为了评估术前磁共振成像（MRI）测量方法通过机器学习（ML）驱动的模型来预测口腔舌鳞状细胞癌（OTSCC）的病理T（pT）分期和颈部淋巴结转移（CLNM）的能力，我们纳入了108例新诊断的OTSCC患者。术前磁共振成像研究包括所有患者的高分辨率T1加权后增强图像。从病灶体积分割中提取了基于MRI的浸润深度（DOI）和肿瘤尺寸，以及基于形状和强度的特征。整个数据集随机分成训练集和验证集，评估和比较了不同类型的ML算法的性能。MRI-based DOI和肿瘤尺寸，以及几个基于形状和强度的特征，能显著区分pT分期和LN状态。预测pT分期的模型的整体准确度在训练集和验证集中分别为0.86（95%CI，0.78-0.92）和0.81（0.64-0.91）。在包括基于形状和强度的特征后，模型性能没有改进。基于DOI和肿瘤尺寸的CLNM预测模型在训练集和验证集中的准确度分别为0.68（0.57-0.78）和0.69（0.51-0.84）。基于形状和强度的特征显示出改善模型敏感性的潜力，并且具有相当的准确度。基于ML算法的MRI模型可以根据pT分期分层评估OTSCC患者。它们具有适度预测颈部淋巴结转移的能力。 © 2023 作者们。

To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC).108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared.MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy.MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.© 2023 The Authors.