Predicting Microvascular Invasion of Hepatocellular Carcinoma by texture analysis of multi-phase MR image

9 pages•Published: March 11, 2020

Qin Yu, Geng Chen, Jiaqi Li, Xiaolong Liu, Xuegong Zhang and Haiming Lu

Abstract

Microvascular invasion (MVI) diagnosis is of vital importance in the curative treatment of hepatocellular carcinoma patients due to its close relationship with prognostic analysis. Currently, MVI detection often bases on surgical specimen, which is invasive for patients. This study, we extracted texture features of multi-phase MR image to predict the presence of MVI. Feature extraction employed neighboring gray level dependency emphasis (NGLDM) method, which is a common texture feature analysis method. Next, we built a SVM classifier to predict the presence of MVI using extracted features. Especially, multi-phase features were designed to enhance the precision of prediction. Enhanced MR images of pre-contrast phase and portal vein phase were used to extracting features. The method was tested by 5-fold cross- validation on the dataset. The precision of prediction was 91.31%, compared with the baseline method of 70.71%. To make the prediction more interpretable, the relationship between NGLDM texture features and the presence of MVI was discussed in the end.

Keyphrases: magnetic resonance image, microvascular invasion, multi phase features, texture features

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 238-246.

Links:	https://easychair.org/publications/paper/q5TT
	https://doi.org/10.29007/r87h

BibTeX entry

@inproceedings{BICOB2020:Predicting_Microvascular_Invasion_Hepatocellular,
  author    = {Qin Yu and Geng Chen and Jiaqi Li and Xiaolong Liu and Xuegong Zhang and Haiming Lu},
  title     = {Predicting Microvascular Invasion of Hepatocellular Carcinoma by texture analysis of multi-phase MR image},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/q5TT},
  doi       = {10.29007/r87h},
  pages     = {238-246},
  year      = {2020}}

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