Harnessing AI for Precision Oncology: Transformative Advances in Non-Small Cell Lung Cancer Treatment
Abstract
The present systematic review scrutinizes the developing function of Artificial Intelligence (AI) in the planning and optimization of treatment for Non-Small Cell Lung Cancer (NSCLC). With an emphasis on patient-tailored therapy planning and improving treatment efficacy using cutting-edge deep learning algorithms, we carefully chose and examined nine excellent research pieces showing AI’s incorporation in NSCLC management. This research demonstrates AI’s capacity to process intricate clinical, radiomic, and genomic data and provide tailored therapy plans.AI technologies, such as deep learning models and machine learning, have shown exceptional promise in predicting immune responses to first treatments. This might result in a radical shift in how non-small cell lung cancer is managed. This review underscores AI’s transformative impact on predicting treatment outcomes, optimizing therapy regimens, and enhancing decision-making processes in NSCLC treatment. The collective findings from these studies reveal a significant trend towards personalized medical approaches, showcasing AI’s capacity to process extensive datasets and forecast individual patient reactions. This contributes to increased treatment efficacy and improved health outcomes for patients.However, this review also addresses the challenges and limitations of current AI applications, emphasizing the need for further research and development in this field. Integrating AI into NSCLC treatment heralds a new era of precision oncology, paving the way for more accurate, efficient, and patient-centric cancer care.References
M. A. Thanoon, M. A. Zulkifley, M. A. A. Mohd Zainuri, and S. R. Abdani, “A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images,” Diagnostics, vol. 13, no. 16, p. 2617, Jan. 2023, number: 16 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/2075-4418/13/16/2617
M. Liu, J. Wu, N. Wang, X. Zhang, Y. Bai, J. Guo, L. Zhang, S. Liu, and K. Tao, “The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis,” PLoS One, vol. 18, no. 3, p. e0273445, Mar. 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10035910/
R. El Sabrouty, A. Elouadi, and M. A. S. Karimoune, “Care Pathway Model for Patients with Localized Lung Adenocarcinoma,” in International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023), M. Ezziyyani, J. Kacprzyk, and V. E. Balas, Eds. Cham: Springer Nature Switzerland, 2024, pp. 344–354.
C. Li, S. Lei, L. Ding, Y. Xu, X. Wu, H. Wang, Z. Zhang, T. Gao, Y. Zhang, and L. Li, “Global burden and trends of
lung cancer incidence and mortality,” Chinese Medical Journal, vol. 136, no. 13, p. 1583, Jul. 2023. [Online]. Available:
https://journals.lww.com/cmj/fulltext/2023/07050/global burden and trends of lung cancer incidence.10.aspx
Stat., Optim. Inf. Comput. Vol. x, Month 202x
D. A. Siegel, S. A. Fedewa, S. J. Henley, L. A. Pollack, and A. Jemal, “Proportion of Never Smokers Among Men and Women With Lung Cancer in 7 US States,” JAMA Oncol, vol. 7, no. 2, p. 302, Feb. 2021. [Online]. Available: https://jamanetwork.com/journals/jamaoncology/fullarticle/2773380
“World Lung Cancer Day 2023: Know the Types and Treatment of Lung Cancer,” Aug. 2023. [Online]. Available: https:
//www.timesnownews.com/health/world-lung-cancer-day-2023-know-the-types-and-treatment-of-lung-cancer-article-102301347
“Lung cancer statistics | World Cancer Research Fund International.” [Online]. Available: https://www.wcrf.org/cancer-trends/lung-cancer-statistics/
B. Audelan, “Probabilistic segmentation modelling and deep learning-based lung cancer screening.”
“Lung cancer.” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/lung-cancer
M. B. Lebrett, E. J. Crosbie, M. J. Smith, E. R. Woodward, D. G. Evans, and P. A. J. Crosbie, “Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors,” J Med Genet, vol. 58, no. 4, pp. 217–226, Apr. 2021. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005792/
C. Choua¨ıd, S. Gendarme, and J.-B. Auliac, “Artificial intelligence to finally enable precision medicine for the management of resected non-small-cell lung cancer,” Ann Oncol, vol. 34, no. 7, pp. 565–566, Jul. 2023.
L. Xiong, C. Zhu, Y. Lu, M. Chen, and M. Li, “Serum THBS2 is a potential biomarker for the diagnosis of non-small
cell lung cancer,” J Cancer Res Clin Oncol, vol. 149, no. 17, pp. 15 671–15 677, Nov. 2023. [Online]. Available:
https://doi.org/10.1007/s00432-023-05330-9
“Time to Treatment Impacts on Early-Stage Lung Cancer Surgery Outcomes,” Mar. 2022. [Online]. Available: https://www.jons-online.com/lung-cancer-monthly-minutes/4466-time-to-treatment-impacts-on-early-stage-lung-cancer-surgery-outcomes
J. M. Archer, M. T. Truong, G. S. Shroff, M. C. B. Godoy, and E. M. Marom, “Imaging of Lung Cancer Staging,” Semin
Respir Crit Care Med, vol. 43, no. 6, pp. 862–873, Dec. 2022, publisher: Thieme Medical Publishers, Inc. [Online]. Available:
http://www.thieme-connect.de/DOI/DOI?10.1055/s-0042-1753476
Y. Kl, T. Ys, Y. Hc, L. Cj, K. Pc, L. Mr, H. Ct, K. Lc, W. Jy, H. Cc, S. Jy, and Y. Cj, “Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study,” BMJ open respiratory research, vol. 10, no. 1, Aug. 2023, publisher: BMJ Open Respir Res. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/37532473/
M. Cellina, M. C`e, G. Irmici, V. Ascenti, N. Khenkina, M. Toto-Brocchi, C. Martinenghi, S. Papa, and G. Carrafiello, “Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future,” Diagnostics (Basel), vol. 12, no. 11, p. 2644, Oct. 2022.
M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. M. Grimshaw, A. Hr´objartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. Stewart, J. Thomas, A. C. Tricco, V. A. Welch, P. Whiting, and D. Moher, “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, Mar. 2021.
Y. Shi, H. Wang, X. Yao, J. Li, J. Liu, Y. Chen, L. Liu, and J. Xu, “Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study,” BMC Medical Informatics and Decision Making, vol. 23, no. 1, p. 197, Sep. 2023. [Online]. Available: https://doi.org/10.1186/s12911-023-02266-5
A. Hosny, D. S. Bitterman, C. V. Guthier, J. M. Qian, H. Roberts, S. Perni, A. Saraf, L. C. Peng, I. Pashtan, Z. Ye, B. H. Kann,
D. E. Kozono, D. Christiani, P. J. Catalano, H. J. W. L. Aerts, and R. H. Mak, “Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study,” Lancet Digit Health, vol. 4, no. 9, pp. e657–e666, Sep. 2022.
H. Tong, J. Sun, J. Fang, M. Zhang, H. Liu, R. Xia, W. Zhou, K. Liu, and X. Chen, “A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study,” Front Immunol, vol. 13, p. 859323, 2022.
Z. Liao, R. Zheng, N. Li, and G. Shao, “Development and validation of a risk model with variables related to non-small cell lung cancer in patients with pulmonary nodules: a retrospective study,” BMC Cancer, vol. 23, p. 872, Sep. 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506295/
Y. She, B. He, F.Wang, Y. Zhong, T.Wang, Z. Liu, M. Yang, B. Yu, J. Deng, X. Sun, C.Wu, L. Hou, Y. Zhu, Y. Yang, H. Hu, D. Dong, C. Chen, and J. Tian, “Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study,” EBioMedicine, vol. 86, p. 104364, Dec. 2022.
K. Deng, L. Wang, Y. Liu, X. Li, Q. Hou, M. Cao, N. N. Ng, H. Wang, H. Chen, K. W. Yeom, M. Zhao, N. Wu, P. Gao, J. Shi, Z. Liu, W. Li, J. Tian, and J. Song, “A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study,” EClinicalMedicine, vol. 51, p. 101541, Sep. 2022.
M. B. Saad, L. Hong, M. Aminu, N. I. Vokes, P. Chen, M. Salehjahromi, K. Qin, S. J. Sujit, X. Lu, E. Young, Q. Al-Tashi, R. Qureshi, C. C. Wu, B. W. Carter, S. H. Lin, P. P. Lee, S. Gandhi, J. Y. Chang, R. Li, M. F. Gensheimer, H. A. Wakelee, J. W. Neal, H.-S. Lee, C. Cheng, V. Velcheti, Y. Lou, M. Petranovic, W. Rinsurongkawong, X. Le, V. Rinsurongkawong, A. Spelman, Y. Y. Elamin, M. V. Negrao, F. Skoulidis, C. M. Gay, T. Cascone, M. B. Antonoff, B. Sepesi, J. Lewis, I. I. Wistuba, J. D. Hazle, C. Chung, D. Jaffray, D. L. Gibbons, A. Vaporciyan, J. J. Lee, J. V. Heymach, J. Zhang, and J. Wu, “Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study,” Lancet Digit Health, vol. 5, no. 7, pp. e404–e420, Jul. 2023.
K. Rounis, D. Makrakis, C. Papadaki, A. Monastirioti, L. Vamvakas, K. Kalbakis, K. Gourlia, I. Xanthopoulos, I. Tsamardinos, D. Mavroudis, and S. Agelaki, “Prediction of outcome in patients with non-small cell lung cancer treated with second line PD-1/PDL-1 inhibitors based on clinical parameters: Results from a prospective, single institution study,” PLoS One, vol. 16, no. 6, p.e0252537, 2021.
H. Pan, N. Zou, Y. Tian, H. Zhu, J. Zhang, W. Jin, Z. Gu, J. Ning, Z. Li, W. Kong, L. Jiang, J. Huang, and Q. Luo, “Short-term outcomes of robot-assisted versus video-assisted thoracoscopic surgery for non-small cell lung cancer patients with neoadjuvant immunochemotherapy: a single-center retrospective study,” Front Immunol, vol. 14, p. 1228451, 2023.
B. Ricciuti, S. E. Dahlberg, A. Adeni, L. M. Sholl, M. Nishino, and M. M. Awad, “Immune Checkpoint Inhibitor Outcomes for Patients With Non–Small-Cell Lung Cancer Receiving Baseline Corticosteroids for Palliative Versus Nonpalliative Indications,” JCO, vol. 37,no. 22, pp. 1927–1934, Aug. 2019, publisher: Wolters Kluwer. [Online]. Available: https://ascopubs.org/doi/10.1200/JCO.19.00189
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