APPLICATION OF CONTEMPORARY COMPUTER METHODS IN LARYNGEAL CANCER DIAGNOSIS AND TREATMENT
Abstract
Early detection of disease and accurate assessment of its extent are of paramount importance for the course of treatment and prognosis of larynx cancer. Machine learning and artificial intelligence tools have the potential to accelerate and improve diagnostic procedures in medicine, as well as to predict disease outcomes and response to specific therapies. Computer algorithms can analyze two-dimensional images obtained during procedures such as laryngeal spectroscopy and endoscopy. Radiological images can be evaluated using appropriate algorithms to determine whether the laryngeal tissue is benign or malignantly altered. In recent years, machine learning tools have been developed to determine the precise radiation doses, predict tumor radiosensitivity, as well as the possibility and severity of complications based on radiological image analysis. In the field of pathology, significant progress has been made by creating digital records of histopathological preparations, which can be further analyzed. This allows changes in intercellular interaction and tissue architecture that cannot be detected by conventional microscopic methods to be identified. With innovative computer techniques, it is possible to quantify tissue and cell structure parameters, which are calculated based on mathematical formulas and used to measure structural homogeneity and uniformity in both normal and pathologically altered tissue. Future multidisciplinary research aimed at developing new and innovative biosensors for the detection of discrete morphological changes characteristic of squamous cell carcinoma of the larynx will make a significant contribution to the advancement of diagnosis and treatment in the field of otolaryngology. In the future, the use of artificial intelligence and machine learning could enable the fusion of algorithms that combine data obtained from radiological, endoscopic, and histopathological findings, which could significantly increase the accuracy and precision of diagnosis, facilitate the process of deciding on therapeutic options, and improve the success rate of larynx cancer treatment.
References
1. Wang JY, Zhang QW, Wen K, Wang C, Ji X, Zhang L. Temporal trends in incidence and mortality rates of laryngeal cancer at the global, regional and national levels, 1990-2017. BMJ Open. 2021;11(10):e050387.
2. Lin C, Cheng W, Liu X, Li H, Song Y. The global, regional, national burden of laryngeal cancer and its attributable risk factors (1990-2019) and predictions to 2035. Eur J Cancer Care. 2022;31(6):e13689.
3. Bhattacharyya S, Mandal S, Banerjee S, Mandal GK, Bhowmick AK, Murmu N. Cannabis smoke can be a major risk factor for early-age laryngeal cancer--a molecular signaling-based approach. Tumour Biol. 2015;36(8):6029-36.
4. Zidar N. Gale N. Cancer of the Larynx; Pathology and Genetics. In: Boffetta P, Hainaut P, editors. Encyclopedia of Cancer, 3rd Edition. Academic Press: Elsevier; 2019. p.346-355.
5. Mourad M, Jetmore T, Jategaonkar AA, Moubayed S, Moshier E, Urken ML. Epidemiological Trends of Head and Neck Cancer in the United States: A SEER Population Study. J Oral Maxillofac Surg. 2017;75(12):2562-2572.
6. Dickstein DR, Lehrer EJ, Hsieh K, Hotca A, Jones BM, Powers A, et al. Management of Older Adults with Locally Advanced Head and Neck Cancer. Cancers (Basel). 2022;14(11):2809.
7. Marioni G, Marchese-Ragona R, Cartei G, Marchese F, Staffieri A. Current opinion in diagnosis and treatment of laryngeal carcinoma. Cancer Treat Rev 2006;32:504–15.
8. Karatzanis AD, Psychogios G, Waldfahrer F, Kapsreiter M, Zenk J, Veegrakis GA, et al. Management of locally advanced laryngeal cancer. J Otolaryngol Head Neck Surg. 2014;43(1):4.
9. Huang Q, Hsueh C, Guo Y, Wu X, Li J, Zhou L. Lack of miR‐1246 in small extracellular vesicle blunts tumorigenesis of laryngeal carcinoma cells by regulating Cyclin G2. IUBMB Life. 2020;72(7):1491-1503.
10. Jovanović MB. Diagnosis of laryngeal carcinoma. Med Pregl. 2008;61(11-12):591-595.
11. Ahn D, Kwak JH, Lee GJ, Sohn JH. Ultrasonography for masses of the pharynx and larynx and assessment of laryngeal squamous cell carcinoma. Auris Nasus Larynx. 2022;49(5):868-874.
12. Joshi VM, Wadhwa V, Mukherji SK. Imaging in laryngeal cancers. Indian J Radiol Imaging. 2012;22(3):209-226.
13. Eckel HE, Simo R, Quer M, Odell E, Paleri V, Klussmann JP, et al. European Laryngological Society position paper on laryngeal dysplasia Part II: diagnosis, treatment, and follow-up. Eur Arch Otorhinolaryngol. 2021;278(6):1723-1732.
14. Sharaf K, Lechner A, Haider SP, Wiebringhaus R, Walz C, Kranz G, et al. Discrimination of cancer stem cell markers ALDH1A1, BCL11B, BMI-1, and CD44 in different tissues of HNSCC patients. Curr. Oncol. 2021;28(4):2763-2774.
15. Arutyunyan IV, Soboleva AG, Gordon KB, Kudashkina DS, Miroshnichenko DA, Polyakov AP, et al. Differential Markers of Subpopulations of Epithelial Cells of the Larynx in Squamous Cell Carcinoma. Bull Exp Biol Med. 2022;173(4):553-559.
16. Yi CH, Jim Zhai Q, Wang BY. Updates on immunohistochemical and molecular markers in selected head and neck diagnostic problems. Arch. Pathol. Lab. Med. 2017;141(9):1214-1235.
17. Haenlein M, Kaplan A. A briefhistory of artificial intelligence:on the past, present and future of artificial intelligence. Calif Manage Rev 2019;61(August (4)):5–14.
18. Bassani S, Santonicco N, Eccher A, Scarpa A, Vianini M, Brunelli M, et al. Artificial intelligence in head and neck cancer diagnosis. J Pathol Inform. 2022;13:100153.
19. Alsuliman T, Humaidan D, Sliman L. Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? Curr Res Transl Med. 2020;68(4):245-251.
20. Fang SH, Tsao Y, Hsiao MJ, Chen JY, Lai YH, Lin FC, et al. Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach. J Voice. 2019;33(5):634-641.
21. Crowson MG, Ranisau J, Eskander A, Babier A, Xu B, Kahmke RR, et al. A contemporary review of machine learning in otolaryngology-head and neck surgery. Laryngoscope. 2020;130(1):45-51.
22. Mahmood H, Shaban M, Rajpoot N, Khurram SA. Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer. 2021;124(12):1934-1940.
23. Mirestean CC, Pagute O, Buzea C, Iancu RI, Iancu DT. Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019;14(2):126-130.
24. Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, et al. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers (Basel). 2022;14(15):3573.
25. Tanaka S, Kadoya N, Sugai Y, Umeda M, Ishizawa M, Katsuta Y, et al. A Deep Learning-Based Radiomics Approach to Predict Head and Neck Tumor Regression for Adaptive Radiotherapy. Sci. Rep. 2022;12:8899.
26. Devakumar D, Sunny G, Sasidharan BK, Bowen SR, Nadaraj A, Jeyseelan L, et al. Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers. J Med Phys. 2021;46(3):181-188.
27. Sheikh K, Lee SH, Cheng Z, Lakshminarayanan P, Peng L, Han P, et al. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol. 2019;14(1):131.
28. Santer M, Kloppenburg M, Gottfried TM, Runge A, Schmutzhard J, Vorbach SM, et al. Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review. Cancers (Basel). 2022;14(21):5397.
29. Chen L, Dohopolski M, Zhou Z, Wang K, Wang R, Sher D, et al. Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer. Int. J. Radiat. Oncol. Biol. Phys. 2021;110:1171–1179.
30. Chen L, Zhou Z, Sher D, Zhang Q, Shah J, Pham NL, et al. Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer. Phys. Med. Biol. 2019;64:075011.
31. Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, et al. Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks. Sci. Rep. 2018;8: 14036.
32. Chinnery T, Arifin A, Tay KY, Leung A, Nichols AC, Palma DA, et al. Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging. Can Assoc Radiol J. 2021;72(1):73-85.
33. Pantić I, Paunović Pantić J, Radojević-Škodrić S. Application of fractal and textural analysis in medical physiology, pathophysiology and pathology Medicinska istraživanja 2022;55(3):43-51.
34. Pantic I, Pantic S, Paunovic J, Perovic M. Nuclear entropy, angular second moment, variance and texture correlation of thymus cortical and medullar lymphocytes: grey level co-occurrence matrix analysis. An Acad Bras Cienc. 2013;85(3):1063-72.
35. Pantic I, Petrovic D, Paunovic J, Vucevic D, Radosavljevic T, Pantic S. Age-related reduction of chromatin fractal dimension in toluidine blue - stained hepatocytes. Mech Ageing Dev. 2016;157:30-34.
36. Lian MJ, Huang CL, Lee TM. Automation Characterization for Oral Cancer by Pathological Image Processing with Gray-Level Co-occurrence Matrix Journal of Image and Graphics. 2018;6(1):80-83.
37. Rahman TY, Mahanta LB, Das AK, Sarma JD. Histopathological imaging database for oral cancer analysis. Data Brief. 2020;29:105114.
38. Davidovic LM, Cumic J, Dugalic S, Vicentic S, Sevarac Z, Petroianu G, et al. Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach. Microsc Microanal. 2022;28(1):265-271.
39. Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun. 2021;12(1):5645.