Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence

被引:7
作者
Borsekofsky, Sarah [1 ]
Tsuriel, Shlomo [1 ]
Hagege, Rami R. [1 ]
Hershkovitz, Dov [1 ,2 ]
机构
[1] Tel Aviv Sourasky Med Ctr, Inst Pathol, 6 Weizmann St, IL-6423906 Tel Aviv, Israel
[2] Tel Aviv Univ, Fac Med, Tel Aviv, Israel
关键词
CANCER; PANCREATICODUODENECTOMY; VALIDATION; RECURRENCE; SURVIVAL;
D O I
10.1038/s41598-023-40833-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:10
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