DataSheet_1_Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.pdf

BackgroundOwing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features using deep learning training to evaluate the response of patients with IMN to immunosuppressive therapy.
MethodsThe 291 patients were randomly categorized into training (n = 219) and validation (n = 72) cohorts. Patch-level convolutional neural network training in a weakly supervised manner was utilized to analyze whole-slide histopathological features. We developed a machine-learning model to assess the predictive value of pathological signatures compared to clinical factors. The performance levels of the models were evaluated using the area under the receiver operating characteristic curve (AUC) on the training and validation tests, and the prediction accuracies of the models for immunotherapy response were compared.
ResultsMultivariate analysis indicated that diabetes and smoking were independent risk factors affecting the response to immunotherapy in IMN patients. The model integrating pathologic features had a favorable predictive value for determining the response to immunotherapy in IMN patients, with AUCs of 0.85 and 0.77 when employed in the training and test cohorts, respectively. However, when incorporating clinical features into the model, the predictive efficacy diminishes, as evidenced by lower AUC values of 0.75 and 0.62 on the training and testing cohorts, respectively.
ConclusionsThe model incorporating pathological signatures demonstrated a superior predictive ability for determining the response to immunosuppressive therapy in IMN patients compared to the integration of clinical factors.

背景：由于个体异质性，特发性膜性肾病（idiopathic membranous nephropathy, IMN）患者对免疫治疗的敏感性存在差异。本研究旨在通过深度学习训练构建并验证一款整合病理与临床特征的模型，以评估IMN患者对免疫抑制治疗的应答情况。
方法：本研究将291例患者随机分为训练队列（n=219）与验证队列（n=72）。采用弱监督模式下的斑块级卷积神经网络（patch-level convolutional neural network）训练，分析全切片组织病理学特征。我们构建了机器学习模型，以评估病理特征相较于临床因素的预测价值。通过训练与验证测试中的受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUC）评估模型性能，并比较各模型对免疫治疗应答的预测准确率。
结果：多因素分析显示，糖尿病与吸烟是影响IMN患者免疫治疗应答的独立危险因素。整合病理特征的模型对IMN患者免疫治疗应答具有良好的预测价值，在训练队列与测试队列中的AUC分别为0.85与0.77。然而，当模型纳入临床特征后，预测效能有所下降，训练队列与测试队列的AUC分别降至0.75与0.62。
结论：相较于仅整合临床特征的模型，纳入病理特征的模型对IMN患者免疫抑制治疗应答的预测能力更优。