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
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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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Presented at RSNA 2017
Date/Time: 11/27/2017 - 11:00 AM ;
Room: N230B; Presenter: Darryl H. Hwang, PhD
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.
Session Time: 11/30/2017 12:45 - 1:15 PM
Station Number: Station #5
Presenter: Janis Yee , MD
Kevin G. King, Sumeet Bhanvadia, Saum Ghodoussipour, Darryl H. Hwang, Bino Varghese, Steven Y. Cen, Siamak Daneshmand, Vinay A. Duddalwar, ummary: 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.