Abstract
Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, in this paper, we present a pilot study by adopting multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually segmented primary tumors which is time-consuming and not practical. Instead, we only need the coordinate of the center point and the number of tumor slices given by a subspecialty-trained pediatric radiologist. Specifically, CNN-based method uses pre-trained convolutional neural network, and radiomics-based method extracts radiomics features. Our results show that CNN-based method outperforms the radiomics-based method.
X. Xiang—Also with the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, China.
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Acknowledgement
This research was supported by HUST Independent Innovation Research Fund (2021XXJS096), Sichuan University Interdisciplinary Innovation Research Fund (RD-03-202108), and the Key Lab of Image Processing and Intelligent Control, Ministry of Education, China.
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Xiang, X., Zhang, Z., Peng, X., Shao, J. (2022). Learning-Based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study. In: Li, X., Lv, J., Huo, Y., Dong, B., Leahy, R.M., Li, Q. (eds) Multiscale Multimodal Medical Imaging. MMMI 2022. Lecture Notes in Computer Science, vol 13594. Springer, Cham. https://doi.org/10.1007/978-3-031-18814-5_9
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