将影像组学特征注入深度模型，提升胰腺癌检测精度
From Global Radiomics to Parametric Maps: A Unified Workflow Fusing Radiomics and Deep Learning for PDAC Detection
- 融合全局与体素级影像组学特征，增强nnUNet模型
- 在PANORAMA数据集上AUC达0.96，外部队列AUC为0.95
- 适合医学影像分析、放射组学与AI结合的研究者
影像组学与深度学习均为定量医学影像的强大工具，但现有融合方法多仅利用全局影像组学特征，忽视了空间解析的参数图的互补价值。本文提出统一框架：先筛选具有判别力的影像组学特征，再将其注入增强型nnUNet模型，在全局和体素两个层面进行胰腺导管腺癌（PDAC）检测。在PANORAMA数据集上，该方法交叉验证AUC为0.96，平均精度（AP）为0.84；在外部院内队列中，AUC达0.95，AP为0.78，优于基线nnUNet，并在PANORAMA Grand Challenge中排名第二。结果表明，手工设计的影像组学特征在全局与体素层级注入，可为深度学习模型提供互补信号。
原文摘要 · Abstract (English)
Radiomics and deep learning both offer powerful tools for quantitative medical imaging, but most existing fusion approaches only leverage global radiomic features and overlook the complementary value of spatially resolved radiomic parametric maps. We propose a unified framework that first selects discriminative radiomic features and then injects them into a radiomics-enhanced nnUNet at both the global and voxel levels for pancreatic ductal adenocarcinoma (PDAC) detection. On the PANORAMA dataset, our method achieved AUC = 0.96 and AP = 0.84 in cross-validation. On an external in-house cohort, it achieved AUC = 0.95 and AP = 0.78, outperforming the baseline nnUNet; it also ranked second in the PANORAMA Grand Challenge. This demonstrates that handcrafted radiomics, when injected at both global and voxel levels, provide complementary signals to deep learning models for PDAC detection. Our code can be found at https://github.com/briandzt/dl-pdac-radiomics-global-n-paramaps
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