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The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning


  • Dimitriou, N., Arandjelović, O., Harrison, D. J. & Caie, P. D. A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis. NPJ Digit. Med. 1(1), 52 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, Y., Ju, L., Tong, J., Zhou, C.-M. & Yang, J.-J. Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection. Sci. Rep. 10(1), 2519 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xi, Y. & Xu, P. International colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 14(10), 101174 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rawla, P., Sunkara, T. & Barsouk, A. Epidemiology of colorectal cancer: incidence, mortality, survival, and threat elements. Gastroenterol. Rev./Prz. Gastroenterol.. 14(2), 89–103 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Wan, N. et al. Machine learning permits detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA. BMC Cancer. 19, 1–10 (2019).

    Article 

    Google Scholar
     

  • Takamatsu, M. et al. Prediction of early colorectal cancer metastasis by machine learning utilizing digital slide photographs. Comput. Strategies Progr. Biomed. 178, 155–161 (2019).

    Article 

    Google Scholar
     

  • Prosnitz, R. G. et al. High quality measures for the use of adjuvant chemotherapy and radiation remedy in sufferers with colorectal cancer: a scientific overview. Cancer 107(10), 2352–2360 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Siegel, R. et al. Preoperative short-course radiotherapy versus mixed radiochemotherapy in regionally superior rectal cancer: A multi-centre prospectively randomised examine of the Berlin Cancer Society. BMC Cancer 9, 1–6 (2009).

    Article 

    Google Scholar
     

  • Almarzouki, H. Z. Deep-learning-based cancer profiles classification utilizing gene expression knowledge profile. J. Healthc. Eng. 2022(1), 4715998 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mostavi, M., Chiu, Y.-C., Huang, Y. & Chen, Y. Convolutional neural community fashions for cancer kind prediction primarily based on gene expression. BMC Med. Genom. 13, 1–13 (2020).

    Article 

    Google Scholar
     

  • Du, M. et al. Built-in multi-omics approach to distinct molecular characterization and classification of early-onset colorectal cancer. Cell Rep. Med. 4(3), 100974 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haj-Hassan, H. et al. Classifications of multispectral colorectal cancer tissues utilizing convolution neural community. J. Pathol. Inform. 8(1), 1 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pal, A., Garain, U., Chandra, A., Chatterjee, R. & Senapati, S. Psoriasis pores and skin biopsy picture segmentation utilizing deep convolutional neural community. Comput. Strategies Progr. Biomed. 159, 59–69 (2018).

    Article 

    Google Scholar
     

  • George, Okay., Faziludeen, S. & Sankaran, P. Breast cancer detection from biopsy photographs utilizing nucleus guided switch learning and perception primarily based fusion. Comput. Biol. Med. 124, 103954 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Srivastava, G., Chauhan, A. & Pradhan, N. Cjt-deo: Condorcet’s jury theorem and differential evolution optimization primarily based ensemble of deep neural networks for pulmonary and colorectal cancer classification. Appl. Mushy Comput. 132, 109872 (2023).

    Article 

    Google Scholar
     

  • Bychkov, D. et al. Deep learning primarily based tissue evaluation predicts final result in colorectal cancer. Sci. Rep. 8(1), 3395 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, J., Wang, P., Zhou, Y., Liang, H. & Luan, Okay. Completely different machine learning and deep learning strategies for the classification of colorectal cancer lymph node metastasis photographs. Entrance. Bioeng. Biotechnol. 8, 620257 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nazari, E., Aghemiri, M., Avan, A., Mehrabian, A. & Tabesh, H. Machine learning approaches for classification of colorectal cancer with and with out function choice technique on microarray knowledge. Gene Rep. 25, 101419 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Escorcia-Gutierrez, J. et al. Galactic swarm optimization with deep switch learning pushed colorectal cancer classification for picture guided intervention. Comput. Electr. Eng. 104, 108462 (2022).

    Article 

    Google Scholar
     

  • Gao, Y., Zhu, Z. & Solar, F. Growing prediction efficiency of colorectal cancer illness standing utilizing random forests classification primarily based on metagenomic shotgun sequencing knowledge. Synth. Syst. biotechnol. 7(1), 574–585 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, Z., Solar, Y., An, F., Chen, H. & Liao, J. Self-supervised clustering evaluation of colorectal cancer biomarkers primarily based on multi-scale complete slides picture and mass spectrometry imaging fused photographs. Talanta. 263, 124727 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Xu, H. et al. Classification of colorectal cancer consensus molecular subtypes utilizing attention-based multi-instance learning community on whole-slide photographs. Acta Histochem. 125(6), 152057 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhou, C. et al. Histopathology classification and localization of colorectal cancer utilizing international labels by weakly supervised deep learning. Comput. Med. Imaging Gr. 88, 101861 (2021).

    Article 

    Google Scholar
     

  • Lo, C.-M. et al. Modeling the survival of colorectal cancer sufferers primarily based on colonoscopic options in a function ensemble imaginative and prescient transformer. Comput. Med. Imaging Gr. 107, 102242 (2023).

    Article 

    Google Scholar
     

  • Schirris, Y., Gavves, E., Nederlof, I., Horlings, H. M. & Teuwen, J. DeepSMILE: Contrastive self-supervised pre-training advantages MSI and HRD classification straight from H&E whole-slide photographs in colorectal and breast cancer. Med. Picture Anal. 79, 102464 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Raghav, S. et al. A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression knowledge. Intell. Med. 4(1), 43–51 (2024).

    Article 

    Google Scholar
     

  • Chang, X. et al. Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural community. Cell Rep. Med. 4(2), 100914 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Luo, R. & Bocklitz, T. A scientific examine of switch learning for colorectal cancer detection. Inform. Med. Unlocked. 40, 101292 (2023).

    Article 

    Google Scholar
     

  • Kumar, A., Vishwakarma, A. & Bajaj, V. Crccn-net: Automated framework for classification of colorectal tissue utilizing histopathological photographs. Biomed. Sign Course of. Management. 79, 104172 (2023).

    Article 

    Google Scholar
     

  • Arowolo, M. O., Aigbogun, H. E., Michael, P. E., Adebiyi, M. O. & Tyagi, A. Okay. A predictive mannequin for classifying colorectal cancer utilizing principal element evaluation 205–216 (Elsevier, 2023).


    Google Scholar
     

  • Parhami, P., Fateh, M., Rezvani, M. & Alinejad-Rokny, H. A comparability of deep neural community fashions for cluster cancer sufferers by way of somatic level mutations. J. Ambient Intell. Humaniz. Comput. 14(8), 10883–10898 (2023).

    Article 

    Google Scholar
     

  • Kim, S.-H., Koh, H. M. & Lee, B.-D. Classification of colorectal cancer in histological photographs utilizing deep neural networks: An investigation. Multimed. Instruments Appl. 80(28), 35941–35953 (2021).

    Article 

    Google Scholar
     

  • Su, Y. et al. Colon cancer analysis and staging classification primarily based on machine learning and bioinformatics evaluation. Comput. Boil. Med. 145, 105409 (2022).

    Article 

    Google Scholar
     

  • Wang, T. et al. scMultiGAN: Cell-specific imputation for single-cell transcriptomes with a number of deep generative adversarial networks. Temporary. Bioinform. https://doi.org/10.1093/bib/bbad384 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, T. et al. Precisely deciphering spatial domains for spatially resolved transcriptomics with stCluster. Temporary. Bioinform. https://doi.org/10.1093/bioinformatics/btac837 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, T. et al. DFinder: A novel end-to-end graph embedding-based technique to establish drug–meals interactions. Bioinformatics https://doi.org/10.1093/bioinformatics/btac837 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, T. et al. Enhancing discoveries of molecular QTL research with small pattern dimension utilizing abstract statistic imputation. Temporary. Bioinform. https://doi.org/10.1093/bib/bbab370 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, T. et al. Exploring causal results of sarcopenia on threat and development of Parkinson illness by Mendelian randomization. npj Parkinson’s Dis. 10(1), 164 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Wang, T. et al. PostGWAS: An online server for deciphering the causality put up the genome-wide affiliation research. Comput. Biol. Med. 171, 108108 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • VOSviewer model 1.6.20. (Leiden College, 2024).

  • Kotsiantis, S. B. Resolution timber: A latest overview. Artif Intell. Rev. 39, 261–283 (2013).

    Article 

    Google Scholar
     

  • Yeon, Y.-Okay., Han, J.-G. & Ryu, Okay. H. Landslide susceptibility mapping in Injae, Korea, utilizing a choice tree. Eng. Geol. 116(3–4), 274–283 (2010).

    Article 

    Google Scholar
     

  • Mitchell, T. M. & Mitchell, T. M. Machine learning (McGraw-hill, New York, 1997).


    Google Scholar
     

  • Freund, Y. & Schapire, R. E. A call-theoretic generalization of on-line learning and an utility to boosting. J. comput. Syst. Sci. 55(1), 119–139 (1997).

    Article 
    MathSciNet 

    Google Scholar
     

  • Alizamir, M. et al. Growing an efficient explainable artificial intelligence approach for correct reverse osmosis desalination plant efficiency prediction: Utility of SHAP evaluation. Eng. Appl. Comput. Fluid Mech. 18(1), 2422060 (2024).


    Google Scholar
     

  • Hastie, T., Tibshirani, R., Friedman, J. & Franklin, J. The parts of statistical learning: knowledge mining, inference and prediction. Math. Intell. 27(2), 83–85 (2005).

    Article 

    Google Scholar
     

  • Breiman, L. Random forests. Mach. be taught. 45, 5–32 (2001).

    Article 

    Google Scholar
     

  • Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J. P. An evaluation of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogram. Distant Sensing. 67, 93–104 (2012).

    Article 

    Google Scholar
     

  • Alizamir, M., Gholampour, A., Kim, S., Keshtegar, B. & Jung, W.-t. Designing a dependable machine learning system for precisely estimating the last word situation of FRP-confined concrete. Sci. Rep. 14(1), 20466 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dang, V.-H., Dieu, T. B., Tran, X.-L. & Hoang, N.-D. Enhancing the accuracy of rainfall-induced landslide prediction alongside mountain roads with a GIS-based random forest classifier. Bull. Eng. Geol. Environ. 78, 2835–2849 (2019).

    Article 

    Google Scholar
     

  • Alizamir, M., Heddam, S., Kim, S. & Mehr, A. D. On the implementation of a novel data-intelligence mannequin primarily based on excessive learning machine optimized by bat algorithm for estimating each day chlorophyll-a focus: Case research of river and lake in USA. J. Clear. Prod. 285, 124868 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Alizamir, M. et al. Modelling each day soil temperature by hydro-meteorological knowledge at completely different depths utilizing a novel data-intelligence mannequin: Deep echo state community mannequin. Artif. Intell. Rev. 54, 2863–2890 (2021).

    Article 

    Google Scholar
     

  • Friedman, J. H. Grasping perform approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Article 
    MathSciNet 

    Google Scholar
     

  • Bentéjac, C., Csörgő, A. & Martínez-Muñoz, G. A comparative evaluation of gradient boosting algorithms. Artif. Intell. Rev. 54, 1937–1967 (2021).

    Article 

    Google Scholar
     

  • Alizamir, M., Kim, S., Kisi, O. & Zounemat-Kermani, M. A comparative examine of a number of machine learning primarily based non-linear regression strategies in estimating photo voltaic radiation: Case research of the USA and Turkey areas. Power. 197, 117239 (2020).

    Article 

    Google Scholar
     

  • Alizamir, M., Kisi, O., Kim, S. & Heddam, S. A novel technique for lake degree prediction: Deep echo state community. Arab. J. Geosci. 13, 1–18 (2020).

    Article 

    Google Scholar
     

  • Alizamir, M. et al. Bettering the accuracy of each day photo voltaic radiation prediction by climatic knowledge utilizing an efficient hybrid deep learning mannequin: Lengthy short-term reminiscence (LSTM) community coupled with wavelet rework. Eng. Appl. Artif. Intell. 123, 106199 (2023).

    Article 

    Google Scholar
     

  • Alizamir, M. et al. Prediction of each day chlorophyll-a focus in rivers by water high quality parameters utilizing an efficient data-driven mannequin: On-line sequential excessive learning machine. Acta Geophys. 69, 2339–2361 (2021).

    Article 

    Google Scholar
     

  • Kisi, O. & Alizamir, M. Modelling reference evapotranspiration utilizing a brand new wavelet conjunction heuristic technique: Wavelet excessive learning machine vs wavelet neural networks. Agric. For. Meteorol. 263, 41–48 (2018).

    Article 

    Google Scholar
     

  • Alizamir, M. et al. Investigating landfill leachate and groundwater high quality prediction utilizing a strong built-in artificial intelligence mannequin: Gray wolf metaheuristic optimization algorithm and excessive learning machine. Water. 15(13), 2453 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Heidari, E., Sobati, M. A. & Movahedirad, S. Correct prediction of nanofluid viscosity utilizing a multilayer perceptron artificial neural community (MLP-ANN). Chemom. Intel. Lab. syst. 155, 73–85 (2016).

    Article 
    CAS 

    Google Scholar
     

  • LeCun, Y., Touresky, D., Hinton, G., Sejnowski, T. (ed.) A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist fashions summer time faculty (1988).

  • LeCun, Y. & Bengio, Y. Convolutional networks for photographs, speech, and time sequence. Handb. Mind Concept Neur. Netw. 3361(10), 1995 (1995).


    Google Scholar
     

  • Thenmozhi, Okay. & Reddy, U. S. Crop pest classification primarily based on deep convolutional neural community and switch learning. Comput. Electron. Agric. 164, 104906 (2019).

    Article 

    Google Scholar
     

  • Agostini, M. et al. A purposeful organic community centered on XRCC3: A brand new potential marker of chemoradiotherapy resistance in rectal cancer sufferers. Cancer Biol. ther. 16(8), 1160–1171 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boyle, P. & Langman, M. J. ABC of colorectal cancer: Epidemiology. Bmj 321((Suppl S6)), 0012452 (2000).

    Article 

    Google Scholar
     

  • Parkin, D. M. Worldwide variation. Oncogene. 23(38), 6329–6340 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Moradi, A. et al. Survival of colorectal cancer in Iran. Asian Pac J. Cancer Prev. 10(4), 583–586 (2009).

    PubMed 

    Google Scholar
     

  • Ai, D. et al. Utilizing resolution tree aggregation with random forest mannequin to establish intestine microbes related to colorectal cancer. Genes. 10(2), 112 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cai, D. et al. A metabolism-related radiomics signature for predicting the prognosis of colorectal cancer. Entrance. Mol. Biosci. 7, 613918 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ansari, A.A., Iqbal, A., Sahoo, B. (eds.) Heterogeneous defect prediction utilizing ensemble learning method. In Artificial intelligence and evolutionary computations in engineering techniques (Springer, 2020).

  • Abbas, A., Abdelsamea, M. M. & Gaber, M. M. Detrac: Switch learning of class decomposed medical photographs in convolutional neural networks. IEEE Entry. 8, 74901–74913 (2020).

    Article 

    Google Scholar
     

  • Azzawi, H., Hou, J., Xiang, Y. & Alanni, R. Lung cancer prediction from microarray knowledge by gene expression programming. IET Syst. Biol. 10(5), 168–178 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cowl, T. M. Parts of data idea (John Wiley & Sons, 1999).


    Google Scholar
     

  • Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual data. Phys. Rev. E––Stat. Nonlinear Mushy Matter Phys. 69(6), 066138 (2004).

    Article 
    MathSciNet 

    Google Scholar
     

  • Fan, X.-N., Zhang, S.-W., Zhang, S.-Y., Zhu, Okay. & Lu, S. Prediction of lncRNA-disease associations by integrating various heterogeneous data sources with RWR algorithm and optimistic pointwise mutual data. BMC Bioinform. 20, 1–12 (2019).

    Article 

    Google Scholar
     

  • Fux, A. et al. Goal video-based evaluation of ADHD-like canine habits utilizing machine learning. Animals. 11(10), 2806 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fawcett, T. An introduction to ROC evaluation. Sample Recognit. Lett. 27(8), 861–874 (2006).

    Article 
    MathSciNet 

    Google Scholar
     

  • Ahn, J. H. et al. Development of a novel prognostic mannequin for predicting lymph node metastasis in early colorectal cancer: Evaluation primarily based on the surveillance, epidemiology, and finish outcomes database. Entrance. Oncol. 11, 614398 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Masud, M., Sikder, N., Nahid, A.-A., Bairagi, A. Okay. & AlZain, M. A. A machine learning approach to diagnosing lung and colon cancer utilizing a deep learning-based classification framework. Sensors. 21(3), 748 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, T. et al. Clever imaging know-how in analysis of colorectal cancer utilizing deep learning. IEEE Entry. 7, 178839–178847 (2019).

    Article 

    Google Scholar
     

  • Hanley, J. A. & McNeil, B. J. The which means and use of the realm beneath a receiver working attribute (ROC) curve. Radiology. 143(1), 29–36 (1982).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, S., Wu, X. & Tan, M. Gene choice utilizing hybrid particle swarm optimization and genetic algorithm. Mushy Comput. 12, 1039–1048 (2008).

    Article 

    Google Scholar
     

  • Zhang, Z., Yang, P., Wu, X. & Zhang, C. An agent-based hybrid system for microarray knowledge evaluation. IEEE Intell. Syst. 24(5), 53–63 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Yang, P., Zhou, B. B., Zhang, Z. & Zomaya, A. Y. A multi-filter enhanced genetic ensemble system for gene choice and pattern classification of microarray knowledge. BMC Bioinform. 11, 1–12 (2010).

    Article 

    Google Scholar
     

  • Kulkarni, A., Kumar, B. N., Ravi, V. & Murthy, U. S. Colon cancer prediction with genetics profiles utilizing evolutionary methods. Professional Syst. Appl. 38(3), 2752–2757 (2011).

    Article 

    Google Scholar
     

  • Al-Rajab, M., Lu, J. & Xu, Q. Analyzing making use of excessive efficiency genetic knowledge function choice and classification algorithms for colon cancer analysis. Comput. Strategies Progr. Biomed. 146, 11–24 (2017).

    Article 

    Google Scholar
     

  • Salem, H., Attiya, G. & El-Fishawy, N. Classification of human cancer ailments by gene expression profiles. Appl. Mushy Comput. 50, 124–134 (2017).

    Article 

    Google Scholar
     

  • Zhao, D. et al. A dependable technique for colorectal cancer prediction primarily based on function choice and help vector machine. Med. Boil. Eng. Comput. 57, 901–912 (2019).

    Article 

    Google Scholar
     

  • Al-Rajab, M., Lu, J. & Xu, Q. A framework mannequin utilizing multifilter function choice to improve colon cancer classification. Plos One. 16(4), e0249094 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     



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