周围神经侵袭（PNI）是指癌细胞存在于或周围神经内，增加残留肿瘤的风险。PNI与胰腺导管腺癌（PDAC）预后较差有关，也被用作治疗靶点的研究。本研究的目的是构建一个PNI检测算法，以提高在PDAC标本中检测PNI的准确性和效率。训练使用了来自6个扫描的PDAC病例的260张经手工分割的神经和肿瘤高清图像；分析性能分析使用了额外的168张图像；临床分析使用了59个PDAC病例。该算法能够准确定位肿瘤和神经接近的关键区域，供病理学家确认。分析性能达到了对神经和肿瘤的敏感性分别为88%和54%，特异性分别为78%和85%。将肿瘤和神经的距离纳入临床评估后，PNI检测的比例从所有病例的52%提高到81%。有趣的是，病理学家分析每个病例仅需要平均24秒的时间。这种时间高效的工具通过模拟病理学家的思考过程，能够准确识别PDAC中的PNI，即使只有一个小的训练队列。© 2023. Springer Nature Limited.

Perineural invasion (PNI) refers to the presence of cancer cells around or within nerves, raising the risk of residual tumor. Linked to worse prognosis in pancreatic ductal adenocarcinoma (PDAC), PNI is also being explored as a therapeutic target. The purpose of this work was to build a PNI detection algorithm to enhance accuracy and efficiency in identifying PNI in PDAC specimens. Training used 260 manually segmented nerve and tumor HD images from 6 scanned PDAC cases; Analytical performance analysis used 168 additional images; clinical analysis used 59 PDAC cases. The algorithm pinpointed key areas of tumor-nerve proximity for pathologist confirmation. Analytical performance reached sensitivity of 88% and 54%, and specificity of 78% and 85% for the detection of nerve and tumor, respectively. Incorporating tumor-nerve distance in clinical evaluation raised PNI detection from 52 to 81% of all cases. Interestingly, pathologist analysis required an average of only 24 s per case. This time-efficient tool accurately identifies PNI in PDAC, even with a small training cohort, by imitating pathologist thought processes.© 2023. Springer Nature Limited.