Computerized classification of intraductal breast lesions using histopathological images.
Computerized classification of intraductal breast lesions using histopathological images.
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DOI：
10.1109/tbme.2011.2110648
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发表时间：
2011-07
期刊：
影响因子：
--
通讯作者：
Gurcan MN
中科院分区：
文献类型：
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作者：
Dundar MM;Badve S;Bilgin G;Raykar V;Jain R;Sertel O;Gurcan MN
在浸润前乳腺癌的诊断中，一些导管内增生是一个特殊的挑战。乳腺导管内病变的连续体包括常见的导管增生（UDH）、非典型导管增生（ADH）和导管原位癌（DCIS）。目前的护理标准是进行经皮穿刺活检，以诊断可触及和图像检测的乳腺异常。UDH被认为是良性的，诊断为UDH的患者接受常规随访，而ADH和DCIS被认为是可采取行动的，诊断为这两种亚型的患者接受额外的外科手术。每年约有25万例新的导管内乳腺病变被诊断出来。据保守估计，这些患者中至少有50%的人不必要地接受了不必要的手术。因此，提高诊断重现性和准确性对于这些患者的有效临床管理至关重要。在这项研究中，介绍了一个原型系统，用于自动分类乳腺显微组织，以区分UDH和可操作的亚型（ADH和DCIS）。该系统自动评估组织的数字化载玻片的某些细胞学标准，并分类的基础上衍生的图像的定量特征的组织。该系统使用在62个患者病例中收集的总共327个感兴趣区域（ROI）进行训练，并使用在33个患者病例中收集的149个ROI的隔离集进行测试。在整个测试数据上实现了87.9%的总体准确度。临界病例的检测准确率为84.6%（33个测试案例中的26个），与同一组中9名病理学家的诊断准确性进行比较时（平均81.2%），表明该系统作为独立的诊断工具与专家病理学家具有很强的竞争力，并且当用作“第二阅读器”时，在提高诊断准确性和可重复性方面具有很大的潜力。和病理学家一起
In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250,000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus improvement in the diagnostic re-producibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in improving diagnostic accuracy and reproducability when used as a “second reader” in conjunction with the pathologists.