全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

Overview of Cancer Management—The Role of Medical Imaging and Machine Learning Techniques in Early Detection of Cancer: Prospects, Challenges, and Future Directions

DOI: 10.4236/oalib.1110014, PP. 1-21

Subject Areas: Oncology, Image Processing, Bioengineering, Computational Biology, Biological Engineering, Machine Learning, Artificial Intelligence

Keywords: Cancer, Machine Learning, Medical Imaging, Neural Network, Computer-Aided Diagnosis

Full-Text   Cite this paper   Add to My Lib

Abstract

Globally, the advent of new cases of cancer has been steadily increasing, with rising mortality and a significant impact on the economy. Most malignancy outcomes are linked to early detection, prompt diagnosis, and treatment. The need for early detection is crucial to cancer management. With these increasing numbers, there is a need for the adoption of emerging technologies such as machine learning to help improve the outcome of cancer management. For these reasons, in this paper, we reviewed the role of medical imaging and machine learning techniques in the management of cancer. In general, the technology used in imaging generates enormous data and hence, these data can be analysed using machine learning techniques and the output can be used to predict potential tumour cells resulting in a significant difference in the management of cancer. However, despite these advantages, there are some challenges in using machine learning which has also been discussed in this review, as well as some recommendations and future directions for the successful utilization of machine learning techniques in cancer management.

Cite this paper

Suleiman, T. A. , Tolulope, A. M. , Wuraola, F. O. , Olorunfemi, R. , Kasali, W. A. , Okorocha, B. O. , Dirisu, C. and Njoku, P. C. (2023). Overview of Cancer Management—The Role of Medical Imaging and Machine Learning Techniques in Early Detection of Cancer: Prospects, Challenges, and Future Directions. Open Access Library Journal, 10, e014. doi: http://dx.doi.org/10.4236/oalib.1110014.

References

[1]  Shah, S.C., Kayamba, V., Peek, R.M. and Heimburger, D. (2019) Cancer Control in Low- and Middle-Income Countries: Is It Time to Consider Screening? Journal of Global Oncology, 5, 1-8. https://doi.org/10.1200/JGO.18.00200
[2]  Mattiuzzi, C. and Lippi, G. (2019) Current Cancer Epidemiology. Journal of Epidemiology and Global Health, 9, 217. https://doi.org/10.2991/jegh.k.191008.001
[3]  Sung, H., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249. https://doi.org/10.3322/caac.21660
[4]  GlobalSurg Collaborative and NIHR Global Health Research Unit on Global Surgery (2022) Effects of Hospital Facilities on Patient Outcomes after Cancer Surgery: An International, Prospective, Observational Study. The Lancet Global Health, 10, E1003-E1011. https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(22)001681/fulltext
[5]  Smetana, K., Lacina, L., Szabo, P., Dvoránková, B., Broz and Sedo, A. (2016) Ageing as an Important Risk Factor for Cancer. Anticancer Research, 36, 5009-5017. https://doi.org/10.21873/anticanres.11069
[6]  Petrucelli, N., Daly, M.B. and Pal, T. (1993) BRCA1- and BRCA2-Associated Hereditary Breast and Ovarian Cancer. University of Washington, Seattle. http://www.ncbi.nlm.nih.gov/books/NBK1247
[7]  Preisler, L., et al. (2021) Heterozygous APC Germline Mutations Impart Predisposition to Colorectal Cancer. Scientific Reports, 11, Article No. 5113. https://doi.org/10.1038/s41598-021-84564-4
[8]  Whiteman, D.C. and Wilson, L.F. (2016) The Fractions of Cancer Attributable to Modifiable Factors: A Global Review. Cancer Epidemiology, 44, 203-221. https://doi.org/10.1016/j.canep.2016.06.013
[9]  Jedy-Agba, E., et al. (2012) Cancer Incidence in Nigeria: A Report from Population-Based Cancer Registries. Cancer Epidemiology, 36, e271-e278. https://doi.org/10.1016/j.canep.2012.04.007
[10]  Olasehinde, O., et al. (2021) Contemporary Management of Breast Cancer in Nigeria: Insights from an Institutional Database. International Journal of Cancer, 148, 2906-2914. https://doi.org/10.1002/ijc.33484
[11]  Kühn, T. (2010) Ductal Carcinoma in Situ: Clinical Perspective. Breast Care, 5, 227-232. https://doi.org/10.1159/000319325
[12]  Tomlinson-Hansen, S., Khan, M. and Cassaro, S. (2022) Breast Ductal Carcinoma in Situ. StatPearls Publishing, Treasure Island. http://www.ncbi.nlm.nih.gov/books/NBK567766
[13]  Britton, P. and Sinnatamby, R. (2007) Investigation of Suspected Breast Cancer. BMJ, 335, 347-348. https://doi.org/10.1136/bmj.39234.386470.BE
[14]  Rawla, P. (2019) Epidemiology of Prostate Cancer. World Journal of Oncology, 10, 63-89. https://doi.org/10.14740/wjon1191
[15]  Haris, M., et al. (2015) Molecular Magnetic Resonance Imaging in Cancer. Journal of Translational Medicine, 13, Article No. 313. https://doi.org/10.1186/s12967-015-0659-x
[16]  Kumar, A., Bi, L., Kim, J. and Feng, D.D. (2020) Machine Learning in Medical Imaging. In: Feng, D.D., Ed., Biomedical Information Technology, Elsevier, Amsterdam, 167-196. https://doi.org/10.1016/B978-0-12-816034-3.00005-5
[17]  Chung, Y.-A. and Weng, W.-H. (2017) Learning Deep Representations of Medical Images Using Siamese CNNs with Application to Content-Based Image Retrieval.
[18]  Giger, M.L. (2018) Machine Learning in Medical Imaging. Journal of the American College of Radiology, 15, 512-520. https://doi.org/10.1016/j.jacr.2017.12.028
[19]  Benning, L., Peintner, A. and Peintner, L. (2022) Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer. Cancers (Basel), 14, Article 623. https://doi.org/10.3390/cancers14030623
[20]  El-Gamal, F.E.Z.A., Elmogy, M. and Atwan, A. (2016) Current Trends in Medical Image Registration and Fusion. Egyptian Informatics Journal, 17, 99-124. https://doi.org/10.1016/j.eij.2015.09.002
[21]  Houssein, E.H., Emam, M.M., Ali, A.A. and Suganthan, P.N. (2021) Deep and Machine Learning Techniques for Medical Imaging-Based Breast Cancer: A Comprehensive Review. Expert Systems with Applications, 167, Article ID: 114161. https://doi.org/10.1016/j.eswa.2020.114161
[22]  Fass, L. (2008) Imaging and Cancer: A Review. Molecular Oncology, 2, 115-152. https://doi.org/10.1016/j.molonc.2008.04.001
[23]  Mallidi, S., Luke, G.P. and Emelianov, S. (2011) Photoacoustic Imaging in Cancer Detection, Diagnosis, and Treatment Guidance. Trends in Biotechnology, 29, 213-221. https://doi.org/10.1016/j.tibtech.2011.01.006
[24]  Vangestel, C., et al. (2012) Single-Photon Emission Computed Tomographic Imaging of the Early Time Course of Therapy-Induced Cell Death Using Technetium 99 m Tricarbonyl His-Annexin A5 in a Colorectal Cancer Xenograft Model. Molecular Imaging, 11, 135-147. https://doi.org/10.2310/7290.2011.00034
[25]  Hadjipanayis, C.G., Jiang, H., Roberts, D.W. and Yang, L. (2011) Current and Future Clinical Applications for Optical Imaging of Cancer: From Intraoperative Surgical Guidance to Cancer Screening. Seminars in Oncology, 38, 109-118. https://doi.org/10.1053/j.seminoncol.2010.11.008
[26]  Peldschus, K. and Ittrich, H. (2014) Magnetic Resonance Imaging of Metastases in Xenograft Mouse Models of Cancer. Methods in Molecular Biology, 1070, 213-222. https://doi.org/10.1007/978-1-4614-8244-4_16
[27]  Hoeks, C.M.A., et al. (2011) Prostate Cancer: Multiparametric MR Imaging for Detection, Localization, and Staging. Radiology, 261, 46-66. https://doi.org/10.1148/radiol.11091822
[28]  El-Bendary, M., Salama, D., Kasban, H., El-Bendary, M.A.M. and Salama, D.H. (2015) A Comparative Study of Medical Imaging Techniques. International Journal of Information Science and Intelligent System, 4, 37-58. https://www.researchgate.net/profile/Mohsen-El-Bendary/publication/274634575_A_Compara-tive_Study_of_Medical_Imaging_Techniques/links/56a565c708ae232fb207b75f/A-Comparative-Study-of-Medical-Imaging-Techniques.pdf
[29]  Spahn, M. (2013) X-Ray Detectors in Medical Imaging. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, 731, 57-63. https://doi.org/10.1016/j.nima.2013.05.174
[30]  Xu, J. and Tsui, B.M.W. (2014) Quantifying the Importance of the Statistical Assumption in Statistical X-Ray CT Image Reconstruction. IEEE Transactions on Medical Imaging, 33, 61-73. https://doi.org/10.1109/TMI.2013.2280383
[31]  Sahuquillo, P., Tembl, J.I., Parkhutik, V., Vázquez, J.F., Sastre, I. and Lago, A. (2013) The Study of Deep Brain Structures by Transcranial Duplex Sonography and Imaging Resonance Correlation. Ultrasound in Medicine and Biology, 39, 226-232. https://doi.org/10.1016/j.ultrasmedbio.2012.09.008
[32]  Øvland, R. (2012) Coherent Plane-Wave Compounding in Medical Ultrasound Imaging: Quality Investigation of {2D} {B}-Mode Images of Stationary and Moving Objects. Master Thesis, Norwegian University of Science and Technology, Trondheim, 1-78. http://www.google.ca/search?q=FULLTEXT01-2&ie=UTF-8&oe=UTF-8&gws_rd=cr&ei=hi51VKqTF47hsASmwIG4Cw%5Cnfile:///Users/Charles/Documents/Papers/Øvland/2012/NorwegianUniversityofScienceandTechnology(MasterThesis)2012Øvland.pdf
[33]  Turkbey, B., Pinto, P.A. and Choyke, P.L. (2009) Imaging Techniques for Prostate Cancer: Implications for Focal Therapy. Nature Reviews Urology, 6, 191-203. https://doi.org/10.1038/nrurol.2009.27
[34]  Kraft, O. and Havel, M. (2012) Sentinel Lymph Node Identification in Breast Cancer—Comparison of Planar Scintigraphy and SPECT/CT. The Open Nuclear Medicine Journal, 4, 5-13. https://doi.org/10.2174/1876388X01204010005
[35]  Carstensen, M.H., Al-Harbi, M., Urbain, J.L. and Belhocine, T.Z. (2011) SPECT/CT Imaging of the Lumbar Spine in Chronic Low Back Pain: A Case Report. Chiropractic & Manual Therapies, 19, Article No. 2. https://doi.org/10.1186/2045-709X-19-2
[36]  Makris, N.E., et al. (2014) Multicenter Harmonization of 89Zr PET/CT Performance. Journal of Nuclear Medicine, 55, 264-267. https://doi.org/10.2967/jnumed.113.130112
[37]  Strååt, S.J., Jacobsson, H., Noz, M.E. andreassen, B., Näslund, I. and Jonsson, C. (2013) Dynamic PET/CT Measurements of Induced Positron Activity in a Prostate Cancer Patient after 50-MV Photon Radiation Therapy. EJNMMI Research, 3, Article No. 6. https://doi.org/10.1186/2191-219X-3-6
[38]  Pichler, B.J., Kolb, A., Nägele, T. and Schlemmer, H.P. (2010) PET/MRI: Paving the Way for the Next Generation of Clinical Multimodality Imaging Applications. Journal of Nuclear Medicine, 51, 333-336. https://doi.org/10.2967/jnumed.109.061853
[39]  Mehmood, I., Ejaz, N., Sajjad, M. and Baik, S.W. (2013) Prioritization of Brain MRI Volumes Using Medical Image Perception Model and Tumor Region Segmentation. Computers in Biology and Medicine, 43, 1471-1483. https://doi.org/10.1016/j.compbiomed.2013.07.001
[40]  Dheeba, J., Albert Singh, N. and Tamil Selvi, S. (2014) Computer-Aided Detection of Breast Cancer on Mammograms: A Swarm Intelligence Optimized Wavelet Neural Network Approach. Journal of Biomedical Informatics, 49, 45-52. https://doi.org/10.1016/j.jbi.2014.01.010
[41]  Shen, R., Yan, K., Tian, K., Jiang, C. and Zhou, K. (2019) Breast Mass Detection from the Digitized X-Ray Mammograms Based on the Combination of Deep Active Learning and Self-Paced Learning. Future Generation Computer Systems, 101, 668-679. https://doi.org/10.1016/j.future.2019.07.013
[42]  Fischerova, D. and Burgetova, A. (2014) Imaging Techniques for the Evaluation of Ovarian Cancer. Best Practice & Research Clinical Obstetrics & Gynaecology, 28, 697-720. https://doi.org/10.1016/j.bpobgyn.2014.04.006
[43]  Kasban, H., El-Bendary, M.A.M. and Salama, D.H. (2015) A Comparative Study of Medical Imaging Techniques. International Journal of Information Science and Intelligent System, 4, 37-58.
[44]  Erickson, B.J., Korfiatis, P., Akkus, Z. and Kline, T.L. (2017) Machine Learning for Medical Imaging. Radiographics, 37, 505. https://doi.org/10.1148/rg.2017160130
[45]  Suleiman, T.A. and Adinoyi, A. (2023) Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies. International Journal of Communications, Network and System Sciences, 16, 31-51. https://doi.org/10.4236/ijcns.2023.163003
[46]  Latif, A., et al. (2019) Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering, 2019, Article ID: 9658350. https://doi.org/10.1155/2019/9658350
[47]  Shi, Y., Liao, S., Gao, Y., Zhang, D., Gao, Y. and Shen, D. (2013) Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, 23-28 June 2013, 2227-2234. https://doi.org/10.1109/CVPR.2013.289
[48]  Alanís-Reyes, E.A., Hernández-Cruz, J.L., Cepeda, J.S., Castro, C., Terashima-Marín, H. and Conant-Pablos, S.E. (2012) Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection. Journal of Cancer Therapy, 3, 1020-1028. https://doi.org/10.4236/jct.2012.36132
[49]  Hulsen, T. et al. (2019) From Big Data to Precision Medicine. Frontiers in Medicine, 6, 34. https://www.frontiersin.org/article/10.3389/fmed.2019.00034 https://doi.org/10.3389/fmed.2019.00034
[50]  Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V. and Fotiadis, D.I. (2015) Machine Learning Applications in Cancer Prognosis and Prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.005
[51]  Dash, S., Shakyawar, S.K., Sharma, M. and Kaushik, S. (2019) Big Data in Healthcare: Management, Analysis and Future Prospects. Journal of Big Data, 6, 54. https://doi.org/10.1186/s40537-019-0217-0
[52]  Bayer, J.M.M., et al. (2022) Site Effects How-To & When: An Overview of Retrospective Techniques to Accommodate Site Effects in Multi-Site Neuroimaging Analyses. https://doi.org/10.31234/osf.io/mpufv
[53]  Kumar, Y., Gupta, S., Singla, R. and Hu, Y.-C. (2022) A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. Archives of Computational Methods in Engineering, 29, 2043-2070. https://doi.org/10.1007/s11831-021-09648-w
[54]  Kilani, A., Hamida, A.B. and Hamam, H. (2018) Artificial Intelligence Review. In: Khosrow-Pour, M., Ed., Encyclopedia of Information Science and Technology, 4th Edition, IGI Global, Hershey, 106-119. https://doi.org/10.4018/978-1-5225-2255-3.ch010
[55]  Zhou, H., Myrzashova, R. and Zheng, R. (2020) Diabetes Prediction Model Based on an Enhanced Deep Neural Network. EURASIP Journal on Wireless Communications and Networking, 2020, Article No. 148. https://doi.org/10.1186/s13638-020-01765-7
[56]  Guo, Y. (2018) A Survey on Methods and Theories of Quantized Neural Networks.
[57]  Ayer, T., Alagoz, O., Chhatwal, J., Shavlik, J.W., Kahn Jr., C.E. and Burnside, E.S. (2010) Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration. Cancer, 116, 3310-3321. https://doi.org/10.1002/cncr.25081
[58]  Chen, Y.-C., Ke, W.-C. and Chiu, H.-W. (2014) Risk Classification of Cancer Survival Using ANN with Gene Expression Data from Multiple Laboratories. Computers in Biology and Medicine, 48, 1-7. https://doi.org/10.1016/j.compbiomed.2014.02.006
[59]  Delen, D., Walker, G. and Kadam, A. (2005) Predicting Breast Cancer Survivability: A Comparison of Three Data Mining Methods. Artificial Intelligence in Medicine, 34, 113-127. https://doi.org/10.1016/j.artmed.2004.07.002
[60]  Soguero-Ruiz, C., Mora-Jiménez, I., Mohedano-Munoz, M.A., Rubio-Sanchez, M., de Miguel-Bohoyo, and Sanchez, A. (2020) Visually Guided Classification Trees for Analyzing Chronic Patients. BMC Bioinformatics, 21, 92. https://doi.org/10.1186/s12859-020-3359-3
[61]  Cruz, J.A. and Wishart, D.S. (2006) Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Informatics, 2, 59-77. https://doi.org/10.1177/117693510600200030
[62]  Waddell, M., Page, D. and Shaughnessy Jr., J. (2005) Predicting Cancer Susceptibility from Single-Nucleotide Polymorphism Data: A Case Study in Multiple Myeloma. Proceedings of the 5th International Workshop on Bioinformatics, Chicago, 21 August 2005, 21-28. https://doi.org/10.1145/1134030.1134035
[63]  Listgarten, J., et al. (2004) Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms. Clinical Cancer Research, 10, 2725-2737. https://doi.org/10.1158/1078-0432.CCR-1115-03
[64]  Kim, W., et al. (2012) Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine. Journal of Breast Cancer, 15, 230-238. https://doi.org/10.4048/jbc.2012.15.2.230
[65]  Tseng, C.-J., Lu, C.-J., Chang, C.-C. and Chen, G.-D. (2014) Application of Machine Learning to Predict the Recurrence-Proneness for Cervical Cancer. Neural Computing and Applications, 24, 1311-1316. https://doi.org/10.1007/s00521-013-1359-1
[66]  Ahmad, L.G., Eshlaghy, A.T., Poorebrahimi, A., Ebrahimi, M. and Razavi, A.R. (2013) Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence. Journal of Health and Medical Informatics, 4, 2.
[67]  Chang, S.-W., Abdul-Kareem, S., Merican, A.F. and Zain, R.B. (2013) Oral Cancer Prognosis Based on Clinicopathologic and Genomic Markers Using a Hybrid of Feature Selection and Machine Learning Methods. BMC Bioinformatics, 14, Article No. 170. https://doi.org/10.1186/1471-2105-14-170
[68]  Xu, X., Zhang, Y., Zou, L., Wang, M. and Li, A. (2012) A Gene Signature for Breast Cancer Prognosis Using Support Vector Machine. 2012 5th International Conference on Biomedical Engineering and Informatics, Chongqing, 16-18 October 2012, 928-931. https://doi.org/10.1109/BMEI.2012.6513032
[69]  Rosado, P., Lequerica-Fernández, P., Villallaín, L., Peña, I., Sanchez-Lasheras, F. and De Vicente, J.C. (2013) Survival Model in Oral Squamous Cell Carcinoma Based on Clinicopathological Parameters, Molecular Markers and Support Vector Machines. Expert Systems with Applications, 40, 4770-4776. https://doi.org/10.1016/j.eswa.2013.02.032
[70]  Cruz, J. and Wishart, D. (2007) Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Informatics, 2, 59-77. https://doi.org/10.1177/117693510600200030
[71]  K.S.P. Chawla Dr Priyanka, (2019) Lung Cancer Prediction Using Machine Learning Algorithms. Think India Journal, 22, 1142-1152. https://www.thinkindiaquarterly.org/index.php/think-india/article/view/18154
[72]  Andries, E., Hagstrom, T., Atlas, S.R. and Willman, C. (2007) Regularization Strategies for Hyperplane Classifiers: Application to Cancer Classification with Gene Expression Data. Journal of Bioinformatics and Computational Biology, 5, 79-104. https://doi.org/10.1142/S0219720007002539
[73]  De Laurentiis, M., De Placido, S., Bianco, A.R., Clark, G.M. and Ravdin, P.M. (1999) A Prognostic Model That Makes Quantitative Estimates of Probability of Relapse for Breast Cancer Patients. Clinical Cancer Research, 5, 4133-4139.
[74]  Lafourcade, A., His, M., Baglietto, L., Boutron-Ruault, M.C., Dossus, L. and Rondeau, V. (2018) Factors Associated with Breast Cancer Recurrences or Mortality and Dynamic Prediction of Death Using History of Cancer Recurrences: The French E3N Cohort. BMC Cancer, 18, Article No. 171. https://doi.org/10.1186/s12885-018-4076-4
[75]  Tao, Z., Shi, A., Li, R., et al. (2017) Microarray Bioinformatics in Cancer—A Review. Journal BUON, 22, 838-843.
[76]  Whitworth, G.B. (2010) An Introduction to Microarray Data Analysis and Visualization, Methods in Enzymology, 470, 19-50. https://doi.org/10.1016/S0076-6879(10)70002-1
[77]  Peters, B., Brenner, S., Wang, E., et al. (2018) Putting Benchmarks in Their Rightful Place: The Heart of Computational Biology. PLOS Computational Biology, 14, e1006494. https://doi.org/10.1371/journal.pcbi.1006494
[78]  Ang, J.C., Mirzal, A., Haron, H., et al. (2016) Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13, 971-989. https://doi.org/10.1109/TCBB.2015.2478454
[79]  Chen, M., Hao, Y., Hwang, K., et al. (2017) Disease Prediction by Machine Learning over Big Data from Healthcare Communities. IEEE Access, 5, 8869-8879. https://doi.org/10.1109/ACCESS.2017.2694446
[80]  Chen, M., Zhang, Y., Qiu, M., et al. (2018) SPHA: Smart Personal Health Advisor Based on Deep Analytics. IEEE Communications Magazine, 56, 164-169. https://doi.org/10.1109/MCOM.2018.1700274
[81]  Qin, Y., Imrie, F., Hüyük, A., Jarrett, D., Gimson, A.E. and van der Schaar, M. (2021) Closing the Loop in Medical Decision Support by Understanding Clinical Decision-Making: A Case Study on Organ Transplantation.
[82]  Juwara, L., Arora, N., Gornitsky, M., Saha-Chaudhuri, and Velly, A.M. (2020) Identifying Predictive Factors for Neuropathic Pain after Breast Cancer Surgery Using Machine Learning. International Journal of Medical Informatics, 141, Article ID: 104170. https://doi.org/10.1016/j.ijmedinf.2020.104170

Full-Text


comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413