Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study

Guzzo M, Locati LD, Prott FJ, Gatta G, McGurk M, Licitra L: Major and minor salivary gland tumors. Critical Reviews in Oncology/Hematology, 74(2):134–148, 2010

Article  PubMed  Google Scholar 

Spiro RH: Salivary neoplasms: Overview of a 35-year experience with 2,807 patients. Head & Neck Surgery, 8(3):177–184, 1986

Article  CAS  Google Scholar 

Reerds STH, Van Engen–Van Grunsven ACH, Hoogen FJA, Takes RP, Marres HAM, Honings J: Accuracy of parotid gland FNA cytology and reliability of the Milan System for Reporting Salivary Gland Cytopathology in clinical practice. Cancer Cytopathology, 129(9):719–728, 2021

Article  PubMed  PubMed Central  Google Scholar 

Correia-Sá I, Correia-Sá M, Costa-Ferreira P, Silva Á, Marques M: Fine-needle aspiration cytology (FNAC): Is it useful in preoperative diagnosis of parotid gland lesions? Acta Chirurgica Belgica, 117(2):110–114, 2017

Article  PubMed  Google Scholar 

Tartaglione T, Botto A, Sciandra M, Gaudino S, Danieli L, Parrilla C, Paludetti G, Colosimo C: Differential diagnosis of parotid gland tumours: Which magnetic resonance findings should be taken in account? Acta Otorhinolaryngologica Italica, 35(5):314–320, 2015

Article  CAS  PubMed  PubMed Central  Google Scholar 

El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM: Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications, 41(11):5526–5545, 2014

Article  Google Scholar 

Wiens J, Shenoy ES: Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology. Clinical Infectious Diseases, 66(1):149–153, 2018

Article  PubMed  Google Scholar 

Zhang G, Zheng C, He J, Yi S: PCT: Pyramid convolutional transformer for parotid gland tumor segmentation in ultrasound images. Biomedical Signal Processing and Control, 81:104498, 2023

Article  Google Scholar 

Sunnetci KM, Kaba E, Celiker FB, Alkan A: Deep network-based comprehensive parotid gland tumor detection. Academic Radiology, 31(1):157–167, 2024

Article  PubMed  Google Scholar 

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ: Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48(4):441–446, 2012

Article  PubMed  PubMed Central  Google Scholar 

Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, 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, 30(9):1234–1248, 2012

Article  PubMed  PubMed Central  Google Scholar 

Ortiz-Ramón R, Larroza-Santacruz A, Ruiz-España S, Arana Fernandez De Moya E, Moratal D: Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study, in European Radiology, volume 28, pages 4514–4523, Springer-Verlag, 2018

Zhang J, Jin J, Ai Y, Zhu K, Xiao C, Xie C, Jin X: Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images. European Radiology, 31(2):1022–1028, 2021

Article  PubMed  Google Scholar 

Organisation mondiale de la santé, Centre international de recherche sur le cancer, editors: WHO Classification of Head and Neck Tumours, number 9 in World Health Organization Classification of Tumours, International agency for research on cancer, Lyon, 4th ed edition, 2017

Zheng Ym, Li J, Liu S, Cui Jf, Zhan Jf, Pang J, Zhou Rz, Li Xl, Dong C: MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. European Radiology, 2020

Gabelloni M, Faggioni L, Attanasio S, Vani V, Goddi A, Colantonio S, Germanese D, Caudai C, Bruschini L, Scarano M, Seccia V, Neri E: Can Magnetic Resonance Radiomics Analysis Discriminate Parotid Gland Tumors? A Pilot Study. Diagnostics, 10(11):900, 2020

Article  PubMed  PubMed Central  Google Scholar 

Khodabakhshi Z, Motisi L, Bink A, Broglie MA, Rupp NJ, Fleischmann M, von der Grün J, Guckenberger M, Tanadini-Lang S, Balermpas P: MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and “proof of principle”. Scientific Reports, 14(1):9945, 2024, https://www.nature.com/articles/s41598-024-60200-9

Mao K, Wong LM, Zhang R, So TY, Shan Z, Hung KF, Ai QYH: Radiomics analysis in characterization of salivary gland tumors on MRI: A systematic review. Cancers, 15(20):4918, 2023, https://www.mdpi.com/2072-6694/15/20/4918

Zhang R, Ai QYH, Wong LM, Green C, Qamar S, So TY, Vlantis AC, King AD: Radiomics for discriminating benign and malignant salivary gland tumors; which radiomic feature categories and MRI sequences should be used? Cancers, 14(23):5804, 2022, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740105/

Xia X, Feng B, Wang J, Hua Q, Yang Y, Sheng L, Mou Y, Hu W: Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images. Frontiers in Oncology, 11:632104, 2021

Article  PubMed  PubMed Central  Google Scholar 

Shinohara RT, Sweeney EM, Goldsmith J, Shiee N, Mateen FJ, Calabresi PA, Jarso S, Pham DL, Reich DS, Crainiceanu CM: Statistical normalization techniques for magnetic resonance imaging. NeuroImage: Clinical, 6:9–19, 2014

Nyúl LG, Udupa JK: On standardizing the MR image intensity scale. Magnetic Resonance in Medicine, 42(6):1072–1081, 1999

Article  PubMed  Google Scholar 

Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RT, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EA, Rahmim A, Rao AU, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJ, Tanadini-Lang S, Thorwarth D, Troost EG, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FH, Whybra P, Richter C, Löck S: The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology, 295(2):328–338, 2020

Article  PubMed  Google Scholar 

Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F, Ayache N, Frouin F, Duron L, Buvat I: How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. European Radiology, 2020

Beer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, Linn KA: Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data. NeuroImage, 220:117129, 2020

Article  PubMed  Google Scholar 

Caruana R, Karampatziakis N, Yessenalina A: An empirical evaluation of supervised learning in high dimensions, in Proceedings of the 25th International Conference on Machine Learning, ICML ’08, pages 96–103, Association for Computing Machinery, New York, NY, USA, 2008

Fernández-Delgado M, Cernadas E, Barro S, Amorim D: Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research, 15(1):3133–3181, 2014

Google Scholar 

Kniep HC, Madesta F, Schneider T, Hanning U, Schönfeld MH, Schön G, Fiehler J, Gauer T, Werner R, Gellissen S: Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. Radiology, 290(2):479–487, 2018

Article  PubMed  Google Scholar 

Wang H, Zhang J, Bao S, Liu J, Hou F, Huang Y, Chen H, Duan S, Hao D, Liu J: Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study. Journal of Magnetic Resonance Imaging, 52(3):873–882, 2020

Article  PubMed  Google Scholar 

Breiman L: Random Forests. Machine Learning, 45(1):5–32, 2001

Article  Google Scholar 

James G, Witten D, Hastie T, Tibshirani R, Taylor J: An Introduction to Statistical Learning: with Applications in Python, Springer Texts in Statistics, Springer International Publishing, 2023

Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer Series in Statistics, Springer-Verlag, New York, second edition, 2009

Book  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S: Radiomics: The bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 14(12):749–762, 2017

Article  PubMed  Google Scholar 

Kuhn M, Johnson K: An Introduction to Feature Selection, pages 487–519, Springer New York, New York, NY, 2013

Kuhn M, Johnson K: Feature Engineering and Selection: A Practical Approach for Predictive Models, CRC Press, 2019

Matsuo H, Nishio M, Kanda T, Kojita Y, Kono AK, Hori M, Teshima M, Otsuki N, Nibu Ki, Murakami T: Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: Discriminating malignant parotid tumors in MRI. Scientific Reports, 10(1):19388, 2020

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yabuuchi H, Kamitani T, Sagiyama K, Yamasaki Y, Hida T, Matsuura Y, Hino T, Murayama Y, Yasumatsu R, Yamamoto H: Characterization of parotid gland tumors: Added value of permeability MR imaging to DWI and DCE-MRI. European Radiology, 30(12):6402–6412, 2020

Article  PubMed  Google Scholar 

Chang YJ, Huang TY, Liu YJ, Chung HW, Juan CJ: Classification of parotid gland tumors by using multimodal MRI and deep learning. NMR in Biomedicine, 34(1), 2021

Wang CW, Chu YH, Chiu DY, Shin N, Hsu HH, Lee JC, Juan CJ: JOURNAL CLUB: The Warthin Tumor Score: A Simple and Reliable Method to Distinguish Warthin Tumors From Pleomorphic Adenomas and Carcinomas. American Journal of Roentgenology, 210(6):1330–1337, 2018

Article  PubMed  Google Scholar 

Zwanenburg A, Leger S, Agolli L, Pilz K, Troost EGC, Richter C, Löck S: Assessing robustness of radiomic features by image perturbation. Scientific Reports, 9(1):614, 2019

Article  PubMed  PubMed Central  Google Scholar 

Ikeda M, Motoori K, Hanazawa T, Nagai Y, Yamamoto S, Ueda T, Funatsu H, Ito H: Warthin tumor of the parotid gland: Diagnostic value of MR imaging with histopathologic correlation. AJNR American journal of neuroradiology, 25(7):1256–1262, 2004

PubMed  PubMed Central  Google Scholar 

Okahara M, Kiyosue H, Hori Y, Matsumoto A, Mori H, Yokoyama S: Parotid tumors: MR imaging with pathological correlation. European Radiology, 13(S06):L25–L33, 2003

Article  PubMed 

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