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

Samy Ammari, Arnaud Quillent, Víctor Elvira, François Bidault, Gabriel C T E Garcia, Dana M Hartl, Corinne Balleyguier, Nathalie Lassau, Émilie Chouzenoux
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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.

在磁共振成像放射组学中使用机器学习诊断腮腺肿瘤,然后再与放射科医生进行比较:试点研究。
腮腺是主要唾液腺中最大的腺体。腮腺既可罹患良性肿瘤,也可罹患恶性肿瘤。术前检查主要依靠核磁共振图像和细针穿刺活检,但这些诊断工具的灵敏度和特异性较低,往往导致为诊断目的而进行手术。本文的目的是:(1) 开发一种基于磁共振图像特征的机器学习算法,自动对腮腺肿瘤进行分类;(2) 将其结果与初级和高级放射科医生的诊断结果进行比较,以评估其在常规实践中的实用性。虽然过去已经开发了应用于腮腺肿瘤分类的自动算法,但我们认为我们的研究是首批利用四种不同磁共振成像序列并提出与临床医生进行比较的研究之一。在这项研究中,我们利用了134名接受良性或恶性腮腺肿瘤治疗的患者的数据。利用从腺体核磁共振图像中提取的放射组学数据,我们训练了随机森林和逻辑回归来预测相应的组织病理学亚型。在测试集中,随机森林的结果最好:在所有组织病理学亚型中,我们获得了 0.720 的准确率、0.860 的特异性和 0.720 的灵敏度,平均 AUC 为 0.838。在区分良性肿瘤和恶性肿瘤时,该算法的准确率为 0.760,AUC 为 0.769(均在测试集上)。此外,临床实验表明,我们的模型有助于提高初级放射科医生的诊断能力,因为使用我们提出的方法后,他们的灵敏度和准确度提高了 6%。该算法可用于医生培训。采用机器学习算法的放射组学可能有助于提高良性和恶性腮腺肿瘤的鉴别能力,从而减少诊断性手术的需要。有必要开展进一步研究,以验证我们的算法是否可用于常规用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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