Abstract
This research was created to examine the recurrence of non-small lung cancer (NSCLC) using computed-tomography images (CT-images) to avoid biopsy from patients because the cancer cells may have an uneven distribution which can lead to the investigation mistake. This work presents a comparison of the operations of two different methods: Hand-Crafted Radiomics model and deep learning-based radiomics model using 88 patient samples from open-access dataset of non-small cell lung cancer in The Cancer Imaging Archive (TCIA) Public Access. In Hand-Crafted Radiomics Models, the pattern of NSCLC CT-images was analyzed in various statistics as radiomics features. The radiomics features associated with recurrence are selected through three statistical calculations: LASSO, Chi-2, and ANOVA. Then, those selected radiomics features were processed using different models. In the Deep Learning-based Radiomics Model, the proposed artificial neural network has been used to enhance the recurrence prediction. The Hand-Crafted Radiomics Model with non-selected, Lasso, Chi-2, and ANOVA, give the following results: 76.56% (AUC 0.6361), 76.83% (AUC 0.6375), 78.64% (AUC 0.6778), and 78.17% (AUC 0.6556), respectively, and the Deep Learning-based Radiomic Models, including ResNet50 and DenseNet121 give the following results: 79.00% (AUC 0.6714), and 79.31% (AUC 0.6712), respectively.
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Acknowledgments
This work was supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture, and Sports (MEXT) under the Grant No. 18H03267 and No.17H00754 and in part by the Zhejiang Lab Program under Grant No.2018DG0ZX01.
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Aonpong, P., Iwamoto, Y., Wang, W., Lin, L., Chen, YW. (2020). Hand-Crafted and Deep Learning-Based Radiomics Models for Recurrence Prediction of Non-Small Cells Lung Cancers. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare. Smart Innovation, Systems and Technologies, vol 192. Springer, Singapore. https://doi.org/10.1007/978-981-15-5852-8_13
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