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| Research Progress of Radiomics and its Application in Clinical of Colorectal Cancer |
| Wei Wei1, 2, 3,Liu Zhenyu2,Wang Shuo2,Tian Jie2#* |
1(School of Life Sciences and Technology, Xidian University, Xi′an 710126)
2(CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190)
3(School of Applied Technology, Xi′an Polytechnic University, Xi′an 710048) |
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Abstract Medical images contain a great deal of information that cannot be recognized by the human eye. It may not only fully express the heterogeneity of the tumor, but also reflect important information such as prognosis information of patients. In recent years, with the development of image processing and artificial intelligence technology, the application of medical image big data analysis method to assist doctors to make decisions or to solve the thorny problems in clinical practice has become a research hotspot. This emerging field is called “radiomics”. On the other hand, colorectal cancer is one of the most common and fatal cancer species in China. The number of patients and deaths has increased year by year. There are many hot research questions in the three different stages of preoperative, intraoperative and postoperative. In this paper, we introduced the basic principle and technology of the radiomics. Taking the research of radiomics in colorectal cancer as an example, the following different clinical questions of each stage, including diagnose the status of pathological complete remission after neoadjuvant chemoradiotherapy, the decision of operation plan and the survival analysis after operation, were introduced respectively.
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Received: 01 July 2018
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[1] Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis [J]. European Journal of Cancer, 2012, 48: 441-446.
[2] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine [J]. Nature Reviews Clinical Oncology, 2017, 14: 749-762.
[3] Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data [J]. Radiology, 2016, 278: 563-577.
[4] Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J]. Nature Communications, 2014, 5:4006.
[5] Wang S, Zhou M, Liu ZY, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation [J]. Medical Image Analysis, 2017, 40: 172-183.
[6] Song JD, Yang CY, Fan L, et al. Lung lesion extraction using a toboggan based growing automatic segmentation approach [J]. IEEE Transactions on Medical Imaging, 2016, 35: 337-353.
[7] Wang S, Zhou M, Gevaert O, et al. A multi-view deep convolutional neural networks for lung nodule segmentation[C]//The 39th Annual International Conference of IEEE EMBS.Jeju Island: IEEE, 2017: 1752-1755
[8] Liu Z, Wang Y, Liu X, et al. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas [J]. NeuroImage: Clinical, 2018, 19: 271-278.
[9] Guo J, Liu Z, Shen C, et al. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation [J]. European Radiology, 2018,28:3872-3881.
[10] Tang ZC, Liu ZY, Li RL, et al. Identifying the white matter impairments among ART-na < ve HIV patients: A multivariate pattern analysis of DTI data [J]. European Radiology, 2017, 27: 4153-4162.
[11] Shen C, Liu ZY, Wang ZQ, et al. BuildingCT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction [J]. Translational Oncology, 2018, 11: 815-824.
[12] Shen C, Liu ZY, Guan M, et al. 2D and 3DCT radiomics features prognostic performance comparison in non-small cell lung cancer [J]. Translational Oncology, 2017, 10: 886-894.
[13] Zhu X, Dong D, Chen Z, et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer [J]. European Radiology, 2018, 28:2772\|2778.
[14] Zhou H, Dong D, Chen B, et al. Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features [J]. Transl Oncol, 2018, 11: 31-36.
[15] Xi YB, Guo F, Xu ZL, et al. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma [J]. J Magn Reson Imaging, 2018,47:1380\|1387.
[16] Qin JB, Liu Z, Zhang H, et al. Grading ofgliomas by using radiomic features on multiple magneticresonance imaging (MRI) sequences [J]. Med Sci Monit, 2017, 23: 2168-2178.
[17] Liu ZY, Zhang XY, Shi YJ, et al. Radiomicsanalysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer [J]. Clinical Cancer Research, 2017, 23: 7253-7262.
[18] Zhang B, He X, Ouyang F, et al. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma [J]. Cancer Letters, 2017, 403: 21-27.
[19] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks [J]. Nature, 2017, 542: 115-118.
[20] Kermany DS, Goldbaum M, Cai WJ, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning [J]. Cell, 2018, 172:893\|895.
[21] Chen WQ, Zheng RS, Baade PD, et al. Cancer statistics in China, 2015 [J]. CA Cancer Journal for Clinicians, 2016, 66: 115-132.
[22] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018 [J]. CA Cancer J Clin, 2018, 68: 7-30.
[23] Yang L, Dong D, Fang MJ, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? [J]. European Radiology, 2018, 28: 2058-2067.
[24] Lee SJ, Zea R, Kim DH, et al. CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer [J]. European Radiology, 2018, 28: 1520-1528.
[25] Lovinfosse P, Polus M, Van Daele D, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer [J]. European Journal of Nuclear Medicine and Molecular Imaging, 2018, 45: 365-375.
[26] Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: Radiomics analysis to assesstreatment response after neoadjuvant therapy [J]. Radiology, 2018, 287: 833-843.
[27] Roelofs E, Dekker A, Meldolesi E, et al. International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining [J]. Radiotherapy and Oncology, 2014, 110: 370-374.
[28] Larue RTHM, Defraene G, De Ruysscher D,et al. Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures [J]. British Journal of Radiology, 2017, 90:20160665.
[29] Hatt M, Tixier F, Pierce L, et al. Characterization of PET/CT images using texture analysis: the past, the presenta... any future? [J]. European Journal of Nuclear Medicine and Molecular Imaging, 2017, 44: 151-165.
[30] Vickers AJ. Prediction models: Revolutionary in principle, but do they do more good than harm? [J]. Journal of Clinical Oncology, 2011, 29: 2951-2952.
[31] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313: 504-507.
[32] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18: 1527-1554.
[33] LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521: 436-444.
[34] Goodfellow IJ, Pouget-Abadie J, Mirza M,et al. Generative adversarial nets[C]//The 28th Annual Conference on Neural Information Processing Systems. Montreal: NIPS, 2014:2672-2680
[35] Lee H, Grosse R, Ranganath R, Ng AY. Unsupervised learning of hierarchical representations with convolutional deep belief networks [J]. Communications of the ACM, 2011, 54: 95-103.
[36] van de Velde CJH, Boelens PG, Borras JM, et al. EURECCA colorectal: Multidisciplinary management: European consensus conference colon & rectum [J]. European Journal of Cancer, 2014, 50:1.e1\|1.e34.
[37] van Gijn W, Marijnen CAM, Nagtegaal ID, et al. Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer: 12-year follow-up of the multicentre, randomised controlled TME trial [J]. Lancet Oncology, 2011, 12: 575-582.
[38] Sanghera P, Wong DWY, McConkey CC,et al. Chemoradiotherapy for rectal cancer: An updated analysis of factors affecting pathological response [J]. Clinical Oncology, 2008, 20: 176-183.
[39] Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data [J]. Lancet Oncology, 2010, 11: 835-844.
[40] Maas M, Beets-Tan RGH, Lambregts DMJ, et al. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer [J]. Journal of Clinical Oncology, 2011, 29: 4633-4640.
[41] Renehan AG, Malcomson L, Emsley R, et al. Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer (the OnCoRe project): A propensity-score matched cohort analysis [J]. Lancet Oncology, 2016, 17: 174-183.
[42] Marijnen CAM. Organ preservation in rectal cancer: have all questions been answered? [J]. Lancet Oncology, 2015, 16: E13-E22.
[43] Nie K, Shi LM, Chen Q, et al. Rectal cancer:Assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI [J]. Clinical Cancer Research, 2016, 22: 5256-5264.
[44] Chang GJ, Rodriguez-Bigas MA, SkibberJM, et al. Lymph node evaluation and survival after curative resection of colon cancer: Systematic review [J]. Journal of the National Cancer Institute, 2007, 99: 433-441.
[45] Glasgow SC, Bleier JIS, Burgart LJ,et al. Meta-analysis of histopathological features of primary colorectal cancers that predict lymph node metastases [J]. Journal of Gastrointestinal Surgery, 2012, 16: 1019-1028.
[46] Toiyama Y, Inoue Y, Shimura T, et al. Serum angiopoietin-like protein 2 improves preoperative detection of lymph node metastasis in colorectal cancer [J]. Anticancer Research, 2015, 35: 2849-2856.
[47] Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer [J]. Journal of Clinical Oncology, 2016, 34: 2157-2164.
[48] Van Cutsem E, Lenz HJ, Kohne CH, et al. Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer [J]. Journal of Clinical Oncology, 2015, 33: 692-700.
[49] Peeters M, Oliner KS, Price TJ, et al. Analysis of KRAS/NRAS mutations in a phase III study of panitumumab with FOLFIRI compared with FOLFIRI alone as second-line treatment for metastatic colorectal cancer [J]. Clinical Cancer Research, 2015, 21: 5469-5479.
[50] Barras D, Missiaglia E, Wirapati P, et al. BRAF V600E mutant colorectal cancer subtypes based on gene expression [J]. Clinical Cancer Research, 2017, 23: 104-115.
[51] Douillard JY, Oliner KS, Siena S, et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer [J]. New England Journal of Medicine, 2013, 369: 1023-1034.
[52] Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer [J]. New England Journal of Medicine, 2004, 351: 1731-1740.
[53] Rodel C, Liersch T, Becker H, et al. Preoperative chemoradiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial [J]. Lancet Oncology, 2012, 13: 679-687.
[54] Bosset JF, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer [J]. New England Journal of Medicine, 2006, 355: 1114-1123.
[55] Meng Y, Zhang Y, Dong D, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer [J]. J Magn Reson Imaging, 13Feb, 2018[Epub ahead of Print]. |
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