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Abstract #1685

Quantitative MR image analysis for predicting histopathological growth patterns of liver metastases from colorectal cancer: standard mono-compartmental vs bi-compartmental model

Pietro Andrea Bonaffini1, Peter Savadjiev1, Sahir Bhatnagar1,2, Ayat Salman3, Zu-Hua Gao4, Anthoula Lazaris3, Peter Metrakos5, Benoit Gallix1, and Caroline Reinhold1

1Diagnostic Radiology, McGill University Health Center, Montreal, QC, Canada, 2Epidemiology, Biostatistics and Occupational Health, McGill University Health Center, Montreal, CA, Canada, 3HBP and Transplant Clinical Research, McGill University Health Center, Montreal, QC, Canada, 4Pathology, McGill University Health Center, Montreal, QC, Canada, 5General Surgery, McGill University Health Center, Montreal, QC, Canada

Morphologic and quantitative imagine biomarkers able to reliably and noninvasively determine the different histopathological growth patterns (HGP) of colorectal cancer liver metastases (CRCLM) are currently missing. We aimed to evaluate if a bi-compartmental model (tumour border region, in addition to an inner core region) can outperform the traditional mono-compartmental model for HGP subtype prediction. Our results show an improvement in HGP subtype classification when using the bi-compartmental tumour model, likely because the information arising from the borders are separate from those pertaining to the inner core. As reported, the main differences for HGP tend to occur at the tumour-liver parenchyma interface. This would allow accurate and potentially more effective patient treatment stratification, since the different HGP subtypes have reported variable response rates to anti VEGF-A therapy.

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