可视化影像决策模型在评估肺结节浸润程度中的价值

目的:探讨基于临床资料、影像征象和影像组学特征构建的联合模型在术前对肺结节浸润程度的预测价值,并通过决策热图及Shapley算法对模型进行可视化分析。方法:回顾性搜集2018年1月-2022年3月在本院经病理确诊的179例肺结节患者的临床资料和术前CT图像(肺窗平扫)。根据肺肿瘤新分类,分为腺体前驱病变组(78例)和浸润性肺腺癌组(101例)。采用Deepwise软件,分别提取瘤灶、瘤周3 mm和5 mm区域的影像组学特征。使用单因素分析、相关性分析、Boruta算法和逐步logistic回归分析等特征筛选算法确定各区域的最佳组学特征,然后采用logistics方法分别构建3个单区域及2个多区域(肿瘤+瘤周3 mm及肿瘤+瘤周5 mm)共5个影像组学模型,分析各模型的预测效能并计算其影像组学评分(Radsocre)。通过单因素和多因素logistic回归方法筛选相关临床指标和结节的主要CT征象,并采用XGBoost算法将筛选出的高危因素结合瘤灶+瘤周3 mm联合模型的影像组学得分构建临床影像联合模型。额外收集浙江省嘉兴市中医医院经病理证实的69例肺结节患者的临床和CT资料来完成联合模型的泛化性验证。利用决策热图和Shapley算法对模型分别进行可视化和特征贡献度分析。结果:相比单区域影像组学模型(训练集:AUC=0.740、0753、0.768;验证集:AUC=0.841、0.856、0.809),多区域影像组学模型在两个数据集中均显示出更高的预测效能(AUC=0.878和0.834)。XGBoost联合模型的预测效能得到进一步地提高(AUC=0.948和0.886)。Shapley分析显示影像组学得分、CT值和结节长度为预测肺结节浸润程度的最重要的3个特征。决策热图算法实现了对浸润性预测推演过程的可视化。结论:XGBoost模型对肺结节浸润性的评估具有较高的准确性和泛化性。决策热图实现了可解释机器学习算法的可视化从而保障了模型的实用性,为肺结节的临床处理及管理提供了一种无创性的辅助诊断工具。

MRI血流动力学半定量分析及形态学特征对乳腺良恶性囊实性病变的诊断价值

目的:探讨联合应用MRI血流动力学半定量分析及囊壁形态学特征对乳腺良恶性囊实性病变的诊断价值。方法:回顾性选取75例乳腺良恶性囊实性病变患者的影像资料,对比分析良恶性囊实性病变囊壁的形态、厚度及壁结节;由MRI增强扫描图像获得时间-信号强度曲线(time-signal intensity curve,TIC),计算血流动力学半定量参数(达峰时间Tmax、早期强化率Ee、最大相对强化斜率Eslope、最大强化速率Vmax),对比良恶性病变间的差异,并绘制ROC曲线分析联合诊断效能。采用SPSS 19.0软件进行统计学分析。结果:良性囊实性病变大多内壁光滑,壁结节单发、形态规则;恶性囊实性病变内壁凹凸不平,壁结节多发、形态不规则;良性囊实性病变的壁厚小于恶性囊实性病变,差异有统计学意义(P<0.05)。乳腺囊实性病变强化方式以环形强化为主,良性病变TIC-Ⅰ型多见,恶性病变TIC-Ⅲ型多见;恶性病变的Ee、Eslope、Vmax均大于良性病变,恶性病变的达峰时间Tmax较良性病变短,差异均有统计学意义(P<0.05)。联合应用血流动力学半定量分析及囊壁形态学特征诊断乳腺良恶性囊实性病变的准确率、阳性预测值、阴性预测值、敏感度度、特异度分别为92.0%、95.1%、88.2%、90.1%、93.8%。结论:MRI血流动力学半定量分析及囊壁的形态学特征能够较好地鉴别乳腺囊实性病变的良恶性,二者联合分析有助于提高诊断效能。

Judging or setting weight steady-state of rational Bézier curves and surfaces

Many works have investigated the problem of reparameterizing rational B^zier curves or surfaces via MSbius transformation to adjust their parametric distribution as well as weights, such that the maximal ratio of weights becomes smallerthat some algebraic and computational properties of the curves or surfaces can be improved in a way. However, it is an indication of veracity and optimization of the reparameterization to do prior to judge whether the maximal ratio of weights reaches minimum, and verify the new weights after MSbius transfor- mation. What＇s more the users of computer aided design softwares may require some guidelines for designing rational B6zier curves or surfaces with the smallest ratio of weights. In this paper we present the necessary and sufficient conditions that the maximal ratio of weights of the curves or surfaces reaches minimum and also describe it by using weights succinctly and straightway. The weights being satisfied these conditions are called being in the stable state. Applying such conditions, any giving rational B6zier curve or surface can automatically be adjusted to come into the stable state by CAD system, that is, the curve or surface possesses its optimal para- metric distribution. Finally, we give some numerical examples for demonstrating our results in important applications of judging the stable state of weights of the curves or surfaces and designing rational B6zier surfaces with compact derivative bounds.

Constrained multi-degree reduction of rational Bézier curves using reparameterization

Applying homogeneous coordinates, we extend a newly appeared algorithm of best constrained multi-degree reduction for polynomial Bezier curves to the algorithms of constrained multi-degree reduction for rational Bezier curves. The idea is introducing two criteria, variance criterion and ratio criterion, for reparameterization of rational Bezier curves, which are used to make uniform the weights of the rational Bezier curves as accordant as possible, and then do multi-degree reduction for each component in homogeneous coordinates. Compared with the two traditional algorithms of ＂cancelling the best linear common divisor＂ and ＂shifted Chebyshev polynomial＂, the two new algorithms presented here using reparameterization have advantages of simplicity and fast computing, being able to preserve high degrees continuity at the end points of the curves, do multi-degree reduction at one time, and have good approximating effect.