Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel.
Sci Rep. 2023 Aug 21;13(1):13628. doi: 10.1038/s41598-023-40833-y.
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.
神经周围侵犯（PNI）是指癌细胞出现在神经周围或神经内，增加了肿瘤残留的风险。在胰腺导管腺癌（PDAC）中，PNI 与更差的预后相关，因此也被探索作为一种治疗靶点。本研究旨在构建一种 PNI 检测算法，以提高 PDAC 标本中 PNI 识别的准确性和效率。该算法使用了 6 个扫描 PDAC 病例中 260 张手动分割的神经和肿瘤高清图像进行训练；使用了另外 168 张图像进行分析性能分析；使用了 59 个 PDAC 病例进行临床分析。该算法确定了肿瘤与神经接近的关键区域，供病理学家确认。分析性能达到了神经和肿瘤检测的灵敏度 88%和 54%，特异性 78%和 85%。在临床评估中纳入肿瘤-神经距离，将所有病例的 PNI 检测率从 52%提高到 81%。有趣的是，病理学家分析每个病例平均仅需 24 秒。这个高效的工具通过模拟病理学家的思维过程，即使在训练队列较小的情况下，也能准确识别 PDAC 中的 PNI。
