Harinath Lakshmi, Elishaev Esther, Ye Yuhong, Matsko Jonee, Colaizzi Amy, Wharton Stephanie, Bhargava Rohit, Pantanowitz Liron, Zhao Chengquan
Department of Pathology, UPMC Magee-Womens Hospital, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Cancer Cytopathol. 2025 Jan;133(1):e22918. doi: 10.1002/cncy.22918. Epub 2024 Nov 5.
Artificial intelligence (AI)-based systems are transforming cytopathology practice. The aim of this study was to evaluate the sensitivity of high-grade squamous intraepithelial lesion (HSIL) Papanicolaou (Pap) diagnosis assisted by the Hologic Genius Digital Diagnostics System (GDDS).
A validation study was performed with 890 ThinPrep Pap tests with the GDDS independently. From this set, a subset of 183 cases originally interpreted as HSIL confirmed histologically were included in this study. The sensitivity for detecting HSIL by three cytopathologists was calculated.
Most HSIL cases were classified as atypical glandular cell/atypical squamous cell-high grade not excluded (AGC/ASC-H) and above by all cytopathologists. Of these cases, 11.5% were classified as low-grade squamous intraepithelial lesion (LSIL) by pathologist A (P-A), 6% by pathologist B (P-B), and 5.5% by pathologist C (P-C); 3.8%, 2.7%, and 1.6% of these cases were classified as atypical squamous cell of unknown significance (ASC-US) by P-A, P-B, and P-C, respectively. The sensitivity for detection of cervical intraepithelial neoplasia 2 and above (CIN2+) lesions was 100% if ASC-US and above (ASC-US+) abnormalities were counted among all three pathologists. The sensitivity for detection of CIN2+ lesions was 84.7%, 91.3%, and 92.9% by P-A, P-B, and P-C, respectively, for ASC-H and above abnormalities. The Kendall W coefficient was 0.722, which indicated strong agreement between all pathologists.
New-generation AI-assisted Pap test screening systems such as the GDDS have the potential to transform cytology practice. In this study, the GDDS aided in interpreting HSIL in ThinPrep Pap tests, with good sensitivity and agreement between the pathologists who interacted with this system.
基于人工智能（AI）的系统正在改变细胞病理学实践。本研究的目的是评估Hologic Genius数字诊断系统（GDDS）辅助诊断高级别鳞状上皮内病变（HSIL）巴氏涂片的敏感性。
独立使用GDDS对890例薄层液基细胞学涂片进行了验证研究。从该组中，选取183例最初经组织学确诊为HSIL的病例纳入本研究。计算了三位细胞病理学家检测HSIL的敏感性。
所有细胞病理学家将大多数HSIL病例分类为非典型腺细胞/不排除高级别非典型鳞状细胞（AGC/ASC-H）及以上。在这些病例中，病理学家A（P-A）将11.5%分类为低级别鳞状上皮内病变（LSIL），病理学家B（P-B）将6%分类为LSIL，病理学家C（P-C）将5.5%分类为LSIL；P-A、P-B和P-C分别将这些病例的3.8%、2.7%和1.6%分类为意义不明确的非典型鳞状细胞（ASC-US）。如果将ASC-US及以上（ASC-US+）异常计算在内，三位病理学家检测宫颈上皮内瘤变2级及以上（CIN2+）病变的敏感性为100%。对于ASC-H及以上异常，P-A、P-B和P-C检测CIN2+病变的敏感性分别为84.7%、91.3%和92.9%。肯德尔W系数为0.722，表明所有病理学家之间有很强的一致性。
新一代AI辅助巴氏涂片筛查系统，如GDDS，有可能改变细胞学实践。在本研究中，GDDS有助于解读薄层液基细胞学涂片中的HSIL，具有良好的敏感性，且与使用该系统的病理学家之间具有一致性。
