利用粗针活检样本中突触素、雌激素受体以及CK14/p63的免疫组化结果进行计算机辅助诊断（CADx）对乳腺上皮增生性病变进行分类的有效性。

Effectiveness of computer-aided diagnosis (CADx) of breast pathology using immunohistochemistry results of core needle biopsy samples for synaptophysin, oestrogen receptor and CK14/p63 for classification of epithelial proliferative lesions of the breast.

作者信息

Maeda Ichiro, Kubota Manabu, Ohta Jiro, Shinno Kimika, Tajima Shinya, Ariizumi Yasushi, Doi Masatomo, Oana Yoshiyasu, Kanemaki Yoshihide, Tsugawa Koichiro, Ueno Takahiko, Takagi Masayuki

机构信息

Department of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan.

1st Development Department, 1st Business Development Unit, 3rd Division, Advanced Business Center, Dai Nippon Printing Co., Ltd, Tokyo, Japan.

出版信息

J Clin Pathol. 2017 Dec;70(12):1057-1062. doi: 10.1136/jclinpath-2017-204478. Epub 2017 Jun 19.

DOI:10.1136/jclinpath-2017-204478

PMID:28630050

Abstract

AIMS

The aim of this study was to develop a computer-aided diagnosis (CADx) system for identifying breast pathology.

METHODS

Two sets of 100 consecutive core needle biopsy (CNB) specimens were collected for test and validation studies. All 200 CNB specimens were stained with antibodies targeting oestrogen receptor (ER), synaptophysin and CK14/p63. All stained slides were scanned in a whole-slide imaging system and photographed. The photographs were analysed using software to identify the proportions of tumour cells that were positive and negative for each marker. In the test study, the cut-off values for synaptophysin (negative and positive) and CK14/p63 (negative and positive) were decided using receiver operating characteristic (ROC) analysis. For ER analysis, samples were divided into groups with <10% positive or >10% positive cells and decided using receiver operating characteristic (ROC) analysis. Finally, these two groups categorised as ER-low, ER-intermediate (non-low and non-high) and ER-high groups. In the validation study, the second set of immunohistochemical slides were analysed using these cut-off values.

RESULTS

The cut-off values for synaptophysin, <10% ER positive, >10% ER positive and CK14/p63 were 0.14%, 2.17%, 77.93% and 18.66%, respectively. The positive predictive value for malignancy (PPV) was 100% for synaptophysin-positive/ER-high/(CK14/p63)-any or synaptophysin-positive/ER-low/(CK14/p63)-any. The PPV was 25% for synaptophysin-positive/ER-intermediate/(CK14/p63)-positive. For synaptophysin-negative/(CK14/p63)-negative, the PPVs for ER-low, ER-intermediate and ER-high were 100%, 80.0% and 95.8%, respectively. The PPV was 4.5% for synaptophysin-negative/ER-intermediate/(CK14/p63)-positive.

CONCLUSION

The CADx system was able to analyse sufficient data for all types of epithelial proliferative lesions of the breast including invasive breast cancer. This system may be useful for pathological diagnosis of breast CNB in routine investigations.

摘要

目的

本研究旨在开发一种用于识别乳腺病变的计算机辅助诊断（CADx）系统。

方法

收集两组各100例连续的粗针活检（CNB）标本用于测试和验证研究。所有200例CNB标本均用靶向雌激素受体（ER）、突触素和CK14/p63的抗体进行染色。所有染色玻片在全玻片成像系统中扫描并拍照。使用软件分析照片，以确定每个标记物阳性和阴性肿瘤细胞的比例。在测试研究中，使用受试者操作特征（ROC）分析确定突触素（阴性和阳性）以及CK14/p63（阴性和阳性）的临界值。对于ER分析，将样本分为阳性细胞<10%或>10%的组，并使用受试者操作特征（ROC）分析确定。最后，这两组被分类为ER低、ER中（非低非高）和ER高组。在验证研究中，使用这些临界值分析第二组免疫组织化学玻片。

结果

突触素、ER阳性<10%、ER阳性>10%和CK14/p63的临界值分别为0.14%、2.17%、77.93%和18.66%。突触素阳性/ER高/（CK14/p63）-任意或突触素阳性/ER低/（CK14/p63）-任意时，恶性肿瘤的阳性预测值（PPV）为100%。突触素阳性/ER中/（CK14/p63）-阳性时，PPV为25%。对于突触素阴性/（CK14/p63）-阴性，ER低、ER中、ER高的PPV分别为100%、80.0%和95.8%。突触素阴性/ER中/（CK14/p63）-阳性时，PPV为4.5%。