Artificial intelligence: characterization, segmentation of lung nodules through high-resolution

• Miguel Ángel Hernández Neira Division of Graduate Studies, Faculty of Medicine, University of Zulia.
• Edunice Mora López Division of Graduate Studies, Faculty of Medicine, University of Zulia.
• Juan Pablo Monroy Division of Graduate Studies, Faculty of Medicine, University of Zulia.
• Edilia Elena Noguera Hernández

DOI: https://doi.org/10.5281/zenodo.17981140

Keywords: Pulmonary nodules, artificial intelligence, chest tomography

Abstract

Objective: to evaluate artificial intelligence (AI) through high-resolution tomography in the segmentation and characterization of pulmonary nodules (NP) in patients referred to the Hospital Clinic de Maracaibo. Methodology: It was a descriptive investigation, cross-sectional, prospective. A sample of 19 patients over 18 years of age who required a chest tomography. A GE brand multidetector tomograph, were used. Results: they were 11 (57.9%) were female and 8 (42.1%) were male, with an average age of 65±11.0 years. The characterization of the NPs through artificial intelligence, in terms of the size of the nodules the average was 1.6 cm, with the greatest number of cases 6 (31.6%) between 0.5 to 1 cm, multiple 10 (52.6%). Referring to the type; solid and contour; regular 14 (73.7%), predominated in upper lobes: Right 8(42.1%) and Left 5(26.3%).There was no correlation of the number of nodules between the operators with the AI In relation to the type of nodules by the AI were Solid (n=13), coinciding 7 with the first operator and 12 with the second operator and likewise the regular contour of 13 cases detected by the AI, 12 coinciding on with the second operator, which yields a concordance of 92.3%. According to the segmentation through the AI, they were from the Upper Lobe D (n=8), agreeing 4 with the first observer and 5 with the second observer, noting that there was no agreement with 12 cases of the first operator and with 8 cases of the second operator. Conclusion: AI system remains operator dependent, future studies centralized in large-scale validation of new algorithms based on deep learning and new reading paradigms.

References

Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, et al. (2011) Reduced Lung-Cancer Mortality With Low-Dose Computed Tomographic Screening. N Engl J Med; 365(5):395–409. doi: 10.1056/NEJMoa1102873

Armato SG, 3rd, McLennan G, Bidaut L. (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 2011;38:915-31.10.1118/1.3528204

Ather S, Kadir T, Gleeson F. (2020) Artificial Intelligence and Radiomics in Pulmonary Nodule Management: Current Status and Future Applications. Clin Radiol 75(1):13–9. doi: 10.1016/j.crad.2019.04.017

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. (2018) Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin (2018) 1168(6):394–424. doi: 10.3322/caac.21492

Chang, P. (2018) What to make of the increased hype surrounding AI in radiology. Radiology Business. Disponible en: https://www.radiologybusiness.com/sponsored/9667/topics/ imaging- informa-tics/rsna-2018-hype-ai-radiology-

Chartrand G, Cheng PM, Vorontsov E. (2017) learning: a primer for radiologists. Radiographics.37(7):2113–2131

Chung K, Mets OM, Gerke PK. (2018) Brock malignancy risk calculator for pulmonary nodules: validation outside a lung cancer screening population. Thorax; 73 (1):857-863.

Ciompi F, Chung K, van Riel S. (2017) Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep 2017;7:46479. 10.1038/srep46479.

Devaraj A, van Ginneken B, Nair A. (2017) Use of Volumetry for Lung Nodule Management: Theory and Practice. Radiology;284:630-44. 10.1148/radiol.2017151022

Gould MK, Donington J, Lynch WR. (2013) Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest; 143 :( 5):e93S–e120S.

Jacobs C, Scholten E, Schreuder A. (2019) An observer study comparing radiologists with the prize-winning lung cancer detection algorithms from the 2017. Kaggle Data Science Bowl. Annual Meeting of the Radiological Society of North America, 2019.

Krizhevsky A, Sutskever I, Hinton G E. (2012) “Imagenet classification with deep convolutional neural networks. Disponible en: https://dl.acm.org/doi/10. 5555/2999134.2999257.

Lachance, Chantelle C., Walter, Melissa. (2020) Artificial Intelligence for Classification of Lung Nodules: A Review of Clinical Utility, Diagnostic Accuracy, Cost-Effectiveness, and Guidelines. Canadian Agency for Drugs and Technologies in Health. Disponible en: https://pubmed.ncbi.nlm.nih.gov/33074628/

Li D, Mikela Vilmun B, Frederik Carlsen J, et al. (2019) The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review. Diagnostics (Basel); 9:207.10.3390/diagnostics9040207

MacMahon H, Naidich DP, Goo JM. (2017) Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society Radiol.; 284(1):228–243.

Moyer VA, Moyer VA, LeFevre ML, Siu AL, Peters JJ, Baumann LC. (2014) Screening for Lung Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med; 160(5):330–8. Disponible en: doi: 10.7326/M13-2771

McWilliams A, Tammemagi MC, Mayo JR, et al. (2013) Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med;369:910-9. 10.1056/NEJMoa1214726

Patel, V.K. (2013) A Practical Algorithmic Approach to the Diagnosis and Management of Solitary Pulmonary Nodules Diagnosis of Pulmonary Nodules Part 2: Pretest Probability and Algorithm. Chest, 143, pp. 840 http://dx.doi.org/10.1378/chest.12-1487

Sociedad Venezolana Anticancerosa (2019). Boletín de los Pronósticos de la Mortalidad e Incidencia del Cáncer en Venezuela 2019. 3a Edición - Centro de Estadística y Matemática Aplicada de la Universidad Simón Bolívar. Disponible en: www.cancervenezuela.org

Pinsky PF, Gierada DS, Nath PH. ( 2013) National lung screening trial: variability in nodule detection rates in chest CT studies. Radiology; 268:865-73.

Rubin GD, Lyo JK, Paik DS. (2005) Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 2005; 234:274-83. 10.1148/radiol.2341040589

Setio AAA, Traverso A, de Bel T. (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal 2017;42:1-13.

Tammemagi M, Ritchie AJ, Atkar-Khattra S. (2019) Predicting Malignancy Risk of Screen-Detected Lung Nodules-Mean Diameter or Volume. J Thorac Oncol; 14: 203-211.

Van Riel SJ, Ciompi F, Winkler Wille M. (2017) Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers. PLoS One 2017;12:e0185032.

Wu W, Hu H, Gong J, Li X, Huang G, Nie S. (2019) Malignant-benign classification of pulmonary nodules based on random forest aided by clustering analysis. Phys Med Biol; 64(3):035017.

Yasaka K, Katsura M, Hanaoka S, Sato J, Ohtomo K. (2016) High-Resolution CT With New Model-Based Iterative Reconstruction With Resolution Preference Algorithm in Evaluations of Lung Nodules: Comparison With Conventional Model-Based Iterative Reconstruction and Adaptive Statistical Iterative Reconstruction. Eur J Radiol (2016)85(3):599–606. doi: 10.1016/j.ejrad.2016.01.001

Zaharchuk G, Gong E, M. Wintermark, M. (2018) Deep Learning in Neuroradiology. Am. J. Neuro-radioL, October 2018, 39 (10) 1776-1784. https://doi.org/10.3174/ajnr.A5543

Zhang C, Sun X, Dang K, Li K, Guo XW, Chang J, et al. (2019) Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network. Oncologist (2019) 24(9):1159–65. doi: 10.1634/theoncologist.2018-0908.