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Research Paper

A novel classification method of lymph node metastasis in colorectal cancer

, , , , &
Pages 2007-2021 | Received 26 Feb 2021, Accepted 08 May 2021, Published online: 23 May 2021

References

  • Roth GA, Abate D, Abate KH, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1736–1788.
  • Parkin DM, Bray FI, Ferlay J, et al. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55(2):74–108.
  • Jemal A, Bray FI, Ferlay J, et al. Global cancer statistics. CA Cancer J Clin. 1999;61(2):69–90.
  • Torre LA, Bray FI, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108.
  • Bray FI, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
  • Sun Y, Tian H, Xu X, et al. Low expression of adenomatous polyposis coli 2 correlates with aggressive features and poor prognosis in colorectal cancer. Bioengineered. 2020;11(1):1027–1033.
  • Park S, Jee S H. Epidemiology of colorectal Cancer in Asia-Pacific region[M]//Surgical treatment of colorectal Cancer. Springer, Singapore, 2018: 3–10.
  • Patel SG, Ahnen DJ. Colorectal cancer in the young. Curr Gastroenterol Rep. 2018;20(4):15.
  • Shi Z, Shen C, Yu C, et al. Long non-coding RNA LINC00997 silencing inhibits the progression and metastasis of colorectal cancer by sponging miR-512-3p. Bioengineered. 2021;12(1):627–639. .
  • Li F, Hu J, Jiang H, et al. Diagnosis of lymph node metastasis on rectal cancer by PET-CT computer imaging combined with MRI technology. J Infect Public Health. 2019;13:1347–1353.
  • Nasu T, Oku Y, Takifuji K, et al. Predicting lymph node metastasis in early colorectal cancer using the CITED1 expression. J Surg Res. 2013;185(1):136–142.
  • Yang Z, Liu Z. The efficacy of 18F-FDG PET/CT-based diagnostic model in the diagnosis of colorectal cancer regional lymph node metastasis. Saudi J Biol Sci. 2020;27(3):805–811.
  • Ding L, Liu G, Zhao B, et al. Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer. Chin Med J (Engl). 2019;132(4):379–387.
  • Zhou L, Wang J-Z, Wang J-T, et al. Correlation analysis of MR/CT on colorectal cancer lymph node metastasis characteristics and prognosis. Eur Rev Med Pharmacol Sci. 2017;21(6):1219–1225.
  • Ishihara S, Kawai K, Tanaka T, et al. Oncological outcomes of lateral pelvic lymph node metastasis in rectal cancer treated with preoperative chemoradiotherapy. Dis Colon Rectum. 2017;60(5):469–476.
  • Fleming M, Ravula S, Tatishchev S, et al. Colorectal carcinoma: pathologic aspects. J Gastrointest Oncol. 2012;3(3):153–173.
  • Li J, Guo BC, Sun LR, et al. TNM staging of colorectal cancer should be reconsidered by T stage weighting. World J Gastroenterol. 2014;20(17):5104–5112. . PubMed PMID: 24803826; PubMed Central PMCID: PMC4009548
  • Pages F, Mlecnik B, Marliot F, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391(10135):2128–2139. .
  • Taylor FGM, Quirke P, Heald RJ, et al. Preoperative high-resolution magnetic resonance imaging can identify good prognosis stage I, II, and III rectal cancer best managed by surgery alone: a prospective, multicenter, European study. Ann Surg. 2011;253(4):711–719. .
  • Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19(1):221–248.
  • Li X, Jiao H, Wang YJB. Edge detection algorithm of cancer image based on deep learning. Bioengineered. 2020;11(1):693–707.
  • Spanhol FA, Oliveira LS, Petitjean C, et al. editors. Breast cancer histopathological image classification using convolutional neural networks. International Joint Conference on Neural Networks (IJCNN 2016); 2016. Vancouver, BC, Canada.
  • Atsushi T, Tetsuya T, Yuka K, et al. Automated classification of lung cancer types from cytological images using deep convolutional neural networks. Biomed Res Int. 2017 August 13;2017:1–6.
  • Bychkov D, Linder N, Turkki R, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8(1):3395. .
  • Liu Y, Gadepalli KK, Norouzi M, et al. Detecting cancer metastases on gigapixel pathology images[J]. arXiv preprint arXiv:1703.02442, 2017.
  • Lambregts DM, Beets GL, Maas M, et al. Accuracy of gadofosveset-enhanced MRI for nodal staging and restaging in rectal cancer. Ann Surg. 2011;253(3):539. .
  • Beetstan RGH, Beets GL. Rectal cancer: review with emphasis on MR imaging. Radiology. 2004;232(2):335–346.
  • Park JS, Jang Y, Choi G, et al. Accuracy of preoperative MRI in predicting pathology stage in rectal cancers: node-for-node matched histopathology validation of MRI features. Dis Colon Rectum. 2014;57(1):32–38.
  • Group MSJBJoS. Relevance of magnetic resonance imaging-detected pelvic sidewall lymph node involvement in rectal cancer. Br J Surg. 2011;98(12):1798–1804. .
  • Ma X, Shen F, Jia Y, et al. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019;19(1):1–7.
  • Grone J, Loch FN, Taupitz M, et al. Accuracy of various lymph node staging criteria in rectal cancer with magnetic resonance imaging. J Gastrointestinal Surg. 2018;22(1):146–153.
  • Pan SJ, Yang QA. Survey on Transfer Learning. IEEE Trans Knowledge Data Eng. 2010;22(10):1345–1359.
  • Al-Absi H R H, Samir B B, Shaban K B, et al. Computer aided diagnosis system based on machine learning techniques for lung cancer[C]//2012 international conference on computer & information science (ICCIS). IEEE, 2012, 1: 295–300.
  • Wang C, Elazab A, Wu J, et al. Lung nodule classification using deep feature fusion in chest radiography. Computerized Med Imaging Graphics. 2017;57:10–18.
  • Yu L, Chen H, Dou Q, et al. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging. 2017;36(4):994–1004.
  • Havaei M, Davy A, Wardefarley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18–31.
  • Tarando S R, Fetita C, Faccinetto A, et al. Increasing CAD system efficacy for lung texture analysis using a convolutional network[C]//Medical Imaging 2016: Computer-Aided Diagnosis. International Society for Optics and Photonics, 2016, 9785: 97850Q.
  • Hosseiniasl E, Gimelfarb G, Elbaz A. Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. IEEE International Conference on Image Processing - ICIP 2016. 2016. Phoenix, AZ, USA.
  • Tan C, Sun F, Kong T, et al. A survey on deep transfer learning.  In: Kůrková V., Manolopoulos Y., Hammer B., Iliadis L., Maglogiannis I.editors. Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science, vol 11141. Springer, Cham.
  • Deepak S. PMA. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med. 2019;111:103345.
  • Cheng PM, Malhi H. Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. J Digit Imaging. 2017;30(2):234–243.
  • Ragab DA, Sharkas M, Marshall S, et al. Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ. 2019;7:e6201.
  • Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13(1):281–305.
  • Lai Z, Deng H. Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput Intell Neurosci. 2018;2018:1–13.
  • Zhang Z, Zhang X, Peng C, et al., editors. ExFuse: enhancing feature fusion for semantic segmentation. European conference on computer vision; 2018.  Munich, Germany.
  • Choudhary M, Tiwari VUV. An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM. Future Gener Comput Syst. 2019;101:1259–1270.
  • Li H, Zhuang S, Li D-A, et al. Benign and malignant classification of mammogram images based on deep learning. Biomed Signal Process Control. 2019;51:347–354.
  • Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Neural Inf Process Syst. 2012;141(5):1097–1105.
  • Tajbakhsh N, Shin JY, Gurudu SR, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging. 2016;35(5):1299–1312. .
  • Margeta J, Criminisi A, Lozoya RC, et al. Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Comput Methods Biomech Biomed Eng Imaging Visualization. 2017;5(5):339–349. .
  • Lu S, Lu Z, Zhang Y-D. Pathological brain detection based on AlexNet and transfer learning. J Computat Sci. 2019;30:41–47.
  • Zhang X, Pan W, Xiao P, editors. In-vivo skin capacitive image classification using AlexNet convolution neural network. IEEE International Conference on Image; 2018. Chongqing, China.
  • Aliyu H, Razak M, Sudirman R, et al. A deep learning AlexNet model for classification of red blood cells in sickle cell anemia. IAES Int J Artif Intell (IJ-AI). 2020;9:221.
  • He K., Zhang X., Ren S., Sun J. (2016) Identity Mappings in Deep Residual Networks. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham
  • Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700–4708.
  • Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural networks? Eprint Arxiv. 2014;27:3320–3328.
  • Tzeng E, Hoffman J, Zhang N, et al. Deep domain confusion: maximizing for domain invariance. arXiv: 14123474 Computer Vision and Pattern Recognition; 2014.
  • Long M, Cao Y, Wang J, et al. Learning transferable features with deep adaptation networks. arXiv:150202791 Machine Learning; 2015.
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770–778
  • Fawaz HI, Forestier G, Weber J, et al. Transfer learning for time series classification. international conference on big data. 2018 1367–1376. Seattle, WA, USA.
  • Bottou L. Stochastic gradient descent tricks.Berlin: Springer ; 2012. p. 421–436.
  • Lipton ZC. The mythos of model interpretability. ACM Queue. 2018;16(10):30. .
  • Lee J, Ha EJ, Kim JH. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT. Eur Radiol. 2019;, 29(10): 5452–5457.
  • Ketkar N, Santana E. Deep learning with python[M]. Berkeley, CA: Apress, 2017.
  • Zaccone G. Getting started with TensorFlow. Packt Publishing Ltd; 2016.
  • Pan SJ, Tsang IW, Kwok JT, et al. Domain adaptation via transfer component analysis. IEEE Trans Neural Networks. 2011;22(2):199–210.
  • Csurka G. Domain adaptation for visual applications: a comprehensive survey. Adv Comput Vision Pattern Recognit. 2017. doi:10.1007/978-3-319-58347-1_1
  • Wang M, Deng W. Deep visual domain adaptation: a survey. Neurocomputing. 2018;312:135–153.
  • Gretton A, Borgwardt KM, Rasch MJ, et al. A kernel two-sample test. J Mach Learn Res. 2012;13(1):723–773.
  • Saenko K, Kulis B, Fritz M, et al.  Adapting visual category models to new domains[C]//European conference on computer vision. Springer, Berlin, Heidelberg, 2010: 213–226.