Advances in the Application of Radiomics in Colorectal Cancer
ZHOU Xue-zhi1,3, WEI Wei2,3, LIU Zhen-yu3, WANG Shuo3, TIAN Jie1,3,4
1.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710126, China;
2.School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China;
3.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100191, China;
4.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China
Advances in the Application of Radiomics in Colorectal Cancer
ZHOU Xue-zhi1,3, WEI Wei2,3, LIU Zhen-yu3, WANG Shuo3, TIAN Jie1,3,4
1. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710126, China;
2. School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China;
3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100191, China;
4. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China
摘要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. With the development of image processing and artificial intelligence technology, an emerging field called "radiomics" has become a research hotspot, aiming to assist doctors to make decisions or to solve the thorny problems in clinical practice by advanced image analysis technology. In this paper, taking applications of radiomics in colorectal cancer as an example, we introduced the basic principle and technology of radiomics followed by studies focusing on different clinical questions during treatment cycle, including assessment and prediction of the pathological response status after neoadjuvant chemoradiotherapy, the decision of operative plan and the survival analysis after operation. We believe that advances in medical image analysis will promote the development of precision medicine.
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. With the development of image processing and artificial intelligence technology, an emerging field called "radiomics" has become a research hotspot, aiming to assist doctors to make decisions or to solve the thorny problems in clinical practice by advanced image analysis technology. In this paper, taking applications of radiomics in colorectal cancer as an example, we introduced the basic principle and technology of radiomics followed by studies focusing on different clinical questions during treatment cycle, including assessment and prediction of the pathological response status after neoadjuvant chemoradiotherapy, the decision of operative plan and the survival analysis after operation. We believe that advances in medical image analysis will promote the development of precision medicine.
基金资助:grant sponsor: Innovation Project of Medicine and Health Science and Technology of Chinese Aceademy of Medical Sciences; grant number: 216-I2M-3-018
通讯作者:
TIAN Jie. E-mail: jie.tian@ia.ac.cn
引用本文:
ZHOU Xue-zhi, WEI Wei, LIU Zhen-yu, WANG Shuo, TIAN Jie. Advances in the Application of Radiomics in Colorectal Cancer[J]. 中国生物医学工程学报(英文版）, 2021, 30(1): 1-12.
ZHOU Xue-zhi, WEI Wei, LIU Zhen-yu, WANG Shuo, TIAN Jie. Advances in the Application of Radiomics in Colorectal Cancer. Chinese Journal of Biomedical Engineering, 2021, 30(1): 1-12.
[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(4): 441-446.
[2] Lambin P, Leijenaar RT, Deist TM, et al.Radiomics: the bridge between medical imaging and personalized medicine[J]. Nature Reviews Clinical Oncology, 2017, 14(12): 749-762.
[3] Gillies RJ, Kinahan PE, Hricak H.Radiomics: Images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 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.
[5] Liu Z, Wang S, Dong D, et al.The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-1322.
[6] 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.
[7] Song J, Yang C, Fan L, et al.Lung lesion extraction using a toboggan based growing automatic segmentation approach[J]. IEEE Transactions on Medical Imaging, 2016, 35(1): 337-353.
[8] Liu Zhenyu, Wang Yinyan.Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas[J]. NeuroImage: Clinical, 2018, 19(undefined): 271-278.
[9] Guo Jian, Liu Zhenyu, Shen Chen, et al.MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation[J]. European Radiology, 2018, 28(9): 3872-3881.
[10] Liu Z, Zhang XY, Shi YJ, et al.Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Clinical Cancer Research, 2017, 23(23): 7253-7262.
[11] Liu Zhenyu, Li Zhuolin, Qu Jinrong, et al.Radiomics of multi-parametric MRI for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study[J]. Clinical Cancer Research, 2019, 25(12): 3538-3547.
[12] Tian Yuan, Liu Zhenyu, Tang Zhenchao, et al.Radiomics analysis of DTI data to assess vision outcome after intravenous methylprednisolone therapy in neuromyelitis optic neuritis[J]. Journal of Magnetic Resonance Imaging, 2019, 49(5): 1365-1373.
[13] Zhang Bin, He Xin, OUyang Fusheng.Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma[J]. Cancer Lett, 2017, 403: 21-27.
[14] Wang S, Zhen Y, Yu R, et al.Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer[J]. Radiotherapy and Oncology, 2019, 132: 171-177.
[15] Shen Chen, Liu Zhenyu, Guan Min, et al.2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer[J]. Translational Oncology, 2017, 10(6): 886-894.
[16] Andre Esteva, Brett Kuprel, Roberto A.Novoa, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118.
[17] Kermany DS, Goldbaum M, Cai W, et al.Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5): 1122-1131.
[18] Chen Wanqing, Zheng Rongshou, Badde PD, et al.Cancer statistics in China, 2015[J]. Ca-a Cancer Journal for Clinicians, 2016, 66(2): 115-132.
[19] Siegel RL, Miller KD, Jemal A.Cancer statistics, 2018[J]. CA Cancer J Clin, 2018, 68(1): 7-30.
[20] Yang Lei, Dong Li, Fang Mengjie.Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer[J]? European Radiology, 2018, 28(5): 2058-2067.
[21] 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(4): 1520-1528.
[22] 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(3): 365-375.
[23] Horvat N, Veeraraghavan H, Khan M.et al.MR Imaging of rectal cancer: Radiomics analysis to assess treatment response after neoadjuvant therapy[J]. Radiology, 2018, 287(3): 833-843.
[24] RRoelofs E,Dekker A, Meldolesi E.International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining[J]. Radiotherapy and Oncology, 2014, 110(2): 370-374.
[25] 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(1070): 201606605.
[26] 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(1): 151-165.
[27] Vickers AJ.Prediction models: Revolutionary in principle, but do they do more good than harm[J]? Journal of Clinical Oncology, 2011, 29(22): 2951-2952.
[28] Hinton GE, Salakhutdinov RR.Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[29] Hinton GE, Osindero S, Teh YW.A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
[30] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[31] Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. in 28th Annual Conference on Neural Information Processing Systems2014, NIPS 2014, December 8, 2014 - December 13, 2014. 2014[C]. Montreal, QC, Canada: Neural Information Processing Systems Foundation.
[32] Lee H, Grosse R, Ranganath R, et al.Unsupervised learning of hierarchical representations with convolutional deep belief networks[J]. Communications of the Acm, 2011, 54(10): 95-103.
[33] Van Custem 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(7): 692-700.
[34] 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(24): 5469-5479.
[35] BBarras D, Missiaglia E, Wirapati P, et al.BRAF V600E Mutant colorectal cancer subtypes based on gene expression[J]. Clinical Cancer Research, 2017, 23(1): 104-115.
[36] 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(11): 1023-1034.
[37] van de Velde CJ, Boelens PG, Borras JM, et al. EURECCA colorectal: Multidisciplinary management: European consensus conference colon & rectum[J]. European Journal of Cancer, 2014, 50(1):e1-e34.
[38] Van GW, Marijnen CA, Nagtegaal ID.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(6): 575-582.
[39] Sanghera P, Wong WD, McConkey CC. Chemoradiotherapy for rectal cancer: An updated analysis of factors affecting pathological response[J]. Clinical Oncology, 2008, 20(2): 176-183.
[40] 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(9): 835-844.
[41] Maas M, Beets-Tan RG, Lambregts DM, et al.Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer[J]. Journal of Clinical Oncology, 2011, 29(35): 4633-4640.
[42] 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(2): 174-183.
[43] Marijnen CAM.Organ preservation in rectal cancer: have all questions been answered[J]? Lancet Oncology, 2015, 16(1): E13-E22.
[44] Nie K, Shi L, Chen Q, et al.Rectal cancer: Assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI[J]. Clinical Cancer Research, 2016, 22(21): 5256-5264.
[45] Tang Zhenchao, Zhang Xiaoyan, Liu Zhenyu.Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer[J]. Radiotherapy and Oncology, 2019, 132: 100-108.
[46] Zhou Xuezhi, Yi Yongju, Liu Zhenyu.Radiomics-based pretherapeutic prediction of non-response to neoadjuvant therapy in locally advanced rectal cancer[J]. Annals of Surgical Oncology, 2019,26: 1667-1684.
[47] Giannini V, Mazzetti S, Bertotto I, et al.Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features[J]. European Journal of Nuclear Medicine Molecular Imaging, 2019, 46(4): 878-888.
[48] Chang GJ, Rodriguez-Bigas MA, Skibber JM,et al.Lymph node evaluation and survival after curative resection of colon cancer: Systematic review[J]. Journal of the National Cancer Institute, 2007, 99(6): 433-441.
[49] Glasgow SC, Bleier JIS, Burgart LJ, et al.Meta-analysis of histopathological features of primary colorectal cCancers that predict lymph node metastases[J]. Journal of Gastrointestinal Surgery, 2012, 16(5): 1019-1028.
[50] 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(5): 2849-2856.
[51] 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(18): 2157-64.
[52] Zhang X, Zhu H,Wang L, et al.Locally advanced rectal cancer: an MRI radiomics study on lymph node re-evaluation after neoadjuvant chemoradiotherapy[J]. Chinese Journal of Radiology, 2017, 51(12): 926-932.
[53] Sauer R, Becker H,Hohenberger W, et al.Preoperative versus postoperative chemoradiotherapy for rectal cancer[J]. New England Journal of Medicine, 2004, 351(17): 1731-1740.
[54] Rdel C, Liersch T, Becker H.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(7): 679-687.
[55] Bosset JF, Collette L, Calais G, et al.Chemotherapy with preoperative radiotherapy in rectal cancer[J]. New England Journal of Medicine, 2006, 355(11): 1114-1123.
[56] Meng Yankai, Zhang Yuchen,Dong Di.Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer[J]. Journal of Magnetic Resonance Imaging, 2018, 48(3): 605-614.
