Wolberg W H, Street W N, Mangasarian O L
Department of Surgery, University of Wisconsin, Madison, USA.
Anal Quant Cytol Histol. 1995 Apr;17(2):77-87.
Fine needle aspiration (FNA) accuracy is limited by, among other factors, the subjective interpretation of the aspirate. We have increased breast FNA accuracy by coupling digital image analysis methods with machine learning techniques. Additionally, our mathematical approach captures nuclear features ("grade") that are prognostically more accurate than are estimates based on tumor size and lymph node status. An interactive computer system evaluates, diagnoses and determines prognosis based on nuclear features derived directly from a digital scan of FNA slides. A consecutive series of 569 patients provided the data for the diagnostic study. A 166-patient subset provided the data for the prognostic study. An additional 75 consecutive, new patients provided samples to test the diagnostic system. The projected prospective accuracy of the diagnostic system was estimated to be 97% by 10-fold cross-validation, and the actual accuracy on 75 new samples was 100%. The projected prospective accuracy of the prognostic system was estimated to be 86% by leave-one-out testing.
细针穿刺抽吸活检（FNA）的准确性受到多种因素的限制，其中包括对抽吸物的主观解读。我们通过将数字图像分析方法与机器学习技术相结合，提高了乳腺FNA的准确性。此外，我们的数学方法能够捕捉核特征（“分级”），这些特征在预后方面比基于肿瘤大小和淋巴结状态的估计更为准确。一个交互式计算机系统基于直接从FNA玻片数字扫描中获取的核特征来评估、诊断并确定预后。连续的569例患者为诊断研究提供了数据。166例患者的子集为预后研究提供了数据。另外75例连续的新患者提供了样本以测试诊断系统。通过10倍交叉验证，诊断系统的预计前瞻性准确性估计为97%，而在75个新样本上的实际准确性为100%。通过留一法测试，预后系统的预计前瞻性准确性估计为86%。
