Related Experiment Video
Updated: Jul 4, 2026
Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones
Published on: November 30, 2016
MRI-based radiomics predicts the pathologic response of colorectal liver metastases to systemic therapy: A
Angela Ammirabile1, Riccardo Levi1, Luca Boldrini2
1Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
Background:
Tumor response to preoperative chemotherapy is a key prognostic factor for colorectal liver metastases (CRLM), but an accurate non-invasive assessment remains an unmet need.
Objective:
To assess the contribution of radiomic analysis of preoperative, post-chemotherapy, gadoxetic acid (Gd-EOB-DTPA)-enhanced MRI to the non-invasive prediction of the pathologic response to systemic therapy of CRLM.
Methods:
This retrospective bi-institutional study included all consecutive patients undergoing resection for CRLM (2018-2021) after preoperative oxaliplatin/irinotecan-based chemotherapy. We selected patients with stable disease or partial response at the last restaging, and with post-chemotherapy MRI performed ≤60 days before surgery. The largest CRLM (≥10 mm) was analyzed. Pathologic response was evaluated according to the TRG. The tumor (Tumor-VOI) was manually segmented on the portal venous phase (PVP) and hepatobiliary phase (HBP) sequences; a 5-mm ring of peritumoral tissue was automatically generated (Margin-VOI) and manually corrected. The predictive models underwent internal validation.
Results:
Overall, 162 patients (median age 62.5 years, 102 men) were evaluated. Of the 131 patients with a radiologic partial response, 59 (45 %) had no tumor regression at pathology (TRG4-5). The model including both clinical variables and radiomic features extracted from the Tumor-VOI/Margin-VOI of PVP and HBP achieved the best performances: at validation, Accuracy = 0.773, Sensitivity = 0.724, Specificity = 0.812, and ROC-AUC = 0.860. The combined clinical-radiomic model outperformed the pure clinical one (p < 0.001). The features extracted from the Tumor-VOI in PVP and Margin-VOI in HBP had the highest impact.
Conclusion:
The addition of radiomic features extracted from the PVP and HBP of post-chemotherapy Gd-EOB-DTPA-enhanced MRI enhanced standard radiologic and clinical assessment of CRLM response to chemotherapy, providing a reliable non-invasive assessment of TRG.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
08:12Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer
Published on: March 14, 2019
