Biography and Research Information
OverviewAI-generated summary
Aditi Chaurasia's research focuses on the application of advanced imaging and machine learning techniques for the detection and characterization of kidney cancers. Her work investigates the use of magnetic resonance imaging (MRI) and deep learning algorithms, such as YOLOv7, to automate the detection of renal masses and to predict tumor growth rates. A significant portion of her recent publications addresses clear cell renal cell carcinoma, particularly in the context of von Hippel-Lindau syndrome, exploring non-invasive methods for tumor grade evaluation and surveillance strategies.
Chaurasia collaborates with researchers at the University of Arkansas for Medical Sciences, including Shiva M. Singh, with whom she has co-authored five publications. Her scholarship metrics include an h-index of 4 and 32 total citations across 10 publications. Her recent activity includes publications in 2024 and 2025, indicating ongoing research in this area.
Metrics
- h-index: 4
- Publications: 10
- Citations: 33
Selected Publications
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Deciphering Wrist Pain: A Comprehensive MRI-Based Classification and Interpretation Approach (2025)
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Commentary: Leveraging Large Language Models for Radiology Education and Training (2025)
Collaboration Network
Top Collaborators
Mark W. Ball
8 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 8 shared publications
Ashkan A. Malayeri
National Institutes of Health Clinical Center (US)
8 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 8 shared publications
Pouria Yazdian Anari
National Institutes of Health Clinical Center (US)
7 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 7 shared publications
Nathan Lay
National Institutes of Health (US)
7 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 7 shared publications
Evrim Türkbey
National Institutes of Health Clinical Center (US)
7 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 7 shared publications
Barış Türkbey
National Institutes of Health (US)
7 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 7 shared publications
W. Marston Linehan
National Institutes of Health (US)
7 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
Showing 5 of 7 shared publications
Maria J. Merino
National Institutes of Health (US)
6 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
- Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
Showing 5 of 6 shared publications
Elizabeth C. Jones
National Institutes of Health Clinical Center (US)
6 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
- Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
Showing 5 of 6 shared publications
Rabindra Gautam
National Cancer Institute (US)
5 shared publications
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
- Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
- Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.
- Is Active Surveillance a Suitable Approach for Bilateral Multifocal Renal Oncocytomas? The 20-Year National Cancer Institute Experience
Shiva M. Singh
University of Arkansas Medical Center
5 shared publications
In database
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
- Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
- Commentary: Leveraging Large Language Models for Radiology Education and Training
- Deciphering Wrist Pain: A Comprehensive MRI-Based Classification and Interpretation Approach
Nikhil Gopal
National Cancer Institute (US)
4 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.
- Is Active Surveillance a Suitable Approach for Bilateral Multifocal Renal Oncocytomas? The 20-Year National Cancer Institute Experience
Fatemeh Dehghani Firouzabadi
4 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
- Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
Aryan Zahergivar
National Institutes of Health Clinical Center (US)
4 shared publications
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results
- Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI
- <scp>Non‐Invasive</scp> Tumor Grade Evaluation in Von Hippel–<scp>Lindau‐Associated</scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging‐Based</scp> Study
- Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
Safa Samimi
National Institutes of Health Clinical Center (US)
3 shared publications
- An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome
- Deep learning‐based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI
- Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.
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