Kok M R, Boon M E
Leiden Cytology and Pathology Laboratory, Leiden, The Netherlands.
Cancer. 1996 Jul 1;78(1):112-7. doi: 10.1002/(SICI)1097-0142(19960701)78:1<112::AID-CNCR16>3.0.CO;2-2.
Screening programs for the early detection of cervical carcinoma are criticized because of the problem of false-negative diagnoses. A successful approach for solving this problem is applying neural network technology (PAP-NET) to assist the cytotechnologist (CT) in finding the (few) abnormal cells in the smear.
In 3 consecutive years (1992, 1993, and 1994), 25,767 smears were screened conventionally and 65,527 with the aid of PAPNET by 7 CTs. For each CT, the scores for atypias of undetermined significance, squamous or glandular (ASCUC/AGUS according to the Bethesda classification system), indicated by Positive 1, for low grade precursor lesions, by Positive II, for high grade lesions and invasive carcinoma, by Positive III, were calculated for both screening methods. The histologic scores were also calculated.
The mean positive scores of the seven CTs were higher for PAPNET than for conventional screening, and the coefficients of variability were lower. For Positive III smears, the consistency in screening was significantly higher for PAPNET than for conventional screening. The higher histologically positive scores for carcinoma in situ and invasive carcinoma indicated an increased screening sensitivity.
As demonstrated by the improvement in the performances of all CTs involved, screening efficacy was enhanced by the use of neural network technology.
由于存在假阴性诊断问题，宫颈癌早期检测的筛查项目受到批评。解决这一问题的一种成功方法是应用神经网络技术（PAP-NET）来协助细胞技术人员（CT）在涂片检查中找出（少量）异常细胞。
在连续3年（1992年、1993年和1994年）中，7名细胞技术人员采用传统方法筛查了25,767份涂片，并借助PAP-NET筛查了65,527份涂片。对于每位细胞技术人员，计算两种筛查方法中意义不明确的非典型鳞状或腺细胞（根据贝塞斯达分类系统为ASCUC/AGUS）的阳性1分、低级别前驱病变的阳性II分、高级别病变和浸润癌的阳性III分的得分。还计算了组织学得分。
PAP-NET筛查时7名细胞技术人员的平均阳性得分高于传统筛查，且变异系数更低。对于阳性III级涂片，PAP-NET筛查的一致性显著高于传统筛查。原位癌和浸润癌在组织学上更高的阳性得分表明筛查敏感性提高。
正如所有参与的细胞技术人员的表现改善所证明的那样，使用神经网络技术提高了筛查效率。
