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Habitat imaging’s role in predicting HCC percentage in combined hepatocellular carcinoma-cholangiocarcinoma and its biologic underpinnings
Yuyao Xiao1, Yunfei Zhang2, Chun Yang1, and Mengsu Zeng1
1radiology, Zhongshan Hospital Fudan University, Shanghai, China, 2Central Research Institute, United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: Liver, Liver

Motivation: The prognostic value of component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) was verified, but the identification of percentage of each component relies on extensive tumor sampling.

Goal(s): Therefore, the aim of this study was to investigate if MRI-based habitat imaging helps to predict component percentage in cHCC-CCA preoperatively,

Approach: and to further verify the biologic underpinnings of habitat imaging in cHCC-CCA by analyzing RNA sequencing data.

Results: We found that preoperative prediction of HCC percentage in patients with cHCC-CCA can be achieved using an MRI-based habitat imaging model, which was also associated with signaling pathways regulating cell migration and tumor metastasis.

Impact: We achieved prediction of HCC percentage through an MRI-based habitat imaging model, and revealed the biologic underpinnings of habitat imaging, that is, habitat imaging may identify patients at risk of metastasis. These results may guide individual management in cHCC-CCA patients.

Background & Aims

Given the prognostic value of component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA), we aimed to construct an MRI-based habitat imaging model to predict component percentage in cHCC-CCA preoperatively, and investigate the biologic underpinnings of habitat imaging.

Methods

The study consisted of one retrospective model-building dataset and one prospective validation dataset from two hospitals. All voxels were assigned into different clusters according to the similarity of enhancement pattern by using K-means clustering method, and each habitat’s volume fraction in each lesion was calculated. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select optimal predictors among three habitats’ volume fractions, conventional imaging features and clinical findings, and then to establish an MRI-based habitat imaging model. R-squared was calculated to evaluate performance of the prediction models. Performance of the model was verified in the prospective dataset with RNA sequencing data, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was then applied to investigate the biologic underpinnings of habitat imaging.

Results

Three habitats, habitat1 (HCC-alike habitat), habitat2 (iCCA-alike habitat) and habitat3 (in-between habitat), were identified. Habitat 1’s volume fraction, habitat 3’s volume fraction, nonrim APHE, nonperipheral washout and LI-RADS categorization were selected to develop an HCC percentage prediction model with R-squared of 0.611 in the model-building set and of 0.543 in the validation set. Habitat 1’s volume fraction was correlated with genes involved in regulation of actin cytoskeleton and Rap1 signaling pathway, which regulate cell migration and tumor metastasis.

Conclusion

Preoperative prediction of HCC percentage in patients with cHCC-CCA was achieved using an MRI-based habitat imaging model, which may correlate with signaling pathways regulating cell migration and tumor metastasis.

Acknowledgements

no acknowledgements in this paper.

References

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Figures

Detailed description of each habitat and correlation analysis between HCC percentage and habitat’s volume fraction.

R-squared of prediction model based on conventional imaging features and clinical findings (A, B), and MRI-based habitat imaging model (C, D), in the model-building set and validation set.

Two examples of application of habitat imaging in HCC percentage prediction. Habitat 1 is in bule, with habitat 2 in green and habitat 3 in red. APHE = arterial phase hyperenhancement, LR = LI-RADS.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
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DOI: https://doi.org/10.58530/2024/4135