Machine-Learning Applications in Oral Cancer: A Systematic Review

被引：24

作者：

Lopez-Cortes, Xaviera A. ^{[1
]}

Matamala, Felipe ^{[1
]}

Venegas, Bernardo ^{[2
]}

Rivera, Cesar ^{[3
]}

机构：

[1] Univ Catolica Maule, Dept Comp Sci & Ind, Talca 3480112, Chile

[2] Univ Talca, Fac Hlth Sci, Dept Stomatol, Talca 3460000, Chile

[3] Univ Talca, Fac Hlth Sci, Dept Basic Biomed Sci, Talca 3460000, Chile

来源：

APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 11期

关键词：

oral cancer; OSCC; machine learning; applications; SQUAMOUS-CELL CARCINOMA; ARTIFICIAL NEURAL-NETWORK; LYMPH-NODE METASTASIS; AUTOMATED CLASSIFICATION; RISK-FACTORS; TISSUE; MODEL; PREDICTION; DIAGNOSIS; SPECTRA;

D O I：

10.3390/app12115715

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.

引用

页数：18

共 78 条

[1] 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 [J].

Ariji, Yoshiko ;

Fukuda, Motoki ;

Kise, Yoshitaka ;

Nozawa, Michihito ;

Yanashita, Yudai ;

Fujita, Hiroshi ;

Katsumata, Akitoshi ;

Ariji, Eiichiro .

ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY, 2019, 127 (05) :458-463

[2] Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning [J].

Aubreville, Marc ;

Knipfer, Christian ;

Oetter, Nicolai ;

Jaremenko, Christian ;

Rodner, Erik ;

Denzler, Joachim ;

Bohr, Christopher ;

Neumann, Helmut ;

Stelzle, Florian ;

Maier, Andreas .

SCIENTIFIC REPORTS, 2017, 7

[3] Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer [J].

Banerjee, Satarupa ;

Pal, Mousumi ;

Chakrabarty, Jitamanyu ;

Petibois, Cyril ;

Paul, Ranjan Rashmi ;

Giri, Amita ;

Chatterjee, Jyotirmoy .

ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2015, 407 (26) :7935-7943

[4] Performance of a computer simulated neural network trained to categorise normal, premalignant and malignant oral smears [J].

Brickley, MR ;

Cowpe, JG ;

Shepherd, JP .

JOURNAL OF ORAL PATHOLOGY & MEDICINE, 1996, 25 (08) :424-428

[5] Age Effects and Temporal Trends in HPV-Related and HPV-Unrelated Oral Cancer in the United States: A Multistage Carcinogenesis Modeling Analysis [J].

Brouwer, Andrew F. ;

Eisenberg, Marisa C. ;

Meza, Rafael .

PLOS ONE, 2016, 11 (03)

[6] Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma [J].

Bur, Andres M. ;

Holcomb, Andrew ;

Goodwin, Sara ;

Woodroof, Janet ;

Karadaghy, Omar ;

Shnayder, Yelizaveta ;

Kakarala, Kiran ;

Brant, Jason ;

Shew, Matthew .

ORAL ONCOLOGY, 2019, 92 :20-25

[7] Use of fuzzy neural networks in modeling relationships of HPV infection with apoptotic and proliferation markers in potentially malignant oral lesions [J].

Campisi, G ;

Di Fede, O ;

Giovannelli, L ;

Capra, G ;

Greco, I ;

Calvino, F ;

Florena, AM ;

Lo Muzio, L .

ORAL ONCOLOGY, 2005, 41 (10) :994-1004

[8] PERI-TUMORAL INFLAMMATORY CELL INFILTRATION IN OSCC: A RELIABLE MARKER OF LOCAL RECURRENCE AND PROGNOSIS? AN INVESTIGATION USING ARTIFICIAL NEURAL NETWORKS [J].

Campisi, G. ;

Calvino, F. ;

Carinci, F. ;

Matranga, D. ;

Carella, M. ;

Mazzotta, M. ;

Rubini, C. ;

Panzarella, V. ;

Santarelli, A. ;

Fedele, S. ;

Lo Muzio, L. .

INTERNATIONAL JOURNAL OF IMMUNOPATHOLOGY AND PHARMACOLOGY, 2011, 24 (02) :113-120

[9] A 3-mRNA-based prognostic signature of survival in oral squamous cell carcinoma [J].

Cao, Ruoyan ;

Wu, Qiqi ;

Li, Qiulan ;

Yao, Mianfeng ;

Zhou, Hongbo .

PEERJ, 2019, 7

[10] Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer [J].

Carnielli, Carolina Moretto ;

Soares Macedo, Carolina Carneiro ;

De Rossi, Tatiane ;

Granato, Daniela Campos ;

Rivera, Cesar ;

Domingues, Romenia Ramos ;

Pauletti, Bianca Alves ;

Yokoo, Sami ;

Heberle, Henry ;

Busso-Lopes, Ariane Fidelis ;

Cervigne, Nilva Karla ;

Sawazaki-Calone, Iris ;

Meirelles, Gabriela Vaz ;

Marchi, Fabio Albuquerque ;

Telles, Guilherme Pimentel ;

Minghim, Rosane ;

Prado Ribeiro, Ana Carolina ;

Brandao, Thais Bianca ;

Castro, Gilberto de, Jr. ;

Alejandro Gonzalez-Arriagada, Wilfredo ;

Gomes, Alexandre ;

Penteado, Fabio ;

Santos-Silva, Alan Roger ;

Lopes, Marcio Ajudarte ;

Rodrigues, Priscila Campioni ;

Sundquist, Elias ;

Salo, Tuula ;

da Silva, Sabrina Daniela ;

Alaoui-Jamali, Moulay A. ;

Graner, Edgard ;

Fox, Jay W. ;

Della Coletta, Ricardo ;

Paes Leme, Adriana Franco .

NATURE COMMUNICATIONS, 2018, 9

← 1 2 3 4 5 6 7 8 →