用可解释的生成模型预测直肠癌淋巴结转移，准确率超现有方法。
Interpretable Prediction of Lymph Node Metastasis in Rectal Cancer MRI Using Variational Autoencoders
- 用变分自编码器提取MRI图像特征，生成更清晰的潜在表示。
- 在168例患者数据上达到AUC 0.86、灵敏度0.79、特异度0.85。
- 模型结果可解释，适合临床医生辅助决策使用。
直肠癌治疗依赖于淋巴结转移（LNM）的准确分期。然而，基于淋巴结大小、形状和纹理的影像学标准诊断准确率有限。本文探索使用变分自编码器（VAE）作为特征编码器，替代现有方法中大型预训练卷积神经网络（CNN）。VAE通过重建图像，直接编码视觉特征与数据中的有意义模式，生成解耦且结构化的潜在空间，比CNN更具可解释性。模型在无新辅助治疗的院内MRI数据集（168名患者）上部署，以术后病理N分期为真实标签进行评估。所提模型VAE-MLP在交叉验证中表现最佳，各项指标为AUC 0.86 ± 0.05，敏感性0.79 ± 0.06，特异性0.85 ± 0.05。代码已开源：https://github.com/benkeel/Lymph_Node_Classification_MIUA。
原文摘要 · Abstract (English)
Effective treatment for rectal cancer relies on accurate lymph node metastasis (LNM) staging. However, radiological criteria based on lymph node (LN) size, shape and texture morphology have limited diagnostic accuracy. In this work, we investigate applying a Variational Autoencoder (VAE) as a feature encoder model to replace the large pre-trained Convolutional Neural Network (CNN) used in existing approaches. The motivation for using a VAE is that the generative model aims to reconstruct the images, so it directly encodes visual features and meaningful patterns across the data. This leads to a disentangled and structured latent space which can be more interpretable than a CNN. Models are deployed on an in-house MRI dataset with 168 patients who did not undergo neo-adjuvant treatment. The post-operative pathological N stage was used as the ground truth to evaluate model predictions. Our proposed model 'VAE-MLP' achieved state-of-the-art performance on the MRI dataset, with cross-validated metrics of AUC 0.86 +/- 0.05, Sensitivity 0.79 +/- 0.06, and Specificity 0.85 +/- 0.05. Code is available at: https://github.com/benkeel/Lymph_Node_Classification_MIUA.
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