Lee Cooper
A5034820084- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Cell Image Analysis Techniques
- Glioma Diagnosis and Treatment
- Cancer Genomics and Diagnostics
- Medical Image Segmentation Techniques
- Digital Imaging for Blood Diseases
- Single-cell and spatial transcriptomics
- Cancer Immunotherapy and Biomarkers
- Ferroptosis and cancer prognosis
- Advanced MRI Techniques and Applications
- Bioinformatics and Genomic Networks
- Dental materials and restorations
- Cancer-related molecular mechanisms research
- Dental Erosion and Treatment
- Pregnancy and preeclampsia studies
- Medical Imaging Techniques and Applications
- Genetics, Bioinformatics, and Biomedical Research
- Hematological disorders and diagnostics
- Artificial Intelligence in Healthcare and Education
- Gene expression and cancer classification
- Image Retrieval and Classification Techniques
- Cancer Cells and Metastasis
- Computational Drug Discovery Methods
- Advanced Neural Network Applications
Emory University
2011-2025
Northwestern University
2019-2025
University of Miami
2025
Duke University
2025
University of Liverpool
2009-2025
McCormick (United States)
2023-2024
RELX Group (United States)
2023-2024
Northwestern University
2024
Brigham and Women's Hospital
2023
Northwestern Medicine
2022
Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II III) have highly variable clinical behavior that is not adequately predicted on basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty compounded by interobserver variability in diagnosis. Mutations IDH, TP53, ATRX codeletion chromosome arms 1p 19q (1p/19q codeletion) been implicated as clinically relevant...
Significance Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances genomic and imaging technologies provide physicians vast amounts data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed computational approach based on deep learning predict overall survival brain tumors from microscopic images tissue biopsies biomarkers. This method uses adaptive feedback simultaneously learn visual...
Abstract Inhibition of vascular endothelial growth factor-A (VEGF) signaling is a promising therapeutic approach that aims to stabilize the progression solid malignancies by abrogating tumor-induced angiogenesis. This may be accomplished inhibiting kinase activity VEGF receptor-2 (KDR), which has key role in mediating VEGF-induced responses. The novel indole-ether quinazoline AZD2171 highly potent (IC50 < 1 nmol/L) ATP-competitive inhibitor recombinant KDR tyrosine vitro. Concordant...
Purpose To conduct a comprehensive analysis of radiologist-made assessments glioblastoma (GBM) tumor size and composition by using community-developed controlled terminology magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, patient survival. Materials Methods Because all study patients had been previously deidentified the Cancer Genome Atlas (TCGA), publicly available data set that contains no linkage identifiers is HIPAA compliant,...
Abstract Purpose: Lower-grade gliomas (WHO grade II/III) have been classified into clinically relevant molecular subtypes based on IDH and 1p/19q mutation status. The purpose was to investigate whether T2/FLAIR MRI features could distinguish between lower-grade glioma subtypes. Experimental Design: scans from the TCGA/TCIA lower database (n = 125) were evaluated by two independent neuroradiologists assess (i) presence/absence of homogenous signal T2WI; (ii) “T2–FLAIR mismatch” sign; (iii)...
Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts transfer after in vitro fertilization (IVF). However, the produces different results between embryologists as a result, success rate IVF remains low. To overcome uncertainties quality, multiple embryos are often implanted resulting undesired pregnancies complications. Unlike other imaging fields, embryology have not yet leveraged artificial intelligence (AI) unbiased, automated...
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications AI in healthcare have the potential to improve our ability detect, diagnose, prognose, and intervene on human disease. For models be used clinically, they need made safe, reproducible robust, underlying software framework must aware particularities (e.g. geometry, physiology, physics) medical data being processed. This work introduces MONAI, freely available, community-supported,...
The Cancer Genome Atlas (TCGA) project has generated gene expression data that divides glioblastoma (GBM) into four transcriptional classes: proneural, neural, classical, and mesenchymal. Because class is only partially explained by underlying genomic alterations, we hypothesize the tumor microenvironment may also have an impact. In this study, focused on necrosis angiogenesis because their presence both prognostically biologically significant. These features were quantified in digitized...
While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due the effort and experience required carefully delineate tissue structures, difficulties related sharing markup whole-slide images.We recruited 25 participants, ranging from senior pathologists medical students, regions 151 breast cancer slides using Digital Slide Archive....
Abstract Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in medicine. Many prediction methods face limitations learning from high-dimensional profiles these platforms, and rely on experts to hand-select small number features for training models. In this paper, we demonstrate how deep Bayesian optimization that have been remarkably successful general tasks can be adapted problem predicting cancer outcomes. We...
Abstract As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute dysregulated oncogenic pathways. Integration of into networks offers opportunities protein–protein interactions (PPIs) with functional and therapeutic significance. Here, we report generation cancer-focused PPI network, termed OncoPPi, identification >260 cancer-associated PPIs not in other large-scale interactomes. hubs new regulatory mechanisms for like MYC , STK11...
Abstract Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the research communities. We evaluated sources of variability sTIL assessment pathologists three previous ring studies. identify common challenges evaluate impact...
Tumor-infiltrating lymphocytes (TIL) have prognostic significance in many cancers, yet their roles glioblastoma not been fully defined. We hypothesized that TILs are associated with molecular alterations, histologies, and survival.
Background The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed whole-slide images, outcome, genetic events. pairing genomics rich clinical descriptions with imaging provided by TCGA presents unique opportunity to perform these correlative studies. However, better tools are needed integrate the vast disparate types.
Tissue-based cancer studies can generate large amounts of histology data in the form glass slides. These slides contain important diagnostic, prognostic, and biological information be digitized into expansive high-resolution whole-slide images using slide-scanning devices. Effectively utilizing digital pathology research requires ability to manage, visualize, share, perform quantitative analysis on these image data, tasks that are often complex difficult for investigators with current state...
The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of '-omic' data on glioblastoma (GBM), resulting in several key insights expression signatures. Despite the richness TCGA GBM data, absence lower grade gliomas this set prevents analysis genes related to progression and uncovering predictive A complementary dataset exists form NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular clinical diffuse across full spectrum histologic class...
Abstract Phenotypic heterogeneity is widely observed in cancer cell populations. Here, to probe this heterogeneity, we developed an image-guided genomics technique termed spatiotemporal genomic and cellular analysis (SaGA) that allows for precise selection amplification of living rare cells. SaGA was used on collectively invading 3D packs create purified leader follower lines. The cultures are phenotypically stable highly invasive contrast cultures, which show phenotypic plasticity over time...
The standard of care for glioblastoma (GBM) is maximal safe resection followed by radiation therapy with chemotherapy. Currently, contrast-enhanced MRI used to define primary treatment volumes surgery and therapy. However, enhancement does not identify the tumor entirely, resulting in limited local control. Proton spectroscopic (sMRI), a method reporting endogenous metabolism, may better margin. Here, we develop whole-brain sMRI pipeline validate metrics quantitative measures infiltration....
Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human has limitations that can result low reproducibility inter-observer agreement. Computerized image analysis partially overcome these shortcomings due to its capacity quantitatively reproducibly measure structures on a large-scale. In this paper, we present an end-to-end data integration pipeline large-scale morphologic pathology images demonstrate ability...