Advances in nano research

  • 제15권5호
  • /
  • Pages.451-466
  • /
  • 2023
  • /
  • 2287-237X(pISSN)
  • /
  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Nanotechnology in early diagnosis of gastro intestinal cancer surgery through CNN and ANN-extreme gradient boosting

  • Y. Wenjing (Institute of Life Science,Wenzhou University) ;
  • T. Yuhan (Institute of Life Science,Wenzhou University) ;
  • Y. Zhiang (Institute of Life Science,Wenzhou University) ;
  • T. Shanhui (Institute of Life Science,Wenzhou University) ;
  • L. Shijun (Institute of Life Science,Wenzhou University) ;
  • M. Sharaf (Industrial Engineering Department, College of Engineering, King Saud University)
  • 투고 : 2022.02.01
  • 심사 : 2023.07.11
  • 발행 : 2023.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

키워드

과제정보

Wenzhou City Public Welfare Science and Technology Project (ZY2019005). Zhejiang Provincial Natural Science Foundation of China under Grant No. LGF21H040001. The authors present their appreciation to King Saud University for funding this research through Researchers Supporting Program number (RSPD2023R704), King Saud University, Riyadh, Saudi Arabia. The authors present their appreciation to King Saud University for funding this research through Researchers Supporting Program number (RSPD2023R704), King Saud University, Riyadh, Saudi Arabia.

참고문헌

  1. Abdelhafiz, D., C. Yang, R. Ammar and S. Nabavi (2019), "Deep convolutional neural networks for mammography: advances, challenges and applications", BMC Bioinform., 20(11), 281 http://doi.org/10.1186/s12859-019-2823-4.
  2. Aghakhani, M., M. Suhatril, M. Mohammadhassani, M. Daie and A. Toghroli (2015), "A simple modification of homotopy perturbation method for the solution of Blasius equation in semi-infinite domains", Math. Probl. Eng., 343, 100-126. https://doi.org/10.1155/2015/671527.
  3. Ali, Z., Y. Deng and C. Ma (2012), "Progress of research in gastric cancer", J. Nanosci. Nanotechnol., 12(11), 8241-8248. https://doi.org/10.1166/jnn.2012.6692.
  4. Bisschops, R., M. Areia, E. Coron, D. Dobru, B. Kaskas, R. Kuvaev, O. Pech, K. Ragunath, B. Weusten and P. Familiari (2016), "Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative", Endoscopy, 48(9), 843-864. http://doi.org/10.1055/s-0042-113128.
  5. Brinker, T.J., A. Hekler, A.H. Enk and C. von Kalle (2019), "Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions", PloS one, 14(6), e0218713 https://doi.org/10.1371/journal.pone.0218713.
  6. Chartrand, G., P.M. Cheng, E. Vorontsov, M. Drozdzal, S. Turcotte, C.J. Pal, S. Kadoury and A. Tang (2017), "Deep learning: a primer for radiologists", Radiographics, 37(7), 2113-2131. https://doi.org/10.1148/rg.2017170077
  7. Chen, T. and C. Guestrin (2016), "Xgboost: A scalable tree boosting system", Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining.
  8. Choi, K.S., J.K. Jun, E.C. Park, S. Park, K.W. Jung, M.A. Han, I.J. Choi and H.Y. Lee (2012), "Performance of different gastric cancer screening methods in Korea: a population-based study", PLoS One, 7(11), e50041.
  9. Choi, S., S. Han, M. Roh and J. Lee (2007), "Predictive significance of serum IL-6, VEGF, and CRP in gastric adenoma and mucosal carcinoma before endoscopic submucosal dissection", Korean J. Gastroenterol., 1598(9992), 2233-6869.
  10. Conti, C.B., S. Agnesi, M. Scaravaglio, P. Masseria, M.E. Dinelli, M. Oldani and F. Uggeri (2023), "Early gastric cancer: update on prevention, diagnosis and treatment", Int. J. Environ. Res. Publ. Health, 20(3), 2149. https://doi.org/10.3390/ijerph20032149
  11. Dang, W., Xiang, L., Liu, S., Yang, B., Liu, M., Yin, Z. and Yin, L. and Zheng, W. (2023), "A feature matching method based on the convolutional neural network", J. Imag. Sci. Technol., 67(3). https://doi.org/10.2352/J.ImagingSci.Technol.2023.67.3.030402
  12. Das, N., M. Topalovic and W. Janssens (2018), "Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential", Curr. Opin. Pulm. Med., 24(2), 117-123. https://doi.org/10.1097/MCP.0000000000000459
  13. Delen, D., G. Walker and A. Kadam (2005), "Predicting breast cancer survivability: A comparison of three data mining methods", Artif. Intell. Med., 34(2), 113-127 https://doi.org/10.1016/j.artmed.2004.07.002.
  14. Esteva, A., B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau and S. Thrun (2017), "Dermatologist-level classification of skin cancer with deep neural networks", Nature, 542(7639), 115-118. http://doi.org/10.1038/nature21056.
  15. Ezoe, Y., M. Muto, N. Uedo, H. Doyama, K. Yao, I. Oda, K. Kaneko, Y. Kawahara, C. Yokoi, Y. Sugiura, H. Ishikawa, Y. Takeuchi, Y. Kaneko and Y. Saito (2011), "Magnifying narrowband imaging is more accurate than conventional whitelight imaging in diagnosis of gastric mucosal cancer", Gastroenterology, 141(6), 2017-2025.e2013 https://doi.org/10.1053/j.gastro.2011.08.007.
  16. Fujishiro, M., S. Yoshida, R. Matsuda, A. Narita, H. Yamashita and Y. Seto (2017), "Updated evidence on endoscopic resection of early gastric cancer from Japan", Gastr. Cancer, 20(1), 39-44. http://doi.org/10.1007/s10120-016-0647-8.
  17. He, B., Zhang, Y., Zhou, Z., Wang, B., Liang, Y., Lang, J., Lin, H., Bing, P., Yu, L., Sun, D., Luo, H, Yang, J. and Tian, G. (2020), "A neural network framework for predicting the tissue-of-origin of 15 common cancer types based on RNA-seq data", Front. Bioeng. Biotechnol., 8, 737. https://doi.org/10.3389/fbioe.2020.00737
  18. Hirasawa, T., K. Aoyama, T. Tanimoto, S. Ishihara, S. Shichijo, T. Ozawa, T. Ohnishi, M. Fujishiro, K. Matsuo, J. Fujisaki and T. Tada (2018), "Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images", Gastr. Cancer, 21(4), 653-660. http://doi.org/10.1007/s10120-018-0793-2.
  19. Horie, Y., T. Yoshio, K. Aoyama, S. Yoshimizu, Y. Horiuchi, A. Ishiyama, T. Hirasawa, T. Tsuchida, T. Ozawa, S. Ishihara, Y. Kumagai, M. Fujishiro, I. Maetani, J. Fujisaki and T. Tada (2019), "Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks", Gastrointest. Endosc., 89(1), 25-32. https://doi.org/10.1016/j.gie.2018.07.037.
  20. Huang, Y., J. Xu, Y. Zhou, T. Tong, X. Zhuang and A.S.D.N. Initiative (2019), "Diagnosis of Alzheimer's disease via multimodality 3D convolutional neural network", Front. Neurosci., 13, 509. https://doi.org/10.3389/fnins.2019.00509.
  21. Jagerman, R., X. Wang, H. Zhuang, Z. Qin, M. Bendersky and M. Najork (2022), "Rax: Composable learning-to-rank using JAX", Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington D.C., U.S.A., August.
  22. Kadowaki, S., K. Tanaka, H. Toyoda, R. Kosaka, I. Imoto, Y. Hamada, M. Katsurahara, H. Inoue, M. Aoki and T. Noda (2009), "Ease of early gastric cancer demarcation recognition: a comparison of four magnifying endoscopy methods", J. Gastroenterol. Hepatol., 24(10), 1625-1630. https://doi.org/10.1111/j.1440-1746.2009.05918.x.
  23. Kaise, M., M. Kato, M. Urashima, Y. Arai, H. Kaneyama, Y. Kanzazawa, J. Yonezawa, Y. Yoshida, N. Yoshimura and T. Yamasaki (2009), "Magnifying endoscopy combined with narrow-band imaging for differential diagnosis of superficial depressed gastric lesions", Endoscopy, 41(4), 310-315. http://doi.org/10.1055/s-0028-1119639.
  24. Kalan Farmanfarma, K., N. Mahdavifar, S. Hassanipour and H. Salehiniya (2020), "Epidemiologic study of gastric cancer in iran: a systematic review", Clin. Experim. Gastroenterol. 13, 511-542. http://doi.org/10.2147/CEG.S256627.
  25. Kim, Y.S. (2008), "Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size", Expert Syst. Appl., 34(2), 1227-1234. https://doi.org/10.1016/j.eswa.2006.12.017.
  26. Kim, Y.S. (2010), "Performance evaluation for classification methods: A comparative simulation study", Expert Syst. Appl., 37(3), 2292-2306. https://doi.org/10.1016/j.eswa.2009.07.043.
  27. Li, B., S. Ding, G. Song, J. Li and Q. Zhang (2019), "Computeraided diagnosis and clinical trials of cardiovascular diseases based on artificial intelligence technologies for risk-early warning model", J. Med. Syst., 43(7), 228. http://doi.org/10.1007/s10916-019-1346-x.
  28. Liang, Y., W. Wang, C. Fang, S.S. Raj, W.M. Hu, Q. W. Li and Z. W. Zhou (2016), "Clinical significance and diagnostic value of serum CEA, CA19-9 and CA72-4 in patients with gastric cancer", Oncotarget, 7(31), 49565-49573 http://doi.org/10.18632/oncotarget.10391.
  29. Lu, S., Yang, B., Xiao, Y., Liu, S., Liu, M., Yin, L. and Zheng, W. (2023), "Iterative reconstruction of low-dose CT based on differential sparse", Biomed. Signal Pr. Control, 79, 104204. https://doi.org/10.1016/j.bspc.2022.104204
  30. Lu, S., Yang, J., Yang, B., Yin, Z., Liu, M., Yin, L. and Zheng, W. (2023), "Analysis and design of surgical instrument localization algorithm", Comput. Model. Eng. Sci., 137(1), 669-685. https://doi.org/10.32604/cmes.2023.027417
  31. Mahmoodi, S.A., K. Mirzaie and S.M. Mahmoudi (2016), "A new algorithm to extract hidden rules of gastric cancer data based on ontology", SpringerPlus, 5(1), 312. http://doi.org/10.1186/s40064-016-1943-9.
  32. Mansouri, I., M. Safa, Z. Ibrahim, O. Kisi, M.M. Tahir, S. Baharom and M. Azimi (2016), "Strength prediction of rotary brace damper using MLR and MARS", Struct. Eng. Mech. 60(3), 471-488. http://doi.org/10.12989/sem.2016.60.3.471.
  33. Mohammadhassani, M., A.M.D. Saleh, M. Suhatril and M. Safa (2015), "Fuzzy modelling approach for shear strength prediction of RC deep beams", Smart Struct. Syst., 16(3), 497-519. https://doi.org/10.12989/sss.2015.16.3.497.
  34. Muto, M., K. Yao, M. Kaise, M. Kato, N. Uedo, K. Yagi and H. Tajiri (2016), "Magnifying endoscopy simple diagnostic algorithm for early gastric cancer (MESDA-G)", Digest. Endosc., 28(4), 379-393. https://doi.org/10.1111/den.12638.
  35. Pimentel-Nunes, P., M. Dinis-Ribeiro, J. Soares, R. Marcos-Pinto, C. Santos, C. Rolanda, R. Bastos, M. Areia, L. Afonso and J. Bergman (2012), "A multicenter validation of an endoscopic classification with narrow band imaging for gastric precancerous and cancerous lesions", Endoscopy, 44(03), 236-246. http://doi.org/10.1055/s-0031-1291537.
  36. Safa, M., M. Ahmadi, J. Mehrmashadi, D. Petkovic, M. Mohammadhassani, Y. Zandi and Y. Sedghi (2020), "Selection of the most influential parameters on vectorial crystal growth of highly oriented vertically aligned carbon nanotubes by adaptive neuro-fuzzy technique", Int. J. Hydromechatr., 3(3), 238-251. https://doi.org/10.1504/IJHM.2020.109919.
  37. Sari, P.A., M. Suhatril, N. Osman, M.A. Mu'azu, H. Dehghani, Y. Sedghi, M. Safa, M. Hasanipanah, K. Wakil and M. Khorami (2019), "An intelligent based-model role to simulate the factor of safe slope by support vector regression", Eng. Comput., 35(4), 1521-1531. https://doi.org/10.1007/s00366-018-0677-4.
  38. Shibagaki, K., Y. Amano, N. Ishimura, H. Taniguchi, H. Fujita, S. Adachi, E. Kakehi, R. Fujita, K. Kobayashi and Y. Kinoshita (2015), "Diagnostic accuracy of magnification endoscopy with acetic acid enhancement and narrow-band imaging in gastric mucosal neoplasms", Endoscopy, 16-25. https://doi.org/10.1055/s-0034-1392542
  39. Shibata, T., A. Teramoto, H. Yamada, N. Ohmiya, K. Saito and H. Fujita (2020), "Automated detection and segmentation of early gastric cancer from endoscopic images using mask R-CNN", Appl. Sci., 10(11), 3842. https://doi.org/10.3390/app10113842.
  40. Shichijo, S., S. Nomura, K. Aoyama, Y. Nishikawa, M. Miura, T. Shinagawa, H. Takiyama, T. Tanimoto, S. Ishihara, K. Matsuo and T. Tada (2017), "Application of convolutional neural networks in the diagnosis of helicobacter pylori infection based on endoscopic images", EBioMedicine, 25, 106-111. https://doi.org/10.1016/j.ebiom.2017.10.014.
  41. Shitrit, D., B. Zingerman, A.B.G. Shitrit, D. Shlomi and M.R. Kramer (2005), "Diagnostic value of CYFRA 21-1, CEA, CA 19-9, CA 15-3, and CA 125 assays in pleural effusions: analysis of 116 cases and review of the literature", The Oncologist, 10(7), 501-507. http://doi.org/10.1634/theoncologist.10-7-501.
  42. Song, X., Li, Q. and Zhang, J. (2023), "A double-edged sword: DLG5 in diseases", Biomed. Pharmacother., 162, 114611. https://doi.org/10.1016/j.biopha.2023.114611
  43. Taninaga, J., Y. Nishiyama, K. Fujibayashi, T. Gunji, N. Sasabe, K. Iijima and T. Naito (2019), "Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study", Sci. Rep., 9(1), 12384. http://doi.org/10.1038/s41598-019-48769-y.
  44. Toghroli, A., E. Darvishmoghaddam, Y. Zandi, M. Parvan, M. Safa, M.M. Abdullahi, A. Heydari, K. Wakil, S.A.M. Gebreel and M. Khorami (2018), "Evaluation of the parameters affecting the Schmidt rebound hammer reading using ANFIS method", Comput. Concr., 21(5), 525-530. http://doi.org/10.12989/cac.2018.21.5.525.
  45. Ture, M., F. Tokatli and I. Kurt Omurlu (2009), "The comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer data", Exp. Syst. Appl., 36(4), 8247-8254. https://doi.org/10.1016/j.eswa.2008.10.014.
  46. Watanabe, Y., H.S. Kim, R.J. Castoro, W. Chung, M.R.H. Estecio, K. Kondo, Y. Guo, S.S. Ahmed, M. Toyota, F. Itoh, K.T. Suk, M.Y. Cho, L. Shen, J. Jelinek and J.P.J. Issa (2009), "Sensitive and specific detection of early gastric cancer with DNA methylation analysis of gastric washes", Gastroenterology, 136(7), 2149-2158. https://doi.org/10.1053/j.gastro.2009.02.085.
  47. J.R., S.S. Sami, D. Reddiar, J. Mannath, J. Ortiz-Fernandez-Sordo, S. Beg, R. Scott, P. Thiagarajan, S. Ahmad, A. Parra-Blanco, M. Kasi, E. Telakis, A.A. Sultan, J. Davis, A. Figgins, P. Kaye, K. Robinson, J.C. Atherton and K. Ragunath (2018), "Narrow band imaging and serology in the assessment of premalignant gastric pathology", Scand. J. Gastroenterol., 53(12), 1611-1618. http://doi.org/10.1080/00365521.2018.1542455.
  48. Wu, J., G. Li, Z. Wang, Y. Yao, R. Chen, X. Pu and J. Wang (2015), "Circulating MicroRNA-21 is a potential diagnostic biomarker in gastric cancer", Disease Markers, 435656. http://doi.org/10.1155/2015/435656.
  49. Yamaguchi, Y., Y. Nagata, R. Hiratsuka, Y. Kawase, T. Tominaga, S. Takeuchi, S. Sakagami and S. Ishida (2016), "Gastric cancer screening by combined assay for serum anti-Helicobacter pylori IgG antibody and serum pepsinogen levels-the ABC method", Digestion, 93(1), 13-18. https://doi.org/10.1159/000441742.
  50. Yao, K. (2015), "Clinical application of magnifying endoscopy with narrow-band imaging in the stomach", Clin. Endosc., 48(6), 481-490. http://doi.org/10.5946/ce.2015.48.6.481.
  51. Zhang, Q., Z.Y. Chen, C.D. Chen, T. Liu, X.W. Tang, Y.T. Ren, S.L. Huang, X.B. Cui, S.L. An, B. Xiao, Y. Bai, S.D. Liu, B. Jiang, F.C. Zhi and W. Gong (2015), "Training in early gastric cancer diagnosis improves the detection rate of early gastric cancer: an observational study in China", Medicine (Baltimore), 94(2), e384. http://doi.org/10.1097/md. 384.
  52. Zhou, B., Z. Zhou, Y. Chen, H. Deng, Y. Cai, X. Rao, Y. Yin and L. Rong (2020), "Plasma proteomics-based identification of novel biomarkers in early gastric cancer", Clin. Biochem., 76, 5-10. https://doi.org/10.1016/j.clinbiochem.2019.11.001.
  53. Zhu, Y., Huang, R., Wu, Z., Song, S., Cheng, L. and Zhu, R. (2021), "Deep learning-based predictive identification of neural stem cell differentiation", Nature Commun., 12(1), 2614. https://doi.org/10.1038/s41467-021-22758-0
  54. Zhu, Y., S. Ge, L. Zhang, X. Wang, X. Xing, Y. Hu, Y. Li, Y. Jia, Y. Lin and B. Fan (2012), "Clinical value of serum CEA, CA19-9, CA72-4 and CA242 in the diagnosis and prognosis of gastric cancer", Chinese J. Gastrointest. Surg., 15(2), 161-164.
  55. Zhuang, Y., Chen, S., Jiang, N. and Hu, H. (2022), "An effective wssenet-based similarity retrieval method of large lung CT image databases", KSII Transact. Internet Inform. Syst., 16(7). https://doi.org/10.3837/tiis.2022.07.013