Research

Radiomics-Pipeline

Our projects often involve developing of image-processing pipelines, feature analysis toolkits, including but not limited to shape and texture analysis, tumor identification and classification based on tumor type and grade of malignancy, etc., across various diseases and using a variety of imaging and non-imaging data. The resultant tumor behavior models relating imaging features to tumor behavior is potentially of great value as a research tool and a clinical decision support tool, to improve individualized treatment selection and aid advanced treatment monitoring.

Results from our projects feed into our RADIOMICS platform -the high throughput extraction of tumor features using standard-of-care imaging platform, where imaging features can be combined with clinical, laboratory, genomic, and epigenetic data to improve identification of diagnostic and prognostic features. A comprehensive capture of tumor heterogeneity via the capture of its various phenotypes using standard-of-care imaging will aid in advancement of precision medicine.

Whole lesion quantitative CT evaluation of renal cell carcinoma: differentiation of clear cell from papillary renal cell carcinoma

Our study suggests that voxel-based whole lesion enhancement parameters

  1. Can differentiate between ccRCC and pRCC.
  2. Provide only a slight improvement over single ROI-based enhancement techniques in differentiating between ccRCC and renal oncocytoma.

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Radiomics-Based Quantitative Biomarker Discovery: Development of a Robust Image Processing Infrastructure

Here, we focus on the key components of our Radiomics workflow, specifically a file organization schema for centralized data storage, deployment of image registration strategies, and frontend GUI design for ease of use by the clinical researcher, all of which increase the transparency, flexibility, and portability of our Radiomics platform.

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MP08-13: MR RADIOMICS IN THE RISK STRATIFICATION OF PROSTATE CANCER

Presented at American Urological Association, May 2017

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MP18-13: TEXTURE ANALYSIS OF ENHANCING, NON-LIPID CONTAINING SOLID RENAL MASSES: DIFFERENTIATION OF MALIGNANT FROM BENIGN RENAL TUMORS.

Presented at American Urological Association, May 2017

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Wavelets Analysis for Differentiating Solid, Non-Macroscopic Fat Containing, Enhancing Renal Masses: A Pilot Study (Scientific Presentation: SSC06-04)

Presented at RSNA 2017


Variability of Texture Analysis from Imaging of Renal Tumors: Role of Necrosis (Scientific Presentation: 17011182)

Date/Time: 11/27/2017 - 11:00 AM ;

Room: N230B; Presenter: Darryl H. Hwang, PhD


Texture Analysis of Bladder Cancer: Differentiating Transitional Cell Carcinoma from Micropapillary Carcinoma (Poster Presentation: 17040612)

Session Time: 11/30/2017 12:45 - 1:15 PM

Station Number: Station #5

Presenter: Ting-Wei Fan , Medical Student

Student Travel Award Winner


The simple cyst (blue ROI) is anechoic and demonstrates no enhancement on qualitative visualization and on TIC analysis.

Contrast-Enhanced Ultrasound with Time-Intensity Curve Analysis: A diagnostic tool for management of indeterminate cystic kidney lesions (Poster Presentation: 17007591 )

Session Time: 11/30/2017 12:45 - 1:15 PM

Station Number: Station #5

Presenter: Janis Yee , MD



Distinguishing Fibrosis/Necrosis from Teratoma or Viable Disease in the Retroperitoneum in Post-Chemotherapy, Nonseminomatous Testicular Germ Cell Tumor using Quantitative CT Texture Analysis (Scientific Poster)

Kevin G. King, Sumeet Bhanvadia, Saum Ghodoussipour, Darryl H. Hwang, Bino Varghese, Steven Y. Cen, Siamak Daneshmand, Vinay A. Duddalwar,

Summary: CT texture analysis shows promise in differentiating RP LNs with necrosis/fibrosis from LNs with teratoma or viable malignancy, in post-chemotherapy patients with metastatic testicular NSGT. A larger study is needed for further validation, towards a long-term goal of potentially allowing some patients to avoid PC-RPLND.