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    • 3. 发明申请
    • Magnetic resonance spectroscopy to classify tissue
    • 磁共振光谱法分类组织
    • US20050107683A1
    • 2005-05-19
    • US11012959
    • 2004-12-15
    • Carolyn MountfordPeter RussellIan SmithRajmund Somorjai
    • Carolyn MountfordPeter RussellIan SmithRajmund Somorjai
    • G01N24/08G01R33/46G01R33/465A61B5/05
    • G06F19/366G01N24/08G01R33/20G01R33/4625G01R33/465G16H10/40Y10T436/24
    • Robust classification methods analyse magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumours. 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 tumour vascularisation with an overall accuracy of 92%.
    • 鲁棒分类方法分析从乳腺肿瘤获取的细针抽吸物的磁共振谱(MRS)数据(光谱)。 与组织病理学和临床标准相比,得到的数据提供了非常高的精度和可靠性的基于计算机分类的诊断和预后。 在光谱和标准天气病理学发现之间进行的诊断相关性包括关于病理学(恶性与良性),原发性癌症的血管浸润和切除的腋窝淋巴结的淋巴结的细节的细节。 分类策略包括三个阶段:预处理MR幅度谱以识别最佳光谱区域,交叉验证线性判别分析和通过计算机共识诊断分类聚合。 恶性组织与良性病变区分开,总体准确率为93%。 从同一频谱,预测淋巴结参与率为95%,肿瘤血管形成的准确度为92%。