Research article Special Issues

Comparative study of imaging staging and postoperative pathological staging of esophageal cancer based on smart medical big data


  • Received: 02 January 2023 Revised: 23 March 2023 Accepted: 27 March 2023 Published: 10 April 2023
  • Esophageal cancer has become a malignant tumor disease with high mortality worldwide. Many cases of esophageal cancer are not very serious in the beginning but become severe in the late stage, so the best treatment time is missed. Less than 20% of patients with esophageal cancer are in the late stage of the disease for 5 years. The main treatment method is surgery, which is assisted by radiotherapy and chemotherapy. Radical resection is the most effective treatment method, but a method for imaging examination of esophageal cancer with good clinical effect has yet to be developed. This study compared imaging staging of esophageal cancer with pathological staging after operation based on the big data of intelligent medical treatment. MRI can be used to evaluate the depth of esophageal cancer invasion and replace CT and EUS for accurate diagnosis of esophageal cancer. Intelligent medical big data, medical document preprocessing, MRI imaging principal component analysis and comparison and esophageal cancer pathological staging experiments were used. Kappa consistency tests were conducted to compare the consistency between MRI staging and pathological staging and between two observers. Sensitivity, specificity and accuracy were determined to evaluate the diagnostic effectiveness of 3.0T MRI accurate staging. Results showed that 3.0T MR high-resolution imaging could show the histological stratification of the normal esophageal wall. The sensitivity, specificity and accuracy of high-resolution imaging in staging and diagnosis of isolated esophageal cancer specimens reached 80%. At present, preoperative imaging methods for esophageal cancer have obvious limitations, while CT and EUS have certain limitations. Therefore, non-invasive preoperative imaging examination of esophageal cancer should be further explored.Esophageal cancer has become a malignant tumor disease with high mortality worldwide. Many cases of esophageal cancer are not very serious in the beginning but become severe in the late stage, so the best treatment time is missed. Less than 20% of patients with esophageal cancer are in the late stage of the disease for 5 years. The main treatment method is surgery, which is assisted by radiotherapy and chemotherapy. Radical resection is the most effective treatment method, but a method for imaging examination of esophageal cancer with good clinical effect has yet to be developed. This study compared imaging staging of esophageal cancer with pathological staging after operation based on the big data of intelligent medical treatment. MRI can be used to evaluate the depth of esophageal cancer invasion and replace CT and EUS for accurate diagnosis of esophageal cancer. Intelligent medical big data, medical document preprocessing, MRI imaging principal component analysis and comparison and esophageal cancer pathological staging experiments were used. Kappa consistency tests were conducted to compare the consistency between MRI staging and pathological staging and between two observers. Sensitivity, specificity and accuracy were determined to evaluate the diagnostic effectiveness of 3.0T MRI accurate staging. Results showed that 3.0T MR high-resolution imaging could show the histological stratification of the normal esophageal wall. The sensitivity, specificity and accuracy of high-resolution imaging in staging and diagnosis of isolated esophageal cancer specimens reached 80%. At present, preoperative imaging methods for esophageal cancer have obvious limitations, while CT and EUS have certain limitations. Therefore, non-invasive preoperative imaging examination of esophageal cancer should be further explored.

    Citation: Linglei Meng, XinFang Shang, FengXiao Gao, DeMao Li. Comparative study of imaging staging and postoperative pathological staging of esophageal cancer based on smart medical big data[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10514-10529. doi: 10.3934/mbe.2023464

    Related Papers:

  • Esophageal cancer has become a malignant tumor disease with high mortality worldwide. Many cases of esophageal cancer are not very serious in the beginning but become severe in the late stage, so the best treatment time is missed. Less than 20% of patients with esophageal cancer are in the late stage of the disease for 5 years. The main treatment method is surgery, which is assisted by radiotherapy and chemotherapy. Radical resection is the most effective treatment method, but a method for imaging examination of esophageal cancer with good clinical effect has yet to be developed. This study compared imaging staging of esophageal cancer with pathological staging after operation based on the big data of intelligent medical treatment. MRI can be used to evaluate the depth of esophageal cancer invasion and replace CT and EUS for accurate diagnosis of esophageal cancer. Intelligent medical big data, medical document preprocessing, MRI imaging principal component analysis and comparison and esophageal cancer pathological staging experiments were used. Kappa consistency tests were conducted to compare the consistency between MRI staging and pathological staging and between two observers. Sensitivity, specificity and accuracy were determined to evaluate the diagnostic effectiveness of 3.0T MRI accurate staging. Results showed that 3.0T MR high-resolution imaging could show the histological stratification of the normal esophageal wall. The sensitivity, specificity and accuracy of high-resolution imaging in staging and diagnosis of isolated esophageal cancer specimens reached 80%. At present, preoperative imaging methods for esophageal cancer have obvious limitations, while CT and EUS have certain limitations. Therefore, non-invasive preoperative imaging examination of esophageal cancer should be further explored.Esophageal cancer has become a malignant tumor disease with high mortality worldwide. Many cases of esophageal cancer are not very serious in the beginning but become severe in the late stage, so the best treatment time is missed. Less than 20% of patients with esophageal cancer are in the late stage of the disease for 5 years. The main treatment method is surgery, which is assisted by radiotherapy and chemotherapy. Radical resection is the most effective treatment method, but a method for imaging examination of esophageal cancer with good clinical effect has yet to be developed. This study compared imaging staging of esophageal cancer with pathological staging after operation based on the big data of intelligent medical treatment. MRI can be used to evaluate the depth of esophageal cancer invasion and replace CT and EUS for accurate diagnosis of esophageal cancer. Intelligent medical big data, medical document preprocessing, MRI imaging principal component analysis and comparison and esophageal cancer pathological staging experiments were used. Kappa consistency tests were conducted to compare the consistency between MRI staging and pathological staging and between two observers. Sensitivity, specificity and accuracy were determined to evaluate the diagnostic effectiveness of 3.0T MRI accurate staging. Results showed that 3.0T MR high-resolution imaging could show the histological stratification of the normal esophageal wall. The sensitivity, specificity and accuracy of high-resolution imaging in staging and diagnosis of isolated esophageal cancer specimens reached 80%. At present, preoperative imaging methods for esophageal cancer have obvious limitations, while CT and EUS have certain limitations. Therefore, non-invasive preoperative imaging examination of esophageal cancer should be further explored.



    加载中


    [1] B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He, et al., Incorporating intelligence in fog computing for big data analysis in smart cities, IEEE Trans. Ind. Inf., 13 (2017), 2140–2150. https://doi.org/10.1109/TⅡ.2017.2679740 doi: 10.1109/TⅡ.2017.2679740
    [2] R. K. Barik, R. Priyadarshini, H. Dubey, V. Kumar, K. Mankodiya, FogLearn: leveraging fog-based machine learning for smart system big data analytics, Int. J. Fog Comput., 1 (2018), 15–34. https://doi.org/10.4018/IJFC.2018010102 doi: 10.4018/IJFC.2018010102
    [3] M. Chen, J. Yang, J. Zhou, Y. Hao, J. Zhang, C. H. Youn, 5G-smart diabetes: Toward personalized diabetes diagnosis with healthcare big data clouds, IEEE Commun. Mag., 56 (2018), 16–23. https://doi.org/10.1109/MCOM.2018.1700788 doi: 10.1109/MCOM.2018.1700788
    [4] N. Y. Ilyasova, A. S. Shirokanev, A. V. Kupriyanov, R. A. Paringev, D. V. Kirsh, A. V. Soifer, Methods of intellectual analysis in medical diagnostic tasks using smart feature selection, Pattern Recognit. Image Anal., 28 (2018), 637–645. https://doi.org/10.1134/S1054661818040144 doi: 10.1134/S1054661818040144
    [5] F. Hao, D. S. Park, S. Y. Woo, S. D. Min, S. Park, Treatment planning in smart medical: A sustainable strategy, J. Inf. Process. Syst., 12 (2016), 711–723.
    [6] W. El-Shafai, F. Khallaf, E. S. M. El-Rabaie, F. E. A. El-Samie, Proposed neural SAE-based medical image cryptography framework using deep extracted features for smart IoT healthcare applications, Neural Comput. Appl., 34 (2022), 10629–10653. https://doi.org/10.1007/s00521-022-06994-z doi: 10.1007/s00521-022-06994-z
    [7] Y. Z. Yu, Y. Liu, C. Zhu, Application of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology, J. Healthcare Eng., 1 (2021), 1–11. https://doi.org/10.1155/2021/5529798 doi: 10.1155/2021/5529798
    [8] M. Rath, Real time analysis based on intelligent applications of big data and iot in smart health care systems, Int. J. Big Data Anal. Healthcare, 3 (2018), 45–61. https://doi.org/10.4018/IJBDAH.2018070104 doi: 10.4018/IJBDAH.2018070104
    [9] Y. Zhang, M. Qiu, C. W. Tsai, M. M. Hassan, A. Alamri, Health-CPS: Healthcare cyber-physical system assisted by cloud and big data, IEEE Syst. J., 11 (2017), 88–95. https://doi.org/10.1109/JSYST.2015.2460747 doi: 10.1109/JSYST.2015.2460747
    [10] M. S. Kang, Y. G. Jung, Big data analysis using Python in agriculture forestry and fisheries, Int. J. Adv. Smart Converg., 5 (2016), 47–50. https://doi.org/10.7236/IJASC.2016.5.1.47 doi: 10.7236/IJASC.2016.5.1.47
    [11] C. Cao, S. Kan, X. Dong, Preoperative staging and postoperative pathological staging of esophageal carcinoma by endoscopic ultrasonography, Modern Digest. Interventional Diagn. Treat., 022 (2017), 1–4
    [12] T. L. Tio, F. C. A. D. H. Jager, P. Coene, Esophageal carcinoma: Clinical TNM staging with endosonography and computed tomography, J. Canad. De Gastroenterol., 4 (2016), 603–607. https://doi.org/10.1155/1990/835307 doi: 10.1155/1990/835307
    [13] G. Li, L. Qian, Application of diffusion weighted magnetic resonance imaging in radiation for esophageal cancer, Chin. J. Radiat. Oncol., 26 (2017), 239–242
    [14] M. Li, H. Xie, F. Zhen, H. Wang, Z. Peng, L. Xu, Clinicopathologic factors associated with pathologic upstaging in patients clinically diagnosed stage T2N0M0 squamous cell esophageal carcinoma, J. Cancer Res. Ther., 16 (2020), 1106–1111. https://doi.org/10.4103/jcrt.JCRT_1171_19 doi: 10.4103/jcrt.JCRT_1171_19
    [15] X. Wang, M. Liu, Y. Wang, J. Fan, E. Meijering, A 3D tubular flux model for centerline extraction in neuron volumetric images, IEEE Trans. Med. Imaging, 41 (2022), 1069–1079. https://doi.org/10.1109/TMI.2021.3130987 doi: 10.1109/TMI.2021.3130987
    [16] S. Ren, D. K. Jain, K. Guo, T. Xu, T. Chi, Towards efficient medical lesion image super-resolution based on deep residual networks, Signal Process. Image Commun., 75 (2019), 1–10. https://doi.org/10.1016/j.image.2019.03.008 doi: 10.1016/j.image.2019.03.008
    [17] M. I. Razzak, M. Imran, G. Xu, Big data analytics for preventive medicine, Neural Comput. Appl., 32 (2020), 4417–4451. https://doi.org/10.1007/s00521-019-04095-y doi: 10.1007/s00521-019-04095-y
    [18] J. H. Hong, H. H. Kim, E. J. Han, J. H. Byun, H. S. Jang, E. K. Choi, et al., Total lesion glycolysis using 18F-FDG PET/CT as a prognostic factor for locally advanced esophageal cancer, J. Korean Med. Sci., 31 (2016), 39–46. https://doi.org/10.3346/jkms.2016.31.1.39 doi: 10.3346/jkms.2016.31.1.39
    [19] T. DaVee, J. A. Ajani, J. H. Lee, Is endoscopic ultrasound examination necessary in the management of esophageal cancer?, World J. Gastroenterol., (2017), 751–762. https://doi.org/10.3346/jkms.2016.31.1.39 doi: 10.3346/jkms.2016.31.1.39
    [20] L. Wu, C. Wang, X. Tan, Z. Cheng, K. Zhao, L.Yan, et al., Radiomics approach for preoperative identification of stages Ⅲ and ⅢIV of esophageal cancer, Chin. J. Cancer Res., 30 (2018), 96–405. https://doi.org/10.21147/j.issn.1000-9604.2018.04.02 doi: 10.21147/j.issn.1000-9604.2018.04.02
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1821) PDF downloads(111) Cited by(0)

Article outline

Figures and Tables

Figures(5)  /  Tables(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog