用生成式深度学习无创预测胆管癌神经侵犯，提升诊断准确性。
NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification
- 构建端到端3D框架，结合图像生成与注意力机制增强特征提取。
- 在5折交叉验证中达AUC 0.7903，优于传统3D模型。
- 适合医学影像分析、肿瘤无创诊断方向研究者参考。
减少侵入性诊断操作以降低患者损伤和感染风险是医学影像的核心目标。然而，由于缺乏明确一致的影像学标准，非侵入性诊断胆管癌中的神经侵犯（PNI）仍具挑战性。为此，我们提出NeoNet，一种无需预定义图像特征的端到端3D深度学习框架，用于胆管癌中PNI的预测。该框架包含三个模块：(1) NeoSeg，采用肿瘤定位感兴趣区域裁剪（TLCR）算法；(2) NeoGen，基于3D潜在扩散模型（LDM）与ControlNet，以解剖掩码为条件生成合成图像块，将数据集平衡至1:1比例；(3) NeoCls，最终预测模块。其中，我们设计了冻结的LDM编码器与专用3D双注意力块（DAB），用于检测提示PNI的微弱强度变化与空间模式。在5折交叉验证中，NeoNet超越基线3D模型，取得最高AUC 0.7903。
原文摘要 · Abstract (English)
Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural invasion (PNI), a critical prognostic factor involving infiltration of tumor cells along the surrounding nerve, still remains challenging, due to the lack of clear and consistent imaging criteria criteria for identifying PNI. To address this challenge, we present NeoNet, an integrated end-to-end 3D deep learning framework for PNI prediction in cholangiocarcinoma that does not rely on predefined image features. NeoNet integrates three modules: (1) NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR) algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM) with ControlNet, conditioned on anatomical masks to generate synthetic image patches, specifically balancing the dataset to a 1:1 ratio; and (3) NeoCls, the final prediction module. For NeoCls, we developed the PNI-Attention Network (PattenNet), which uses the frozen LDM encoder and specialized 3D Dual Attention Blocks (DAB) designed to detect subtle intensity variations and spatial patterns indicative of PNI. In 5-fold cross-validation, NeoNet outperformed baseline 3D models and achieved the highest performance with a maximum AUC of 0.7903.
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