切除胰腺导管腺癌生存预测的多模态数据集
A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma
- Giessen University（吉森大学）
- University Medical Center Göttingen（哥廷根大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究构建了包含302例PDAC患者的多模态数据集，评估多种生存预测模型，其中多模态融合达到最高C指数0.619，为后续研究提供基准。
AI中文摘要：
胰腺导管腺癌（PDAC）的生存研究受限于缺乏将全切片组织学与临床、分子及长期预后数据关联的数据集。我们提出了一个回顾性单中心队列，包含302名在哥廷根大学医学中心接受PDAC切除术的患者。该数据集包括446张H&E全切片图像、临床病理变量、154名患者的靶向测序数据以及总生存期结果。随访期间，253名患者死亡，中位随访时间为76个月。为建立初始参考值，我们使用相同的五次重复蒙特卡洛交叉验证分区评估了十四种生存预测配置。使用数值临床病理变量的Ridge Cox回归实现了平均一致性指数$0.649 \pm 0.042$，在加入KRAS和TP53突变状态后达到$0.652 \pm 0.046$。仅图像注意力模型达到$0.603 \pm 0.030$，而多模态融合达到$0.619 \pm 0.025$，这是神经模型中最高的C指数。这些结果为未来使用该胰腺特异性多模态数据集的研究建立了有前景的初始基准，为外部验证铺平了道路。
英文摘要：
Survival research in pancreatic ductal adenocarcinoma (PDAC) is limited by the scarcity of datasets linking whole-slide histology with clinical, molecular, and long-term outcome data. We present a retrospective single-centre cohort of 302 patients who underwent PDAC resection at University Medical Center Gottingen. The dataset comprises 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall-survival outcomes. During follow-up, 253 patients died, and the median follow-up was 76 months. To establish initial reference values, we evaluated fourteen survival-prediction configurations using identical five-repetition Monte Carlo cross-validation partitions. Ridge Cox regression using numeric clinicopathological variables achieved a mean concordance of $0.649 \pm 0.042$ and $0.652 \pm 0.046$ after adding KRAS and TP53 mutation status. The image-only attention model achieved $0.603 \pm 0.030$, while multimodal fusion achieved $0.619 \pm 0.025$, the highest concordance among the neural models. These results establish promising initial benchmarks for future research using this pancreas-specific multimodal dataset, paving the way for external validation.
