Wojcik E M, Miller M C, O'Dowd G J, Veltri R W
UroCor, Inc., Oklahoma City, Oklahoma, USA.
Anal Quant Cytol Histol. 1998 Feb;20(1):69-76.
To evaluate the ability of computer-assisted quantitative nuclear grading (QNG) using a microspectrophotometer and morphometry software to differentiate Feulgen-stained nuclei captured from normal urothelium, low grade transitional cell carcinoma (LG-TCC) and high grade transitional cell carcinoma (HG-TCC) cytology specimens.
Feulgen-stained nuclei from a series of normal volunteers (urologic disease-free history) and from biopsy-confirmed cases of LG-TCC and HG-TCC were evaluated using a CAS-200 image analysis system. Thirty-eight nuclear morphometric descriptors (NMDs) were measured for each nucleus using a software conversion system. Backwards stepwise logistic regression analysis was applied to assess which of the NMDs contributed to QNG statistical models that could differentiate between nuclei from normals vs. LG-TCC, normals vs. HG-TCC, and LG-TCC vs. HG-TCC. Receiver operating characteristic curves and areas under the curve (AUC), as well as cell classification accuracy, were used to assess these differences.
Statistically significant differences (P < .0001) were observed between all three categories. In the LG-TCC vs. normals, the QNG solution model required 16/38 features, with an AUC = 93%, a sensitivity = 85%, specificity = 86%, positive predictive value (PPV) = 87% and negative predictive value (NPV) = 84%. The QNG solution model for normals vs. HG-TCC required 12/38 nuclear features yielding an AUC = 99%, sensitivity = 99%, specificity = 98%, PPV = 98% and NPV = 99%. The QNG solution model for LG-TCC vs. HG-TCC required 17/38 nuclear features, with an AUC = 99%, sensitivity = 96%, specificity = 97%, PPV = 97% and NPV = 96%.
Computer-assisted QNG cell classifiers based upon the measurement of 38 nuclear features, including size, shape and chromatin organization, are capable of differentiating normal urothelial nuclei from LG-TCC and HG-TCC nuclei as well as LG-TCC from HG-TCC nuclei. The QNG cell classifier has shown conclusively that there are morphometric differences between normal urothelial and LG-TCC nuclei that may not be apparent to the naked eye and that it may be useful in helping the pathologist determine the presence or absence of LG-TCC in bladder cytology specimens.
评估使用显微分光光度计和形态测量软件的计算机辅助定量核分级(QNG)区分从正常尿路上皮、低级别移行细胞癌(LG-TCC)和高级别移行细胞癌(HG-TCC)细胞学标本中获取的福尔根染色细胞核的能力。
使用CAS-200图像分析系统对一系列正常志愿者(无泌尿系统疾病病史)以及活检确诊的LG-TCC和HG-TCC病例的福尔根染色细胞核进行评估。使用软件转换系统对每个细胞核测量38个核形态测量描述符(NMD)。应用向后逐步逻辑回归分析来评估哪些NMD有助于建立可区分正常细胞核与LG-TCC细胞核、正常细胞核与HG-TCC细胞核以及LG-TCC细胞核与HG-TCC细胞核的QNG统计模型。使用受试者工作特征曲线和曲线下面积(AUC)以及细胞分类准确性来评估这些差异。
在所有三个类别之间观察到具有统计学意义的差异(P <.0001)。在LG-TCC与正常细胞核的比较中,QNG解决方案模型需要16/38个特征,AUC = 93%,灵敏度 = 85%,特异性 = 一、选择题(每题3分,共30分)
A. (y = 2x + 1) B. (y=(x - 1)^2 - x^2) C. (y = 2x^2 - 7) D. (y = -\frac{1}{x^2})
A. ((-2,5)) B. ((-2,-5)) C. ((2,5)) D. ((2,-5))
A. 3 B. 4 C. 5 D. 6
A. ((1,0)),((3,0)) B. ((-1,0)),((-3,0)) C. ((0,1)),((0,3)) D. ((0,-1)),((0,-3))
A. (a > 0) B. (c < 0) C. (b^2 - 4ac < 0) D. (a + b + c > 0)
A. (y = 2(x + 1)^2 - 3) B. (y = 2(x - 1)^2 - 3) C. (y = 2(x + 1)^2 + 3) D. (y = 2(x - 1)^2 + 3)
A. (y = x^2 + 2x - 3) B. (y = x^2 - 2x - 3) C. (y = x^2 + 2x + 3) D. (y = x^2 - 2x + 3)
A. (abc < 0) B. (2a + b = 0) C. (a - b + c = 0) D. 当(x > 1)时,(y)随(x)的增大而增大
A. (y = -10x^2 + 110x + 2100) B. (y = -10x^2 + 100x + 2100) C. (y = -10x^2 + 110x + 210) D. (y = -10x^2 + 100x + 210)
A. (1)个 B. (2)个 C. (3)个 D. (4)个
二、填空题(每题3分,共15分)
二次函数(y = 3(x - 1)^2 + 2)的图象的开口方向是______,对称轴是______,顶点坐标是______。
抛物线(y = -2x^2 + 8x - 6)的对称轴是______,顶点坐标是______。
已知二次函数(y = x^2 - 4x + k)的图象与(x)轴有两个交点,则(k)的取值范围是______。
若二次函数(y = ax^2 + bx + c)的图象经过点((-1,0)),((3,0)),则此二次函数图象的对称轴是______。
某公司的一种产品,每件成本为2元,售价为3元,年销售量为10万件。为了获得更好的效益,公司准备拿出一定的资金做广告。根据经验,每年投入的广告费用为(x)(万元)时,产品的年销售量将是原销售量的(y)倍,且(y = -\frac{1}{10}x^2 + \frac{7}{10}x + 1)。如果把利润看作是销售总额减去成本费和广告费,则当每年投入的广告费用为______万元时,公司所获利润最大,最大利润为______万元。
三、解答题(共55分)
(1)该二次函数图象的顶点坐标;
(2)当(x)取何值时,(y)随(x)的增大而增大;
(3)当(x)取何值时,(y = 3)。
(1)求二次函数的解析式;(86%),阳性预测值(PPV) = (87%),阴性预测值(NPV) = (84%)。在正常细胞核与HG-TCC细胞核的比较中,QNG解决方案模型需要12/38个核特征,AUC = (99%),灵敏度 = (99%),特异性 = (98%),PPV = (98%),NPV = (99%)。在LG-TCC与HG-TCC细胞核的比较中,QNG解决方案模型需要17/38个核特征,AUC = (99%),灵敏度 = (96%),特异性 = (97%),PPV = (97%),NPV = (96%)。
基于对包括大小、形状和染色质组织在内的38个核特征的测量的计算机辅助QNG细胞分类器能够区分正常尿路上皮细胞核与LG-TCC和HG-TCC细胞核以及LG-TCC与HG-TCC细胞核。QNG细胞分类器已确凿表明正常尿路上皮和LG-TCC细胞核之间存在形态测量差异,这些差异可能肉眼难以察觉,并且它可能有助于病理学家确定膀胱细胞学标本中是否存在LG-TCC。