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Advances in application of various artificial intelligence algorithms on tumor based on radiology
CHEN Yong-ye  ZHANG En-long  ZHANG Jia-hui  LANG Ning  YUAN Huishu 

DOI:10.12015/issn.1674-8034.2018.10.016.


[Abstract] Accompany with the continuous development of computer algorithm and hardware, artificial intelligence (AI) has showed promising prospective in medical image, comparing to radiologist, AI owns extreme speed in diagnosis for certain diseases and have well enough accuracy in the meantime. This article is aimed to introduce the advances in application of various artificial intelligence algorithms in tumor based on radiology, in addition, pointing out the current deficiencies of AI, in order to provide assistances to doctor for the better transition of AI from researches to clinical application in the future.
[Keywords] Artificial intelligence；Computer aided diagnosis；Medical image；Tumor；Radiology；Machine learning

CHEN Yong-ye Department of Radiology, Peking University Third Hospital, Beijing 100191, China

ZHANG En-long Departmrnt of Radiology, Peking University International Hospital, Beijing 102206, China

ZHANG Jia-hui Department of Radiology, Peking University Third Hospital, Beijing 100191, China

LANG Ning* Department of Radiology, Peking University Third Hospital, Beijing 100191, China

YUAN Huishu Department of Radiology, Peking University Third Hospital, Beijing 100191, China

*Corresponding to: Lang N, E-mail: 13501241339@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Natural Science Foundation in China No.81701648, 81471634
Received  2018-05-29
DOI: 10.12015/issn.1674-8034.2018.10.016
DOI:10.12015/issn.1674-8034.2018.10.016.

[1]
曾毅，刘成林，谭铁牛.类脑智能研究的回顾与展望．计算机学报, 2016(1): 212-222.
[2]
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527-1554.
[3]
Cicero M, Bilbily A, Colak E, et al. Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol, 2017, 52(5): 281-287.
[4]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Analy, 2017, 35: 18-31.
[5]
Soltaninejad M, Yang G, Lambrou T, et al. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed, 2018, 157: 69-84.
[6]
Sunwoo L, Kim YJ, Choi SH, et al. Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study. PLoS One, 2017, 12(6): e178265.
[7]
Yuehao P, Weimin H, Zhiping L, et al. Brain tumor grading based on neural networks and convolutional neural networks. Conf Proc IEEE Eng Med Biol Soc, 2015, 2015: 699-702.
[8]
Zhang X, Yan LF, Hu YC, et al. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget, 2017, 8(29): 47816-47830.
[9]
Hu X, Wong KK, Young GS, et al. Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging, 2011, 33(2): 296-305.
[10]
Emblem KE, Pinho MC, Zollner FG, et al. A generic support vector machine model for preoperative glioma survival associations. Radiology, 2015, 275(1): 228-234.
[11]
Nie D, Zhang H, Adeli E, et al. 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. Med Image Comput Comput Assist Interv, 2016, 9901: 212-220.
[12]
Barthel FP, Wesseling P, Verhaak R. Reconstructing the molecular life history of gliomas. Acta Neuropathol, 2018, 135(5): 649-670.
[13]
Ellingson BM. Radiogenomics and imaging phenotypes in glioblastoma: Novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep, 2015, 15(1): 506.
[14]
Korfiatis P, Kline TL, Lachance DH, et al. Residual deep convolutional neural network predicts MGMT methylation status. J Digit Imaging, 2017, 30(5): 622-628.
[15]
Zhang K, Wang XQ, Zhou B, et al. The prognostic value of MGMT promoter methylation in Glioblastoma multiforme: A meta-analysis. Fam Cancer, 2013, 12(3): 449-458.
[16]
Akkus Z, Ali I, Sedlar J, et al. Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J Digit Imaging, 2017, 30(4): 469-476.
[17]
van den Bent MJ, Baumert B, Erridge SC, et al. Interim results from the CATNON trial (EORTC study 26053-22054) of treatment with concurrent and adjuvant temozolomide for 1p/19q non-co-deleted anaplastic glioma: a phase 3, randomised, open-label intergroup study. Lancet, 2017, 390(10103): 1645-1653.
[18]
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer, 2015, 136(5): E359-E386.
[19]
Masood A, Sheng B, Li P, et al. Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. J Biomed Inform, 2018, 79: 117-128.
[20]
Sun W, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
[21]
陈万青，郑荣寿．中国女性乳腺癌发病死亡和生存状况．中国肿瘤临床, 2015, 42(13): 668-674.
[22]
李静，柯承露．乳腺影像检查方法优选及临床应用中需注意的问题．中华全科医师杂志, 2018, 17(3): 167-170.
[23]
Becker AS, Marcon M, Ghafoor S, et al. Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol, 2017, 52(7): 434-440.
[24]
Lu W, Li Z, Chu J. A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning. Comput Biol Med, 2017, 83: 157-165.
[25]
Becker AS, Mueller M, Stoffel E, et al. Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: A pilot study. Br J Radiol, 2018, 91(1083): 20170576.
[26]
Ben-Cohen A, Klang E, Diamant I, et al. CT image-based decision support system for categorization of liver metastases into primary cancer sites. Acade Radiol, 2017, 24(12): 1501-1509.
[27]
Sasaki R, Taura N, Miyazoe Y, et al. Ketone bodies as a predictor of prognosis of hepatocellular carcinoma after transcatheter arterial chemoembolization. Nutrition, 2018, 50: 97-103.
[28]
Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning: An artificial intelligence concept. J Vasc Interv Radiol, 2018, 29(6): 850-857.
[29]
Ye D, Zhu Y. Epidemiology of prostate cancer in China: an overview and clinical implication. Chin J Surg, 2015, 53(4): 249-252.
[30]
Wang X, Yang W, Weinreb J, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Scientific Reports, 2017, 7(1): 15415.
[31]
Piccioli A, Maccauro G, Spinelli MS, et al. Bone metastases of unknown origin: epidemiology and principles of management. J Orthop Traumatol, 2015, 16(2): 81-86.
[32]
Ugras N, Yalcinkaya U, Akesen B, et al. Solitary bone metastases of unknown origin. Acta Orthop Belg, 2014, 80(1): 139-143.
[33]
Wang J, Fang Z, Lang N, et al. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput in Biology and Med, 2017, 84: 137-146.

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