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用于宫颈涂片质量控制的PAPNET细胞学筛查系统评估

Evaluation of the PAPNET cytologic screening system for quality control of cervical smears.

作者信息

Koss L G, Lin E, Schreiber K, Elgert P, Mango L

机构信息

Department of Pathology, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York 10467.

出版信息

Am J Clin Pathol. 1994 Feb;101(2):220-9. doi: 10.1093/ajcp/101.2.220.

Abstract

The PAPNET system is an automated interactive instrument for analysis of conventional (Papanicolaou) cervical smears. The instrument, described in this paper, introduces several important innovations to cytology automation. The cell selection system is composed of two stages: an algorithmic classifier, followed by a trained neural network allowing for great flexibility and precision in recognition of abnormal cell images. Contrary to other attempts at cytology automation, this machine does not attempt to diagnose cell abnormalities. Instead, it is interactive, leaving the assessment of the cells displayed on a high-resolution video screen to trained human observers. The slides judged to contain abnormal cells or to be inadequate are referred for a second microscopic review. Two versions of the instrument (Alpha and Beta) were evaluated in several modes. Initial testing was performed on archival smears with known, histologically confirmed neoplastic lesions of the uterine cervix. These lesions comprised the entire spectrum of abnormalities, from low-grade lesions to invasive cancers of several types. The Alpha machine displayed recognizable abnormal cells in 97% of the 201 cases, and the Beta machine displayed such cells in 97.2% of 176 cases. The Beta instrument was subsequently tested on 500 sequential archival cervical smears that had been previously subjected to a rigorous quality control. One hundred forty smears (28%), which either displayed atypical cells or were considered "inadequate," were referred for further rescreening. Fifteen of 16 previously diagnosed neoplastic smears were appropriately identified with the help of the machine. The one missed case contained a single cluster of vacuolated cancer cells from an endometrial carcinoma. As a result of PAPNET-triggered review, three new cases of low-grade squamous intraepithelial lesions view, three new cases of low-grade squamous intraepithelial lesions (SIL) came to light in previously negative smears; three additional cases, previously classified as atypical, were also reclassified as SIL, for a net gain of six neoplastic abnormalities. In two additional atypical cases, colposcopic follow-up was recommended, even though the diagnosis was not modified. Two cases of cervical intraepithelial neoplasia, represented by tiny single clusters of abnormal cells missed on original screening, quality control, and on machine rescreening, came to light on second review of the residual 360 cases. The initial experience with the PAPNET system suggests that the instrument may be valuable in quality control and may assist in significantly reducing false-negative cervical smears in an efficient and timely manner. Further testing of the instrument on a much larger number of cervical smears is in progress.

摘要

PAPNET系统是一种用于分析传统(巴氏)宫颈涂片的自动化交互式仪器。本文所述的该仪器为细胞学自动化引入了几项重要创新。细胞筛选系统由两个阶段组成:一个算法分类器,随后是一个经过训练的神经网络,可在识别异常细胞图像时具有极大的灵活性和精确性。与其他细胞学自动化尝试不同,该机器并不试图诊断细胞异常。相反,它是交互式的,将在高分辨率视频屏幕上显示的细胞评估留给训练有素的人类观察者。被判定含有异常细胞或不合格的玻片会被送去进行二次显微镜检查。该仪器的两个版本(Alpha和Beta)在几种模式下进行了评估。最初的测试是在已知有组织学确诊的子宫颈肿瘤性病变的存档涂片上进行的。这些病变涵盖了从低度病变到几种类型的浸润性癌的整个异常范围。Alpha机器在201例病例中的97%显示出可识别的异常细胞,Beta机器在176例病例中的97.2%显示出此类细胞。随后,Beta仪器在500份先前经过严格质量控制的连续存档宫颈涂片上进行了测试。140份涂片(28%)显示非典型细胞或被认为“不合格”,被送去进一步复查。在该机器的帮助下,16份先前诊断为肿瘤性的涂片中的15份被正确识别。漏诊的1例病例包含来自子宫内膜癌的单个空泡状癌细胞簇。由于PAPNET触发的复查,在先前阴性的涂片中发现了3例新的低度鳞状上皮内病变病例;另外3例先前分类为非典型的病例也被重新分类为低度鳞状上皮内病变,肿瘤性异常净增加6例。在另外2例非典型病例中,尽管诊断未改变,但建议进行阴道镜随访。在对剩余360例病例的二次复查中,发现了2例宫颈上皮内瘤变病例,这些病例在最初筛查、质量控制和机器复查时均因异常细胞的微小单个簇而漏诊。PAPNET系统的初步经验表明,该仪器在质量控制方面可能有价值,并可能有助于高效、及时地显著减少宫颈涂片的假阴性。正在对该仪器进行更多宫颈涂片的进一步测试。

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