预先手术诊断肌层浸润性膀胱癌（MIBC）患者中泌尿上皮癌伴鳞状分化（UC w/SD）等变异组织学类型与纯UC的不同是至关重要的，因为它们的治疗策略存在显著差异。我们开发了一种无创的自动化机器学习（AutoML）模型，用于预先手术识别具有鳞状分化的UC w/SD与纯UC之间的差异。本研究纳入了进行基线膀胱MRI的119例MIBC患者，包括38例UC w/SD患者和81例纯UC患者。这些患者随机分配到训练集或测试集（3:1）。我们从训练集中建立了一个AutoML模型，使用T2加权成像的13个选择性放射组学特征、语义特征（ADC值）和临床特征（肿瘤长度、肿瘤分期、淋巴结转移情况），并进行了十折交叉验证。测试集用于验证提出的模型。计算ROC曲线下的面积（AUC）以评估模型。该AutoML模型能够在训练集（十折交叉验证AUC = 0.955，95％置信区间[CI]:0.944-0.965）和测试集（AUC = 0.932，95％CI: 0.812-1.000）中鲁棒地区分UC w/SD和纯UC。所提出的基于MRI的AutoML模型，结合了基线MRI的放射组学、语义和临床特征，可用于UC w/SD和纯UC的术前鉴别。这种基于MRI的自动化机器学习（AutoML）研究提供了一种无创且低成本的术前预测工具，可用于鉴别具有变异组织学的肌层浸润性膀胱癌患者，对于临床决策具有实用性。© 2023. 作者经欧洲放射学会独家许可。

It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC.A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model.This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000).The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC.This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making.• It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.© 2023. The Author(s), under exclusive licence to European Society of Radiology.