Application progress of radiomics in digestive system diseases

LI Hua-xiu; LI Zhen-hui; WANG Guan-shun

Journal of China Clinic Medical Imaging ›› 2017, Vol. 28 ›› Issue (9) : 672-674.

LI Hua-xiu, LI Zhen-hui, WANG Guan-shun

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Abstract

adiomics is an important part of precision medicine, which is also an inevitable trend of the development of medical imaging. In recent years, the application of radiomics in the diagnosis and treatment of disease has been paid more and more attention by scholars and clinical workers. However, the radiomics study methods, scanning parameters and analysis methods have not been standardized and the repeatability of research results needs to be validated. In addition, there is a big difference between the research progress of the disease. This article is to review the research and application of imaging in the diagnosis, treatment, curative effect and prognosis of digestive system neoplasms(esophageal cancer, liver cancer, colorectal cancer).

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Digestive system neoplasms / Radiomics

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LI Hua-xiu, LI Zhen-hui, WANG Guan-shun. Application progress of radiomics in digestive system diseases[J]. Journal of China Clinic Medical Imaging. 2017, 28(9): 672-674

References

[1]Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
[2]Lambin P, Riosvelazquez 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.
[3]Levy MA, Freymann JB, Kirby JS, et al. Informatics Methods to Enable Sharing of Quantitative Imaging Research Data[J]. Magn Reson Imaging, 2012, 30(9): 1249-1256.
[4]苏会芳，周国锋，谢传淼，等. 放射组学的兴起和研究进展[J]. 中华医学杂志，2015，95（7）：553-556.
[5]王敏，宋彬，黄子星，等. 大数据时代的精准影像医学：放射组学[J]. 中国普外基础与临床杂志，2016，（6）：752-755.
[6]刘慧，王小宜，龙学颖. 基于CT图像纹理分析肿瘤异质性的研究进展及应用[J]. 国际医学放射学杂志， 2016，39（5）：543-548.
[7]Davnall F, Yip CSP, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?[J]. Insights Imaging, 2012, 3(6): 573-589.
[8]Wang J, Kato F, Oyamamanabe N, et al. Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study[J]. Plos One, 2015, 10(11): e0143308.
[9]Vallières M, Freeman CR, Skamene SR, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[J]. Phys Med Biol, 2015, 60(14): 5471-5496.
[10]Velazquez ER, Parmar C, Jermoumi M, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer[J]. Sci Rep, 2013, 3(12): 3529-3535.
[11]Antunes J, Viswanath S, Rusu M, et al. Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study[J]. Transl Oncol, 2016, 9(2): 155-162.
[12]Parmar C, Leijenaar RT, Grossmann P, et al. Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer[J]. Sci Rep, 2015, 5: 11044.
[13]Ypsilantis PP, Siddique M, Sohn HM, et al. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks[J]. Plos One, 2015, 10(9): e0137036.
[14]Yip C, Davnall F, Kozarski R, et al. Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer[J]. Dis Esophagus, 2014, 28(2): 172-179.
[15]Tan S, Kligerman S, Chen W, et al. Spatial-temporal [18F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy[J]. Int J Radiat Oncol Biol Phys, 2013, 85(5): 1375-1382.
[16]Nakajo M, Jinguji M, Nakabeppu Y, et al. Texture analysis of (18)F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy[J]. Eur J Nucl Med Mol Imaging, 2016, 44(2): 206-214.
[17]Yip SS, Coroller TP, Sanford NN, et al. Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients[J]. Front Oncol, 2016, 6: 72.
[18]Echegaray S, Gevaert O, Shah R, et al. Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma[J]. J Med Imaging, 2015, 2(4): 041011.
[19]Huang YL, Chen JH, Shen WC. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images[J]. Academic Radiology, 2006, 13(6): 713-720.
[20]Li M, Fu S, Zhu Y, et al. Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma[J]. Oncotarget, 2016, 7(11): 13248-13259.
[21]Bowen SR, Chapman TR, Borgman J, et al. Measuring total liver function on sulfur colloid SPECT/CT for improved risk stratification and outcome prediction of hepatocellular carcinoma patients[J]. EJNMMI Res, 2016, 6(1): 1-10.
[22]Hu P, Wang J, Zhong H, et al. Reproducibility with repeat CT in radiomics study for rectal cancer[J]. 2016.
[23]Liang C, Huang Y, He L, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and stage Ⅲ-Ⅳ colorectal cancer[J]. Oncotarget, 2016, 7(44): 71440-71446.
[24]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]. J Clin Oncol, 2016, 34(18): 2157-2164.
[25]Nie K, Shi L, Chen Q, et al. Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI[J]. Clin Cancer Res, 2016, 22(21): 5256-5264.
[26]Ganeshan B, Miles KA, Young RCD, et al. Hepatic Enhancement in Colorectal Cancer 1: Texture Analysis Correlates with Hepatic Hemodynamics and Patient Survival[J]. Academic Radiol, 2007, 14(12): 1520-1530.