Keywords: Radiomics, Machine Learning/Artificial Intelligence, Histopathological growth patterns; colorectal liver metastases; transformationOur study used an MRI-based radiomics model to predict the transformation of the histopathological growth pattern (HGP) of colorectal liver metastases (CRLMs) before and after chemotherapy. After collecting data, drawing regions of interest and analyzing radiomics, we enrolled 152 patients and 299 liver metastases (99 pure desmoplastic (pdHGP) and 174 non-pdHGP). The pdHGP in the non-chemotherapy group and the post-chemotherapy group accounted for 28.3% and 42.5%, respectively (P=0.019). The fused MRI-based radiomics model demonstrated good predictive performance, and it could predict pdHGP before chemotherapy (25.3%), which was significantly different (p=0.034) compared with postoperative pdHGP after chemotherapy (39.1%).
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