Department of Nephrology, The First Hospital of Jilin University, Changchun, China.
Front Endocrinol (Lausanne). 2024 Mar 8;15:1328579. doi: 10.3389/fendo.2024.1328579. eCollection 2024.
Owing 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.
The 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.
Multivariate 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.
The 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.
由于个体的异质性，特发性膜性肾病（IMN）患者对免疫治疗的敏感性存在差异。本研究旨在建立并验证一个基于深度学习训练的纳入病理和临床特征的模型，以评估 IMN 患者对免疫抑制治疗的反应。
291 例患者被随机分为训练（n=219）和验证（n=72）队列。采用弱监督的补丁级卷积神经网络训练来分析全片组织病理学特征。我们开发了一个机器学习模型，以评估病理特征与临床因素相比的预测价值。使用训练和验证测试中的受试者工作特征曲线（ROC）下面积（AUC）评估模型的性能水平，并比较模型对免疫治疗反应的预测准确性。
多因素分析表明，糖尿病和吸烟是影响 IMN 患者免疫治疗反应的独立危险因素。整合病理特征的模型对预测 IMN 患者免疫治疗反应具有较好的预测价值，在训练和测试队列中的 AUC 分别为 0.85 和 0.77。然而，当将临床特征纳入模型时，预测效果会降低，在训练和测试队列中的 AUC 值分别为 0.75 和 0.62。
与整合临床因素相比，纳入病理特征的模型在预测 IMN 患者对免疫抑制治疗的反应方面具有更好的预测能力。
