膀胱癌治疗决策取决于检测肌肉侵袭。 2018 年“膀胱成像报告和数据系统”(VI-RADS) 标准化了多参数 MRI (mp-MRI) 的使用。放射组学是一种分析框架，提供比传统方法更深入的信息。为了确定使用 mp-MRI 放射组学特征区分 MIBC（肌肉浸润性膀胱癌）和 NMIBC（非肌肉浸润性膀胱癌）的效果。我们进行了一项研究73 名经病理诊断的膀胱癌患者于 2020 年 1 月至 2022 年 7 月期间接受了术前 mp-MRI。利用 3D Slicer（版本 4.8.1）和 Pyradiomics，我们从扩散加权创建的表观扩散系数 (ADC) 图手动提取放射组学特征成像。 LASSO 方法确定了最佳特征，并且我们使用 SMOTE 解决了样本不平衡问题。我们开发了一个单独使用纹理特征或与 VI-RADS 结合使用的分类模型，采用具有 10 倍交叉验证的随机森林分类器。使用 ROC 曲线下面积分析来评估诊断性能。在 73 名患者（63 名男性，10 名女性；中位年龄：63 岁）中，41 名患有肌肉浸润癌，32 名患有浅表性膀胱癌。 41 名 VI-RADS 4 和 5 评分患者中的 25 名观察到肌肉侵犯，32 名 VI-RADS 1、2 和 3 评分患者中的 12 名观察到肌肉侵犯（准确度：77.5%，敏感性：67.7%，特异性：88.8%）。在此数据集中，VI-RADS 评分和放射组学模型的组合 (AUC = 0.92 ± 0.12) 优于使用 ADC MRI 的单一放射组学模型（AUC = 0.83 ± 0.22，10 倍交叉验证）。在接受手术之前，膀胱癌浸润肌肉可能会使用基于 mp-MRI 的放射组学特征进行预测。© 2024。外科肿瘤学会。

Bladder cancer treatment decisions hinge on detecting muscle invasion. The 2018 "Vesical Imaging Reporting and Data System" (VI-RADS) standardizes multiparametric MRI (mp-MRI) use. Radiomics, an analysis framework, provides more insightful information than conventional methods.To determine how well MIBC (Muscle Invasive Bladder Cancer) and NMIBC (Non-Muscle Invasive Bladder Cancer) can be distinguished using mp-MRI radiomics features.We conducted a study with 73 bladder cancer patients diagnosed pathologically, who underwent preoperative mp-MRI from January 2020 to July 2022. Utilizing 3D Slicer (version 4.8.1) and Pyradiomics, we manually extracted radiomic features from apparent diffusion coefficient (ADC) maps created from diffusion-weighted imaging. The LASSO approach identified optimal features, and we addressed sample imbalance using SMOTE. We developed a classification model using textural features alone or combined with VI-RADS, employing a random forest classifier with 10-fold cross-validation. Diagnostic performance was assessed using the area under the ROC curve analysis.Among 73 patients (63 men, 10 women; median age: 63 years), 41 had muscle-invasive and 32 had superficial bladder cancer. Muscle invasion was observed in 25 of 41 patients with VI-RADS 4 and 5 scores and 12 of 32 patients with VI-RADS 1, 2, and 3 scores (accuracy: 77.5%, sensitivity: 67.7%, specificity: 88.8%). The combined VI-RADS score and radiomics model (AUC = 0.92 ± 0.12) outperformed the single radiomics model using ADC MRI (AUC = 0.83 ± 0.22 with 10-fold cross-validation) in this dataset.Before undergoing surgery, bladder cancer invasion in muscle might potentially be predicted using a radiomics signature based on mp-MRI.© 2024. Society of Surgical Oncology.