Tissue characterization of renal masses using Nakagami-modeling of ultrasound-based texture

Bino A. Varghese; Marielena Rivas; Steven Cen; Xiaomeng Lei; Michael Chang; KwangJu Lee; Janet Jamie; Renata L. Amoedo; Mario Franco; Darryl H. Hwang; Bhushan Desai; Kevin G. King; Phillip M. Cheng; Vinay Duddalwar

doi:10.1117/12.2670273

6 March 2023 Tissue characterization of renal masses using Nakagami-modeling of ultrasound-based texture

Bino A. Varghese, Marielena Rivas, Steven Cen, Xiaomeng Lei, Michael Chang, KwangJu Lee, Janet Jamie, Renata L. Amoedo, Mario Franco, Darryl H. Hwang, Bhushan Desai, Kevin G. King, Phillip M. Cheng, Vinay Duddalwar

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Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 1256712 (2023) https://doi.org/10.1117/12.2670273
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, prospective study, uncompressed envelope data (RF data) were collected from 100 patients with focal renal masses using an RS80A ultrasound scanner with B-mode and CEUS. By summing and averaging the Nakagami images formed using sliding windows, we use the average ‘m’ to stratify manually segmented masses, using data from both the B-mode and CEUS scans. Wilcoxon rank sum test using an alpha value of 0.05 was used detect differences between the groups. Logistic regression was used for classification and the area under the receiver operator curve (AUC) was used to assess performance. Among the 100 masses, 40 were benign, 37 were malignant based on histopathology, and 23 were radiologically and clinically presumed malignant but with no pathological proof at the time of data analysis. Univariate analyses showed significant (p<0.01) differences between the benign and non-benign masses on both B-mode and CEUS, with non-benign masses having smaller ‘m’. Predictive models constructed using Nakagami parameters extracted from Bmode and CEUS-based RF scans showed an AUC of 0.67 95% CI: (0.56, 0.78) and 0.61 95% CI: (0.5, 0.73), respectively for discriminating benign from non-benign renal masses. The concordance between the two assessments was 95%. We present a framework for characterizing images using speckle textural properties, for example Nakagami analysis, to aid in objective tissue characterization using ultrasound.

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Bino A. Varghese, Marielena Rivas, Steven Cen, Xiaomeng Lei, Michael Chang, KwangJu Lee, Janet Jamie, Renata L. Amoedo, Mario Franco, Darryl H. Hwang, Bhushan Desai, Kevin G. King, Phillip M. Cheng, and Vinay Duddalwar "Tissue characterization of renal masses using Nakagami-modeling of ultrasound-based texture", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 1256712 (6 March 2023); https://doi.org/10.1117/12.2670273

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KEYWORDS

Ultrasonography

Tissues

Scanners

Backscatter

Visualization

Performance modeling

Image processing

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