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| Prognostic information extract from CT images of patients with liver cancer using two-dimensional lattice complexity |
| WU Ruixia1, ZHANG Zirui1, CHEN Yubin1, YE Suzhe1, Zheng Minghua2, KE Daguan1. |
| 1.School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325035; 2.Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015 |
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WU Ruixia,ZHANG Zirui,CHEN Yubin, et al. Prognostic information extract from CT images of patients with liver cancer using two-dimensional lattice complexity[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2018, 48(6): 396-400.
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Abstract Objective: To verify whether the two-dimensional (2-D) lattice complexity algorithm can effectively extract hidden prognostic information in some medical images. Methods: The preoperative abdominal CT images of 92 patients with hepatocellular carcinoma (HCC) were converted into binary images with a size of 32×32 pixels and then into one-dimensional binary symbolic sequence by using two-dimensional Hilbert curves for calculating lattice complexity. All cases were scanned by support vector machine (SVM) for 10-fold cross-validation to select the best characteristics. And then, based on the characteristics of 46 patients, a classification model was established to identify the pattern recognition of other 46 patients’ survival period. Results: When using 28 images for each patient, the best average accuracy of classification in 10-fold cross-validation was 75.0% with the lattice complexity under the control parameter 19. For the 46 patients, the test accuracy was 69.6%. Conclusion: With 2-D lattice complexity, we could extract from CT images some prognostic information not previously captured.
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Received: 22 January 2018
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