Mola Nazanin, Weishaupt Hrafn, Krasontovitsch Valentin, Hodneland Erlend, Leh Sabine
Department of Pathology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Department of Clinical Medicine, Faculty of Medicine, University of Bergen, Bergen, Norway.
Sci Rep. 2025 Jul 29;15(1):27700. doi: 10.1038/s41598-025-08778-6.
The Ki67 score is a crucial prognostic biomarker for neuroendocrine tumors, but its manual assessment is labor-intensive, requiring the counting of 500-2,000 cells in hotspots. Digital image analysis could streamline this process, yet few comprehensive comparisons exist between different tools. We compared a non-machine learning (non-ML) tool (ImageScope, Leica Biosystems) with a machine learning (ML) tool (Aiforia Create, Aiforia Technologies) on Ki67-stained slides from 10 low proliferative neuroendocrine tumor cases (Ki67 score < 5%, eight regions per slide). Performance metrics based on the coordinates of detected cells were used to assess the capability of image analysis tools to detect (i) total and (ii) Ki67 positive tumor cells, and consequently calculate the (iii) Ki67 score. Manual scoring by an experienced pathologist was used as the reference standard. The ML compared to the non-ML tool showed better performance metrics (F-score 0.90 vs. 0.74) in detecting the tumor cells. Also, the ML tool had a higher agreement with the reference standard in detecting tumor cells (ICC 0.91 vs. 0.62), Ki67 positive tumor cells (ICC 0.70 vs. 0.24), and the Ki67 score (ICC 0.86 vs. 0.45). Our findings highlight the enhanced accuracy of ML-based image analysis in detecting the correct tumor cells, outperforming traditional methods.
Ki67评分是神经内分泌肿瘤的关键预后生物标志物，但其手动评估劳动强度大，需要在热点区域计数500 - 2000个细胞。数字图像分析可以简化这一过程，但不同工具之间很少有全面的比较。我们在10例低增殖性神经内分泌肿瘤病例（Ki67评分<5%，每张载玻片8个区域）的Ki67染色载玻片上，将一种非机器学习（non-ML）工具（ImageScope，徕卡生物系统公司）与一种机器学习（ML）工具（Aiforia Create，Aiforia Technologies）进行了比较。基于检测到的细胞坐标的性能指标用于评估图像分析工具检测（i）总肿瘤细胞和（ii）Ki67阳性肿瘤细胞的能力，并由此计算（iii）Ki67评分。由经验丰富的病理学家进行的手动评分用作参考标准。与非ML工具相比，ML工具在检测肿瘤细胞方面表现出更好的性能指标（F值分别为0.90和0.74）。此外，在检测肿瘤细胞（组内相关系数分别为0.91和0.62）、Ki67阳性肿瘤细胞（组内相关系数分别为0.70和0.24）以及Ki67评分（组内相关系数分别为0.86和0.45）方面，ML工具与参考标准的一致性更高。我们的研究结果突出了基于ML的图像分析在检测正确肿瘤细胞方面的准确性提高，优于传统方法。
