Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma

2026-09-10

AI Summary

为解决术前评估肝内胆管癌神经周围侵犯风险的问题，提出了一种基于T2加权MRI的Order-Aware Slab多实例学习方法，通过利用轴向顺序信息提高了预测性能。

Abstract

Perineural invasion (PNI) is an adverse histopathologic marker in intrahepatic cholangiocarcinoma (ICC), but it is usually confirmed only after resection. Preoperative T2-weighted MRI may provide noninvasive imaging cues predictive of PNI, although labels are available only at the patient level without slice- or voxel-level annotations. We propose Order-Aware Slab Multiple Instance Learning (OAS-MIL), a weakly supervised framework for patient-level PNI prediction. Each tumor-centered MRI crop is represented as an ordered sequence of overlapping 2.5D slabs formed from contiguous axial slices. A shared encoder extracts slab-level features, which are aggregated by a permutation-invariant set-attention branch and a bidirectional sequence-attention branch. Using five-fold label-stratified cross-validation at the patient level, OAS-MIL achieved a mean AUROC of 0.770, outperforming the evaluated volumetric and MIL baselines. These results suggest that axial order provides a useful inductive bias for weakly supervised PNI prediction from MRI.

Problem

• Perineural Invasion
• Intrahepatic Cholangiocarcinoma
• Preoperative MRI
• Patient-level Prediction

Innovation

• Order-Aware Slab Multiple Instance Learning
• weakly supervised framework
• 2.5D slabs
• permutation-invariant set-attention
• bidirectional sequence-attention