影像组学和深度学习在胰腺癌诊疗和评估中的研究进展

doi:10.3969/j.issn.0258-8021.2023.03.012

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (822 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Imaging examinations are used for preoperative diagnosis and evaluation of pancreatic cancer, which play great role in clinical decision-making and prognosis prediction. Radiomics and deep learning provide new means for accurate diagnosis and treatment of pancreatic cancer. In this paper, the developments of radiomics and deep learning in pancreatic cancer were reviewed. The paper reviewed the application of radiomics and deep learning in predicting malignant potential of precursor lesions, diagnosis and differentiating diagnosis, therapeutic effect evaluation, prognosis prediction, and gene expression status. Radiomics and deep learning may provide comprehensive information for clinical decision-making and provide help for precise diagnosis and treatment of pancreatic cancer, with standardized quality control and multi-center database establishment.

Key words： radiomics deep learning pancreatic cancer

Received: 31 December 2021

PACS:

R318

Corresponding Authors: ^* E-mail: husu@suda.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Chen Can
	Bao Jiayi
	Hu Su

Cite this article:

Chen Can,Bao Jiayi,Hu Su. Research Progress of Radiomics and Deep Learning in Pancreatic Cancer[J]. Chinese Journal of Biomedical Engineering, 2023, 42(3): 353-359.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.03.012 OR http://cjbme.csbme.org/EN/Y2023/V42/I3/353

[1] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018 [J].CA Cancer J Clin, 2018, 68: 7-30.
[2] Chen Wanqing, Zheng Rongshou, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016,66(2):115-132.
[3] 胰腺癌诊疗规范(2018年版)[J].中华消化病与影像杂志(电子版),2019,9(5):224-240.
[4] Tempero MA, Malafa MP, Al-Hawary M, et al. Pancreatic adenocarcinoma, version 2.2017, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2017,15(8): 1028-1061.
[5] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
[6] Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J].Eur J Cancer, 2012, 48(4): 441-446.
[7] Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016,278(2):563-577.
[8] Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA: Cancer J Clin, 2019, 69(2): 127-157.
[9] He Ming, Liu Zhenyu, Lin Yusong, et al. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics[J]. Eur J Radiol, 2019, 117: 102-111.
[10] Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis[J]. Eur Radiol Exp, 2018, 2(1): 36.
[11] 张娴, 刘玉林, 高智勇. 深度学习在医学影像中的应用研究进展[J]. 临床放射学杂志, 2021,40(10):2041-2044.
[12] 马福林,王新刚,王琛.胰腺癌早期诊断的研究进展[J].中国微创外科杂志,2020,20(4):358-361.
[13] 赵练, 王巍. 胰腺导管内乳头状黏液性肿瘤诊疗进展 [J] . 中华肝胆外科杂志, 2021, 27(7): 552-557.
[14] Tobaly D, Santinha J, Sartoris R, et al. CT-based radiomics analysis to predict malignancy in patients with Intraductal papillary mucinous neoplasm (IPMN) of the pancreas[J]. Cancers, 2020,12(11):3089.
[15] Chakraborty J, Midya A, Gazit L, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas[J]. Medical Physics, 2018,45(11):5019-5029.
[16] Chen Poting, Chang Dawei, Yen Huisuan, et al. Radiomic features at CT can distinguish pancreatic cancer from noncancerous pancreas[J]. Radiol Imaging Cancer, 2021,3(4):e210010.
[17] Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network[J]. Frontiers in Artificial Intelligence, 2019,2:2.
[18] Si Ke, Xue Ying, Yu Xiazhen, et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors[J]. Theranostics, 2021,11(4):1982-1990.
[19] Le N, Sund M, Vinci A, Pancreas G. Prognostic and predictive markers in pancreatic adenocarcinoma[J]. Dig Liver Dis, 2016,48:223-230.
[20] Xing Haiqun, Hao Zhixin, Zhu Wenjia, et al. Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics[J]. EJNMMI Research, 2021,11(1):19.
[21] Chang Na, Cui Lingling, Luo Yahong, et al. Development and multicenter validation of a CT-based radiomics signature for discriminating histological grades of pancreatic ductal adenocarcinoma[J]. Quantitative imaging in medicine and surgery, 2020,10(3):692-702.
[22] Liu Peng, Gu Qianbiao, Hu Xiaoli, et al. Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma[J]. J Xray Sci Technol, 2020, 28(6): 1113-1121.
[23] Li Ke, Yao Qiandong, Xiao Jingjing, et al. Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study[J]. Cancer Imaging, 2020,20(1):12.
[24] Chen Fangming, Zhou Yongping, Qi Xiumin, et al. Radiomics-assisted presurgical prediction for surgical portal vein-superior mesenteric vein invasion in pancreatic ductal adenocarcinoma[J]. Front Oncol. 2020;10:523543.
[25] Li Jiali, Lu Jingyu, Liang Ping, et al. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole tumor CT texture analysis as quantitative biomarkers[J]. Cancer Med, 2018, 7(10): 4924-4931.
[26] Zhang Yuquan, Cheng Chaocheng, Liu Zhaobang, et al. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT[J]. Med Phys, 2019,46 (10):4520-4530.
[27] Ziegelmayer S, Kaissis G, Harder F, et al. Deep convolutional neural network-assisted feature extraction for diagnostic discrimination and feature visualization in pancreatic ductal adenocarcinoma (PDAC) versus autoimmune pancreatitis (AIP)[J]. Journal of Clinical Medicine, 2020,9(12):4013.
[28] Park S, Chu LC, Hruban RH, et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features[J]. Diagnostic and Interventional Imaging, 2020,101(9):555-564.
[29] Ren Shuai, Zhao Rui, Zhang Jingjing, et al. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma[J]. Abdom Radiol (NY), 2020, 45(5): 1524-1533.
[30] Van Heerde MJ, Biermann K, Zondervan PE, et al. Prevalence of autoimmune pancreatitis and other benign disorders in pancreatoduodenectomy for presumed malignancy of the pancreatic head[J]. Dig Dis Sci, 2012, 57: 2458-2465.
[31] Cheng Meifang, Guo Yueleon, Yen Ruohfang, et al. Clinical utility of FDG PET/CT in patients with autoimmune pancreatitis: a case-control study[J]. Sci Rep, 2018,8:3651.
[32] de Castro SM, de Nes LC, Nio CY, et al. Incidence and characteristics of chronic and lymphoplasmacytic sclerosing pancreatitis in patients scheduled to undergo a pancreatoduodenectomy[J]. HPB (Oxford), 2010,12(1):15-21.
[33] Nakazawa T, Ohara H, Sano H, et al. Difficulty in diagnosing autoimmune pancreatitis by imaging findings[J]. Gastrointest Endosc, 2006,65:99-108.
[34] Hardacre JM, Iacobuzio-Donahue CA, Sohn TA, et al. Results of pancreaticoduodenectomy for lymphoplasmacytic sclerosing pancreatitis[J]. Ann Surg, 2003,237(6):853-859.
[35] Marya NB, Powers PD, Chari ST, et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis[J]. Gut, 2021,70(7):1335-1344.
[36] Jeon SK, Lee JM, Joo I, et al. Nonhypervascular pancreatic neuroendocrine tumors: differential diagnosis from pancreatic ductal adenocarcinomas at MR imaging retrospective cross sectional study[J]. Radiology, 2017, 284(1): 77-87.
[37] He Ming, Liu Zhenyu, Lin Yusong, et al. Differentiation of atypical non functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics[J]. Eur J Radiol, 2019, 117: 102-111.
[38] Wang Yingwei, Zhang Xinghua, Wang Botao, et al. Value of texture analysis of intravoxel incoherent motion parameters in differential diagnosis of pancreatic neuroendocrine tumor and pancreatic adenocarcinoma[J]. Chin Med Sci J, 2019, 34(1): 1-9.
[39] Huang Zixing, Li Mouli, He Du, et al. Two dimensional texture analysis based on ct images to differentiate pancreatic lymphoma and pancreatic adenocarcinoma: a preliminary study[J]. Acad Radiol, 2019, 26(8): e189-e195.
[40] Katz MH, Fleming JB, Bhosale P, et al. Response of borderline resectable pancreatic cancer to neoadjuvant therapy is not reflected by radiographic indicators[J]. Cancer, 2012, 118(23): 5749-5756.
[41] Chen Xiaojian, Oshima K, Schott D, et al. Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study[J]. PLoS ONE, 2017, 12(6): e178961.
[42] Ciaravino V, Cardobi N, DE Robertis R, et al. CT Texture analysis of ductal adenocarcinoma downstaged after chemotherapy[J]. Anticancer Res, 2018, 38(8): 4889-4895.
[43] Kim BR, Kim JH, Ahn SJ, et al. CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis[J]. Eur Radiol, 2019, 29(1): 362-372.
[44] Nasief H, Hall W, Zheng C, et al. Improving treatment response prediction for chemoradiation therapy of pancreatic cancer using a combination of delta-radiomics and the clinical biomarker CA19-9[J]. Front Oncol, 2019, 9: 1464.
[45] Liang L, Ding Y, Yu Y, et al. Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study[J]. BMC Med Imaging, 2021,21(1):75.
[46] Groot VP, Gemenetzis G, Blair AB, et al. Defining and predicting early recurrence in 957 patients with resected pancreatic ductal adenocarcinoma[J]. Ann Surg, 2019, 269(6):1154-1162.
[47] Wolfgang CL, Herman JM, Laheru DA, et al. Recent progress in pancreatic cancer[J]. CA: A Cancer Journal for Clinicians, 2013, 63:318e48.
[48] Cheng Sihang, Cheng Yuejuan, Jin Zhengyu, et al. Unresectable pancreatic ductal adenocarcinoma: role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy[J]. Eur J Radiol, 2019, 113: 188-197.
[49] Parr E, Du Q, Zhang C, et al. Radiomics-based outcome prediction for pancreaticcancer following stereotactic body radiotherapy[J]. Cancers, 2020,12(4):1051.
[50] Tong Zhou, Liu Yu, Ma Hongtao, et al. Development, validation and comparison of artificial neural network models and logistic regression models predicting survival of unresectable pancreatic cancer[J]. Frontiers in Bioengineering and Biotechnology, 2020;8:196.
[51] Shi Hongyuan, Wei Yun, Cheng Shenhao, et al. Survival prediction after upfront surgery in patients with pancreatic ductal adenocarcinoma: Radiomic, clinic-pathologic and body composition analysis[J]. Pancreatology, 2021,21(4):731-737.
[52] Lee J W, Park S, Ahn H, et al. Predicting survival in patients with pancreatic cancer by integrating bone marrow FDG uptake and radiomic features of primary tumor in PET/CT[J]. Cancers, 2021,13(14):3563.
[53] Wei Miaoyan, Gu Bingxin, Song Shaoli, et al. A novel validated recurrence stratification system based on 18F-FDG PET/CT radiomics to guide surveillance after resection of pancreatic cancer[J]. Frontiers in Oncology, 2021;11:650266.
[54] Sala Elarre P, Oyaga-Iriarte E, Yu K H, et al. Use of machine-learning algorithms in intensified preoperative therapy of pancreatic cancer to predict individual risk of relapse[J]. Cancers, 2019,11(5):606.
[55] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA: A Cancer Journal for Clinicians,2020; 70(1):7-30.
[56] Hang Junjie, Wu Lixia, Zhu Lina, et al. Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real-world study[J]. Cancer Medicine, 2018, 7(7):2974-2984.
[57] Hang Junjie, Xu Kequn, Yin Ruohan, et al. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases[J]. Journal of Cancer, 2021,12(8):2351-2358.
[58] Yachida S, Iacobuzio-Donahue CA. Evolution and dynamics of pancreatic cancer progression[J]. Oncogene. 2013;32(45):5253-5260.
[59] Chand S, O′Hayer K, Blanco FF, et al.The landscape of pancreatic cancer therapeutic resistance mechanisms[J]. Int J Biol Sci. 2016;12:273-82.
[60] Lim C H, Cho Y S, Choi J Y, et al. Imaging phenotype using 18F-fluorodeoxyglucose positron emission tomography-based radiomics and genetic alterations of pancreatic ductal adenocarcinoma[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2020,47(9):2113-2122.
[61] Morton JP, Timpson P, Karim SA, et al. Mutant p53 drives metastasis and overcomes growth arrest/senescence in pancreatic cancer [J].Proc Natl Acad Sci, 2010,107(1):246-251.
[62] Gao Jing, Chen Xiahan, Li Xudong, et al. Differentiating TP53 mutation status in pancreatic ductal adenocarcinoma using multiparametric MRI-derived radiomics[J]. Frontiers in Oncology, 2021,11:632130.
[63] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol. 2017;14(12):749-762.
[64] Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2):328-338.