Tufail Bin A, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, et al. Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions. Comput Math Methods Med. 2021;31(2021):1–28. https://doi.org/10.1155/2021/9025470.
Al-Rawi N, Sultan A, Rajai B, Shuaeeb H, Alnajjar M, Alketbi M, et al. The effectiveness of artificial intelligence in detection of oral cancer. Int Dent J. 2022;72:436–47. https://doi.org/10.1016/j.identj.2022.03.001.
Article PubMed PubMed Central Google Scholar
Yngve Mardal M. Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers. ElecEng Sys Sci. 2019. https://doi.org/10.48550/arXiv.1908.00841.
Vishwanath V, Jafarieh S, Rembielak A. The role of imaging in head and neck cancer: An overview of different imaging modalities in primary diagnosis and staging of the disease. J Contemp Brachyther. 2020;12:512–8. https://doi.org/10.5114/jcb.2020.100386.
Hegde S, Ajila V, Zhu W, Zeng C. Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Pac J Oncol Nurs. 2022;9: 100133. https://doi.org/10.1016/j.apjon.2022.100133.
Article PubMed PubMed Central Google Scholar
Ilhan B, Lin K, Guneri P, Wilder-Smith P. Improving oral cancer outcomes with imaging and artificial intelligence. J Dent Res. 2020;20(99):241–8. https://doi.org/10.1177/0022034520902128.
Mody MD, Rocco JW, Yom SS, Haddad RI, Saba NF. Head and neck cancer. Lancet. 2021;398:2289–99. https://doi.org/10.1016/S0140-6736(21)015506.
Borse V, Konwar AN, Buragohain P. Oral cancer diagnosis and perspectives in India. Sens Int. 2020;1:100046. https://doi.org/10.1016/j.sintl.2020.100046.
Article PubMed PubMed Central Google Scholar
de Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;13(24):1342–50. https://doi.org/10.1038/s41591-018-0107-6.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;7(25):44–56.
Wang D, Gong Z, Zhang Y, Wang S. Convolutional neural network intelligent segmentation algorithm-based magnetic resonance imaging in diagnosis of nasopharyngeal carcinoma foci. Contrast Media Mol Imaging. 2021;2021(13):1–9. https://doi.org/10.1155/2021/2033806.
Wang X, Li Bin B. Deep learning in head and neck tumor multiomics diagnosis and analysis: review of the literature. Front Genet. 2021. https://doi.org/10.3389/fgene.2021.624822.
Article PubMed PubMed Central Google Scholar
Kann BH, Hicks DF, Payabvash S, Mahajan A, Du J, Gupta V, et al. Multi-institutional validation of deep learning for pretreatment identification of extranodal extension in head and neck squamous cell carcinoma. J Clin Oncol. 2020;20(38):1304–11. https://doi.org/10.1200/JCO.19.02031.
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;19(8):14036. https://doi.org/10.1038/s41598-018-32441-y.
Stefaniak B, Cholewiski W, Tarkowska A. Application of artificial neural network algorithm to detection of parathyroid adenoma. Nucl Med Rev Cent East Eur. 2003;6:111–7.
Diamant A, Chatterjee A, Vallières M, Shenouda G, Seuntjens J. Deep learning in head & neck cancer outcome prediction. Sci Rep. 2019;26(9):2764. https://doi.org/10.1038/s41598-019-39206-1.
Sahiner B, Heang-Ping C, Petrick N, Wei D, Helvie MA, Adler DD, et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging. 1996;15:598–610. https://doi.org/10.1109/42.538937.
Das N, Hussain E, Mahanta LB. Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network. Neural Netw. 2020;128:47–60. https://doi.org/10.1016/j.neunet.2020.05.003.
Jeyaraj PR, Samuel Nadar ER. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J Cancer Res Clin Oncol. 2019;3(145):829–37. https://doi.org/10.1007/s00432-018-02834-7.
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;31(72):73–85. https://doi.org/10.1177/0846537120942134.
Cardenas CE, Anderson BM, Aristophanous M, Yang J, Rhee DJ, McCarroll RE, et al. Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks. Phys Med Biol. 2018;63(21):215026. https://doi.org/10.1088/1361-6560/aae8a9.
Lee JH, Ha EJ, Kim D, Jung YJ, Heo S, Jang Ho Y, et al. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training. Eur Radiol. 2020;30:3066–72. https://doi.org/10.1007/s00330-019-06652-4.
Lee JH, Ha EJ, Kim JH. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT. Eur Radiol. 2019;15(29):5452–7. https://doi.org/10.1007/s00330-019-06098-8.
Ariji Y, Fukuda M, Nozawa M, Kuwada C, Goto M, Ishibashi K, et al. Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study. Oral Radiol. 2021;6(37):290–6. https://doi.org/10.1007/s11282-020-00449-8.
van Dijk LV, Fuller CD. Artificial intelligence and radiomics in head and neck cancer care: opportunities, mechanics, and challenges. Am Soc Clin Onco Edu Book. 2021;41:225–35. https://doi.org/10.1200/EDBK_320951.
Daoud B, Morooka K, Kurazume R, Leila F, Mnejja W, Daoud J. 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning. Comput Med Imag Graph. 2019;77: 101644. https://doi.org/10.1016/j.compmedimag.2019.101644.
Schouten JPE, Noteboom S, Martens RM, Mes SW, Leemans CR, de Graaf P, et al. Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN. Canc Imag. 2022;15(22):8. https://doi.org/10.1186/s40644-022-00445-7.
Gerstle RJ, Aylward SR, Kromhout-Schiro S, Mukherji SK. The role of neural networks in improving the accuracy of MR spectroscopy for the diagnosis of head and neck squamous cell carcinoma. AJNR Am J Neuroradiol. 2000;21:1133–8.
PubMed PubMed Central Google Scholar
Ariji Y, Sugita Y, Nagao T, Nakayama A, Fukuda M, Kise Y, et al. CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification. Oral Radiol. 2020;36(2):148–55. https://doi.org/10.1007/s11282-019-00391-4.
Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, et al. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019;127:458–63. https://doi.org/10.1016/j.oooo.2018.10.002.
Deng Y, Li C, Lv X, Xia W, Shen L, Jing B, et al. The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area. Comput Methods Programs Biomed. 2022;217: 106702. https://doi.org/10.1016/j.cmpb.2022.106702.
Ke L, Deng Y, Xia W, Qiang M, Chen X, Liu K, et al. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images. Oral Oncol. 2020;110: 104862. https://doi.org/10.1016/j.oraloncology.2020.104862.
McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM, Clifford T, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies. JAMA. 2018;23(319):388. https://doi.org/10.1001/jama.2017.19163.
Whiting PF. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;18(155):529. https://doi.org/10.7326/0003-4819-155-8-201110180-00009.
Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, et al. Deep learning for caries detection: a systematic review. J Dent. 2022;122: 104115. https://doi.org/10.1016/j.jdent.2022.104115.
Zhang H, Lai H, Wang Y, Lv X, Hong Y, Peng J, et al. Research on the classification of benign and malignant parotid tumors based on transfer learning and a convolutional neural network. IEEE Access. 2021;9:40360–71. https://doi.org/10.1109/ACCESS.2021.3064752.
Men K, Chen X, Zhu J, Yang B, Zhang Y, Yi J, et al. Continual improvement of nasopharyngeal carcinoma segmentation with less labeling effort. Physica Med. 2020;80:347–51. https://doi.org/10.1016/j.ejmp.2020.11.005.
Al-Maaitah M, Alzubi AA. Enhanced computational model for gravitational search optimized echo state neural networks based oral cancer detection. J Med Syst. 2018;20(42):205. https://doi.org/10.1007/s10916-018-1052-0.
Xia X, Feng B, Wang J, Hua Q, Yang Y, Sheng L, et al. Deep learning for differentiating benign from malignant parotid lesions on MR images. Front Oncol. 2021;23:11. https://doi.org/10.3389/fonc.2021.632104.
Wong LM, King AD, Ai QYH, Lam WKJ, Poon DMC, Ma BBY, et al. Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI. Eur Radiol. 2021;25(31):3856–63. https://doi.org/10.1007/s00330-020-07451-y.
Comments (0)