Supplementary Material for: Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple-Z-Plane Scanning

Introduction: Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images. Methods: This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ (SurePath)), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total numbers of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses. Results: The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78-0.91 vs. 0.75-0.88, P=0.451-0.578). Notably, it substantially reduced both scanning time (137.2-635.0 seconds vs. 332.6-1278.8 seconds, P<0.05) and image file size (0.51-2.10 GB vs. 1.16-3.10 GB, P<0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%). Conclusion: We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.引言：细胞病理切片的数字化面临诸多挑战，这源于其三维结构特性与细胞分布不均的特点。尽管多Z平面扫描（multi-Z-plane scan）是当前主流的解决方案，但由于扫描耗时过长、图像文件体积增大，且需要病理医师审阅多张Z平面图像，其在临床数字化细胞病理领域的应用受到了限制。 方法：本研究提出启发式扫描（heuristic scan）作为一种全新解决方案，采用专为细胞病理切片扫描设计的人工智能（AI）方法，作为多Z平面扫描的替代方案。本研究针对来自3种不同细胞制片方式（Cytospin、ThinPrep 以及 BD CytoRich™（SurePath））的52份尿液细胞病理切片，分别采用21层Z平面扫描与启发式扫描模拟两种方法，通过徕卡Aperio AT2数字化扫描仪生成全切片图像（WSIs）。本研究通过AI算法对21层Z平面扫描得到的全切片图像进行分析，定量统计高级别尿路上皮癌及更严重病变可疑细胞（SHGUC+）的总数；启发式扫描模拟则基于21层Z平面扫描的数据，计算得到SHGUC+细胞的总数。本研究分析了多项性能指标以对比两种扫描方法的表现，包括SHGUC+细胞覆盖率（即每张全切片图像中，多Z平面扫描或启发式扫描模拟识别出的SHGUC+细胞数与21层Z平面扫描得到的总SHGUC+细胞数的比值）、扫描时长与文件体积。启发式扫描的各项性能指标均通过21层Z平面扫描数据进行线性估算得到。此外，针对SHGUC+细胞数量稀少的全切片图像，本研究采用基于AI的判读方式，遵循《巴黎系统》（The Paris System）指南进行分析，并将结果与原始诊断进行对比。 结果：在3种细胞制片方式中，启发式扫描的SHGUC+细胞覆盖率中位数与5层Z平面扫描相当（0.78~0.91 vs. 0.75~0.88，P=0.451~0.578）。值得注意的是，启发式扫描可显著缩短扫描时长（137.2~635.0秒 vs. 332.6~1278.8秒，P<0.05），同时降低图像文件体积（0.51~2.10 GB vs. 1.16~3.10 GB，P<0.05）。更为重要的是，与单层Z平面扫描相比，启发式扫描的AI辅助判读准确率更高（62.5% vs. 37.5%）。 结论：本研究证实，启发式扫描可作为临床数字化细胞病理领域中传统多Z平面扫描的高性价比替代方案。其在实现相当的SHGUC+细胞捕获率的同时，可缩短扫描时长并降低图像文件体积，且相较于传统单层（最优）平面扫描，其尿液细胞病理数字化判读的准确率更高。未来仍需开展进一步研究，以评估该新技术与适配数字化扫描仪的集成效果，从而实现实用化的细胞病理切片扫描。