Wolberg W H, Street W N, Mangasarian O L
Department of Surgery, University of Wisconsin, Madison 53792.
Cancer Lett. 1994 Mar 15;77(2-3):163-71. doi: 10.1016/0304-3835(94)90099-x.
An interactive computer system evaluates and diagnoses based on cytologic features derived directly from a digital scan of fine-needle aspirate (FNA) slides. A consecutive series of 569 patients provided the data to develop the system and an additional 54 consecutive, new patients provided samples to test the system. The projected prospective accuracy of the system estimated by tenfold cross validation was 97%. The actual accuracy on 54 new samples (36 benign, 1 atypia, and 17 malignant) was 100%. Digital image analysis coupled with machine learning techniques will improve diagnostic accuracy of breast fine needle aspirates.
一个交互式计算机系统基于直接从细针穿刺抽吸（FNA）玻片的数字扫描中获得的细胞学特征进行评估和诊断。连续569例患者提供了开发该系统的数据，另外54例连续的新患者提供了样本以测试该系统。通过十折交叉验证估计该系统的预期前瞻性准确率为97%。对54个新样本（36个良性、1个非典型性和17个恶性）的实际准确率为100%。数字图像分析与机器学习技术相结合将提高乳腺细针穿刺抽吸的诊断准确率。
