目的:在肌层浸润性膀胱癌(MIBC)患者中,术前诊断变异型组织学如鳞状分化的尿路上皮癌(UCw/SD)是必不可少的,但极具挑战性的,因为他们的治疗策略差异很大。我们开发了一种非侵入性自动机器学习(AutoML)模型,在术前区分MIBC患者的UCw/SD和纯UC。
方法:本研究共纳入119例接受基线膀胱MRI检查的MIBC患者,包括38例UC患者W/SD和81例单纯UC患者。这些患者被随机分配到训练集或测试集(3:1)。从训练集构建了AutoML模型,使用来自T2加权成像的13个选定的放射学特征,语义特征(ADC值),和临床特征(肿瘤长度,肿瘤分期,淋巴结转移状态),随后进行了10倍交叉验证。使用测试集来验证所提出的模型。然后计算模型的ROC曲线的AUC。
结果:该AutoML模型能够在两个训练集(十倍交叉验证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)中对MIBC患者的UCw/SD和纯UC进行稳健区分。
结论:提出的AutoML模型,结合了放射学,语义,和基线MRI的临床特征,可用于术前分化UCw/SD和纯真UC。
结论:这项基于MRI的自动机器学习(AutoML)研究提供了一种非侵入性和低成本的术前预测工具,可用于识别组织学变异的肌层浸润性膀胱癌患者。这可能是临床决策的有用工具。
结论:•在肌层浸润性膀胱癌(MIBC)患者中,术前诊断尿路上皮癌变组织学非常重要,因为他们的治疗策略差异很大。•基于基线膀胱MRI的自动机器学习(AutoML)模型可以识别MIBC患者术前尿路上皮癌的组织学变异(鳞状分化)。•开发的AutoML模型是一种非侵入性和低成本的术前预测工具,这可能对临床决策有用。
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: • 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.