Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.
The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic pu...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1496 - 1509 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280506&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280506 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280506 185280506 185280506 10.1007/s10278-024-01255-y 185280506 ppf: 1496 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study. aug: au: Ammari, Samy Quillent, Arnaud Elvira, Víctor Bidault, François Garcia, Gabriel C. T. E. Hartl, Dana M. Balleyguier, Corinne Lassau, Nathalie Chouzenoux, Émilie affil: https://ror.org/03xjwb503 Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France sug: subj: Machine Learning Algorithms Utilization Magnetic Resonance Imaging Radiomics Radiologists Parotid Neoplasms Diagnosis Parotid Neoplasms Classification Image Interpretation, Computer Assisted Human Pilot Studies Comparative Studies Male Female Adolescence Adult Middle Age Aged Aged, 80 and Over Random Forest ROC Curve Sensitivity and Specificity Logistic Regression Funding Source Prospective Studies Physicians Descriptive Statistics Parotid Neoplasms Therapy Image Processing, Computer Assisted Detection Algorithms Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic purposes. The aim of this paper is (1) to develop a machine learning algorithm based on MR images characteristics to automatically classify parotid gland tumours and (2) compare its results with the diagnoses of junior and senior radiologists in order to evaluate its utility in routine practice. While automatic algorithms applied to parotid tumours classification have been developed in the past, we believe that our study is one of the first to leverage four different MRI sequences and propose a comparison with clinicians. In this study, we leverage data coming from a cohort of 134 patients treated for benign or malignant parotid tumours. Using radiomics extracted from the MR images of the gland, we train a random forest and a logistic regression to predict the corresponding histopathological subtypes. On the test set, the best results are given by the random forest: we obtain a 0.720 accuracy, a 0.860 specificity, and a 0.720 sensitivity over all histopathological subtypes, with an average AUC of 0.838. When considering the discrimination between benign and malignant tumours, the algorithm results in a 0.760 accuracy and a 0.769 AUC, both on test set. Moreover, the clinical experiment shows that our model helps to improve diagnostic abilities of junior radiologists as their sensitivity and accuracy raised by 6 % when using our proposed method. This algorithm may be useful for training of physicians. Radiomics with a machine learning algorithm may help improve discrimination between benign and malignant parotid tumours, decreasing the need for diagnostic surgery. Further studies are warranted to validate our algorithm for routine use. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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