Wolberg W H, Street W N, Heisey D M, Mangasarian O L
Department of Surgery, University of Wisconsin, Madison, USA.
Arch Surg. 1995 May;130(5):511-6. doi: 10.1001/archsurg.1995.01430050061010.
To use digital image analysis and machine learning to (1) improve breast mass diagnosis based on fine-needle aspirates and (2) improve breast cancer prognostic estimations.
An interactive computer system evaluates, diagnoses, and determines prognosis based on cytologic features derived from a digital scan of fine-needle aspirate slides.
The University of Wisconsin (Madison) Departments of Computer Science and Surgery and the University of Wisconsin Hospital and Clinics.
Five hundred sixty-nine consecutive patients (212 with cancer and 357 with benign masses) provided the data for the diagnostic algorithm, and an additional 118 (31 with malignant masses and 87 with benign masses) consecutive, new patients tested the algorithm. One hundred ninety of these patients with invasive cancer and without distant metastases were used for prognosis.
Surgical biopsy specimens were taken from all cancers and some benign masses. The remaining cytologically benign masses were followed up for a year and surgical biopsy specimens were taken if they changed in size or character. Patients with cancer received standard treatment.
Cross validation was used to project the accuracy of the diagnostic algorithm and to determine the importance of prognostic features. In addition, the mean errors were calculated between the actual times of distant disease occurrence and the times predicted using various prognostic features. Statistical analyses were also done.
The predicted diagnostic accuracy was 97% and the actual diagnostic accuracy on 118 new samples was 100%. Tumor size and lymph node status were weak prognosticators compared with nuclear features, in particular those measuring nuclear size. Compared with the actual time for recurrence, the mean error of predicted times for recurrence with the nuclear features was 17.9 months and was 20.1 months with tumor size and lymph node status (P = .11).
Computer technology will improve breast fine-needle aspirate accuracy and prognostic estimations.
运用数字图像分析和机器学习技术，（1）基于细针穿刺抽吸物改进乳腺肿块诊断，（2）改进乳腺癌预后评估。
一个交互式计算机系统基于细针穿刺抽吸物玻片数字扫描得出的细胞学特征进行评估、诊断并确定预后。
威斯康星大学（麦迪逊）计算机科学与外科学系以及威斯康星大学医院和诊所。
569例连续患者（212例患有癌症，357例患有良性肿块）为诊断算法提供数据，另有118例连续的新患者（31例患有恶性肿块，87例患有良性肿块）对该算法进行测试。其中190例患有浸润性癌且无远处转移的患者用于预后评估。
所有癌症和部分良性肿块均采集手术活检标本。其余细胞学检查为良性的肿块随访一年，若其大小或特征发生变化则采集手术活检标本。癌症患者接受标准治疗。
采用交叉验证来预测诊断算法的准确性并确定预后特征的重要性。此外，计算远处疾病实际发生时间与使用各种预后特征预测时间之间的平均误差。还进行了统计分析。
预测诊断准确率为97%，118个新样本的实际诊断准确率为100%。与核特征相比，肿瘤大小和淋巴结状态是较弱的预后指标，尤其是测量核大小的指标。与复发实际时间相比，核特征预测复发时间的平均误差为17.9个月，肿瘤大小和淋巴结状态预测复发时间的平均误差为20.1个月（P = 0.11）。
计算机技术将提高乳腺细针穿刺抽吸的准确性和预后评估。
