Ammari Samy, Quillent Arnaud, Elvira Víctor, Bidault François, Garcia Gabriel C T E, Hartl Dana M, Balleyguier Corinne, Lassau Nathalie, Chouzenoux Émilie
Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805, Villejuif, France.
Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, 94805, Villejuif, France.
J Imaging Inform Med. 2025 Jun;38(3):1496-1508. doi: 10.1007/s10278-024-01255-y. Epub 2024 Oct 10.
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.
腮腺是主要唾液腺中最大的一对。它们可发生良性和恶性肿瘤。术前检查依赖于磁共振成像（MR）和细针穿刺活检，但这些诊断工具的敏感性和特异性较低，常导致为明确诊断而进行手术。本文的目的是：（1）基于MR图像特征开发一种机器学习算法，以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效用。虽然过去已开发出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MRI序列并与临床医生进行比较的研究之一。在本研究中，我们利用了来自134例接受良性或恶性腮腺肿瘤治疗患者队列的数据。使用从腮腺MR图像中提取的影像组学特征，我们训练了一个随机森林模型和一个逻辑回归模型来预测相应的组织病理学亚型。在测试集上，随机森林模型给出了最佳结果：对所有组织病理学亚型，我们获得了0.720的准确率、0.860的特异性和0.720的敏感性，平均曲线下面积（AUC）为0.838。在考虑区分良性和恶性肿瘤时，该算法在测试集上的准确率为0.760，AUC为0.769。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，因为他们在使用我们提出的方法时，敏感性和准确率提高了6%。该算法可能对医生培训有用。结合机器学习算法的影像组学可能有助于提高腮腺良恶性肿瘤的鉴别能力，减少诊断性手术的需求。有必要进行进一步研究以验证我们的算法在常规使用中的有效性。
