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KMID : 1137820230440010025
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의공학회지
2023 Volume.44 No. 1 p.25 ~ p.32
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Radiomics-based Biomarker Validation Study for Region Classification in 2D Prostate Cross-sectional Images
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Park Jun-Young
Kim Young-Jae
Kim Ji-Sup
Kim Kwang-Gi
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Abstract
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Recognizing the size and location of prostate cancer is critical for prostate cancer diagnosis, treatment, and predicting prognosis. This paper proposes a model to classify the tumor region and normal tissue with cross- sectional visual images of prostatectomy tissue. We used specimen images of 44 prostate cancer patients who received prostatectomy at Gachon University Gil Hospital. A total of 289 prostate slice images consist of 200 slices including tumor region and 89 slices not including tumor region. Images were divided based on the presence or absence of tumor, and a total of 93 features from each slice image were extracted using Radiomics: 18 first order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM. We compared feature selection techniques such as LASSO, ANOVA, SFS, Ridge and RF, LR, SVM classifiers for the model's high performances. We evaluated the model's performance with AUC of the ROC curve. The results showed that the combination of feature selection techniques LASSO, Ridge, and classifier RF could be best with an AUC of 0.99±0.005.
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KEYWORD
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Prostate cancer, Radiomics, Machine learning, Feature selection
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