A COMPREHENSIVE REVIEW OF INTEGRATING AI AND MEDICAL IMAGING IN LUNG CANCER DIAGNOSIS

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Mehrnaz Mostafavi
Mahtab Shabani

Keywords

Abstract

Lung cancer, a leading cause of global malignancy-related deaths, poses challenges due to late-stage diagnoses and diverse features in imaging and histopathology. Traditional approaches relying on clinical trials and expert opinions lead to time-consuming processes. Integrating artificial intelligence (AI) addresses these challenges, employing data-driven algorithms for prediction and classification. As AI subclasses, machine learning (ML) and deep learning offer sophisticated models that overcome historical computational obstacles. AI is crucial in lung cancer detection, spanning screening to diagnosis, especially in asymptomatic cases. Challenges in lung cancer screening underscore the need for accurate methods. AI improves the accuracy of low-dose computed tomography (LDCT) for lung cancer detection, with computer-aided diagnosis systems and AI-based programs enhancing radiologists' sensitivity and reducing false-negative rates. The narrative review explores AI applications in lung cancer detection, focusing on its role in the clinical workflow. The article introduces a deep-learning framework for chest radiography analysis, featuring a novel approach with a deep convolutional neural network (DCNN) algorithm-based software. The DCNN aids radiologists in detecting malignant pulmonary nodules, exhibiting improved sensitivity and reduced false-positive rates. While the study suggests potential clinical effectiveness, challenges in generalizability exist. In chest CT screening, AI algorithms match radiologists' performance levels. Collaborative approaches, such as concurrent reading and second-reader paradigms, show increased sensitivity and reduced interpretation time. Ongoing research and validation are emphasized for practical integration into routine clinical practice. Whole Slide Imaging (WSI) integration with AI and deep learning transforms cytopathology, enhancing pathologists' efficiency. The combination facilitates tumor cell recognition and segmentation and predicts gene mutations, envisioning AI assisting pathologists in routine tasks for personalized treatment decisions.


The future trajectory of AI in lung cancer focuses on overcoming challenges through federated learning and proposes "Medomics" for holistic insights. Despite promising results, real-world implementation faces barriers requiring infrastructure development. This comprehensive review provides insights into the evolving landscape of AI applications in lung cancer detection, showcasing advancements and highlighting avenues for future developments with the potential to revolutionize lung cancer diagnosis and treatment.


 

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References

Cancer. Available online: https://www.who.int/news-room/fact-sheets/detail/cancer (accessed on 29 November 2023).
2. Migliore, M.; Palmucci, S.; Nardini, M.; Basile, A. Imaging patterns of early stage lung cancer for the thoracic surgeon. J. Thorac. Dis. 2020, 12, 3349–3356. [CrossRef] [PubMed]
3. Travis, W.D.; Brambilla, E.; Nicholson, A.G.; Yatabe, Y.; Austin, J.H.M.; Beasley, M.B.; Chirieac, L.R.; Dacic, S.; Duhig, E.; Flieder,
4. D.B. et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J. Thorac. Oncol. 2015, 10, 1243–1260. [CrossRef]
5. Nicholson, A.G.; Tsao, M.S.; Beasley, M.B.; Borczuk, A.C.; Brambilla, E.; Cooper,W.A.; Dacic, S.; Jain, D.; Kerr, K.M.; Lantuejoul, S.; et al. The 2021 WHO Classification of Lung Tumors: Impact of advances since 2015. J. Thorac. Oncol. 2021, 17, 362–387. [CrossRef]
6. Klang, E. Deep learning and medical imaging. J. Thorac. Dis. 2018, 10, 1325–1328. [CrossRef]
7. Lawson, C.E.; Marti, J.M.; Radivojevic, T.; Jonnalagadda, S.V.R.; Gentz, R.; Hillson, N.J.; Peisert, S.; Kim, J.; Simmons, B.A.; Petzold, C.J.; et al. Machine learning for metabolic engineering: A review. Metab. Eng. 2021, 63, 34–60. [CrossRef]
8. Leiserson, C.E.; Thompson, N.C.; Emer, J.S.; Kuszmaul, B.C.; Lampson, B.W.; Sanchez, D.; Schardl, T.B. There’s plenty of room at the Top: What will drive computer performance after Moore’s law? Science 2020, 368, eaam9744. [CrossRef]
9. Shalf, J. The future of computing beyond Moore’s Law. Philos. Trans. A Math. Phys. Eng. Sci. 2020, 378, 20190061. [CrossRef]
10. Somvanshi, M.; Chavan, P.; Tambade, S.; Shinde, S. A review of machine learning techniques using decision tree and support vector machine. In Proceedings of the 2016 International Conference on Computing Communication Control and Automation(ICCUBEA), Pune, India, 12–13 August 2016; pp. 1–7
11. Sesen, M.B.; Nicholson, A.E.; Banares-Alcantara, R.; Kadir, T.; Brady, M. Bayesian networks for clinical decision support in lung cancer care. PLoS ONE 2013, 8, e82349. [CrossRef]
12. Gao, R.; Huo, Y.; Bao, S.; Tang, Y.; Antic, S.L.; Epstein, E.S.; Balar, A.B.; Deppen, S.; Paulson, A.B.; Sandler, K.L. Distanced LSTM: Time-distanced gates in long short-term memory models for lung cancer detection. In International Workshop on Machine Learning in Medical Imaging; Springer: New York, NY, USA, 2019; pp. 310–318.
13. In, K.-H.; Kwon, Y.-S.; Oh, I.-J.; Kim, K.-S.; Jung, M.-H.; Lee, K.-H.; Kim, S.-Y.; Ryu, J.-S.; Lee, S.-Y.; Jeong, E.-T. Lung cancer patients who are asymptomatic at diagnosis show favorable prognosis: A Korean Lung Cancer Registry Study. Lung. Cancer 2009, 64, 232–237. [CrossRef]
14. Quadrelli, S.; Lyons, G.; Colt, H.; Chimondeguy, D.; Buero, A. Clinical characteristics and prognosis of incidentally detected lung cancers. Int. J. Surg. Oncol. 2015, 2015, 287604. [CrossRef]
15. Melamed, M.R.; Flehinger, B.J.; Zaman, M.B.; Heelan, R.T.; Perchick, W.A.; Martini, N. Screening for early lung cancer. Results of the Memorial Sloan-Kettering study in New York. Chest 1984, 86, 44–53. [CrossRef]
16. Hocking,W.G.; Hu, P.; Oken, M.M.;Winslow, S.D.; Kvale, P.A.; Prorok, P.C.; Ragard, L.R.; Commins, J.; Lynch, D.A.; Andriole, G.L.; et al. Lung cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. J. Natl. Cancer Inst. 2010, 102, 722–731. [CrossRef]
17. Chu, G.C.W.; Lazare, K.; Sullivan, F. Serum and blood based biomarkers for lung cancer screening: A systematic review. BMC Cancer 2018, 18, 181. [CrossRef]
18. Montani, F.; Marzi, M.J.; Dezi, F.; Dama, E.; Carletti, R.M.; Bonizzi, G.; Bertolotti, R.; Bellomi, M.; Rampinelli, C.; Maisonneuve, P.; et al. miR-Test: A blood test for lung cancer early detection. J. Natl. Cancer Inst. 2015, 107, djv063. [CrossRef]
20. Lodwick, G.S.; Keats, T.E.; Dorst, J.P. The Coding of Roentgen Images for Computer Analysis as Applied to Lung Cancer. Radiology1963, 81, 185–200. [CrossRef]
21. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365(5):395–409.
22. White CS, Salis AI, Meyer CA. Missed lung cancer on chest radiography and computed tomography: imaging and medicolegal issues. J Thorac Imaging 1999;14(1):63–68.
23. Fardanesh M, White C. Missed lung cancer on chest radiography and computed tomography. Semin Ultrasound CT MR 2012;33(4):280–287.
24. Samuel S, Kundel HL, Nodine CF, Toto LC. Mechanism of satisfaction of search: eye position recordings in the reading of chest radiographs. Radiology 1995;194(3):895–902.
25. Del Ciello A, Franchi P, Contegiacomo A, Cicchetti G, Bonomo L, Larici AR. Missed lung cancer: when, where, and why? Diagn Interv Radiol 2017;23(2):118–126.
26. Kundel HL. Predictive value and threshold detectability of lung tumors. Radiology 1981;139(1):25–29.
27. Drayer JA, Vittitoe NF, Vargas-Voracek R, Baydush AH, Ravin CE, Floyd CE Jr. Characteristics of regions suspicious for pulmonary nodules at chest radiography. Acad Radiol 1998;5(9):613–619.
28. Austin JH, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect. Radiology 1992;182(1):115–122.
29. Kakeda S, Moriya J, Sato H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am J Roentgenol 2004;182(2):505–510.
30. de Hoop B, De Boo DW, Gietema HA, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology 2010;257(2):532–540.
31. Novak RD, Novak NJ, Gilkeson R, Mansoori B, Aandal GE. A comparison of computer-aided detection (CAD) effectiveness in pulmonary nodule iden tification using different methods of bone suppression in chest radiographs J Digit Imaging 2013;26(4):651–656.
32. van Beek EJR, Mullan B, Thompson B. Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images: a prospective study. Acad Radiol 2008;15(5):571–575.
33. De Boo DW, Uffmann M, Weber M, et al. Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study. Acad Radiol 2011;18(12):1507–1514.
34. White CS, Flukinger T, Jeudy J, Chen JJ. Use of a computer-aided detec tion system to detect missed lung cancer at chest radiography. Radiology 2009;252(1):273–281.
35. Dellios N, Teichgraeber U, Chelaru R, Malich A, Papageorgiou IE. Computer aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph. J Clin Imaging Sci 2017;7:8.
36. Park SH, Han K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology 2018;286(3):800–809.
37. Mostafavi, M. and Shabani, M. (2024) Leveraging Nanotechnology for Addressing COVID-19: Revealing Antiviral Approaches and Hurdles. World Journal of Nano Science and Engineering, 14, 1-14. doi: 10.4236/wjnse.2024.141001
38. Mehrnaz Mostafavi, Mahsa Alizadeh, Mahtab Shaabani, Hamed Asadi, Metallic nanomaterials in cancer theranostics: A review of Iron oxide and Gold-based nanomaterials (2021) SDRP Journal of Nanotechnology & Material Science 3(1) doi:https://doi.org/10.25177/JNMS.3.1.RA.10736
39. Jaeger, S.; Candemir, S.; Antani, S.; Wang, Y.X.; Lu, P.X.; Thoma, G. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 2014, 4, 475–477. [CrossRef] [PubMed]
40.Nguyen, H.C.; Le, T.T.; Pham, H.; Nguyen, H.Q. VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays. arXiv 2021, arXiv:2107.01327.
41. Rajpurkar, P.; Irvin, J.; Zhu, K.; Yang, B.; Mehta, H.; Duan, T.; Ding, D.; Bagul, A.; Langlotz, C.; Shpanskaya, K. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv 2017, arXiv:1711.05225.
42. Kim, Y.-G.; Cho, Y.;Wu, C.-J.; Park, S.; Jung, K.-H.; Seo, J.B.; Lee, H.J.; Hwang, H.J.; Lee, S.M.; Kim, N. Short-term reproducibility of pulmonary nodule and mass detection in chest radiographs: Comparison among radiologists and four different computer-aided detections with convolutional neural net. Sci. Rep. 2019, 9, 18738. [CrossRef] [PubMed]
43. Tam, M.; Dyer, T.; Dissez, G.; Morgan, T.N.; Hughes, M.; Illes, J.; Rasalingham, R.; Rasalingham, S. Augmenting lung cancer diagnosis on chest radiographs: Positioning artificial intelligence to improve radiologist performance. Clin. Radiol. 2021, 76, 607–614. [CrossRef] [PubMed]
44. Kim, J.H.; Han, S.G.; Cho, A.; Shin, H.J.; Baek, S.-E. Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: A prospective interventional simulation- based study. BMC Med. Inform. Decis. Mak. 2021, 21, 311. [CrossRef]
45. Van Ginneken, B.; Armato, S.G., III; de Hoop, B.; van Amelsvoort-van de Vorst, S.; Duindam, T.; Niemeijer, M.; Murphy, K.; Schilham, A.; Retico, A.; Fantacci, M.E. Comparing and combining algorithms for computer- aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study. Med. Image Anal. 2010, 14, 707–722. [CrossRef]
46. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60:84-90. [Crossref]
47. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88. [Crossref] [PubMed]
48. Willemink MJ, Noël PB. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 2019;29:2185-95. [Crossref] [PubMed]
49. Paks M, Leong P, Einsiedel P, et al. Ultralow dose CT for follow-up of solid pulmonary nodules: A pilot single-center study using Bland-Altman analysis. Medicine (Baltimore) 2018;97:e12019 [Crossref] [PubMed]
50. Sui X, Meinel FG, Song W, et al. Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. Eur J Radiol 2016;85:564-70. [Crossref] [PubMed]
51. Jacobs C, van Rikxoort EM, Murphy K, et al. Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database. Eur Radiol 2016;26:2139-47. [Crossref] [PubMed]
52. Silva M, Schaefer-Prokop CM, Jacobs C, et al. Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis. Invest Radiol 2018;53:441-9. [Crossref] [PubMed]
53. Roos JE, Paik D, Olsen D, et al. Computer-aided detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance. Eur Radiol 2010;20:549-57. [Crossref] [PubMed]
54. Liang M, Tang W, Xu DM, et al. Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers. Radiology 2016;281:279-88. [Crossref] [PubMed]
55. Teague SD, Trilikis G, Dharaiya E. Lung nodule computer-aided detection as a second reader: influence on radiology residents. J Comput Assist Tomogr 2010;34:35-9. [Crossref] [PubMed]
56. White CS, Pugatch R, Koonce T, et al. Lung nodule CAD software as a second reader: a multicenter study. Acad Radiol 2008;15:326-33. [Crossref] [PubMed]
57. Zhao Y, de Bock GH, Vliegenthart R, et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur Radiol 2012;22:2076-84. [Crossref] [PubMed]
58. Lo SB, Freedman MT, Gillis LB, et al. JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function. AJR Am J Roentgenol 2018;210:480-8. [Crossref] [PubMed]
59. Ritchie AJ, Sanghera C, Jacobs C, et al. Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans. J Thorac Oncol 2016;11:709-17. [Crossref] [PubMed]
60. Ghanavi J, Mostafavi M, Ghanavi Z, inventors. Method for the synthesis of metallic nano products. United States patent US 9,487,399. 2016 Nov 8.
61.Jalaledin G, Mehrnaz M, inventors. Method for producing rod-shaped and branched metallic nano-structures by polyol compounds. United States patent application US 12/870,792. 2011 Apr 21.
62. Evans, A.J.; Bauer, T.W.; Bui, M.M.; Cornish, T.C.; Duncan, H.; Glassy, E.F.; Hipp, J.; McGee, R.S.; Murphy, D.; Myers, C. US Food and Drug Administration approval of whole slide imaging for primary diagnosis: A key milestone is reached and new questions are raised. Arch. Pathol. Lab. Med. 2018, 142, 1383–1387. [CrossRef]
63. Abels, E.; Pantanowitz, L. Current state of the regulatory trajectory for whole slide imaging devices in the USA. J. Pathol. Inform. 2017, 8, 23. [CrossRef]
64. Niazi, M.K.K.; Parwani, A.V.; Gurcan, M.N. Digital pathology and artificial intelligence. Lancet Oncol. 2019, 20, e253–e261. [CrossRef]
65. DICOMWhole Slide Imaging (WSI). Available online: https://dicom.nema.org/Dicom/ DICOMWSI/ (accessed on 29 November 2021).
66. Sakamoto, T.; Furukawa, T.; Lami, K.; Pham, H.H.N.; Uegami,W.; Kuroda, K.; Kawai, M.; Sakanashi, H.; Cooper, L.A.D.; Bychkov, A. A narrative review of digital pathology and artificial intelligence: Focusing on lung cancer. Transl. Lung Cancer Res. 2020, 9, 2255. [CrossRef]
67. Giovagnoli, M.R.; Giansanti, D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards. Healthc. Multidiscip. Digit. Publ. Inst. 2021, 9, 858. [CrossRef]
68. Šari´c, M.; Russo, M.; Stella, M.; Sikora, M. CNN-based method for lung cancer detection in whole slide histopathology images. In Proceedings of the 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 18–21 June2019; pp. 1–4.
69.Coudray, N.; Ocampo, P.S.; Sakellaropoulos, T.; Narula, N.; Snuderl, M.; Fenyö, D.; Moreira, A.L.; Razavian, N.; Tsirigos, A. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat. Med. 2018, 24, 1559–1567. [CrossRef]
70. Schaumberg, A. J., Rubin, M. A. & Fuchs, T. J. H&E-stainedwhole slide deep learning predicts spop mutation state in prostate cancer. Preprint at https:// doi.org/10.1101/064279 (2016).
71. Donovan, M. J. et al. A systems pathology model for predicting overall survival in patients with refractory, advanced non-small-cell lung cancer treated with gefitinib. Eur. J. Cancer 45, 1518–1526 (2009).
72. Jochems, A.; Deist, T.M.; Van Soest, J.; Eble, M.; Bulens, P.; Coucke, P.; Dries, W.; Lambin, P.; Dekker, A. Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital–a real life proof of concept. Radiother. Oncol. 2016, 121, 459–467. [CrossRef] [PubMed]

73. Jochems, A.; Deist, T.M.; El Naqa, I.; Kessler, M.; Mayo, C.; Reeves, J.; Jolly, S.; Matuszak, M.; Ten Haken, R.; van Soest, J. Developing and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries. Int. J. Radiat. Oncol. Biol. Phys. 2017, 99, 344–352. [CrossRef]
74. Wang, D.D.; Zhou,W.; Yan, H.;Wong, M.; Lee, V. Personalized prediction of EGFR mutation-induced drug resistance in lung cancer. Sci. Rep. 2013, 3, 2855. [CrossRef]
75. Giang, T.-T.; Nguyen, T.-P.; Tran, D.-H. Stratifying patients using fast multiple kernel learning framework: Case studies of Alzheimer’s disease and cancers. BMC Med. Inform. Decis. Mak. 2020, 20, 108. [CrossRef]
76. Gao, Y.; Zhou, R.; Lyu, Q. Multiomics and machine learning in lung cancer prognosis. J. Thorac. Dis. 2020, 12, 4531. [CrossRef]
77. Wissel, D.; Rowson, D.; Boeva, V. Hierarchical autoencoder-based integration improves performance in multi-omics cancer survival models through soft modality selection. bioRxiv 2022. [CrossRef]
78. Coory, M.; Gkolia, P.; Yang, I.A.; Bowman, R.V.; Fong, K.M. Systematic review of multidisciplinary teams in the management of lung cancer. Lung Cancer 2008, 60, 14–21. [CrossRef]
79. Denton, E.; Conron, M. Improving outcomes in lung cancer: The value of the multidisciplinary health care team. J. Multidiscip. Healthc. 2016, 9, 137–144. [CrossRef] [PubMed]
80. Wichmann, J.L.;Willemink, M.J.; De Cecco, C.N. Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation. Investig. Radiol. 2020, 55, 619–627. [CrossRef] [PubMed]
81. Mostafavi, M., Ghanavi, J., Shaabani, M., Asadi, H., & Alizadeh, M. (2021). Application of semiconductor nanomaterials in cancer theranostics. J Nanomed Nanotechnol, 12, 563–564. https://www.walshmedicalmedia.com/open-access/application-of-semiconductor-nanomaterials-in-cancer-theranostics.pdf
82. Mostafavi M (2021) A Comparison of Virtual Colonoscopy Methods for Colon Cleansing. Colorec Cancer Vol: 7 No: 5.