Modern diagnostic methods in oncogastroenterology

Автор: Pogosov Gabriel R., Osmanov Guseyn G., Ivanova Elizaveta R., Mardanyan Nona T.

Журнал: Cardiometry @cardiometry

Рубрика: Descriptive study

Статья в выпуске: 26, 2023 года.

Бесплатный доступ

Diagnostic procedures occupy a special place in the activities of medical specialists, since the accuracy and timeliness of their implementation depends on the determination of the patient’s treatment package, as well as the prognosis of the course of the disease. In the modern period, innovative technologies come to the aid of doctors, one of which is artificial intelligence. Along with other areas of medicine, artificial intelligence (AI) technology is widely used in oncogastroenterology and is used in the process of diagnosis, prediction and image analysis. AI makes it possible to determine with high accuracy the features of the diagnosed pathology and, accordingly, to develop the most effective strategy for the treatment of the patient. The future of oncogastroenterology is based, without a doubt, precisely on the results of high-precision diagnostics, since advanced methods of treating cancer patients can give a significant effect precisely at the early stages of the development of this disease. For this reason, the appeal to AI as an indispensable assistant to diagnosticians in the future should be widely implemented at all levels of polyclinic and inpatient oncological care to the population.

Еще

Oncology, gastroenterology, diagnostics, modern methods, artificial intelligence

Короткий адрес: https://sciup.org/148326597

ID: 148326597   |   DOI: 10.18137/cardiometry.2023.26.127132

Список литературы Modern diagnostic methods in oncogastroenterology

  • Isomoto H, Shikuwa S, Yamaguchi N, Fukuda E, Ikeda K, Nishiyama H, Ohnita K, Mizuta Y, Shiozawa J, Kohno S. Endoscopic submucosal dissection in early gastric cancer: a large-scale feasibility study. The gut. 2009;58(3):331-36.
  • Farmanpharma K.K., Mahdavifar N., Hassanipour S., Salekhinia H. Epidemiological study of stomach cancer in Iran: a systematic review. Klin Exp Gastroenterol. 2020;13:511.
  • Liu D, Wang X, Li L, Jiang Q, Li X, Liu M, Wang W, Shi E, Zhang C, Wang Y. A machine learning-based model for predicting postoperative stomach cancer. Cancer Manag Res. 2022;14:135.
  • Evans J. A., Chandrasekhara V., Chatadi K. V., Decker G. A., Early D. S., Fisher D. A., Foley K., Hwang J. H., Jue T. L., Lightdale J. R. The role of endoscopy in the treatment of precancerous and malignant diseases stomach. Gastrointest Endosc. 2015;82(1):1-8.
  • Taninaga J., Nishiyama Y., Fujibayashi K., Gunji T., Sasabe N., Iijima K., Naito T. Predicting the risk of stomach cancer in the future using a machine learning algorithm and complex medical examination data: a case-control study. Scientific report 2019;9(1):1-9.
  • Dohi O, Majima A., Naito Yu., Yoshida T., Ishida T., Azuma Yu., Kitae H., Matsumura S., Mizuno N., Yoshida N. (2020) Can image-enhanced endoscopy improve the diagnosis of gastritis according to the Kyoto classification in clinical conditions? 32(2):191–203
  • Lee JH, Kim YJ, Kim YW, Park S, Choi YI, Kim YJ, Park DK, Kim KG, Chung JW (2019)Detection of malignant neoplasms on endoscopic images of the stomach using deep learning. Surg Endosc Other Interv Tech 33(11):3790–3797
  • Li YP, Deng LY, Yang XH, Liu Z, Zhao XP, Huang FR, Zhu SQ, Chen XD, Chen ZQ, Zhang WM (2019) Early diagnosis of gastric cancer based on deep learning in combination with the spectral-spatial classification method. Biomed Opt Express 10(10):4999-5014
  • Alfayes A.A., Kunts H., Lai A.G. Prediction of cancer risk in adults using controlled machine learning: a preliminary review. Open BMJ. 2021;11(9): e047755.
  • T. Kanesaka, T.S. Lee, N. Uedo, etc. Computer diagnostics for the detection and determination of the boundaries of gastric cancer in the early stages with narrow-spectrum imaging with an increase in Gastrointest. Endosc., 87 ( 5 ) ( 2018 ), pages 1339 – 1344
  • Y. Zhu, QC Wang, MD Xu, et al. The use of convolutional neural network in the diagnosis of the depth of invasion of gastric cancer based on traditional Gastrointest endoscopy. Endosc., 89 ( 4 ) ( 2019 ), pp. 806-815
  • K. Kubota, J. Kuroda, M. Yoshida, etc. Analysis of medical images: computer diagnostics of gastric cancer invasion based on endoscopic images of Surg. Endosc., 26 ( 5 ) ( 2012 ), pages 1485 – 1489
  • R. Eid, S. F. Moss Helicobacter pylori infection and the development of gastric cancer N. engl. J. Med., 346 ( 1 ) ( 2002 ), pp. 65-67 _
  • T. Ito, H. Kawahira, H. Nakashima, etc. Deep Learning analyzes Helicobacter pylori infection using endoscopy images of the upper gastrointestinal tract Endosc. International. Open, 6 ( 2 ) ( 2018 ), pp. E139 - E144
  • K. Kusano Artificial intelligence for the treatment of stomach cancer: can we make further progress? Endoscopy, 53 ( 12 ) ( 2021 ), pp. 1208-1209.
  • Z. Li, S. Chen, V. Feng, etc. A pan-cancer analysis of the HER2 index revealed a transcriptional pattern for the precise choice of therapy aimed at HER2. EBio-Medicine, 62 ( 2020 ), article 103074
  • X. Ishii, H. Sasaki, K. Aoyagi, et al. Classification of subtypes of gastric cancer using ICA, MLR and Bayesian Stud network. Technologies of health. Inf., 192 ( 2013 ), p. 1014
  • Z. Yang, V. Xu, Yu Xiong, etc. High-precision twogene signature of gastric cancer Med. Oncol., 30 ( 2 ) ( 2013 ), p. 584
  • MM Liu, L. Wen, YJ Liu, et al. Application of data mining methods to improve the screening of the risk of early gastric cancer BMS Med. Inf. Resh. Making, 18 ( Suppl 5 ) ( 2018 ), p. 121
  • J. N. Kater, A. T. Pearson, N. Halama Deep learning can predict microsatellite instability directly based on the histology of gastrointestinal cancer Nat Med, 25 ( 7 ) ( 2019 ), pp. 1054-1056.
  • Korhani Kangi A., Bahrampur A. Predicting the survival rate of patients with gastric cancer using artificial and Bayesian neural networks. Asian Pac J Cancer Prev. 2018; 19 : 487-490.
  • Bolshweiler E.H., Menig S.P., Hensler K., Baldus S.E., Maruyama K., Helscher A.H. Artificial neural network for predicting lymph node metastases in gastric cancer: a phase II diagnostic study. Ann Surg Oncol. 2004; 11 : 506-511
  • Li C, Zhang S, Zhang H, Pang L, Lam K, Hui C, Zhang S. Using the K-nearest neighbor algorithm to classify lymph node metastases in gastric cancer. Computational mathematical methods med. 2012; 2012 : 876545
  • Korhani Kangi A., Bahrampur A. Predicting the survival rate of patients with gastric cancer using artificial and Bayesian neural networks. Asian Pac J Cancer Prev. 2018; 19 : 487–490
  • Li F, Zhang R, Liang H, Liu H, Quan J. The nature and risk factors of recurrence of proximal gastric cancer after therapeutic resection. J. Surgeon Oncol. 2013; 107 : 130-135
  • Bolshweiler E.H., Menig S.P., Hensler K., Baldus S.E., Maruyama K., Helscher A.H. Artificial neural network for predicting lymph node metastases in gastric cancer: a phase II diagnostic study. Ann Surg Oncol. 2004; 11 : 506-511
  • Jagric T., Potrc S., Jagric T. Prediction of liver metastases after gastric cancer resection using learning neural networks of vector quantization. Dig Dis Sci. 2010; 55:3252-3261.
  • Hensler K, Waschulzik T, Mönig SP, Maruyama K, Hölscher AH, Bollschweiler E. Effective development of neural networks with direct communication (QUEEN) with guaranteed quality — pre-therapeutic assessment of the state of lymph nodes in patients with gastric carcinoma. Methods Inf Med. 2005; 44 : 647-654
Еще
Отчет