Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.
Authors
Affiliations (6)
Affiliations (6)
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France.
- Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France.
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
- School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, UK.
- Department of Otolaryngology Head and Neck Surgery, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France.
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190, Gif-sur-Yvette, France. [email protected].
Abstract
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.