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Magnetic resonance spectroscopy of breast biopsy to determine pathology, vascularization and nodal involvement

机译:乳房活检的磁共振波谱确定病理,血管形成和淋巴结转移

摘要

Robust classification methods analyse magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumors. The resultant data when compared with the histopathology and clinical criteria provide computerized classification-based diagnosis and prognosis with a very high degree of accuracy and reliability. Diagnostic correlation performed between the spectra and standard synoptic pathology findings contain detail regarding the pathology (malignant versus benign), vascular invasion by the primary cancer and lymph node involvement of the excised axillary lymph nodes. The classification strategy consisted of three stages: pre-processing of MR magnitude spectra to identify optimal spectral regions, cross-validated Linear Discriminant Analysis, and classification aggregation via Computerised Consensus Diagnosis. Malignant tissue was distinguished from benign lesions with an overall accuracy of 93%. From the same spectrum, lymph node involvement was predicted with an accuracy of 95% and tumor vascularization with an overall accuracy of 92%.
机译:稳健的分类方法可分析取自乳腺肿瘤的细针抽吸物的磁共振波谱(MRS)数据(光谱)。与组织病理学和临床标准相比,所得数据可提供非常准确和可靠的基于计算机分类的诊断和预后。在频谱和标准天气病理发现之间进行的诊断相关性包含有关病理(恶性与良性),原发癌的血管浸润和切除的腋窝淋巴结受累的细节。分类策略包括三个阶段:MR幅值谱的预处理以识别最佳光谱区,交叉验证的线性判别分析和通过计算机共识诊断进行分类汇总。恶性组织与良性病变区分开,总准确率为93%。从同一频谱来看,预计淋巴结受累的准确度为95%,肿瘤血管形成的总准确度为92%。

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