Open Access Open Access  Restricted Access Subscription or Fee Access

THE EVOLVING ROLE OF ARTIFICIAL INTELLIGENCE (AI) IN THE DISCOVERY OF CANCER DRUG THERAPY

Himanshu Solanki, Nazneen Siddique, Sumit Singh, Jai Naik, Nimisha Solanki

Abstract


The term "cancer" refers to a broad range of diseases that develop in any organ or tissue of the body when malignant cells multiply out of control, cross their usual boundaries to infect surrounding body regions, and spread to other organs, primarily problems that arise in breast cancer and lung cancer. The world recognizes Artificial Intelligence (AI) as a cutting-edge technology. Today, several institutes, organizations, and individuals are aiming to use AI in practically every industry, including healthcare, education, manufacturing, etc. AI is one of many technologies that have been developed to treat cancer. AI is a computerized simulation of human intelligence that solves complex problems by use of personified knowledge and also it has been revealed that lowering cancer fatalities needs early diagnosis so this review focused on the emerging role of AI in cancer diagnosis, cancer treatment, anticancer drug development with (chemotherapy, immunotherapy, radiotherapy, machine deep learning, clinical support system) and different technologies for the treatment of breast cancer and lung cancer. 


Keywords


Artificial intelligence, Cancer diagnosis, Breast cancer, Lung cancer, Technologies

Full Text:

PDF

References


Mesko B. The role of artificial intelligence in precision medicine. Expert Review of Precision Medicine and Drug Development. 2017 Sep 3;2(5):239-41.

Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug discovery today. 2019 Mar 1;24(3):773-80.

Zhang C, Lu Y. Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration. 2021 Sep 1;23:100224.

Raza MA, Aziz S, Noreen M, Saeed A, Anjum I, Ahmed M, et al. Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations. INNOVATIONS in pharmacy. 2022 Jul 25;13(2):13-.

Brunette ES, Flemmer RC, Flemmer CL. A review of artificial intelligence. In2009 4th International Conference on Autonomous Robots and Agents 2009 Feb 10 (pp. 385-392). Ieee.

Khare SS, Gajbhiye AR. Literature Review on Application of Artificial Neural Network (Ann) In Operation of Reservoirs. International Journal of computational Engineering research (IJCER) IJCER| June 2013| VOL 3 ISSUE 6. 1943:63.

Akman V, Blackburn P. Alan Turing and artificial intelligence. Journal of Logic, Language, and Information. 2000 Oct 1:391-5.

Bruderer H. The birth of artificial intelligence: first conference on artificial intelligence in paris in 1951?. InIFIP International Conference on the History of Computing 2016 May 25 (pp. 181-185). Springer, Cham.

Masche J, Le NT. A review of technologies for conversational systems. InInternational conference on computer science, applied mathematics and applications 2017 Jun 30 (pp. 212-225). Springer, Cham.

Suvetha M, Swathi S, Rani M, Vinoth S, Suriya R. A study on artificial intelligence. Bonfring International Journal of Industrial Engineering and Management Science. 2019 Mar;9(1):6-9.

Wang B, Tao F, Fang X, Liu C, Liu Y, Freiheit T. Smart manufacturing and intelligent manufacturing: A comparative review. Engineering. 2021 Jun 1;7(6):738-57.

Feigenbaum EA. Expert systems in the 1980s. State of the art report on machine intelligence. Maidenhead: Pergamon-Infotech. 1981.

Yao X, Zhou J, Zhang J, Boër CR. From intelligent manufacturing to smart manufacturing for industry 4.0 driven by next generation artificial intelligence and further on. In2017 5th international conference on enterprise systems (ES) 2017 Sep 22 (pp. 311-318). IEEE.

Newborn M. Deep Blue's contribution to AI. Annals of Mathematics and Artificial Intelligence. 2000 Oct;28(1):27-30.

Jones JL. Robots at the tipping point: the road to iRobot Roomba. IEEE Robotics & Automation Magazine. 2006 Feb 27;13(1):76-8.

Strickland E. IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care. IEEE Spectrum. 2019 Apr 1;56(4):24-31.

Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019 Jul;8(7):2328.

Larson EJ. The Myth of Artificial Intelligence. InThe Myth of Artificial Intelligence 2021 Dec 31. Harvard University Press.

Marinchak CL, Forrest E, Hoanca B. The impact of artificial intelligence and virtual personal assistants on marketing. InEncyclopedia of Information Science and Technology, Fourth Edition 2018 (pp. 5748-5756). IGI global.

Kooi T, Litjens G, Van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Medical image analysis. 2017 Jan 1;35:303-12.

Maddison CJ, Huang A, Sutskever I, Silver D. Move evaluation in Go using deep convolutional neural networks. arXiv preprint arXiv:1412.6564. 2014 Dec 20.

Elemento O, Leslie C, Lundin J, Tourassi G. Artificial intelligence in cancer research, diagnosis and therapy. Nature Reviews Cancer. 2021 Dec;21(12):747-52.

Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomedicine & Pharmacotherapy. 2020 Aug 1;128:110255.

Chen G, Tsoi A, Xu H, Zheng WJ. Predict effective drug combination by deep belief network and ontology fingerprints. Journal of biomedical informatics. 2018 Sep 1;85:149-54.

Bulik-Sullivan B, Busby J, Palmer CD, Davis MJ, Murphy T, Clark A, et al. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nature biotechnology. 2019 Jan;37(1):55-63.

Wang F, Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA internal medicine. 2019 Mar 1;179(3):293-4.

Simon AB, Vitzthum LK, Mell LK. Challenge of directly comparing imaging-based diagnoses made by machine learning algorithms with those made by human clinicians. Journal of Clinical Oncology. 2020 Jun 6;38(16):1868.

Oke SA. A literature review on artificial intelligence. International journal of information and management sciences. 2008 Jan;19(4):535-70.

Aldhabi MA, Alzoubi K, Almoneef TS, Bamatra SM, Attia H, Ramahi OM. Review of microwaves techniques for breast cancer detection. Vol. 20, Sensors (Switzerland). MDPI AG; 2020.

Abdul Halim AA, Andrew AM, Mohd Yasin MN, Abd Rahman MA, Jusoh M, Veeraperumal V, et al. Existing and emerging breast cancer detection technologies and its challenges: A review. Applied Sciences (Switzerland). 2021 Nov 1;11(22).

Joy JE (Janet E, Penhoet EE, Petitti DB, National Cancer Policy Board (U.S.). Committee on New Approaches to Early Detection and Diagnosis of Breast Cancer. Saving women’s lives : strategies for improving breast cancer detection and diagnosis. National Academies Press; 2005. 361 p.

Modiri A, Goudreau S, Rahimi A, Kiasaleh K. Review of breast screening: Toward clinical realization of microwave imaging: Toward. Vol. 44, Medical Physics. John Wiley and Sons Ltd; 2017. p. e446–58. 33.Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, et al. Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA. 2014 Jun 25;311(24):2499–507.

Zeng J, Liu Z, Shen G, Zhang Y, Li L, Wu Z, et al. MRI evaluation of pulmonary lesions and lung tissue changes induced by tuberculosis. International Journal of Infectious Diseases. 2019 May 1;82:138–46. 35.Zhang L, Ren Z. Comparison of CT and MRI images for the prediction of soft-tissue sarcoma grading and lung metastasis via a convolutional neural networks model. Clin Radiol. 2020 Jan 1;75(1):64–9.

Mann RM, Cho N, Moy L. Breast MRI: State of the art. Radiology. 2019;292(3):520–36.

Carovac A, Smajlovic F, Junuzovic D. Application of Ultrasound in Medicine. Acta Informatica Medica. 2011;19(3):168.

Cosgrove D. Ultrasound contrast agents: An overview. Eur J Radiol. 2006 Dec;60(3):324–30.

Halter RJ, Zhou T, Meaney PM, Hartov A, Barth RJ, Rosenkranz KM, et al. The correlation of in vivo and ex vivo tissue dielectric properties to validate electromagnetic breast imaging: Initial clinical experience. Physiol Meas. 2009;30(6).

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data Brief. 2020 Feb 1;28.

Zhurbenko V. Challenges in the design of microwave imaging systems for breast cancer detection. Advances in Electrical and Computer Engineering. 2011 Feb;11(1):91–6.

Tipa R, Baltag O. MICROWAVE THERMOGRAPHY FOR CANCER DETECTION *. APPLIED PHYSICS-MEDICAL PHYSICS.

Mouty S, Bocquet B, Ringot R, Rocourt N, Devos P. Microwave radiometric imaging (MWI) for the characterisation of breast tumours. Vol. 10, Eur. Phys. J. AP. 2000.

Benny R, Anjit TA, Mythili P. An Overview of Microwave Imaging for Breast Tumor Detection. Vol. 87, Progress In Electromagnetics Research B. 2020.

Abdul Wahab Y, Abdul Rahim R, Fazalul Rahiman MH, Ridzuan Aw S, Mohd Yunus FR, Goh CL, et al. Non-invasive process tomography in chemical mixtures - A review. Sens Actuators B Chem. 2015;210:602–17.

Klemm M, Craddock IJ, Leendertz JA, Preece A, Benjamin R. Radar-based breast cancer detection using a hemispherical antenna array - Experimental results. IEEE Trans Antennas Propag. 2009;57(6):1692–704.

Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008 Oct;455(7216):1069-75.

National Lung Screening Trial Research Team. Reduced lung cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine. 2011 Aug 4;365(5):395-409.

Wang Y, Cai H, Li J, Yang C, Yang F, CHEN L, et al. The value of AI in the diagnosis, treatment, and prognosis of malignant lung cancer. Frontiers in Radiology.:10.

Wang S, Chen A, Yang L, Cai L, Xie Y, Fujimoto J, et al. Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Scientific reports. 2018 Jul 10;8(1):1-9.

Onishi Y, Teramoto A, Tsujimoto M, Tsukamoto T, Saito K, Toyama H, et al. Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. BioMed research international. 2019 Jan 2;2019.

He Y, Zhou C. Tyrosine kinase inhibitors interstitial pneumonitis: diagnosis and management. Translational Lung Cancer Research. 2019 Nov;8(Suppl 3):S318.

Wu J, Savooji J, Liu D. Second-and third-generation ALK inhibitors for non-small cell lung cancer. Journal of hematology & oncology. 2016 Dec;9(1):1-7.

Zhang Y, Chang L, Yang Y, Fang W, Guan Y, Wu A, et al. Intratumor heterogeneity comparison among different subtypes of non-small-cell lung cancer through multi-region tissue and matched ctDNA sequencing. Molecular cancer. 2019 Dec;18(1):1-6.

Teeling EC, Springer MS, Madsen O, Bates P, O'brien SJ, Murphy WJ. A molecular phylogeny for bats illuminates biogeography and the fossil record. Science. 2005 Jan 28;307(5709):580-4.

Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of non–small-cell lung cancer. New England Journal of Medicine. 2015 May 21;372(21):2018-28.

Sun Z, Hu S, Ge Y, Wang J, Duan S, Song J, et al. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features. Journal of X-ray Science and Technology. 2020 Jan 1;28(3):449-59.

Timmerman R, Paulus R, Galvin J, Michalski J, Straube W, Bradley J, Fakiris A, et al. Stereotactic body radiation therapy for inoperable early stage lung cancer. Jama. 2010 Mar 17;303(11):1070-6.

Lambin P, Leijenaar RT, Deist TM, Peerlings J, De Jong EE, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology. 2017 Dec;14(12):749-62.

Chin Snyder K, Kim J, Reding A, Fraser C, Gordon J, Ajlouni M, Movsas B, Chetty IJ. Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge‐based algorithm for treatment planning. Journal of applied clinical medical physics. 2016 Nov;17(6):263-75.




DOI: https://doi.org/10.37591/rrjodfdp.v10i1.1315

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Research & Reviews: A Journal of Drug Formulation, Development and Production