# Deep learning for renal tumor characterization

> **NIH NIH R03** · RHODE ISLAND HOSPITAL · 2021 · $41,552

## Abstract

Our long-term objective is to develop deep learning techniques capable of predicting
characteristics and treatment response or response to surveillance to assist clinical decision-
making in renal tumors that are potential candidates for ablation therapy, biopsy, active
surveillance or surgical resection. An increasing number of renal tumors are being diagnosed,
due in part to incidental detection from the increased use of cross-sectional imaging. Although
partial nephrectomy is still considered the primary treatment for small renal masses,
percutaneous ablation is increasingly performed as a therapeutic, nephron-sparing approach.
One challenge for interventional radiologists and urologists who manage these patients is
selection for therapy, since the average rate of progression is slow for small renal tumors and
metastasis rarely occurs. A technique that could distinguish indolent tumors from those will
progress based on data from the imaging methods used to detect and delineate renal masses
would enable early triage to observation versus invasive treatment. Deep learning, a type of
machine learning technique which takes raw images as input, and applies many layers of
transformations to calculate an output signal, has already led to breakthroughs in other areas of
image recognition, and is increasingly used for medical image analysis. However, its application
in the field of interventional radiology is currently limited. Furthermore, no study in the literature
has applied deep learning to kidney lesion segmentation and characteristics/outcome prediction.
In this project, we propose to develop novel deep learning architectures based on routine MR
imaging that allow for accurate renal mass segmentation and prediction of characteristics and
outcome in renal tumors. Using data from four independent cohorts, we will use our deep
learning architectures to predict (1) benign versus malignant histology (2) growth rate in stage
1a renal cell carcinoma (3) SSIGN score in clear cell renal cell carcinoma and (4) clinical
endpoints. We will integrate segmentation and classification into one net that suitable for clinical
application. In addition, we will compare results with those of experts and traditional machine
learning approaches.

## Key facts

- **NIH application ID:** 10116348
- **Project number:** 5R03CA249554-02
- **Recipient organization:** RHODE ISLAND HOSPITAL
- **Principal Investigator:** Harrison Bai
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $41,552
- **Award type:** 5
- **Project period:** 2020-03-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10116348

## Citation

> US National Institutes of Health, RePORTER application 10116348, Deep learning for renal tumor characterization (5R03CA249554-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10116348. Licensed CC0.

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