The Roles of Ultrasound-Based Radiomics In Precision Diagnosis and Treatment of Different Cancers: A Literature Review

doi:10.37015/AUDT.2020.200051

Abstract

Abstract:

The study aims to review literatures on ultrasound-based radiomics, including ultrasound modalities, and discusses basic methods, applications, and limitations of ultrasound-based radiomics. The search strategy was conducted in form of “Radiomics [Title/Abstract] and Ultrasound [Title/Abstract]”in PubMed. The retrieved articles were initially screened via abstracts. Then, the main objectives, methods, and achievements of selected articles were summarized. Finally, twenty articles focused on malignancies of different organs, such as liver, rectum, breast, and thyroid were included into this review. The multiparametric features exhibited a superior diagnostic performance compared with a single modality. Ultrasound-based radiomics can assist radiologists to improve the accuracy of diagnosis, and it may promote the development of precision diagnosis and treatment of various types of cancer.

Key words: Ultrasound-based radiomics; Precision medicine; Data mining; Multi-parametric features

Bing Mao, MD, Shaobo Duan, MD, Ruiqing Liu, MD, Na Li, PhD, Yaqiong Li, PhD, Lianzhong Zhang, MD. The Roles of Ultrasound-Based Radiomics In Precision Diagnosis and Treatment of Different Cancers: A Literature Review. Advanced Ultrasound in Diagnosis and Therapy, 2020, 4(4): 291-296.

References

[1]	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 2018; 68:7-30.
[2]	Kurland BF, Gerstner ER, Mountz JM, Schwartz LH, Ryan CW, Graham MM, et al. Promise and pitfalls of quantitative imaging in oncology clinical trials. Magn Reson Imaging 2012; 30:1301-1312.
[3]	Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5:4006.
[4]	Lambin P, van Stiphout RG, Starmans MH, Rios-Velazquez E, Nalbantov G, Aerts HJ, et al. Predicting outcomes in radiation oncology--multifactorial decision support systems. Nat Rev Clin Oncol 2013; 10:27-40.
[5]	Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278:563-577.
[6]	Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14:749-762.
[7]	Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30:1234-1248.
[8]	Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 2016; 281:947-957.
[9]	Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, et al. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging 2018; 99:643-651.
[10]	Jiang Y, Chen C, Xie J, Wang W, Zha X, Lv W, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine 2018; 36:171-182.
[11]	Laghi A, Voena C. CT-based radiomics for biliary tract cancer: a possible solution for predicting lymph node metastases. Radiology 2019; 290:99-100.
[12]	Ortiz-Ramón R, Larroza A, Ruiz-Espa?a S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018; 28:4514-4523.
[13]	Wang Y, Yu B, Zhong F, Guo Q, Li K, Hou Y, et al. MRI-based texture analysis of the primary tumor for pre-treatment prediction of bone metastases in prostate cancer. Magn Reson Imaging 2019; 60:76-84.
[14]	Jin X, Zheng X, Chen D, Jin J, Zhu G, Deng X, et al. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol 2019; 29:6080-6088.
[15]	Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 2018; 287:833-843.
[16]	Fan Y, Jiang S, Hua M, Feng S, Feng M, Wang R. Machine learning-based radiomics predicts radiotherapeutic response in patients with acromegaly. Front Endocrinol (Lausanne) 2019; 10:588.
[17]	Hu HT, Wang Z, Huang XW, Chen SL, Zheng X, Ruan SM, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol 2019; 29:2890-2901.
[18]	Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Radiomics: extracting more information from medical images using advanced feature analysis 2012; 48:441-446.
[19]	Dong Y, Wang QM, Li Q, Li LY, Zhang Q, Yao Z, et al. Preoperative prediction of microvascular invasion of hepatocellular carcinoma: radiomics algorithm based on ultrasound original radio frequency signals. Front Oncol 2019; 9:1203.
[20]	D'Souza JC, Sultan LR, Hunt SJ, Schultz SM, Brice AK, Wood AKW, et al. B-mode ultrasound for the assessment of hepatic fibrosis: a quantitative multiparametric analysis for a radiomics approach. Sci Rep 2019; 9:8708.
[21]	Li W, Huang Y, Zhuang BW, Liu GJ, Hu HT, Li X, et al. Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis. Eur Radiol 2019; 29:1496-1506.
[22]	Peng YT, Zhou CY, Lin P, Wen DY, Wang XD, Zhong XZ, et al. Preoperative ultrasound radiomics signatures for noninvasive evaluation of biological characteristics of intrahepatic cholangiocarcinoma. Acad Radiol 2020; 27:785-797.
[23]	Yao Z, Dong Y, Wu G, Zhang Q, Yang D, Yu JH, et al. Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images. BMC Cancer 2018; 18:1089.
[24]	European Association for the Study of the Liver; European Association for the Study of the LiverEuropean Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 2018; 69:182-236.
[25]	Chan AWH, Zhong J, Berhane S, Toyoda H, Cucchetti A, Shi K, et al. Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection. J Hepatol 2018; 69:1284-1293.
[26]	Rodríguez-Perálvarez M, Luong TV, Andreana L, Meyer T, Dhillon AP, Burroughs AK. A systematic review of microvascular invasion in hepatocellular carcinoma: diagnostic and prognostic variability. Ann Surg Oncol 2013; 20:325-339.
[27]	Sumie S, Kuromatsu R, Okuda K, Ando E, Takata A, Fukushima N, et al. Microvascular invasion in patients with hepatocellular carcinoma and its predictable clinicopathological factors. Ann Surg Oncol 2008; 15:1375-1382.
[28]	Chen W, Zheng R, Zhang S, Zeng H, Xia C, Zuo T, et al. Cancer incidence and mortality in China, 2013. Cancer Lett 2017; 401:63-71.
[29]	Guo Y, Hu Y, Qiao M, Wang Y, Yu J, Li J, et al. Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer 2018; 18:e335-e344.
[30]	Lee SE, Han K, Kwak JY, Lee E, Kim EK. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 2018; 8:13546.
[31]	Luo WQ, Huang QX, Huang XW, Hu HT, Zeng FQ, Wang W. Predicting breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A nomogram combining radiomics and BI-RADS. Sci Rep 2019; 9:11921.
[32]	Li Y, Liu Y, Zhang M, Zhang G, Wang Z, Luo J. Radiomics with attribute bagging for breast tumor classification using multimodal ultrasound images. J Ultrasound Med 2020; 39:361-371.
[33]	Yersal O, Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J Clin Oncol 2014; 5:412-424.
[34]	Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 2007; 13:4429-4434.
[35]	Cho N. Molecular subtypes and imaging phenotypes of breast cancer. Ultrasonography 2016; 35:281-288.
[36]	Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 2019; 119:108658.
[37]	Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68:394-424.
[38]	Chen LD, Li W, Xian MF, Zheng X, Lin Y, Liu BX, et al. Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model. Eur Radiol 2020; 30:1969-1979.
[39]	Chen LD, Liang JY, Wu H, Wang Z, Li SR, Li W, et al. Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sci 2018; 208:55-63.
[40]	Li X, Jiang F, Guo Y, Jin Z, Wang Y. Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image. Int J Comput Assist Radiol Surg 2019; 14:1635-1645.
[41]	Greene FL. Tumor deposits in colorectal cancer: a moving target. Ann Surg 2012; 255:214-215.
[42]	Colombo PE, Patani N, Bibeau F, Assenat E, Bertrand MM, Senesse P, et al. Clinical impact of lymph node status in rectal cancer. Surg Oncol 2011; 20:e227-233.
[43]	Sashidharan P, Matele A, Matele U, Al Felahi N, Kassem KF. Gastrointestinal stromal tumors: a case report. Oman Med J 2014; 29:138-141.
[44]	Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin 2016; 66:115-132.
[45]	Liang J, Huang X, Hu H, Liu Y, Zhou Q, Cao Q, et al. Predicting malignancy in thyroid nodules: radiomics score versus 2017 American college of radiology thyroid imaging, reporting and data system. Thyroid 2018; 28:1024-1033.
[46]	Liu T, Zhou S, Yu J, Guo Y, Wang Y, Zhou J, et al. Prediction of lymph node metastasis in patients with papillary thyroid carcinoma: A radiomics method based on preoperative ultrasound images. Technol Cancer Res Treat 2019; 18: 1533033819831713.
[47]	Liu T, Ge X, Yu J, Guo Y, Wang Y, Wang W, et al. Comparison of the application of B-mode and strain elastography ultrasound in the estimation of lymph node metastasis of papillary thyroid carcinoma based on a radiomics approach. Int J Comput Assist Radiol Surg 2018; 13:1617-1627.
[48]	Park VY, Han K, Lee E, Kim EK, Moon HJ, Yoon JH, et al. Association between radiomics signature and disease-free survival in conventional papillary thyroid carcinoma. Sci Rep 2019; 9:4501.
[49]	Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, et al. ACR thyroid imaging, reporting and data system (TI-RADS): White paper of the ACR TI-RADS Committee. J Am Coll Radiol 2017; 14:587-595.
[50]	Smith-Bindman R, Lebda P, Feldstein VA, Sellami D, Goldstein RB, Brasic N, et al. Risk of thyroid cancer based on thyroid ultrasound imaging characteristics: results of a population-based study. JAMA Intern Med 2013; 173:1788-1796.
[51]	Lim H, Devesa SS, Sosa JA, Check D. Trends in thyroid cancer incidence and mortality in the United States, 1974-2013. JAMA 2017; 317:1338-1348.
[52]	Liu Z, Zeng W, Liu C, Wang S, Xiong Y, Guo Y, et al. Diagnostic accuracy of ultrasonographic features for lymph node metastasis in papillary thyroid microcarcinoma: a single-center retrospective study. World J Surg Oncol 2017; 15:32.