Jia Wu

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Assistant Professor

The University of Texas MD Anderson Cancer Center

United States

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About

Dr. Jia Wu is an Assistant Professor at the University of Texas MD Anderson Cancer Center in the United States. His research focuses on lung cancer, particularly in areas such as the clinical efficacy of targeted therapies, risk prediction models for pulmonary nodules, and the molecular landscape of genetic alterations in non-small cell lung cancer (NSCLC). He has authored several recent articles exploring machine learning applications and computational methods in cancer biomarker identification and drug resistance prediction.

Recent Grants

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QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCER

Open Date: 2020-07-01

Close Date: 2023-06-30

Grant: Close

Quantitative Imaging Biomarkers of Treatment Response and Prognosis in Breast Cancer

Open Date: 2018-03-01

Close Date: 2020-02-28

Articles (18)

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2025

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2021

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2020

Collaborators (17)

David Gerber

Professor of Internal Medicine (Hematology-Oncology) and Clinical Sciences

University of Texas Southwestern Medical Center

UNITED STATES

Jia Wu

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About

Recent Grants

Articles (18)

Poziotinib for EGFR exon 20-insertion NSCLC: Clinical efficacy of the phase 2 ZENITH trial and differential impact of EGFR exon 20 insertion location on sensitivity

First-Line MET Tyrosine Kinase Inhibitors versus Immunotherapy ± Chemotherapy for Patients with MET Exon 14 Skipping Mutant Metastatic NSCLC

Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model

Single Stage Adaptive Multi-Attention Network for Image Restoration

Exploring Multiple Instance Learning (MIL): A brief survey

Molecular landscape of ERBB2 alterations in 3000 advanced NSCLC patients

Functional Genetic Variants in TGFβ1 and TGFβR1 in miRNA-Binding Sites Predict Outcomes in Patients with HPV-positive Oropharyngeal Squamous Cell Carcinoma

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Limited Benefit from the Addition of Immunotherapy to Chemotherapy in TKI-Refractory EGFR-Mutant Lung Adenocarcinoma

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering

Radiological tumor classification across imaging modality and histology.

Immune evolution from preneoplasia to invasive lung adenocarcinomas and underlying molecular features

CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in Chronic Lymphocytic Leukemia

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.

Collaborators (17)

David Gerber

Jianjun Zhang

Neil Gross

Qasem Al-Tashi

Chao Li

Ruijiang Li

Shah Nawaz

Hazrat Ali

J. Jack Lee

Jiexin Zhang

Amgad Muneer

Natalie Vokes

Siba El Hussein

David Jaffray

Marcelo Negrao

Peng Wei

Sonia Patel

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