A modeling method of the support vector machine combined with matrix optics is considered; a complete new measurement model for double-four quadrant photoelectric detector is built. According to the analysis of the re...A modeling method of the support vector machine combined with matrix optics is considered; a complete new measurement model for double-four quadrant photoelectric detector is built. According to the analysis of the received light spot size and its motion with the changes of the defocusing amount of detector photosensitive surface and the detector position attitude in the optical path, a mathematic expression of photoelectrical conversion is given, which can be applicable to random setting position of the detector at any time. Based on least square support vector machine (LS SVM), the mapping relationship among the output signal linear characteristic parameters (zero neighborhood gradient and intercept), the defocusing amount of the detector and the installation position attitude angle is established. Thus, the multiple dimensional high accuracy measuring and adjusting control system can be left out, and adaptive measurement of the detector parameters can be realized. Compared with existed measurement model and method, the presented model has the advantages of more clear physical meaning, closer to work mechanism of detector, acquiring more complete sample data and wiping out the dead spots or bad spots in measurement. And the accuracy of displacement measurement is increased to 3?μm. At the same time, this measurement mode provides a technical shortcut for three-dimensional small angle measurement.展开更多
目的:回顾性分析外侧象限乳腺癌患者乳腺原发肿瘤及腋窝淋巴结的超声特征,并构建列线图模型,为临床评估外侧象限乳腺癌患者腋窝淋巴结转移提供影像学依据。方法:回顾性分析无锡市锡山人民医院经病理证实的127例外侧象限乳腺癌患者腋窝...目的:回顾性分析外侧象限乳腺癌患者乳腺原发肿瘤及腋窝淋巴结的超声特征,并构建列线图模型,为临床评估外侧象限乳腺癌患者腋窝淋巴结转移提供影像学依据。方法:回顾性分析无锡市锡山人民医院经病理证实的127例外侧象限乳腺癌患者腋窝淋巴结及乳腺原发肿瘤的超声影像学特征。伴腋窝淋巴结转移者分入阳性组(54例),不伴腋窝淋巴结转移者分入阴性组(73例)。采用单变量和多变量Logistic回归分析,筛选淋巴结转移的危险因素。使用R语言将数据集随机分成训练集和验证集,基于训练集构建列线图预测模型,预测腋窝淋巴结转移风险,并在验证集中验证。受试者工作特征(receiver operating characteristic,ROC)曲线用于评估诊断性能,校正曲线和Hosmer-Lemeshow检验用于评估预测值与实际列线图预测值的一致性。结果:肿瘤针状边缘(OR=4.16,95%CI:1.25~13.79)和淋巴门结构不清晰(OR=19.20,95%CI:1.98~186.36)是外侧象限乳腺癌患者发生腋窝淋巴结转移的独立危险因素。据此构建预测外侧象限乳腺癌腋窝淋巴结转移的列线图模型。ROC曲线显示,训练集的曲线下面积(area under curve,AUC)为0.74(0.62~0.86),验证集AUC为0.73(0.62~0.84)。训练集和验证集的Hosmer-Lemeshow检验分别为P=0.570和P=0.552。结论:超声有助于术前外侧象限乳腺癌患者腋窝淋巴结转移情况的评估;基于Logistic回归构建的列线图预测模型具有良好的安全性、可靠性和实用性。展开更多
文摘A modeling method of the support vector machine combined with matrix optics is considered; a complete new measurement model for double-four quadrant photoelectric detector is built. According to the analysis of the received light spot size and its motion with the changes of the defocusing amount of detector photosensitive surface and the detector position attitude in the optical path, a mathematic expression of photoelectrical conversion is given, which can be applicable to random setting position of the detector at any time. Based on least square support vector machine (LS SVM), the mapping relationship among the output signal linear characteristic parameters (zero neighborhood gradient and intercept), the defocusing amount of the detector and the installation position attitude angle is established. Thus, the multiple dimensional high accuracy measuring and adjusting control system can be left out, and adaptive measurement of the detector parameters can be realized. Compared with existed measurement model and method, the presented model has the advantages of more clear physical meaning, closer to work mechanism of detector, acquiring more complete sample data and wiping out the dead spots or bad spots in measurement. And the accuracy of displacement measurement is increased to 3?μm. At the same time, this measurement mode provides a technical shortcut for three-dimensional small angle measurement.
文摘目的:回顾性分析外侧象限乳腺癌患者乳腺原发肿瘤及腋窝淋巴结的超声特征,并构建列线图模型,为临床评估外侧象限乳腺癌患者腋窝淋巴结转移提供影像学依据。方法:回顾性分析无锡市锡山人民医院经病理证实的127例外侧象限乳腺癌患者腋窝淋巴结及乳腺原发肿瘤的超声影像学特征。伴腋窝淋巴结转移者分入阳性组(54例),不伴腋窝淋巴结转移者分入阴性组(73例)。采用单变量和多变量Logistic回归分析,筛选淋巴结转移的危险因素。使用R语言将数据集随机分成训练集和验证集,基于训练集构建列线图预测模型,预测腋窝淋巴结转移风险,并在验证集中验证。受试者工作特征(receiver operating characteristic,ROC)曲线用于评估诊断性能,校正曲线和Hosmer-Lemeshow检验用于评估预测值与实际列线图预测值的一致性。结果:肿瘤针状边缘(OR=4.16,95%CI:1.25~13.79)和淋巴门结构不清晰(OR=19.20,95%CI:1.98~186.36)是外侧象限乳腺癌患者发生腋窝淋巴结转移的独立危险因素。据此构建预测外侧象限乳腺癌腋窝淋巴结转移的列线图模型。ROC曲线显示,训练集的曲线下面积(area under curve,AUC)为0.74(0.62~0.86),验证集AUC为0.73(0.62~0.84)。训练集和验证集的Hosmer-Lemeshow检验分别为P=0.570和P=0.552。结论:超声有助于术前外侧象限乳腺癌患者腋窝淋巴结转移情况的评估;基于Logistic回归构建的列线图预测模型具有良好的安全性、可靠性和实用性。
