Publications
112
Citations
859
Est. group size
~25
Recurring co-author estimate
Active years
14
Publishing since 2013
Elshad Hasanov's research focuses on kidney (renal cell carcinoma) and other cancers, combining clinical trials of immunotherapy and targeted drug combinations with computational approaches like machine learning and deep learning applied to tumor imaging, genomics, and single-cell data. A recurring theme is studying how cancer spreads to the brain and other organs, and using AI tools to predict patient outcomes and classify tumor subtypes from pathology images and molecular data.
Publication output has grown substantially over the last decade, rising from about 2 papers per year in 2017 to nearly 28 in 2026, with a marked acceleration in the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning
arXiv (Cornell University) · 2026
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning
arXiv (Cornell University) · 2026
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning
Research Square · 2026
- HistoMet: A Pan-Cancer Deep Learning Framework for Prognostic Prediction of Metastatic Progression and Site Tropism from Primary Tumor Histopathology
Open MIND · 2026
- HistoMet: A Pan-Cancer Deep Learning Framework for Prognostic Prediction of Metastatic Progression and Site Tropism from Primary Tumor Histopathology
arXiv (Cornell University) · 2026
- Phase 1B/2 study of combination <sup>177</sup> Lu girentuximab plus cabozantinib and nivolumab in treatment-naïve patients with advanced clear cell RCC.
Journal of Clinical Oncology · 2026
- Abstract 5470: Low-magnification deep learning model for rapid HER2 status prediction from H&E whole-slide images.
Cancer Research · 2026
- Abstract 2771: Integrating image and text-based AI improves identification of metastatic sites from whole-slide pathology images.
Cancer Research · 2026
- Deciphering the Genomic Landscape of Renal Cell Carcinoma Brain Metastases
bioRxiv (Cold Spring Harbor Laboratory) · 2026
- Single-Cell Atlas of Renal Cell Carcinoma Brain Metastasis Uncovers Mechanisms of Immune Dysfunction and Resistance
bioRxiv (Cold Spring Harbor Laboratory) · 2026
- A Spatial Atlas of Muscle-Invasive Bladder Cancer Reveals Lineage-Specific Vulnerabilities and Immune Architecture
Cancer Discovery · 2026
- Spatial transcriptomic profiling of the tumor microenvironment associated with sunitinib response in metastatic renal cell carcinoma.
Journal of Clinical Oncology · 2026
- Deciphering the genomic landscape of renal cell carcinoma brain metastases.
Journal of Clinical Oncology · 2026
- In silico phase III clinical trial of avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma using a machine learning model transfer approach.
Journal of Clinical Oncology · 2026
- Real-world overall survival after PD-1 failure in advanced cutaneous squamous cell carcinoma.
Journal of Clinical Oncology · 2026
- Journal of Clinical Oncology×41
- Cancer Research×6
- The Oncologist×4
- arXiv (Cornell University)×4
- Regular and Young Investigator Award Abstracts×4
- Theodore F. Logan
Medicine · Indiana University
- Megan Hinkley
Medicine · The Ohio State University
- Taylor Goodstein
Medicine · The Ohio State University
- Sreenivasulu Chintala
Medicine · Indiana University
- Rebecca Koenigsberg
Medicine · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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