A5057770699- Lymphoma Diagnosis and Treatment
- Acute Lymphoblastic Leukemia research
- CAR-T cell therapy research
- Bladder and Urothelial Cancer Treatments
- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Protein Degradation and Inhibitors
- Urinary and Genital Oncology Studies
- Epigenetics and DNA Methylation
- Artificial Intelligence in Healthcare and Education
- Pancreatic and Hepatic Oncology Research
- COVID-19 diagnosis using AI
- Colorectal Cancer Screening and Detection
- Histone Deacetylase Inhibitors Research
- Occupational and environmental lung diseases
- Osteoarthritis Treatment and Mechanisms
- Cancer Genomics and Diagnostics
- COVID-19 Clinical Research Studies
- Cytokine Signaling Pathways and Interactions
- Chronic Lymphocytic Leukemia Research
- Urological Disorders and Treatments
- Immune Cell Function and Interaction
- Acute Myeloid Leukemia Research
- Traditional Chinese Medicine Studies
- RNA Research and Splicing
Cornell University
2011-2022
Weill Cornell Medicine
2022
New York Hospital Queens
2012-2022
NewYork–Presbyterian Hospital
2012-2022
Presbyterian Hospital
2013-2022
New York Proton Center
2014
Northwestern University
2013
University of Chicago
2013
BC Cancer Agency
2013
Université de Montréal
2013
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis prediction of treatment outcomes. These have also been used predict gene mutations images, but no comprehensive evaluation their potential extracting molecular features histology slides has yet performed. We show that HE2RNA, a model based on the integration data modes, can be trained systematically RNA-Seq profiles whole-slide images alone, without expert...
Standardized and robust risk-stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies investigate the benefits of adjuvant systemic therapies after curative resection/ablation.In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (whole-slide imaging; WSI) build models predicting survival HCC treated by surgical resection. Two independent series were investigated: a discovery set...
The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, chest CT scan data, from 1003 coronavirus-infected patients two French hospitals. train deep learning model based scans to predict severity. then construct the multimodal AI-severity score includes 5 variables (age, sex, oxygenation, urea, platelet) in addition model. show neural network analysis CT-scans brings...
Although aberrant DNA methylation patterning is a hallmark of cancer, the relevance targeting methyltransferases (DNMT) remains unclear for most tumors. In diffuse large B-cell lymphoma (DLBCL) we observed that chemoresistance associated with programming. Prolonged exposure to low-dose DNMT inhibitors (DNMTI) reprogrammed chemoresistant cells become doxorubicin sensitive without major toxicity in vivo. Nine genes were recurrently hypermethylated DLBCL. Of these, SMAD1 was critical...
What's known on the subject? and What does study add? Radical nephroureterectomy ( RNU ), standard of care treatment for high‐risk urothelial carcinoma upper tract UTUC results in loss a renal unit. Loss function decreases eligibility systemic chemotherapies decreased overall survival various malignancies. The shows that only small proportion patients had preoperative would allow cisplatin‐based chemotherapy. Moreover, eGFR significantly after , thereby lowering rate cisplatin to 16 52%...
Molecular mechanisms associated with frequent relapse of diffuse large B-cell lymphoma (DLBCL) are poorly defined. It is especially unclear how primary tumor clonal heterogeneity contributes to relapse. Here, we explore unique features lymphomas - VDJ recombination and somatic hypermutation address this question.We performed high-throughput sequencing rearranged junctions in 14 pairs matched diagnosis-relapse tumors, among which 7 were further characterized by exome sequencing. We identify...
Abstract Background The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms structure is critical facilitate the development disease-modifying drugs. Methods Using 9280 magnetic resonance (MR) images (3268 patients) from Osteoarthritis Initiative (OAI) database , we implemented a deep learning method predict, MR and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months. Results...
Bladder cancer is among the five most common cancers diagnosed in Western world and causes significant mortality morbidity rates affected patients. Therapeutic options to treat disease advanced muscle-invasive bladder (MIBC) include cystectomy chemotherapy. Neoadjuvant cisplatin-based combination chemotherapy effective MIBC; however, it has not been widely adopted by community. One reason that many patients do respond neoadjuvant chemotherapy, no biomarker currently exists identify these It...
The heterogeneity of bladder cancers (BCs) is a major challenge for the development novel therapies. However, given high rates recurrence and/or treatment failure, identification effective therapeutic strategies an urgent clinical need. We aimed to establish model system drug identification/repurposing in order identify therapies BC. A collection commercially available BC cell lines (n = 32) was comprehensively characterized. panel 23 lines, representing broad spectrum BC, selected perform...
Lapatinib, a dual tyrosine kinase inhibitor of ErbB1 and ErbB2, shows clinical benefit in subset patients with advanced urothelial bladder cancer (UBC). We hypothesized that the corresponding gene, ERBB2, is affected by mutations UBC these impact ErbB2 function, signaling, proliferation, gene expression, predict response to lapatinib. found ERBB2 5 33 cell lines (15%), all which were derived from invasive or high grade tumors. Phosphorylation activation its downstream pathways markedly...
e15076 Background: Precision oncology aims to first break diagnoses into biologically distinct subtypes, then pursue personalized therapies for each group of patients ( Garraway et al, J Clin Onc, 2013). This strategy is enabled by recent advances in technologies medical imaging, molecular profiling, and artificial intelligence. Spatially resolved profiling (“spatial omics”) an emerging technology that harnesses all three these Navarro Science, 2016; Rao Nature, 2021). Cancer driven...
Objectives: Define a clinically usable preprocessing pipeline for MRI data. Predict brain age using various machine learning and deep algorithms. Caveat against common traps. Data Methods: We used 1597 open-access T1 weighted from 24 hospitals. Preprocessing consisted in applying: N4 bias field correction, registration to MNI152 space, white grey stripe intensity normalization, skull stripping tissue segmentation. Prediction of was done with growing complexity data input (histograms, matter...