Research on the Lesion Classification by Radiomics in Laryngoscopy Image |
Park, Jun Ha
(Department of Biomedical Eng., College of Health & Sci., Gachon University)
Kim, Young Jae (Department of Biomedical Eng., College of Health & Sci., Gachon University) Woo, Joo Hyun (Department of Otorhinolaryngology-Head and Neck Surgery, Gachon University School of Medicine, Gil Medical Center) Kim, Kwang Gi (Department of Biomedical Eng., College of Health & Sci., Gachon University) |
1 | Sainju S, Bui FM, Wahid K. Bleeding detection in wireless capsule endoscopy based on color features from histogram probability. 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) IEEE. 2013;1-4 |
2 | Vu DH, Nguyen LT, Nguyen VT, Tran TH, Dao VH, Vu H. Boundary delineation of reflux esophagitis lesions from endoscopic images using color and texture. 2021 International Conference on Multimedia Analysis and Pattern. 2021;1-6. |
3 | Cho WK, Choi SH. Comparison of convolutional neural network models for determination of vocal fold normality in laryngoscopic images. Journal of Voice. 2020;36(5):590-598. |
4 | Azam MA, Sampieri C, Loppi A, Africano S, Vallin A, Mocellin D, Fragale M, Guastini L, Moccia S, Piazza C, Mattos L, Peretti G. Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection. The Laryngoscope. 2021;132(9):1798-1806. |
5 | Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Physics in Medicine & Biology. 2016;61(13):R150. DOI |
6 | Biau G, Scornet E. A random forest guided tour. TEST. 2016;25:197-227. DOI |
7 | Natekin A, and Knoll A. Gradient boosting machines, a tutorial. Frontiers in neurorobotics. 2013;7:21. |
8 | https://pyradiomics.readthedocs.io/en/latest/features.html. Accessed on 4 Jul 2022. |
9 | Mazor T, et al. Intratumoral heterogeneity of the epigenome. Cancer cell. 2016;29(4):440-451. DOI |
10 | Cohen SM, Dupont WD, Courey MS. Quality-of-life impact of non-neoplastic voice disorders: a meta-analysis. Annals of Otology, Rhinology & Laryngology. 2006;115(2):128-134. DOI |
11 | Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F. Radiomics-based prognosis analysis for non-small cell lung cancer. Scientific reports. 2017;7(1):1-8. DOI |
12 | Chandrashekar G, Sahin F. A survey on feature selection methods. Computers & Electrical Engineering. 40.1 (2014):16-28. DOI |
13 | https://scikit-learn.org/stable/modules/linear_model.html. Accessed on 1 Aug 2022. |
14 | McDonald GC. Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics. 2009;1(1):93-100. DOI |
15 | Coroller TP, Grossmann P, Hou Y, Velazquez ER, Leijenaar RTH, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJWL. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiotherapy and Oncology. 2015;114(3)345-350. DOI |
16 | Machicado JD, Koay EJ, Krishna SG. Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions. Diagnostics. 2020;10(7):505. DOI |
17 | Antoine AA. The student's t-test: a brief description. Research & Reviews: Journal of Hospital and Clinical Pharmacy. 2019;5(1):1. |
18 | Aerts HJWL, Pankov A, Song JS, Costello JF. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications. 2014;5(1):1-9. |
19 | Griethuysen JJMV, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts HJWL. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer research. 2017;77(21):e104-e107. DOI |
20 | Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, Tian J, Liang C, Liu Z. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage III and stage III-IV colorectal cancer. Oncotarget. 2016;7(21):31401-31412. DOI |
21 | https://scikit-learn.org/stable/modules/feature_selection.html, Accessed on 29 Jun 2022. |
22 | Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Foster K, Aerts HJWL, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ. Radiomics: the process and the challenges. Magnetic Resonance Imaging. 2012;30(9):1234-1248. DOI |
23 | Ahmadi N, Akbarizadeh G. Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Computing and Applications. 2020;32(7):2267-2281. DOI |
24 | Amadasun M, King R. Textural features corresponding to textural properties. in IEEE Transactions on Systems, Man, and Cybernetics. 1989;19(5):1264-1274. DOI |
25 | https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html sklearn.svm.LinearSVC. Accessed on 30 Jun 2022. |
26 | Kniep HC, Madesta F, Schneider T, Hanning U, Schonfeld MH, Schon G, Fiehler J, Gauer T, Werner R, Gellissen S. Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology. 2019;290(2):479-487. DOI |
27 | Lee SH. Diagnostic Role of Stroboscopy. Journal of the Korean Society of Laryngology. Phoniatrics and Logopedics. 2010;21(1):13-16. |
28 | Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, Niculae V, Prettenhofer P, Gramfort A, Grobler J, Layton R, Vanderplas J, Joly A, Varoquaux G. API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv. 2013;1309.0238. |
29 | Chu EA, Kim YJ. "Laryngeal cancer: diagnosis and preoperative work-up". Otolaryngologic Clinics of North America. 2008;41(4):673-695. DOI |
30 | Kim JO, Lim SE, Park SY, Choi SH, Choi JN, Choi HS. Validity and reliability of Korean-version of voice handicap index and voice-related quality of life. Speech Sciences. 2007;14(3):111-125. |
31 | Xiong H, Lin P, Yu JG, Ye J, Xiao L, Tao Y, Jiang Z, Lin W, Liu M, Xu J, Hu W, Lu Y, Liu H, Li Y, Zheng Y, Yang H. Computeraided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine. 2019;48:92-99. DOI |
32 | Ye G, Du C, Lin T, Yan Y, Jiang J. Deep Learning for Laryngopharyngeal Reflux Diagnosis. Applied Sciences. 2021;11(11):4753. DOI |
33 | Onji K, Yoshida S, Tanaka S, Kawase R, Takemura Y, Oka S, Tamaki T, Raytchev B, Kaneda K, Yoshihara M, Chayama K. Quantitative analysis of colorectal lesions observed on magnified endoscopy images. J Gastroenterol. 2011;46:1382-1390. DOI |
34 | Mohanaiah P, Sathyanarayana P, GuruKumar I. Image texture feature extraction using GLCM approach. International journal of scientific and research publications. 2013;3(5):1-5. |
35 | Ni XG, He S, Xu ZG, Gao L, Lu N, Yuan Z Lai S-Q, Zhang T-M, Yi J-L, Wang X-L. Endoscopic diagnosis of laryngeal cancer and precancerous lesions by narrow band imaging. The Journal of Laryngology & Otology. 2010;125(3):288-296. |
36 | Ren J, Jing X, Wang J, Ren X, Xu Y, Yang Q, Ma L, Sun Y, Xu W, Yang N, Zou J, Zheng Y, Chen M, Gan W, Xiang T, An J, Liu R, Lv C, Lin K, Zheng X, Lou F, Rao Y, Yang Hui, Liu Kai, Liu G, Lu T, Zheng X, Zhao Y. Automatic recognition of laryngoscopic images using a deep-learning technique. The Laryngoscope. 2020;130(11):E686-E693. |
37 | Kandemirli SG, Chopra S, Pyiya S, Ward C, Locke T, Soni N, Srivastava S, Jones K, Bathla G. Presurgical detection of brain invasion status in meningiomas based on first-order histogram-based texture analysis of contrast enhanced imaging. Clinical neurology and neurosurgery. 2020;198:106205. DOI |
38 | Sohail ASM, Bhattacharya P, Mudur SP, Krishnamurthy S. Local relative GLRLM-based texture feature extraction for classifying ultrasound medical images. 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE). 2011;001092-001095. |
39 | Thibault G, Angulo J, Meyer F. Advanced statistical matrices for texture characterization: Application to DNA chromatin and microtubule network classification. 2011 18th IEEE International Conference on Image Processing. 2011;53-56. |