Abstract #0696

Leveraging a multicompartmental signal model for improved classification of prostate-cancer bone metastases in whole-body DWI

Christopher C Conlin¹, Christine H Feng², Leonardino A Digma², Ana E Rodriguez-Soto¹, Joshua M Kuperman¹, Dominic Holland³, Rebecca Rakow-Penner¹, Tyler M Seibert^1,2,4, Anders M Dale^1,3,5, and Michael E Hahn¹

¹Department of Radiology, UC San Diego School of Medicine, La Jolla, CA, United States, ²Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, CA, United States, ³Department of Neurosciences, UC San Diego School of Medicine, La Jolla, CA, United States, ⁴Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, CA, United States, ⁵Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, United States

Multicompartmental diffusion modeling shows promise for overcoming the limitations of conventional DWI methods and may help to improve the clinical evaluation of prostate-cancer bone involvement. In this study, we applied multicompartmental modeling to develop an empirical tissue classifier for identifying bone lesions in whole-body DWI. The proposed classifier relates signal contributions from model compartments with lower diffusion coefficients to the likelihood that such contributions are from cancerous tissue. This approach proved effective for detecting metastatic lesions in whole-body DWI data, considerably outperforming a classifier based on conventional ADC values.

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