Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.
Citation: Meiling Sun, Changlei Cui. Transforming lung cancer care: Synergizing artificial intelligence and clinical expertise for precision diagnosis and treatment[J]. AIMS Bioengineering, 2023, 10(3): 331-361. doi: 10.3934/bioeng.2023020
Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.
[1] | Xia C, Dong X, Li H, et al. (2022) Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J 135: 584-590. https://doi.org/10.1097/CM9.0000000000002108 |
[2] | Kanwal M, Ding XJ, Cao Y (2017) Familial risk for lung cancer. Oncol Lett 13: 535-542. https://doi.org/10.3892/ol.2016.5518 |
[3] | Boloker G, Wang C, Zhang J (2018) Updated statistics of lung and bronchus cancer in United States. J Thorac Dis 10: 1158. https://doi.org/10.21037/jtd.2018.03.15 |
[4] | Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68: 7-30. https://doi.org/10.3322/caac.21442 |
[5] | Planchard D, Popat ST, Kerr K, et al. (2018) Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 29: iv192-iv237. https://doi.org/10.3322/caac.21442 |
[6] | Wadowska K, Bil-Lula I, Trembecki Ł, et al. (2020) Genetic markers in lung cancer diagnosis: a review. Int J Mol Sci 21: 4569. https://doi.org/10.3390/ijms21134569 |
[7] | Pennell NA, Arcila ME, Gandara DR, et al. (2019) Biomarker testing for patients with advanced non–small cell lung cancer: real-world issues and tough choices. Am Soc Clin Oncol Educ Book 39: 531-542. https://doi.org/10.1200/EDBK_237863 |
[8] | Khanna P, Blais N, Gaudreau PO, et al. (2017) Immunotherapy comes of age in lung cancer. Clin Lung Cancer 18: 13-22. https://doi.org/10.1016/j.cllc.2016.06.006 |
[9] | Hansen RN, Zhang Y, Seal B, et al. (2020) Long-term survival trends in patients with unresectable stage iii non-small cell lung cancer receiving chemotherapy and radiation therapy: a seer cancer registry analysis. BMC cancer 20: 1-6. https://doi.org/10.1186/s12885-020-06734-3 |
[10] | Bradley JD, Hu C, Komaki RR, et al. (2020) Long-term results of nrg oncology rtog 0617: standard-versus high-dose chemoradiotherapy with or without cetuximab for unresectable stage iii non–small-cell lung cancer. J Clin Oncol 38: 706. https://doi.org/10.1200/JCO.19.01162 |
[11] | Yoon SM, Shaikh T, Hallman M (2017) Therapeutic management options for stage iii non-small cell lung cancer. World J Clin Oncol 8: 1-20. https://doi.org/10.5306/wjco.v8.i1.1 |
[12] | Wang Y, Liu Z, Xu J, et al. (2022) Heterogeneous network representation learning approach for ethereum identity identification. IEEE Trans Comput Social Syst 10: 890. https://10.1109/TCSS.2022.3164719 |
[13] | Shi Y, Li L, Yang J, et al. (2023) Center-based transfer feature learning with classifier adaptation for surface defect recognition. Mech Syst Signal Process 188: 110001. https://doi.org/10.1016/j.ymssp.2022.110001 |
[14] | Shi Y, Li H, Fu X, et al. (2023) Self-powered difunctional sensors based on sliding contact-electrification and tribovoltaic effects for pneumatic monitoring and controlling. Nano Energy 110: 108339. https://doi.org/10.1016/j.nanoen.2023.108339 |
[15] | Tian C, Xu Z, Wang L, et al. (2023) Arc fault detection using artificial intelligence: challenges and benefits. Math Biosci Eng 20: 12404-12432. https://10.3934/mbe.2023552 |
[16] | Liu Z, Yang D, Wang Y, et al. (2023) Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks. Appl Soft Comput 135: 110040. https://doi.org/10.1016/j.asoc.2023.110040 |
[17] | Wang S, Yang DM, Rong R, et al. (2019) Artificial intelligence in lung cancer pathology image analysis. Cancers 11: 1673. https://doi.org/10.3390/cancers11111673 |
[18] | Asuntha A, Srinivasan A (2020) Deep learning for lung cancer detection and classification. Multimed Tools Appl 79: 7731-7762. https://doi.org/10.1007/s11042-019-08394-3 |
[19] | Riquelme D, Akhloufi MA (2020) Deep learning for lung cancer nodules detection and classification in ct scans. Ai 1: 28-67. https://doi.org/10.3390/ai1010003 |
[20] | Chiu HY, Chao HS, Chen YM (2022) Application of artificial intelligence in lung cancer. Cancers 14: 1370. https://doi.org/10.3390/cancers14061370 |
[21] | Dlamini Z, Francies FZ, Hull R, et al. (2020) Artificial intelligence (ai) and big data in cancer and precision oncology. Comput Struct Biotechnol J 18: 2300-2311. https://doi.org/10.1016/j.csbj.2020.08.019 |
[22] | Mann M, Kumar C, Zeng WF, et al. (2021) Artificial intelligence for proteomics and biomarker discovery. Cell Syst 12: 759-770. https://doi.org/10.1016/j.cels.2021.06.006 |
[23] | Subramanian M, Wojtusciszyn A, Favre L, et al. (2020) Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med 18: 1-12. https://doi.org/10.1186/s12967-020-02658-5 |
[24] | Schork NJ (2019) Artificial intelligence and personalized medicine. Precis Med Cancer Ther 178: 265-283. https://doi.org/10.1007/978-3-030-16391-4_11 |
[25] | Magrabi F, Ammenwerth E, McNair JB, et al. (2019) Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform 28: 128-134. https://doi.org/10.1055/s-0039-1677903 |
[26] | Kim MS, Park HY, Kho BG, et al. (2020) Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res 9: 507. https://doi.org/10.21037/tlcr.2020.04.11 |
[27] | Giordano C, Brennan M, Mohamed B, et al. (2021) Accessing artificial intelligence for clinical decision-making. Frontiers Digit Health 3: 645232. https://doi.org/10.3389/fdgth.2021.645232 |
[28] | Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al. (2021) Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. J Dent Sci 16: 482-492. https://doi.org/10.1016/j.jds.2020.05.022 |
[29] | Zhao J, Lv Y (2023) Output-feedback robust tracking control of uncertain systems via adaptive learning. Int J Control Autom Syst 21: 1108-1118. https://doi.org/10.1007/s12555-021-0882-6 |
[30] | Qi W, Su H (2022) A cybertwin based multimodal network for ecg patterns monitoring using deep learning. IEEE Trans Industr Inform 18: 6663-6670. https://doi.org/10.1109/TII.2022.3159583 |
[31] | Su H, Qi W, Chen J, et al. (2022) Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint. IEEE Trans Fuzzy Syst 30: 1564-1573. https://doi.org/10.1109/TFUZZ.2022.3157075 |
[32] | Kadir T, Gleeson F (2018) Lung cancer prediction using machine learning and advanced imaging techniques. Transl Lung Cancer Res 7: 304. https://doi.org/10.21037/tlcr.2018.05.15 |
[33] | Tuncal K, Sekeroglu B, Ozkan C (2020) Lung cancer incidence prediction using machine learning algorithms. J Adv Inform Technol Vol 11: 91-96. https://doi.org/10.12720/jait.11.2.91-96 |
[34] | Tu SJ, Wang CW, Pan KT, et al. (2018) Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Phys Med Biol 63: 065005. https://doi.org/10.1088/1361-6560/aaafab |
[35] | Li Y, Lu L, Xiao M, et al. (2018) CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study. Sci Rep 8: 17913. https://doi.org/10.1038/s41598-018-36421-0 |
[36] | McBee MP, Awan OA, Colucci AT, et al. (2018) Deep learning in radiology. Acad Radiol 25: 1472-1480. https://doi.org/10.1016/j.acra.2018.02.018 |
[37] | Yasaka K, Abe O (2018) Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med 15: e1002707. https://doi.org/10.1371/journal.pmed.1002707 |
[38] | Hua KL, Hsu CH, Hidayati SC, et al. (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8: 2015-2022. https://doi.org/10.2147/OTT.S80733 |
[39] | Cengil E, Cinar A (2018) A deep learning based approach to lung cancer identification. 2018 International conference on artificial intelligence and data processing (IDAP) 2018: 1-5. https://doi.org/10.1109/IDAP.2018.8620723 |
[40] | Coudray N, Ocampo PS, Sakellaropoulos T, et al. (2018) Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med 24: 1559-1567. https://doi.org/10.1038/s41591-018-0177-5 |
[41] | Thawani R, McLane M, Beig N, et al. (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung cancer 115: 34-41. https://doi.org/10.1016/j.lungcan.2017.10.015 |
[42] | Su H, Qi W, Schmirander Y, et al. (2022) A human activity-aware shared control solution for medical human–robot interaction. Assem Autom 42: 388-394. https://doi.org/10.1108/AA-12-2021-0174 |
[43] | Su H, Qi W, Hu Y, et al. (2020) An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators. IEEE Trans Industr Inform 18: 1864-1872. https://10.1109/TII.2020.3036693 |
[44] | Nair JKR, Saeed UA, McDougall CC, et al. (2021) Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer. Can Assoc Radiol J 72: 109-119. https://doi.org/10.1177/0846537119899526 |
[45] | Singal G, Miller PG, Agarwala V, et al. (2019) Association of patient characteristics and tumor genomics with clinical outcomes among patients with non–small cell lung cancer using a clinicogenomic database. Jama 321: 1391-1399. https://doi.org/10.1001/jama.2019.3241 |
[46] | Huang S, Yang J, Shen N, et al. (2023) Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 89: 30-37. https://doi.org/10.1016/j.semcancer.2023.01.006 |
[47] | Petousis P, Winter A, Speier W, et al. (2019) Using sequential decision making to improve lung cancer screening performance. Ieee Access 7: 119403-119419. https://doi.org/10.1109/ACCESS.2019.2935763 |
[48] | Tortora M, Cordelli E, Sicilia R, et al. (2021) Deep reinforcement learning for fractionated radiotherapy in non-small cell lung carcinoma. Artif Intell Med 119: 102137. https://doi.org/10.1016/j.artmed.2021.102137 |
[49] | Pei Q, Luo Y, Chen Y, et al. (2022) Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med 60: 1974-1983. https://doi.org/10.1515/cclm-2022-0291 |
[50] | Wang M, Herbst RS, Boshoff C (2021) Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 27: 1345-1356. https://doi.org/10.1038/s41591-021-01450-2 |
[51] | Wang L (2022) Deep learning techniques to diagnose lung cancer. Cancers 14: 5569. https://doi.org/10.3390/cancers14225569 |
[52] | Bi WL, Hosny A, Schabath MB, et al. (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69: 127-157. https://doi.org/10.3322/caac.21552 |
[53] | Abid MMN, Zia T, Ghafoor M, et al. (2021) Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing 453: 299-311. https://doi.org/10.1016/j.neucom.2020.06.144 |
[54] | Gu Y, Lu X, Yang L, et al. (2018) Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med 103: 220-231. https://doi.org/10.1016/j.compbiomed.2018.10.011 |
[55] | Setio AAA, Ciompi F, Litjens G, et al. (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35: 1160-1169. https://doi.org/10.1109/TMI.2016.2536809 |
[56] | Xie H, Yang D, Sun N, et al. (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 85: 109-119. https://doi.org/10.1016/j.patcog.2018.07.031 |
[57] | Pezeshk A, Hamidian S, Petrick N, et al. (2018) 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomed Health Inform 23: 2080-2090. https://doi.org/10.1109/JBHI.2018.2879449 |
[58] | Toğaçar M, Ergen B, Cömert Z (2020) Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng 40: 23-39. https://doi.org/10.1016/j.bbe.2019.11.004 |
[59] | Ardila D, Kiraly AP, Bharadwaj S, et al. (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25: 954-961. https://doi.org/10.1038/s41591-019-0447-x |
[60] | Teramoto A, Yamada A, Kiriyama Y, et al. (2019) Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network. Inform Med Unlocked 16: 100205. https://doi.org/10.1016/j.imu.2019.100205 |
[61] | Onishi Y, Teramoto A, Tsujimoto M, et al. (2019) Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. Biomed Res Int 2019: 6051939. https://doi.org/10.1155/2019/6051939 |
[62] | Bharati S, Podder P, Mondal MRH (2020) Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlocked 20: 100391. https://doi.org/10.1016/j.imu.2020.100391 |
[63] | Ke Q, Zhang J, Wei W, et al. (2019) A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Syst Appl 126: 218-232. https://doi.org/10.1016/j.eswa.2019.01.060 |
[64] | Gordienko Y, Gang P, Hui J, et al. (2019) Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. Advances in Computer Science for Engineering and Education 2019: 638-647. https://doi.org/10.48550/arXiv.1712.07632 |
[65] | Ausawalaithong W, Thirach A, Marukatat S, et al. (2018) Automatic lung cancer prediction from chest X-ray images using the deep learning approach. 2018 11th biomedical engineering international conference (BMEiCON) 2018: 1-5. https://doi.org/10.1109/BMEiCON.2018.8609997 |
[66] | Philip B, Jain A, Wojtowicz M, et al. (2023) Current investigative modalities for detecting and staging lung cancers: a comprehensive summary. Indian J Thorac Cardiovasc Surg 39: 42-52. https://doi.org/10.1007/s12055-022-01430-2 |
[67] | Bhandary A, Prabhu GA, Rajinikanth V, et al. (2020) Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recogn Lett 129: 271-278. https://doi.org/10.1016/j.patrec.2019.11.013 |
[68] | Li X, Shen L, Xie X, et al. (2020) Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection. Artif Intell Med 103: 101744. https://doi.org/10.1016/j.artmed.2019.101744 |
[69] | Sim AJ, Kaza E, Singer L, et al. (2020) A review of the role of mri in diagnosis and treatment of early stage lung cancer. Clin Transl Radiat Oncol 24: 16-22. https://doi.org/10.1016/j.ctro.2020.06.002 |
[70] | Rustam Z, Hartini S, Pratama RY, et al. (2020) Analysis of architecture combining convolutional neural network (cnn) and kernel k-means clustering for lung cancer diagnosis. Int J Adv Sci Eng Inf Technol 10: 1200-1206. https://doi.org/10.18517/ijaseit.10.3.12113 |
[71] | Isaksson LJ, Raimondi S, Botta F, et al. (2020) Effects of MRI image normalization techniques in prostate cancer radiomics. Phys Med 71: 7-13. https://doi.org/10.1016/j.ejmp.2020.02.007 |
[72] | Rahman MM, Sazzad TMS, Ferdaus FS (2021) Automated detection of lung cancer using MRI images. 2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI) 2021: 1-5. https://doi.org/10.1109/STI53101.2021.9732603 |
[73] | Wahengbam M, Sriram M (2023) MRI Lung Tumor Segmentation and Classification Using Neural Networks. International Conference on Communication, Electronics and Digital Technology 2023: 605-616. https://doi.org/10.1007/978-981-99-1699-3_42 |
[74] | Baxi V, Edwards R, Montalto M, et al. (2022) Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 35: 23-32. https://doi.org/10.1038/s41379-021-00919-2 |
[75] | Acs B, Rantalainen M, Hartman J (2020) Artificial intelligence as the next step towards precision pathology. J Intern Med 288: 62-81. https://doi.org/10.1111/joim.13030 |
[76] | Garg S, Garg S (2020) Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps. Proceedings of the 2020 3rd Artificial Intelligence and Cloud Computing Conference 2020: 38-45. https://doi.org/10.1145/3442536.3442543 |
[77] | Wang S, Chen A, Yang L, et al. (2018) Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep 8: 10393. https://doi.org/10.1038/s41598-018-27707-4 |
[78] | Šarić M, Russo M, Stella M, et al. (2019) CNN-based method for lung cancer detection in whole slide histopathology images. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) 2019: 1-4. https://doi.org/10.23919/SpliTech.2019.8783041 |
[79] | Sha L, Osinski BL, Ho IY, et al. (2019) Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J Pathol Inform 10: 24. https://doi.org/10.4103/jpi.jpi_24_19 |
[80] | Gertych A, Swiderska-Chadaj Z, Ma Z, et al. (2019) Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep 9: 1483. https://doi.org/10.1038/s41598-018-37638-9 |
[81] | Yu KH, Wang F, Berry GJ, et al. (2020) Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc 27: 757-769. https://doi.org/10.1093/jamia/ocz230 |
[82] | Tiwari A, Trivedi R, Lin SY (2022) Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci 29: 1-27. https://doi.org/10.1186/s12929-022-00866-3 |
[83] | Saltz J, Gupta R, Hou L, et al. (2018) Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 23: 181-193. https://doi.org/10.1016/j.celrep.2018.03.086 |
[84] | Yi F, Yang L, Wang S, et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC bioinformatics 19: 1-9. https://doi.org/10.1186/s12859-018-2055-z |
[85] | Wang S, Wang T, Yang L, et al. (2019) ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 50: 103-110. https://doi.org/10.1016/j.ebiom.2019.10.033 |
[86] | Rączkowski Ł, Paśnik I, Kukiełka M, et al. (2022) Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer. BMC cancer 22: 1001. https://doi.org/10.1186/s12885-022-10081-w |
[87] | Nooreldeen R, Bach H (2021) Current and future development in lung cancer diagnosis. Int J Mol Sci 22: 8661. https://doi.org/10.3390/ijms22168661 |
[88] | Wang S, Zimmermann S, Parikh K, et al. (2019) Current diagnosis and management of small-cell lung cancer. Mayo Clin Proc 94: 1599-1622. https://doi.org/10.1016/j.mayocp.2019.01.034 |
[89] | Li B, Zhu L, Lu C, et al. (2021) circNDUFB2 inhibits non-small cell lung cancer progression via destabilizing IGF2BPs and activating anti-tumor immunity. Nat Commun 12: 295. https://doi.org/10.1038/s41467-020-20527-z |
[90] | Xu Y, Wang Q, Xie J, et al. (2021) The predictive value of clinical and molecular characteristics or immunotherapy in non-small cell lung cancer: a meta-analysis of randomized controlled trials. Front Oncol 11: 732214. https://doi.org/10.3389/fonc.2021.732214 |
[91] | Xiao Y, Wu J, Lin Z, et al. (2018) A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed 153: 1-9. https://doi.org/10.1016/j.cmpb.2017.09.005 |
[92] | Seijo LM, Peled N, Ajona D, et al. (2019) Biomarkers in lung cancer screening: achievements, promises, and challenges. J Thorac Oncol 14: 343-357. https://doi.org/10.1016/j.jtho.2018.11.023 |
[93] | Yuan F, Lu L, Zou Q (2020) Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochim Biophys Acta-Mol Basis Dis 1866: 165822. https://doi.org/10.1016/j.bbadis.2020.165822 |
[94] | Matsubara T, Ochiai T, Hayashida M, et al. (2019) Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. J Bioinf Comput Biol 17: 1940007. https://doi.org/10.1142/S0219720019400079 |
[95] | Wiesweg M, Mairinger F, Reis H, et al. (2020) Machine learning reveals a PD-L1–independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context. Eur J Cancer 140: 76-85. https://doi.org/10.1016/j.ejca.2020.09.015 |
[96] | Khalifa NEM, Taha MHN, Ali DE, et al. (2020) Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach. IEEE Access 8: 22874-22883. https://doi.org/10.1109/ACCESS.2020.2970210 |
[97] | Wang W, Ding M, Duan X, et al. (2019) Diagnostic value of plasma microRNAs for lung cancer using support vector machine model. J Cancer 10: 5090. https://doi.org/10.7150/jca.30528 |
[98] | Selvanambi R, Natarajan J, Karuppiah M, et al. (2020) Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization. Neural Comput Appl 32: 4373-4386. https://doi.org/10.1007/s00521-018-3824-3 |
[99] | Banaganapalli B, Mallah B, Alghamdi KS, et al. (2022) Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer. Plos one 17: e0274629. https://doi.org/10.1371/journal.pone.0274629 |
[100] | Tanaka I, Furukawa T, Morise M (2021) The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: Harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int 21: 1-14. https://doi.org/10.1186/s12935-021-02165-7 |
[101] | Choi Y, Qu J, Wu S, et al. (2020) Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Med Genomics 13: 1-15. https://doi.org/10.1186/s12920-020-00782-1 |
[102] | Khan A, Lee B (2021) Gene transformer: Transformers for the gene expression-based classification of lung cancer subtypes. arXiv preprint arXiv: 2108.11833. https://doi.org/10.48550/arXiv.2108.1183 |
[103] | Oka M, Xu L, Suzuki T, et al. (2021) Aberrant splicing isoforms detected by full-length transcriptome sequencing as transcripts of potential neoantigens in non-small cell lung cancer. Genome Biol 22: 1-30. https://doi.org/10.1186/s13059-020-02240-8 |
[104] | Martínez-Ruiz C, Black JRM, Puttick C, et al. (2023) Genomic–transcriptomic evolution in lung cancer and metastasis. Nature : 1-10. https://doi.org/10.1038/s41586-023-05706-4 |
[105] | Hofman P, Heeke S, Alix-Panabières C, et al. (2019) Liquid biopsy in the era of immuno-oncology: is it ready for prime-time use for cancer patients?. Ann Oncol 30: 1448-1459. https://doi.org/10.1093/annonc/mdz196 |
[106] | Ilie M, Benzaquen J, Hofman V, et al. (2017) Immunotherapy in non-small cell lung cancer: biological principles and future opportunities. Curr Mol Med 17: 527-540. https://doi.org/10.2174/1566524018666180222114038 |
[107] | Pantel K, Alix-Panabières C (2019) Liquid biopsy and minimal residual disease—latest advances and implications for cure. Nat Rev Clin Oncol 16: 409-424. https://doi.org/10.1038/s41571-019-0187-3 |
[108] | He X, Folkman L, Borgwardt K (2018) Kernelized rank learning for personalized drug recommendation. Bioinformatics 34: 2808-2816. https://doi.org/10.1093/bioinformatics/bty132 |
[109] | Luo S, Xu J, Jiang Z, et al. (2020) Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing. Pharmacol Res 160: 105037. https://doi.org/10.1016/j.phrs.2020.105037 |
[110] | Ciccolini J, Benzekry S, Barlesi F (2020) Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC. Br J Cancer 123: 337-338. https://doi.org/10.1038/s41416-020-0918-3 |
[111] | Mu W, Jiang L, Zhang JY, et al. (2020) Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun 11: 5228. https://doi.org/10.1038/s41467-020-19116-x |
[112] | Chang L, Wu J, Moustafa N, et al. (2021) AI-driven synthetic biology for non-small cell lung cancer drug effectiveness-cost analysis in intelligent assisted medical systems. IEEE J Biomed Health Inform 26: 5055-5066. https://doi.org/10.1109/JBHI.2021.3133455 |
[113] | Wang S, Yu H, Gan Y, et al. (2022) Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health 4: e309-e319. https://doi.org/10.1016/S2589-7500(22)00024-3 |
[114] | Khorrami M, Khunger M, Zagouras A, et al. (2019) Combination of peri-and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1: 180012. https://doi.org/10.1148/ryai.2019180012 |
[115] | Song P, Cui X, Bai L, et al. (2019) Molecular characterization of clinical responses to PD-1/PD-L1 inhibitors in non-small cell lung cancer: Predictive value of multidimensional immunomarker detection for the efficacy of PD-1 inhibitors in Chinese patients. Thorac Cancer 10: 1303-1309. https://doi.org/10.1111/1759-7714.13078 |
[116] | Yu KH, Berry GJ, Rubin DL, et al. (2017) Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst 5: 620-627. https://doi.org/10.1016/j.cels.2017.10.014 |
[117] | Lee TY, Huang KY, Chuang CH, et al. (2020) Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication. Comput Biol Chem 87: 107277. https://doi.org/10.1016/j.compbiolchem.2020.107277 |
[118] | She Y, Jin Z, Wu J, et al. (2020) Development and validation of a deep learning model for non–small cell lung cancer survival. JAMA Netw Open 3: e205842-e205842. https://doi.org/10.1001/jamanetworkopen.2020.5842 |
[119] | Emaminejad N, Qian W, Guan Y, et al. (2015) Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng 63: 1034-1043. https://doi.org/10.1109/TBME.2015.2477688 |
[120] | Liu WT, Wang Y, Zhang J, et al. (2018) A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma. Cancer Lett 425: 43-53. https://doi.org/10.1016/j.canlet.2018.03.043 |
[121] | Malik V, Dutta S, Kalakoti Y, et al. (2019) Multi-omics Integration based Predictive Model for Survival Prediction of Lung Adenocarcinaoma. 2019 Grace Hopper Celebration India (GHCI) : 1-5. https://doi.org/10.1109/GHCI47972.2019.9071831 |
[122] | Wang X, Duan H, Li X, et al. (2020) A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics. Phys Med Biol 65: 045006. https://doi.org/10.1088/1361-6560/ab6e51 |
[123] | Johnson M, Albizri A, Simsek S (2022) Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis. Ann Oper Res 308: 275--305. https://doi.org/10.1007/s10479-020-03872-6 |
[124] | Ekins S, Puhl AC, Zorn KM, et al. (2019) Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 18: 435-441. https://doi.org/10.1038/s41563-019-0338-z |
[125] | Chandak T, Mayginnes JP, Mayes H, et al. (2020) Using machine learning to improve ensemble docking for drug discovery. Proteins 88: 1263-1270. https://publons.com/publon/10.1002/prot.25899 |
[126] | Houssein EH, Hosney ME, Oliva D, et al. (2020) A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng 133: 106656. https://doi.org/10.1016/j.compchemeng.2019.106656 |
[127] | Zhao L, Ciallella HL, Aleksunes LM, et al. (2020) Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 25: 1624-1638. https://doi.org/10.1016/j.drudis.2020.07.005 |
[128] | Zhavoronkov A, Ivanenkov YA, Aliper A, et al. (2019) Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37: 1038-1040. https://doi.org/10.1038/s41587-019-0224-x |
[129] | Bhuvaneshwari S, Sankaranarayanan K (2019) Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach. SAR QSAR Environ Res 30: 81-108. https://doi.org/10.1080/1062936X.2019.1566172 |
[130] | He G, Gong B, Li J, et al. (2018) An improved receptor-based pharmacophore generation algorithm guided by atomic chemical characteristics and hybridization types. Front Pharmacol 9: 1463. https://doi.org/10.3389/fphar.2018.01463 |
[131] | Yang H, Wierzbicki M, Du Bois DR, et al. (2018) X-ray crystallographic structure of a teixobactin derivative reveals amyloid-like assembly. J Am Chem Soc 140: 14028-14032. https://doi.org/10.1021/jacs.8b07709 |
[132] | Trebeschi S, Drago SG, Birkbak NJ, et al. (2019) Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol 30: 998-1004. https://doi.org/10.1093/annonc/mdz108 |
[133] | Wang Q, Xu J, Li Y, et al. (2018) Identification of a novel protein arginine methyltransferase 5 inhibitor in non-small cell lung cancer by structure-based virtual screening. Front Pharmacol 9: 173. https://doi.org/10.3389/fphar.2018.00173 |
[134] | Haredi Abdelmonsef A (2019) Computer-aided identification of lung cancer inhibitors through homology modeling and virtual screening. Egypt J Med Hum Genet 20: 1-14. https://doi.org/10.1186/s43042-019-0008-3 |
[135] | Shaik NA, Al-Kreathy HM, Ajabnoor GM, et al. (2019) Molecular designing, virtual screening and docking study of novel curcumin analogue as mutation (S769L and K846R) selective inhibitor for EGFR. Saudi J Biol Sci 26: 439-448. https://doi.org/10.1016/j.sjbs.2018.05.026 |
[136] | Udhwani T, Mukherjee S, Sharma K, et al. (2019) Design of PD-L1 inhibitors for lung cancer. Bioinformation 15: 139. https://doi.org/10.6026/97320630015139 |
[137] | Patel HM, Ahmad I, Pawara R, et al. (2021) In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): a combined approach of structure-based virtual screening and molecular dynamics simulation. J Biomol Struct Dyn 39: 1491-1505. https://doi.org/10.1080/07391102.2020.1734092 |
[138] | Su H, Mariani A, Ovur SE, et al. (2021) Toward teaching by demonstration for robot-assisted minimally invasive surgery. IEEE Trans Autom Sci Eng 18: 484-494. https://doi.org/10.1109/TASE.2020.3045655 |
[139] | Qi W, Aliverti A (2019) A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity. IEEE J Biomed Health Inf 24: 2199-2207. https://doi.org/10.1109/JBHI.2019.2963048 |
[140] | Khan B, Fatima H, Qureshi A, et al. (2023) Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomed Mater Devices 2023: 1-8. https://doi.org/10.1007/s44174-023-00063-2 |
[141] | Hanif A, Zhang X, Wood S (2021) A survey on explainable artificial intelligence techniques and challenges. 2021 IEEE 25th international enterprise distributed object computing workshop (EDOCW) 2021: 81-89. https://doi.org/10.1109/EDOCW52865.2021.00036 |
[142] | Dicuonzo G, Donofrio F, Fusco A, et al. (2023) Healthcare system: Moving forward with artificial intelligence. Technovation 120: 102510. https://doi.org/10.1016/j.technovation.2022.102510 |
[143] | McLennan S, Fiske A, Tigard D, et al. (2022) Embedded ethics: a proposal for integrating ethics into the development of medical AI. BMC Med Ethics 23: 6. https://doi.org/10.1186/s12910-022-00746-3 |
[144] | Steffens D, Pocovi NC, Bartyn J, et al. (2023) Feasibility, reliability, and safety of remote five times sit to stand test in patients with gastrointestinal cancer. Cancers 15: 2434. https://doi.org/10.3390/cancers15092434 |
[145] | Askin S, Burkhalter D, Calado G, et al. (2023) Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol 13: 203-213. https://doi.org/10.1007/s12553-023-00738-2 |
[146] | Albahri AS, Duhaim AM, Fadhel MA, et al. (2023) A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Inf Fusion 96: 156-191. https://doi.org/10.1016/j.inffus.2023.03.008 |