Abstract #3919

Noninvasive prediction of tumor-fibrosis using texture analysis of multiparametric MRI in pancreatic cancer model

Dae Chul Jung¹, Ravneet Vohra², Seon Young Lee³, Kyunghwa Han¹, Helen Hong³, and Donghoon Lee²

¹Radiology, Yonsei University, Seoul, Republic of Korea, ²Radiology, University of Washington, Seattle, WA, United States, ³Software Convergence, Seoul Women’s University, Seoul, Republic of Korea

Authors want to evaluate the correlations between texture features of tumor on multi-parametric MRI (mp-MRI) and tumor-fibrosis in animal model of pancreatic cancer. mp-MRI was performed in a genetically engineered mice model of human pancreatic cancer. Texture features of tumors were extracted from each parametric map using texture analysis. Linear regression with LASSO method was used to evaluate the correlations between the texture features and percentage of fibrosis on histologic slides. Several texture features were correlated with tumor fibrosis. Statistical learning showed preliminary prediction model. Texture analysis of mp-MRI is helpful for predicting and monitoring tumor-fibrosis in pancreatic cancer model.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.