Jia Wu, PhD
Department of Imaging Physics, Division of Diagnostic Imaging
About Jia Wu
My overarching goal is to build AI frameworks that address critical unmet needs across the cancer continuum—from early detection and diagnosis to treatment selection, response assessment, and longitudinal disease monitoring. Central to my work is the principle that impactful AI must be biologically grounded, rigorously validated, and designed to operate within real-world clinical workflows. I lead a highly collaborative data science laboratory that works closely with oncologists, radiologists, pathologists, and translational scientists at MD Anderson and with international partners. Our research tightly integrates methodological innovation with clinical application, enabling bidirectional translation from biological insight to algorithm development and from model output to patient care. A defining feature of my program is the integration of multimodal AI systems—combining imaging, pathology, molecular profiling, blood-based biomarkers, and clinical data—directly into prospective and interventional clinical trials, ensuring that computational advances meaningfully inform patient care and clinical decision-making.
Present Title & Affiliation
Primary Appointment
Associate Professor, Department Thoracic-Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
Associate Professor, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
Education & Training
Degree-Granting Education
| 2013 | University of Pittsburgh, Pittsburgh, Pennsylvania, US, Civil & Bioengineering, Ph.D |
| 2012 | Carnegie Mellon University, Pittsburgh, Pennsylvania, US, Machine Learning, Credit learning |
| 2009 | Harbin Institute of Technology, Heilongjiang, CN, Computational Mechanics, M.S |
| 2007 | Harbin Institute of Technology, Heilongjiang, CN, Mechanical Engineering, BS |
Postgraduate Training
| 2015-2017 | Postdoctoral Fellowship, Radiation Oncology, Stanford University, Palo Alto, California |
| 2013-2015 | Postdoctoral Fellowship, Radiology, University of Pennsylvania, Philadelphia, Pennsylvania |
Experience & Service
Faculty Academic Appointments
Assistant Professor, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, 2020 - 2024
Assistant Professor, Thoracic-Head & Neck Med Onc, The University of Texas MD Anderson Cancer Center, Houston, Texas, 2020 - 2024
Instructor, Radiation Oncology, Stanford University, Palo Alto, CA, 2018 - 2020
Honors & Awards
| 2026 - Present | Diagnostic Imaging Outstanding Service Awards |
| 2026 - Present | AACR Annual Meeting Press Program (Senior Author) |
| 2025 | Distinguished Investigator, Academy for Radiology & Biomedical Imaging Research |
| 2025 | Honoree for Research Excellency, MD Anderson Cancer Center |
| 2024 | Hearst Health Prize Finalist |
| 2023 | Editor's Recognition Awards with Distinction, RSNA Radiology Journal Office |
| 2023 | Scholar-In-Training Award (Senior Author), AACR |
| 2023 | Faculty Scholar Award President's Recognition of Faculty Excellence, The University of Texas MD Anderson Cancer Center |
| 2022 | DI Outstanding Jr. Faculty Award, The University of Texas MD Anderson Cancer Center |
| 2021 - 2025 | Rexanna's Foundation Award for Fighting Lung Cancer |
| 2021 | NCI Awardee Skills Consortium (NASDC) |
| 2021 | UT Rising STARs Award, The University of Texas System |
| 2021 | Goldwater Scholar (Research Mentor) |
| 2019 | Editor's Recognition Awards with Special Distinction, RSNA Radiology Journal Office |
| 2018 | Research Career Accelerator Program, Stanford Center for Clinical & Translational Research & Education |
| 2018 | Pathway to Independence Award (K99/R00), NIH/NCI |
| 2017 | Introduction to Academic Radiology for Scientists Program (ITARSc), RSNA |
Professional Memberships
Selected Presentations & Talks
Regional Presentations
- 2026. Multimodal AI for Cancer Diagnosis, Prevention, and Treatment. Invited. Houston, Texas, US.
- 2026. From Publications to New Horizons: The Next Phase of Multimodal AI in Lung Cancer Care. Invited. Houston, Texas, US.
- 2026. Building the Cancer Digital Twin: Harnessing Multimodal AI Across Radiology, Pathology, and Blood Biomarkers. Invited. Houston, Texas, US.
- 2025. Seeing the Unseen: How Multimodal AI is Rethinking Cancer Biology. Invited, US.
- 2025. Leverage AI and multimodal data to study precancer evolution. Invited. Houston, Texas, US.
- 2025. Multi-Modal AI in Lung Cancer. Invited. Houston, Texas, US.
- 2025. Harnessing Multi-Modal Data with AI to Address Unmet Challenges in Cancer. Invited. Harnessing Multi-Modal Data with AI to Address Unmet Challenges in Cancer. Houston, Texas, US.
National Presentations
- 2025. Year in Review: AI in Oncology. Invited, US.
- 2025. AI-Driven Multimodal Integration to Guide Cancer Treatment and Beyond. Invited, US.
- 2025. Integrating AI and Pathomics to Advance Clinical Decision-Making and Biological Discovery. Invited, US.
- 2025. AI-augmented digital twin frameworks for clinical trial prediction and design. Invited. Rome, IT.
- 2025. Generatating synthetic PET scans to improve diagnosis and prognosis for lung cancer. Invited. Houston, Texas, US.
- 2025. Harnessing Multi-Modal Data with AI to Address Unmet Challenges in Cancer. Invited. Houston, TX, US.
- 2025. AI and Multi-modal Modeling in Lung Cancer,. Invited. Gilbert W. Beebe Symposium on AI and ML Applications in Radiation Therapy, Medical Diagnostics, and Radiation Occupational Health and Safety. Washington, D.C, US.
- 2025. Advancing Clinical Trials: Radiomics and Multiscale Machine Learning for Patient Selection and Stratification. Invited. NRG Developmental Therapeutics/Radiation Oncology Meeting. Phoenix, AZ, US.
- 2024. he World Molecular Imaging Congress. Invited. Naples, IT.
- 2024. AI in Clinical Risk Prediction. Invited. Dallas, Texas, US.
- 2023. Keynote Speaker: Multi-modal Integration and Modeling to Advance Precision Oncology. Conference. Keynote Speaker: Multi-modal Integration and Modeling to Advance Precision Oncology, US.
- 2023. Empower Digital Pathology with Machine Learning and Usher It into Multi-Modal Integration. Conference. Empower Digital Pathology with Machine Learning and Usher It into Multi-Modal Integration, US.
- 2023. Using large language models to predict clinical trial success. Conference. Using large language models to predict clinical trial success, US.
- 2023. Machine learning to analyze radiographic scans and digital pathology slides and integrate genomic data. Conference. Machine learning to analyze radiographic scans and digital pathology slides and integrate genomic data, US.
- 2023. Artificial Intelligence to Surpass Radiomics with Multi-Platform Integration for Precision Oncology. Conference. Artificial Intelligence to Surpass Radiomics with Multi-Platform Integration for Precision Oncology, US.
- 2022. Identification of Nodular Lymphocyte-Predominant Hodgkin Lymphoma Variant Morphology Using Artificial Intelligence. Conference. Identification of Nodular Lymphocyte-Predominant Hodgkin Lymphoma Variant Morphology Using Artificial Intelligence. New Orleans, LA, US.
- 2022. Integrated Imaging and Molecular Analysis to Decipher Tumor Microenvironment in the Era of Immunotherapy. Conference. The 2nd MD Anderson Cancer Center and ShangHai Concord Cancer Center Annual International Academic Meeting, US.
- 2022. Proliferation Center-Focused Artificial Intelligence Algorithm Enhances Detection of Accelerated Phase Chronic Lymphocytic Leukemia. Conference. Proliferation Center-Focused Artificial Intelligence Algorithm Enhances Detection of Accelerated Phase Chronic Lymphocytic Leukemia. Los Angeles, CA, US.
- 2021. Artificial Intelligence-Assisted Mapping of Proliferation Centers in Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma Identifies Patterns That Reliably Distinguish Accelerated Phase and Large Cell Transformation. Conference. Artificial Intelligence-Assisted Mapping of Proliferation Centers in Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma Identifies Patterns That Reliably Distinguish Accelerated Phase and Large Cell Transformation. Atlanta, GA, US.
- 2021. Machine Learning Pipeline with Feature Engineering Provides Robust Diagnostic Predictions in Chronic Lymphocytic Leukemia, Accelerated and Transformed Phases. Conference. Machine Learning Pipeline with Feature Engineering Provides Robust Diagnostic Predictions in Chronic Lymphocytic Leukemia, Accelerated and Transformed Phases. Atlanta, GA, US.
- 2021. Spearhead Clinically Relevant Radiologic Biomarker Discovery in Precision Oncology with Habitat Imaging, Virtual Symposium and Scientific Meeting. Conference. Virtual Symposium and Scientific Meeting, US.
- 2018. Quantitative DCE-MRI Features Can Complement Molecular Markers for Predicting Tumor Infiltrating Lymphocytes in Breast Cancer: Model Discovery and Independent Validation. Conference. Quantitative DCE-MRI Features Can Complement Molecular Markers for Predicting Tumor Infiltrating Lymphocytes in Breast Cancer: Model Discovery and Independent Validation. Chicago, IL, US.
- 2016. Intratumor Partitioning of Serial CT and FDG-PET Images Identifies High-risk Tumor Subregions and Predicts Patterns of Failure in Non-small Cell Lung Cancer After Radiotherapy. Conference. Intratumor Partitioning of Serial CT and FDG-PET Images Identifies High-risk Tumor Subregions and Predicts Patterns of Failure in Non-small Cell Lung Cancer After Radiotherapy. Boston, MA, US.
- 2015. Tumor Heterogeneity Patterns of DCE-MRI Parametric Response Maps May Augment Early Assessment of Neoadjuvant Chemotherapy: A Pilot Study of ACRIN 6657/I-SPY 1. Conference. Tumor Heterogeneity Patterns of DCE-MRI Parametric Response Maps May Augment Early Assessment of Neoadjuvant Chemotherapy: A Pilot Study of ACRIN 6657/I-SPY 1. Chicago, IL, US.
- 2014. A Feasibility Study Investigating the Use of Quantitative Measures of Spatio-temporal Tumor Heterogeneity Derived from 4D Breast Page 4 of 6 DCE-MRI Registration as a Biomarker of Response to Neoadjuvant Chemotherapy. Conference. A Feasibility Study Investigating the Use of Quantitative Measures of Spatio-temporal Tumor Heterogeneity Derived from 4D Breast Page 4 of 6 DCE-MRI Registration as a Biomarker of Response to Neoadjuvant Chemotherapy. Austin, TX, US.
- 2014. A feasibility study on kinematic feature extraction from the human interventricular septum toward hypertension classification. Conference. A feasibility study on kinematic feature extraction from the human interventricular septum toward hypertension classification. Pittsburgh, PA, US.
- 2013. An Investigation of Shape Analysis Methods for Assessment of Organ-Level Functional Changes in the Human Right Ventricle. Conference. An Investigation of Shape Analysis Methods for Assessment of Organ-Level Functional Changes in the Human Right Ventricle. Raleigh, NC, US.
- 2011. Geometric Analysis and Decomposition of Normal and Hypertensive Human Right Ventricle from Diagnostic Medical Imaging. Conference. Geometric Analysis and Decomposition of Normal and Hypertensive Human Right Ventricle from Diagnostic Medical Imaging. Pittsburgh, PA, US.
International Presentations
- 2026. How to validate AI biomarkers for clinical practice. Invited, US.
- 2025. Multimodal AI for Advancing Lung Cancer Diagnosis and Treatment. Invited. Nice, FR.
- 2025. AI Innovations in Lung Cancer. Invited. Rome, IT.
- 2024. Radiomics and Artificial Intelligence for Precision Oncology. Invited, US.
- 2024. Harnessing AI for Enhanced Molecular Imaging in Cancer Research. Invited. Montreal, CA.
Formal Peers
- 2022. AI Bridge Radiology and Pathology. Invited, US.
- 2021. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Invited. Austin, TX, US.
- 2020. Habitat Imaging for COVID-19: Discover Clinically Relevant and Actionable Biomarkers. Invited, US.
- 2019. Empower Radiology with Artificial Intelligence: Discover Clinically Relevant and Actionable Imaging Markers. Invited. Stanford, CA, US.
- 2019. Artificial Intelligence in Radiology: Discover Clinically Relevant & Actionable Imaging Markers, Department of Radiology. Invited. New York, NY, US.
- 2019. Discover Clinically Relevant and Actionable Imaging Markers with Artificial Intelligence, Radiology Research Grand Round. Invited. Los Angeles, CA, US.
- 2018. Artificial Intelligence in Medical Imaging: Converting Pictures into Clinically Useful Information for Precision Medicine. Invited. Atlanta, GA, US.
- 2018. Artificial Intelligence in Radiology: Discover Clinically Relevant and Actionable Imaging Markers in Precision Oncology. Invited. New York City, NY, US.
- 2018. Unraveling Tumor Heterogeneity Using Artificial Intelligence: Radiomics, Radiogenomics and Habitat Imaging, Center for Biomedical Informatics. Invited. Winston-Salem, NC, US.
- 2018. Spearhead Clinically Relevant Radiologic Biomarker Discovery in Precision Oncology with Artificial Intelligence. Invited. New York City, NY, US.
- 2017. Radiomic and Radiogenomic Analysis for Clinically Relevant Imaging Biomarkers in Precision Oncology, Radiology Grand Rounds & Translational Research Seminar. Invited. Danville, PA, US.
- 2013. Computational Statistical Shape Analysis of Medical Images for Hypertension Classification. Invited. Saint Louis, MO, US.
Selected Publications
Peer-Reviewed Articles
- Waqas, M, Bandyopadhyay, R, Showkatian, E, Muneer, A, Zafar, A, Alvarez, FR, Marin, MC, Li, W, Jaffray, D, Haymaker, CL, Heymach, JV, Vokes, N, Solis Soto, LM, Zhang, J, Wu, J. The next layer: augmenting foundation models with structure-preserving and attention-guided learning for local patches to global context awareness in computational pathology. npj Precision Oncology 10(1), 2026. e-Pub 2026. PMID: 41639188.
- Muneer, A, Showkatian, E, Saad, M, Hong, L, Li, S, Salehjahromi, M, Aminu, M, Sujit, S, Xu, H, Waqas, M, Zafar, A, Shroff, G, Wu, CC, Carter, B, Chang, JY, Liao, Z, Altan, M, Vokes, N, Cascone, T, Le, X, Haymaker, CL, Wistuba, II, Chung, C, Jaffray, D, Gibbons, DL, Vaporciyan, AA, Lee, JJ, Lou, Y, Heymach, JV, Zhang, J, Wu, J. Deep learning of CT imaging predicts PD-L1 expression and immunotherapy response in metastatic NSCLC. Cancer Letters 656, 2026. e-Pub 2026. PMID: 42314966.
- Muneer, A, Waqas, M, Saad, M, Showkatian, E, Bandyopadhyay, R, Xu, H, Li, W, Chang, JY, Liao, Z, Haymaker, CL, Solis Soto, LM, Wu, CC, Vokes, N, Le, X, Byers, LA, Gibbons, DL, Heymach, JV, Zhang, J, Wu, J. From classical machine learning to emerging foundation models. Artificial Intelligence Review 59(4), 2026. e-Pub 2026.
- Zhu B, Aminu M, Chen P, Li J, Dong C, Li C, Tian Y, Lu S, Chen H, Ma C, Hu X, Ye J, Liu AY, Huang B, Rojas FR, Edwin Roger PC, Shi O, Nilsson MB, Poteete A, Khan KB, Lu W, Solis Soto LM, Fujimoto J, Haymaker C, Wistuba II, Wei Z, Wang L, Gibbons DL, Chen K, Reuben A, Schenke JM, Heymach JV, Cheng C, Wu J, Zhang J. Spatial Profiling Reveals Distinct Molecular and Immune Evolution of Mouse Lung Adenocarcinoma Precancers with or Without Carcinogen Exposure. Advanced Science 13(17), 2026. e-Pub 2026. PMID: 41580978.
- Zhu, E, Muneer, A, Zhang, J, Xia, Y, Li, X, Zhou, CC, Heymach, JV, Wu, J, Le, X. Progress and challenges of artificial intelligence in lung cancer clinical translation. npj Precision Oncology 9(1), 2025. e-Pub 2025. PMID: 40595378.
- # XH, # WL, # JL, # SL, Saad MB, Kitsel Y, Heeke S, Hong L, Mohamed MQ, Le X, Vokes N, Godoy MC, Carter BW, Shroff GS, Eapen G, Byers LA, Vaporciyan AA, Gibbons DL, Heymach J, Wu CC, Zhang J, Wu J. Development of PET/CT-clinical nomograms for predicting lymph node metastasis in primary lung cancer. European Radiology 36(5):4110-4122, 2025. e-Pub 2025. PMID: 41405691.
- Aminu, M, Zhu, B, Vokes, N, Chen, H, Hong, L, Li, J, Fujimoto, J, Chaib, M, Yang, Y, Wang, B, Poteete, A, Nilsson, M, Le, X, Cascone, T, Jaffray, D, Navin, N, Wang, T, Byers, LA, Gibbons, DL, Heymach, JV, Chen, K, Cheng, C, Zhang, J, Wu, J. CoCo-ST detects global and local biological structures in spatial transcriptomics datasets. Nature cell biology 27(11):2019-2031, 2025. e-Pub 2025. PMID: 41083603.
- Saad, M, Showkatian, E, Verma, V, Al Tashi, Q, Aminu, M, Xu, X, Mohamed, MQ, Salehjahromi, M, Sujit, S, Kitsel, Y, Lin, SH, Liao, Z, Gandhi, S, Qian, DC, Jaffray, D, Chung, C, Vokes, N, Zhang, J, Jack Lee, J, Heymach, JV, Wu, J, Chang, JY. Causal AI-based clinical and radiomic analysis for optimizing patient selection in combined immunotherapy and SABR in early-stage NSCLC. Journal for immunotherapy of cancer 13(10), 2025. e-Pub 2025. PMID: 41052882.
- Salehjahromi M, Li H, Showkatian E, Saad MB, Qayati M, Ismail SM, Sujit SJ, Muneer A, Aminu M, Hong L, Han X, Heeke S, Cascone T, Le X, Vokes N, Gibbons DL, Toumazis I, Ostrin EJ, Antonoff MB, Vaporciyan AA, Jaffray D, Kay FU, Carter BW, Wu CC, B Godoy MC, Lee J, Gerber DE, Heymach JV, Zhang J, Wu J. Radiomics for Dynamic Lung Cancer Risk Prediction in USPSTF-Ineligible Patients. Cancers 17(21), 2025. e-Pub 2025. PMID: 41228201.
- Deboever N, Al-Tashi Q, Eisenberg M, Saad MB, Antonoff MB, Hofstetter WL, Mehran RJ, Rice DC, Roth J, Swisher SG, Vaporciyan AA, Walsh GL, Wu J, Rajaram R. Machine Learning Prediction of Financial Toxicity in Patients with Resected Lung Cancer. Journal of the American College of Surgeons, 2025. e-Pub 2025. PMID: 40028915.
- Saad MB, Al-Tashi Q, Hong L, Verma V, Li W, Boiarsky D, Li S, Petranovic M, Wu CC, Carter BW, Shroff GS, Cascone T, Le X, Elamin YY, Altan M, Heeke S, Sheshadri A, Chang JY, Lee PP, Liao Z, Gibbons DL, Vaporciyan AA, Lee JJ, Wistuba II, Haymaker C, Mirjalili S, Jaffray D, Gainor JF, Lou Y, Di Federico A, Pecci F, Awad M, Ricciuti B, Heymach JV, Vokes NI, Zhang J, Wu J. Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC. Nature communications 16(1), 2025. e-Pub 2025. PMID: 40707438.
- Zhu B, Chen P, Aminu M, Li JR, Fujimoto J, Tian Y, Hong L, Chen H, Hu X, Li C, Vokes N, Moreira AL, Gibbons DL, Solis Soto LM, Parra Cuentas ER, Shi O, Diao S, Ye J, Rojas FR, Vilar E, Maitra A, Chen K, Navin N, Nilsson M, Huang B, Heeke S, Zhang J, Haymaker CL, Velcheti V, Sterman DH, Kochat V, Padron WI, Alexandrov LB, Wei Z, Le X, Wang L, Fukuoka J, Lee JJ, Wistuba II, Pass HI, Davis M, Hanash S, Cheng C, Spira A, Rai K, Lippman SM, Futreal PA, Heymach JV, Reuben A, Wu J, Zhang. Spatial and multiomics analysis of human and mouse lung adenocarcinoma precursors reveals TIM-3 as a putative target for precancer interception. Cancer cell 43(6):1125-1140.e10, 2025. e-Pub 2025. PMID: 40345189.
- Alahdab, F, Saad, M, Ahmed, AI, Al Tashi, Q, Aminu, M, Han, Y, Moody, JB, Murthy, VL, Wu, J, Al-Mallah, MH. Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients’ electrocardiograms. Cell Reports Medicine 5(10), 2024. e-Pub 2024. PMID: 39326409.
- Waqas, M, Tahir, MA, Author, MD, Al-Maadeed, S, Bouridane, A, Wu, J. Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer. Neural Computing and Applications 36(12):6659-6680, 2024. e-Pub 2024.
- Sujit # SJ, # MA, Karpinets TV, Chen P, Saad MB, Salehjahromi M, Boom JD, Qayati M, George JM, Allen H, Antonoff MB, Hong L, Hu X, Heeke S, Tran HT, Le X, Elamin YY, Altan M, Vokes NI, Sheshadri A, Lin J, Zhang J, Lu Y, Behrens C, B Godoy MC, Wu CC, Chang JY, Chung C, Jaffray DA, Wistuba II, Lee J, Vaporciyan AA, Gibbons DL, Heymach J, Zhang J, Cascone T, Wu J. Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights. Nature Communications, 2024. e-Pub 2024. PMID: 38605064.
- Salehjahromi, M, Karpinets, T, Sujit, S, Qayati, M, Chen, P, Aminu, M, Saad, MB, Bandyopadhyay, R, Hong, L, Sheshadri, A, Lin, J, Antonoff, MB, Sepesi, B, Ostrin, EJ, Toumazis, I, Huang, P, Cheng, C, Cascone, T, Vokes, N, Behrens, C, Siewerdsen, JH, Hazle, JD, Chang, JY, Zhang, J, Lu, Y, Godoy, M, Chung, C, Jaffray, D, Wistuba, II, Lee, JJ, Vaporciyan, AA, Gibbons, DL, Gladish, G, Heymach, JV, Wu, CC, Zhang, J, Wu, J. Synthetic PET from CT improves diagnosis and prognosis for lung cancer. Cell Reports Medicine 5(3), 2024. e-Pub 2024. PMID: 38471502.
- Nofal S, Niu J, Resong P, Jin J, Merriman KW, Le X, Katki H, Heymach J, Antonoff MB, Ostrin E, Wu J, Zhang J, Toumazis I. Personal history of cancer as a risk factor for second primary lung cancer: Implications for lung cancer screening. Cancer Medicine, 2024. e-Pub 2024. PMID: 38466021.
- Tran H, Heeke S, Sujit S, Vokes N, Zhang J, Aminu M, Lam V, Vaporciyan A, Swisher S, Godoy M, Cascone T, Sepesi B, Gibbons D, Wu J, Heymach J. Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer. Annals of Oncology, 2024. e-Pub 2024. PMID: 37992871.
- Chen, P, Rojas Alvarez, F, Hu, X, Serrano, A, Zhu, B, Chen, H, Hong, L, Bandyoyadhyay, R, Aminu, M, Kalhor, N, Lee, JJ, El Hussein, S, Khoury, J, Pass, HI, Moreira, AL, Velcheti, V, Sterman, DH, Fukuoka, J, Tabata, K, Su, D, Ying, L, Gibbons, DL, Heymach, JV, Wistuba, II, Fujimoto, J, Solis Soto, LM, Zhang, J, Wu, J. Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma. Modern Pathology 36(12), 2023. e-Pub 2023. PMID: 37678674.
- Diao S, Chen P, Showkatian E, Bandyopadhyay R, Rojas FR, Zhu B, Hong L, Aminu M, Saad MB, Salehjahromi M, Muneer A, Sujit SJ, Behrens C, Gibbons DL, Heymach JV, Kalhor N, Wistuba II, Solis Soto LM, Zhang J, Qin W, Wu J. Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis. Cancers (Basel) 15(19), 2023. e-Pub 2023. PMID: 37835518.
- Aminu M, Daver N, Godoy MC, Shroff G, Wu C, Torre-Sada LF, Goizueta A, Shannon VR, Faiz SA, Altan M, Garcia-Manero G, Kantarjian H, Ravandi-Kashani F, Kadia T, Konopleva M, DiNardo C, Pierce S, Naing A, Kim ST, Kontoyiannis DP, Khawaja F, Chung C, Wu J, Sheshadri A. Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept. Frontiers in Immunology, 2023. e-Pub 2023. PMID: 37841255.
- Saad§ MB, Hong§ L, Aminu§ M, Vokes NI, Chen§ P, Salehjahromi§ M, Qin K, Sujit§ SJ, Lu X, Young E, Al-Tashi§ Q, Qureshi§ R, Wu CC, Carter BW, Lin SH, Lee PP, Gandhi S, Chang JY, Li R, Gensheimer MF, Wakelee HA, Neal JW, Lee H, Cheng C, Velcheti V, Lou Y, Petranovic M, Rinsurongkawong W, Le X, Rinsurongkawong V, Spelman A, Elamin YY, Negrao MV, Skoulidis F, Gay CM, Cascone T, Antonoff MB, Sepesi B, Lewis J, Wistuba II, Hazle JD, Chung C, Jaffray D, Gibbons DL, Vaporciyan A, J Jack Lee J, Heymach JV, Zhang J, Wu J. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study. The Lancet Digit. Health 5(7):e404-e420, 2023. e-Pub 2023. PMID: 37268451.
- Al-Tashi§ Q, Saad§ MB, Sheshadri A, Wu CC, Chang JY, Al-Lazikani B, Gibbons C, Vokes NI, Zhang J, J Jack Lee J, Heymach JV, Jaffray D, Mirjalili S, Wu J. SwarmDeepSurv: Swarm Intelligence Advances Deep Survival Network for Prognostic Radiomics Signatures in Four Solid Cancers. Patterns 4(8), 2023. e-Pub 2023. PMID: 37602223.
- Al-Tashi Q, Saad MB, Muneer A, Qureshi R, Mirjalili S, Sheshadri A, Le X, Vokes NI, Zhang J, Wu J. Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review. International Journal of Molecular Sciences 24(9):7781, 2023. e-Pub 2023. PMID: 37175487.
- Reddy DR, Cuenca JA, Botdorf J, Muthu M, Hanmandlu A, Wegner R, Crommett J, Gutierrez C, Rathi N, Sajith B, Knafl M, Collaborators DT. Clinical Characteristics and Cause of Death Among Hospitalized Decedents with Cancer and COVID-19. Mayo Clinic Proceedings 98(3):451-457, 2023. e-Pub 2023. PMID: 36868753.
- Hong L, Aminu M, Li S, Lu X, Petranovic M, Saad MB, Chen P, Qin K, Varghese S, Rinsurongkawong W, Rinsurongkawong V, Spelman A, Elamin YY, Negrao MV, Skoulidis F, Gay CM, Cascone T, Gandhi SJ, Lin SH, Lee PP, Carter BW, Wu CC, Antonoff MB, Sepesi B, Lewis J, Gibbons DL, Vaporciyan AA, Le X, Jack Lee J, Roy-Chowdhuri S, Routbort MJ, Gainor JF, Heymach JV, Lou Y, Wu J, Zhang J, Vokes NI. Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer. Nat Commun 14(1):695, 2023. e-Pub 2023. PMID: 36755027.
- Aminu M, Yadav D, Hong L, Young E, Edelkamp P, Saad M, Salehjahromi M, Chen P, Sujit SJ, Chen MM, Sabloff B, Gladish G, de Groot PM, Godoy MCB, Cascone T, Vokes NI, Zhang J, Brock KK, Daver N, Woodman SE, Tawbi HA, Sheshadri A, Lee JJ, Jaffray D, D Code Team D, Wu CC, Chung C, Wu J. Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19. Cancers (Basel) 15(1), 2022. e-Pub 2022. PMID: 36612278.
- El Hussein S, Chen P, Medeiros LJ, Hazle JD, Wu J, Khoury JD. Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia. Mod Pathol 35(8):1121-1125, 2022. e-Pub 2022. PMID: 35132162.
- Chen P, El Hussein S, Xing F, Aminu M, Kannapiran A, Hazle JD, Medeiros LJ, Wistuba II, Jaffray D, Khoury JD, Wu J. Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering. Cancers (Basel) 14(10), 2022. e-Pub 2022. PMID: 35626003.
- Jimenez JE, Dai D, Xu G, Zhao R, Li T, Pan T, Wang L, Lin Y, Wang Z, Jaffray D, Hazle JD, Macapinlac HA, Wu J, Lu Y. Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma. Clin Nucl Med 47(3):209-218, 2022. e-Pub 2022. PMID: 35020640.
- El Hussein S, Chen P, Medeiros LJ, Wistuba II, Jaffray D, Wu J, Khoury JD. Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia. J Pathol 256(1):4-14, 2022. e-Pub 2022. PMID: 34505705.
- Chen P, Zhang J, Wu J. Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy. 21 Century Pathol 2(3), 2022. e-Pub 2022. PMID: 36282981.
- Chen MM, Terzic A, Becker AS, Johnson JM, Wu CC, Wintermark M, Wald C, Wu J. Artificial intelligence in oncologic imaging. Eur J Radiol Open 9:100441, 2022. e-Pub 2022. PMID: 36193451.
- Jiang Y, Jin C, Yu H, Wu J, Chen C, Yuan Q, Huang W, Hu Y, Xu Y, Zhou Z, Fisher GA, Li G, Li R. Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study. Ann Surg 274(6):e1153-61, 2021. e-Pub 2021. PMID: 31913871.
- Chen, P, Aminu, M, El Hussein, S, Khoury, J, Wu, J. CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology. Software Impacts 10, 2021. e-Pub 2021. PMID: 36203948.
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Review Articles
- Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 84:310-328, 2022. e-Pub 2022. PMID: 33290844.
Patient Reviews
CV information above last modified September 04, 2026