核极限学习机诊断乳腺良恶性肿块样病变

Kernel extreme learning machine in diagnosis of benign and malignant mass-like breast lesions

目的采用核极限学习机(KELM)方法对乳腺良恶性肿块样病变进行分类,并评估其鉴别诊断效能。方法对93例患者103个经术后病理或长期随访确诊的乳腺肿块样病变行MR检查。由1名低年资和1名高年资乳腺影像学诊断医师参照乳腺影像报告和数据系统(BI-RADS)第2版,选取12个MRI特征及临床特征,分别独立及采用KELM方法对乳腺病变进行良恶性分类,并计算诊断效能。结果低年资和高年资医师使用KELM方法鉴别诊断乳腺良恶性病变的敏感度、特异度、准确率分别为0.88、0.89、0.91和0.93、0.91、0.92,AUC分别为0.84和0.89。低年资和高年资医师独立诊断的敏感度、特异度、准确率分别为0.91、0.74、0.86和0.90、0.85、0.92,AUC分别为0.83和0.90。结论基于影像学特征及临床资料特征的KELM方法可辅助临床鉴别诊断乳腺肿块样良恶性病变,具有较理想的敏感度、特异度和准确率。

Objective To classify benign and malignant breast mass-like lesions by using kernel extreme learning machine (KELM),and to evaluate its effectiveness in differential diagnosis. Methods Totally 93 patients with 103 breast mass-like lesions confirmed by postoperative pathology or long-term follow-up underwent MRI.According to the breast imaging report and data system (BI-RADS) scoring guidelines,12 MR imaging features and clinical features were selected.Then benign and malignant lesions were classified by one junior and one senior radiologist independently.The diagnostic efficacy was calculated. Results The sensitivity,specificity and accuracy of KELM in differential diagnosis of benign and malignant breast mass-like lesions were 0.88,0.89,0.91 and 0.93,0.91,0.92 for junior and senior doctor respectively,and AUC was 0.84 and 0.89.The sensitivity,specificity and accuracy of independent diagnosis of junior and senior doctor were 0.91,0.74,0.86 and 0.90,0.85,0.92,respectively,and AUC was 0.83 and 0.90,respectively. Conclusion KELM based on imaging features and clinical data can be used as asssitant in differential diagnosis of benign and malignant mass-like breast lesions,which has ideal sensitivity,specificity and accuracy.