaewon Sim, Kimberly B Clark, Matan Geron, Karli Monahan, Christine Evans, Patrick O'Connell, Sophie Laumet, Bo Peng, Jiacheng Ma, Cobi J Heijnen, Robert Dantzer, Grégory Scherrer, Annemieke Kavelaars, Matthew Bernard, Yasser A Aldhamen, Joseph K Folger, Alexis Bavencoffe, Geoffroy Laumet

Faculty, Staff and Student Publications

Chronic pain often includes periods of transient amelioration and even remission that alternate with severe relapsing pain. While most research on chronic pain has focused on pain development and maintenance, there is a critical unmet need to better understand the mechanisms that underlie pain remission and relapse. We found that interleukin (IL)-10, a pain resolving cytokine, is produced by resident macrophages in the spinal meninges during remission from pain and signaled to IL-10 receptor-expressing sensory neurons. Using unbiased RNA-sequencing, we identified that IL-10 upregulated expression and antinociceptive activity of δ-opioid receptor (δOR) in the dorsal root ganglion. Genetic or pharmacological …

Privacy-Preserving Collaborative Population Stratification With Dynamic Algorithm And Hyperparameter Selection, Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday

Faculty, Staff and Student Publications

We present a privacy-preserving selection layer for collaborative population stratification under 𝜖-local differential privacy (LDP). Rather than fixing a single pipeline (e.g., PCA+K-Means with preset 𝐾), our framework lets parties choose among three DP pipelines: PCA→Noise, Noise→PCA, and Noise-Only, according to their resources, and has an honest-but-curious server aggregate only DP shares to automatically select the clustering algorithm (K-Means, GMM, or Hierarchical) and 𝐾 that maximize internal metrics (Silhouette, Calinski–Harabasz, Davies–Bouldin). Because selection operates on DP data, it adds no further privacy loss. On openSNP (942 samples, 28,396 SNPs), the PCA-augmented pipelines yield higher utility …

On The Empirical Power Of Goodness-Of-Fit Tests In Watermark Detection, Weiqing He, Xiang Li, Tianqi Shang, Li Shen, Weijie Su, Qi Long

Faculty, Staff and Student Publications

Large language models (LLMs) raise concerns about content authenticity and integrity because they can generate human-like text at scale. Text watermarks, which embed detectable statistical signals into generated text, offer a provable way to verify content origin. Many detection methods rely on pivotal statistics that are i.i.d. under human-written text, making goodness-of-fit (GoF) tests a natural tool for watermark detection. However, GoF tests remain largely underexplored in this setting. In this paper, we systematically evaluate eight GoF tests across three popular watermarking schemes, using three open-source LLMs, two datasets, various generation temperatures, and multiple post-editing methods. We find that general …

Privacy-Preserving Verification Of Ml Preprocessing Via Model Behavior Indicators, Wenbiao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

We present a privacy-preserving framework to verify whether a declared data preprocessing pipeline was correctly applied before training a machine learning model on sensitive data. The verifier has only black-box query access to the model and combines three behavior indicators: shift in prediction accuracy, Kullback-Leibler (KL) divergence between output distributions, and explanation vectors from Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The method requires neither the original training records nor ground-truth labels. It supports two tasks: (i) a binary decision on correctness and (ii) a multi-class diagnosis identifying which step is missing. Experiments on three tabular datasets …

Safeguarding Privacy In Genome Research: A Comprehensive Framework For Authors, Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday

Faculty, Staff and Student Publications

As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers' …

Not Fully Synthetic: Llm-Based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing, Atiquer Rahman Sarkar, Yao-Shun Chuang, Xiaoqian Jiang, Noman Mohammed

Faculty, Staff and Student Publications

The publication and sharing of clinical notes are crucial for healthcare research and innovation. However, privacy regulations such as HIPAA and GDPR pose significant challenges. While de-identification techniques aim to remove protected health information, they often fall short of achieving complete privacy protection. Similarly, the current state of synthetic clinical note generation can lack nuance and content coverage. To address these limitations, we propose an approach that combines de-identification, filtration, and synthetic clinical note generation. Variations of this approach currently retain 36%-61% of the original note's content and fill the remaining gaps using an LLM, ensuring high information coverage. We …

Enhancing Cross-Domain Generalizability In Social Determinants Of Health Extraction With Prompt-Tuning Large Language Models, Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic, Jiang Bian, Yonghui Wu

Faculty, Staff and Student Publications

The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from …

Enhancing Medical Coding Efficiency Through Domain-Specific Fine-Tuned Large Language Models, Zhen Hou, Hao Liu, Jiang Bian, Xing He, Yan Zhuang

Faculty, Staff and Student Publications

Medical coding is essential for healthcare operations yet remains predominantly manual, error-prone (up to 20%), and costly (up to $18.2 billion annually). Although large language models (LLMs) have shown promise in natural language processing, their application to medical coding has produced limited accuracy. In this study, we evaluated whether fine-tuning LLMs with specialized ICD-10 knowledge can automate code generation across clinical documentation. We adopted a two-phase approach: initial fine-tuning using 74,260 ICD-10 code-description pairs, followed by enhanced training to address linguistic and lexical variations. Evaluations using a proprietary model (GPT-4o mini) on a cloud platform and an open-source model (Llama) …

Using Digitally Delivered Measurement-Based Care In Substance Use Disorder Treatment: Qualitative Analysis Of Patients’ Perspectives, Tessa Frohe, Eliza B Cohn, Madeline C Frost, Tascha R Johnson, Kevin A Hallgren

Faculty, Staff and Student Publications

Background: Measurement-based care (MBC) is a clinical practice where patients complete standardized outcome measures throughout treatment to monitor clinical progress and inform clinical decision-making. However, MBC is rarely adopted in routine substance use disorder (SUD) treatment. We developed a digital MBC system and pilot tested it in an outpatient adult community SUD treatment setting.

Objectives:The current study aims to characterize qualitative feedback from the pilot participants about their experiences using the MBC system in SUD treatment, focusing on perceived benefits, drawbacks, and suggestions for improvement.

Methods: Participants (N=30; n=11 female 37%) completed weekly MBC questionnaires via smartphone for 6 …

Medication Prescribing Checklists And Their Impact On Patient Safety: A Scoping Review, Yilei Liu, Tate W Miner, Kawtar Zouaidi, Anika N Roy, Muhammad F Walji, Kristin N Ray, Donald B Rindal, Elsbeth Kalenderian, Katie J Suda

Faculty, Staff and Student Publications

Background: Medication prescribing checklists and the impact on patient safety remain unexplored.

Objectives: This scoping review aimed to identify key elements of these checklists and evaluate their impact on patient safety outcomes.

Methods: We searched PubMed and Embase for studies reporting checklists in medication prescribing practices and the impact on patient safety outcomes as of October 23, 2024. We applied Fisher's exact test to evaluate the association between checklist effectiveness (based on patient safety outcomes) with study design (randomized controlled trials [RCTs] vs non-RCTs) and intervention type (bundled intervention vs checklist-only intervention).

Results: After full-text review, 53 articles met inclusion …

Neutrophils Unveiled In Chronic Lymphocytic Leukemia, Sheighlah Mcmanus, Priyanka Khare, Maria Teresa S Bertilaccio

Faculty, Staff and Student Publications

This review explores neutrophils' roles in chronic lymphocytic leukemia (CLL), highlighting their functions within the immune system. While neutrophils are known for fighting infections, their altered behavior in CLL significantly impacts disease progression. This review notes the reduced phagocytic abilities of neutrophils and the increased formation of neutrophil extracellular traps (NETs) in patients with CLL. It also examines the effects of CLL treatments, including chemotherapy, immunotherapy and targeted therapies, on neutrophils' count and function, stressing the need for improved strategies to manage therapy-induced immune dysfunction. This review also provides detailed information about the interactions between neutrophils and other immune elements …

Microbial Imprints On Colorectal Cancer: The Epigenetic Silencing Of Phlpp1 As A Prognostic Nexus, Xiangsheng Huang, Faraz Bishehsari

Faculty, Staff and Student Publications

No abstract provided.

Editorial: Protein Regulation By Lipids, Mikhail V Bogdanov, Elena G Govorunova

Faculty, Staff and Student Publications

No abstract provided.

Protecting Organs-At-Risk In Cancer Therapies Through Temporary Organ Displacement: A Comprehensive Review, Rance B Tino, Michael Li, Amirreza Heshmat, Ayush Suresh, Anasimone Guillaume, Kristy K Brock, Bruno C Odisio, Eugene J Koay

Faculty, Staff and Student Publications

Radiation therapy (RT) and locoregional ablation are cornerstones of modern oncology, yet their therapeutic potential is frequently limited by the challenge of sparing healthy organs-at-risk (OARs) from treatment-related complications. Temporary organ displacement (TOD) techniques directly address this issue by creating a physical separation using 'spacers' during treatment, thereby minimizing collateral damage while enhancing therapeutic precision. The clinical benefits, including improved tumor control, reduced morbidity, and enhanced survival, are documented across malignancies of the head and neck, thorax, abdomen, and pelvis. To create a unified framework for this evolving field, this comprehensive review provides a systematic classification of TOD techniques based …

Digital Twins In Healthcare: A Comprehensive Review And Future Directions, Hamid Khoshfekr Rudsari, Becky Tseng, Hongxu Zhu, Lulu Song, Chunhui Gu, Abhishikta Roy, Ehsan Irajizad, Joseph Butner, James Long, Kim-Anh Do

Faculty, Staff and Student Publications

Digital Twin (DT) technology has emerged as a transformative force in healthcare, offering unprecedented opportunities for personalized medicine, treatment optimization, and disease prevention. This comprehensive review examines the current state of DTs in healthcare, analyzing their implementation across different physiological levels-from cellular to whole-body systems. We systematically review the latest developments, methodologies, and applications while identifying challenges and opportunities. Our analysis encompasses technical frameworks for cardiovascular, neurological, respiratory, metabolic, hepatic, oncological, and cellular DTs, highlighting significant achievements such as population-scale cardiac modeling (3,461 patient cohort), reduced atrial fibrillation recurrence rates through patient-specific cardiac models, improved brain tumor radiotherapy planning, advanced …

Role Of Mir-204 In Controlling Metabolic Functions Of The Subretinal Microglia, Yan Chen, Sarah E Bounds, Neloy Kundu, James Regun Karmoker, Yin Liu, Dongin Kim, Jiyang Cai

Faculty, Staff and Student Publications

Rationale: MicroRNA-204 (miR-204) is one of the most abundant miRNA species in the retinal pigment epithelium (RPE) and RPE-derived extracellular vesicles (EVs). Knockout (KO) of miR-204 leads to dysfunction and degeneration of both the RPE and the retina. In addition to previously reported retinal pathologies, we observed the accumulation of lipid-laden subretinal microglia in miR-204 KO mice. This study aimed to identify potential molecular targets of miR-204 involved in microglia lipid processing and to determine whether RPE-derived EVs can improve the function of miR-204-deficient retinal microglia.

Methods: Lipid accumulation in microglia was detected by staining with LipidTox, a fluorescent dye …

Advancing Alzheimer's Research By Improving Disease Modeling Of Secondary Tauopathy, Ethan R Roy, Wei Cao

Faculty, Staff and Student Publications

Despite decades of mechanistic investigation of Alzheimer's disease (AD), wide gaps exist in disease modeling, particularly the pathobiological arm of tau pathology. Relying on transgenic models expressing mutated forms of tau has contributed much knowledge about primary tauopathy, yet with limited relevance to human AD. To eliminate blind spots for basic and translational research, we review recent developments in this area and discuss key refinements toward next-generation AD-relevant tauopathy modeling.

Combined Inhibition Of Lysine-Specific Demethylase 1 And Kinase Signaling As A Preclinical Treatment Strategy In Glioblastoma, Lea M Stitzlein, Deokhwa Nam, Faith A Hernandez, Kareena H Patel, Alaina Poche, Huaxian Ma, Katie Impelman, Joy Gumin, Heping Wang, Jing Wang, Samantha Gadd, Wafik Zaky, Oren Becher, Richard W Dudley, Frederick F Lang, Gangadhara R Sareddy, Joya Chandra

Faculty, Staff and Student Publications

Background: Lysine-specific demethylase 1 (LSD1) is overexpressed in glioblastoma, contributing to tumor growth and treatment resistance. LSD1 inhibitors have shown preclinical promise but have had limited clinical development for glioblastoma. Given the frequent kinase pathway alterations seen in glioblastoma, the interplay between LSD1 inhibition and kinase signaling pathways was investigated.

Methods: Glioblastoma stem cell (GSC) lines and normal human astrocytes (NHAs) were treated with catalytic LSD1 inhibitors, NCD38 and bomedemstat, and the LSD1 scaffolding inhibitor, seclidemstat alone and in combination with kinase inhibitors, including osimertinib, afatinib, and ulixertinib. The effect on cell viability, proliferation, and neurosphere formation was assessed, and …

International Multicenter Retrospective Study From The Ultra-Rare Sarcoma Working Group On Low-Grade Fibromyxoid Sarcoma, Sclerosing Epithelioid Fibrosarcoma, And Hybrid Forms: Outcome Of Primary Localized Disease, Claudia Giani, Abdulazeez Salawu, Silva Ljevar, Ryan A Denu, Andrea Napolitano, Emanuela Palmerini, Elizabeth A Connolly, Koichi Ogura, Daniel D Wong, Roberto Scanferla, Evan Rosenbaum, Jyoti Bajpai, Zola Chia-Chen Li, Susie Bae, Lorenzo D'Ambrosio, Steve Bialick, Andrew J Wagner, Alexander T J Lee, Hanna Koseła-Paterczyk, Giacomo G Baldi, Antonella Brunello, Yeh Chen Lee, Herbert H Loong, Sosipatros Boikos, Fernando Campos, Carlo M Cicala, Robert G Maki, Nadia Hindi, Costanza Figura, Shahd S Almohsen, Sheyaskumar Patel, Robin L Jones, Toni Ibrahim, Rooshdiya Karim, Akira Kawai, Richard Carey-Smith, Richard Boyle, Silvia M Taverna, Alexander J Lazar, Elizabeth G Demicco, Judith V M G Bovee, Angelo P Dei Tos, Christopher Fletcher, Daniel Baumhoer, Marta Sbaraglia, Inga-Marie Schaefer, Rosalba Miceli, Alessandro Gronchi, Silvia Stacchiotti

Faculty, Staff and Student Publications

The aim of the study was to report the outcome of primary localized low-grade fibromyxoid sarcoma (LGFMS), sclerosing epithelioid fibrosarcoma (SEF), and hybrid LGFMS/SEF (H-LGFMS/SEF). Patients with primary localized LGFMS, SEF, or H-LGFMS/SEF, surgically treated with curative intent from January 2000 to September 2022, were enrolled from 14 countries and 27 institutions. Pathologic inclusion criteria were predefined by expert pathologists. The primary endpoint was overall survival (OS). Secondary endpoints were crude cumulative incidence (CCI) of local recurrence (LR), CCI of distant metastases (DM), and post-metastases OS (p-OS). Two hundred ninety-four patients (239 LGFMS, 32 SEF, and 23 H-LGFMS/SEF) were identified. …

Histopathologic Progression And Metastatic Relapse Outcomes In Small Cell Neuroendocrine Carcinomas Of The Urinary Tract, Mohammad Jad Moussa, Georges C Tabet, Arlene O Siefker-Radtke, Lianchun Xiao, Nathaniel R Wilson, Jianjun Gao, Christopher J Logothetis, Petros Grivas, Byron Lee, Amishi Y Shah, Pavlos Msaouel, Roger Li, Leticia Campos Clemente, Jianping Zhao, Nizar M Tannir, Ashish M Kamat, Donna E Hansel, Charles C Guo, Matthew T Campbell, Omar Alhalabi

Faculty, Staff and Student Publications

Introduction: Small cell neuroendocrine carcinoma of the urinary tract (SCNEC-URO) has an inferior prognosis compared to conventional urothelial carcinoma (UC). Here, we evaluate the predictors and patterns of relapse after surgery.

Materials and methods: We identified a definitive-surgery cohort (n = 224) from an institutional database of patients with cT1-T4NxM0 SCNEC-URO treated in 1985-2021. Histopathologic review was conducted by independent pathologists. Relapse event was the time-to-event outcome, and relapse probabilities were estimated using a competing risk method with cumulative incidence functions (CIFs). Fine-Gray distribution models assessed covariate associations.

Results: Most patients (161, 71.9%) received neoadjuvant chemotherapy (neoCTX). Ninety two (41%) …

Outcomes Of Patients Treated With Chemotherapy For Breast Cancer During Pregnancy Compared With Nonpregnant Breast Cancer Patients Treated With Systemic Therapy, Helen M Johnson, Juhee Song, Carla L Warneke, Ashley L Martinez, Jennifer K Litton, Oluchi C Oke

Faculty, Staff and Student Publications

Introduction: Prior studies of patients treated for breast cancer during pregnancy (PrBC) report mixed outcomes and are limited by substandard treatment, small cohorts, and short follow-up. This study compared survival outcomes of PrBC patients treated with chemotherapy during pregnancy with nonpregnant patients matched by age, year of diagnosis, stage, and subtype.

Methods: PrBC patients treated from 1989 to 2022 on prospective institutional protocols were eligible. Disease-free survival (DFS), overall survival (OS), and progression-free survival (PFS) were estimated using the Kaplan-Meier method and multivariable Cox proportional hazards regression.

Results: Among 143 PrBC and 285 nonpregnant patients, median follow-up was 11.4 years. …

A Pan-Tumor Review Of The Role Of Poly(Adenosine Diphosphate Ribose) Polymerase Inhibitors, Chadi Hage Chehade, Georges Gebrael, Nicolas Sayegh, Zeynep Irem Ozay, Arshit Narang, Tony Crispino, Talia Golan, Jennifer K Litton, Umang Swami, Kathleen N Moore, Neeraj Agarwal

Faculty, Staff and Student Publications

Poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors, such as olaparib, talazoparib, rucaparib, and niraparib, comprise a therapeutic class that targets PARP proteins involved in DNA repair. Cancer cells with homologous recombination repair defects, particularly BRCA alterations, display enhanced sensitivity to these agents because of synthetic lethality induced by PARP inhibitors. These agents have significantly improved survival outcomes across various malignancies, initially gaining regulatory approval in ovarian cancer and subsequently in breast, pancreatic, and prostate cancers in different indications. This review offers a comprehensive clinical overview of PARP inhibitor approvals, emphasizing their efficacy across different cancers based on landmark phase 3 clinical …

Outcomes With Bridging Radiation Therapy Prior To Chimeric Antigen Receptor T-Cell Therapy In Patients With Aggressive Large B-Cell Lymphomas, Gohar S Manzar, Chelsea C Pinnix, Stephanie O Dudzinski, Kathryn E Marqueen, Elaine E Cha, Lewis F Nasr, Alison K Yoder, Michael K Rooney, Paolo Strati, Sairah Ahmed, Chijioke Nze, Ranjit Nair, Luis E Fayad, Michael Wang, Loretta J Nastoupil, Jason R Westin, Christopher R Flowers, Sattva S Neelapu, Jillian R Gunther, Bouthaina S Dabaja, Susan Y Wu, Penny Q Fang

Faculty, Staff and Student Publications

Background: Select patients with relapsed/refractory aggressive B cell lymphoma may benefit from bridging radiation (bRT) prior to anti-CD19-directed chimeric antigen receptor T cell therapy (CAR-T). Here, we examined patient and treatment factors associated with outcomes and patterns of failure after bRT and CAR-T.

Methods: We retrospectively reviewed adults with diffuse large B-cell lymphoma (DLBCL) who received bRT prior to axicabtagene ciloleucel, tisagenlecleucel, or lisocabtagene maraleucel between 11/2017-4/2023. Clinical/treatment characteristics, response, and toxicity were extracted. Survival was modeled using Kaplan-Meier or Cox regression models for events distributed over time, or binary logistic regression for disease response. Fisher's Exact Test or Mann-Whitney …

Erratum: Long Non-Coding Rna Uiclm Promotes Colorectal Cancer Liver Metastasis By Acting As A Cerna For Microrna-215 To Regulate Zeb2 Expression: Erratum, Dong-Liang Chen, Yun-Xin Lu, Jia-Xing Zhang, Xiao-Li Wei, Feng Wang, Zhao-Lei Zeng, Zhi-Zhong Pan, Yun-Fei Yuan, Feng-Hua Wang, Helene Pelicano, Paul J Chiao, Peng Huang, Dan Xie, Yu-Hong Li, Huai-Qiang Ju, Rui-Hua Xu

Faculty, Staff and Student Publications

No abstract provided.

Novel Volumetric Modulated Arc Therapy Approach For Lattice Radiation Therapy For Bulky Liver Tumors, Christine V Chung, Saurabh S Nair, Meena S Khan, Callistus I Nguyen, Rachael M Martin-Paulpeter, Ethan B Ludmir, Laurence E Court, Joshua S Niedzielski

Faculty, Staff and Student Publications

Purpose: Lattice radiation therapy (LRT) is a type of spatially fractionated radiation therapy that has emerged as an effective treatment approach for bulky solid tumors. RapidArc Dynamic (RAD) is a novel beam delivery approach that may be advantageous for LRT. The purpose of this in silico study was to evaluate and compare a novel RAD-based LRT approach (RAD-LRT) with conventional volumetric modulated arc therapy (VMAT)-based LRT (VMAT-LRT).

Methods: Twenty patients with bulky liver tumors treated with RT were retrospectively identified. VMAT-LRT and RAD-LRT plans were generated for all patients. Lattice spheres were placed in a standardized hexagonal pattern with alternating …