Brem Rachel F, Rapelyea Jocelyn A, Zisman Gilat, Hoffmeister Jeffrey W, Desimio Martin P
Department of Radiology, The George Washington University Medical Center, Washington, DC, USA.
Cancer. 2005 Sep 1;104(5):931-5. doi: 10.1002/cncr.21255.
The objective of this study was to evaluate the performance of a computer-aided detection (CAD) system for the detection of breast cancer, based on mammographic appearance and histopathology.
From 1000 consecutive screening mammograms from women with biopsy-proven breast carcinoma, 273 mammograms were selected randomly for retrospective evaluation by CAD. The sensitivity of the CAD system for breast cancer was assessed from the proportion of masses and microcalcifications detected. The corresponding tumor histopathologies also were evaluated. Normal mammograms (n = 155 patients) were used to determine the false-positive rate of the system.
Of the 273 breast carcinomas, 149 appeared mammographically as masses, and 88 appeared as microcalcifications, including 36 carcinomas that presented as mixed lesions. The CAD system marked 125 of 149 masses correctly (84%), marked 86 of 88 microcalcifications correctly (98%), and marked 32 of 36 of mixed lesions correctly (89%.). The system showed a high sensitivity for the detection of ductal carcinoma in situ (95%; 73 of 77 lesions), invasive lobular carcinoma (95%; 18 of 19 lesions), invasive ductal carcinoma (85%; 125 of 147 lesions), and invasive mammary carcinoma (90%; 27 of 30 lesions). The highest CAD system sensitivity was for all invasive carcinomas that presented as microcalcifications (100%). On normal mammograms, there was an average of 1.3 false-positive CAD marks per image.
The CAD system correctly marked a large majority of biopsy-proven breast cancers, with a greater sensitivity for lesions with microcalcifications and without significant impact of performance based on tumor histopathology. CAD was highly effective in detecting invasive lobular carcinoma (sensitivity, 95%) and ductal carcinoma in situ (sensitivity, 95%). CAD represents a useful tool for the detection of breast cancer.
本研究的目的是基于乳腺钼靶影像表现和组织病理学，评估一种用于检测乳腺癌的计算机辅助检测（CAD）系统的性能。
从1000例经活检证实患有乳腺癌的女性的连续筛查乳腺钼靶片中，随机选择273例钼靶片进行CAD回顾性评估。通过检测到的肿块和微钙化的比例评估CAD系统对乳腺癌的敏感性。同时对相应肿瘤的组织病理学也进行了评估。使用正常乳腺钼靶片（n = 155例患者）来确定该系统的假阳性率。
在273例乳腺癌中，149例在乳腺钼靶影像上表现为肿块，88例表现为微钙化，其中36例癌表现为混合性病变。CAD系统正确标记了149个肿块中的125个（84%），88个微钙化中的86个（98%），以及36个混合性病变中的32个（89%）。该系统对原位导管癌（95%；77个病变中的73个）、浸润性小叶癌（95%；19个病变中的18个）、浸润性导管癌（85%；147个病变中的125个）和浸润性乳腺癌（90%；30个病变中的27个）的检测显示出高敏感性。CAD系统对所有表现为微钙化的浸润性癌的敏感性最高（100%）。在正常乳腺钼靶片上，每张图像平均有1.3个CAD假阳性标记。
CAD系统正确标记了大多数经活检证实的乳腺癌，对伴有微钙化的病变敏感性更高，且基于肿瘤组织病理学对性能无显著影响。CAD在检测浸润性小叶癌（敏感性95%）和原位导管癌（敏感性95%）方面非常有效。CAD是检测乳腺癌的一种有用工具。
