PRIMENA SAVREMENIH RAČUNARSKIH METODA U DIJAGNOSTICI I LEČENJU KARCINOMA LARINKSA

Svetlana Valjarević; Milan  Jovanović; Igor Pantić

doi:10.5937/mp74-43594

Dr Svetlana Valjarević Kl. asist. dr med https://orcid.org/0000-0002-1565-8724
Prof. Milan B. Jovanović Redovni profesor https://orcid.org/0000-0001-7522-7582
Prof. Igor Pantić Vanredni profesor https://orcid.org/0000-0001-6589-8964

DOI: https://doi.org/10.5937/mp74-43594

Ključne reči: karcinom larinksa, veštačka inteligencija, mašinsko učenje

Sažetak

Rana detekcija bolesti i tačna procena proširenosti od najvećeg su značaja za tok lečenja i prognozu karcinoma larinksa. Alati bazirani na mašinskom učenju i veštačkoj inteligenciji imaju potencijal da ubrzaju i unaprede dijagnostičke procedure u ovoj oblasti medicine, kao i da predvide ishod bolesti i odgovor na određenu terapiju. Računarskim metodama mogu se analizirati dvodimenzionalni signali dobijeni u toku procedura kao što su spektroskopija i endoskopija larinksa. Sa radioloških snimaka primenom odgovarajućih algoritama dobijaju se procene o tome da li se radi o maligno izmenjenom ili benignom tkivu larinksa. Poslednjih godina, razvijaju se i alati mašinskog učenja koji na osnovu analize radioloških slika određuju preciznu dozu zračenja, predviđaju radiosenzitivnost tumora, kao i mogućnost i težinu komplikacija. Na polju patologije postignut je značajan napredak formiranjem digitalnih zapisa histopatoloških preparata koji se potom mogu dodatno analizirati. Na taj način detektuju se promene u međućelijskoj interakciji i arhitekturi tkiva, koje se ne mogu otkriti konvencionalnim mikroskopskim metodama. Pomoću inovativnih računarskih tehnika moguće je kvantifikovati veliki broj parametara tkivne i ćelijske strukture. Ovi parametri se mogu izračunati na osnovu kompleksnih matematičkih algoritama i mogu se koristiti za merenje strukturne homogenosti i uniformnosti kako normalnog, tako i patološki izmenjenog tkiva. Buduća multidisciplinarna istraživanja sa ciljem razvoja novih i inovativnih biosenzora za detekciju diskretnih morfoloških promena karakterističnih za planocelularni karcinom larinksa daće značajan doprinos unapređenju dijagnostike i lečenja u ovoj oblasti otorinolaringologije. Primena veštačke inteligencije i mašinskog učenja u budućnosti mogla bi da omogući primenu algoritama koji kombinuju podatke dobijene iz radioloških, endoskopskih i histopatoloških nalaza, što može značajno da poveća tačnost i preciznost dijagnoze, da olakša proces odlučivanja o terapijskim mogućnostima i poboljša uspešnost lečenja karcinoma larinksa.

Biografije autora

Dr Svetlana Valjarević, Kl. asist. dr med

Otorinolaringolog u Službi otorinolaringologije sa maksilofacijalnom hirurgijom, Kliničko-bolničkog centra "Zemun". Klinički asistent na Katedri otorinolaringologije sa maksilofacijalnom hirurgijom Medicinskog fakutleta Univerziteta u Beogradu.

Prof. Milan B. Jovanović, Redovni profesor

Redovni profesor na Katedri otorinolaringologije sa maksilofacijalnom hirurgijom Medicinskog fakulteta Univerziteta u Beogradu

Prof. Igor Pantić, Vanredni profesor

Vanredni profesor na Medicinskom fakultetu Univerziteta u Beogradu, Institut za medicinsku fiziologiju "Rihard Burijan"

Reference

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.