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Abstract：
The depth of myometrial invasion affects the treatment and prognosis of patients with endometrial cancer (EC), conventionally evaluated using MR imaging (MRI). However, only a few computer-aided diagnosis methods have been reported for identifying deep myometrial invasion (DMI) using MRI. Moreover, these existing methods exhibit relatively unsatisfactory sensitivity and specificity. This study proposes a novel computerized method to facilitate the accurate detection of DMI on MRI. This method requires only the corpus uteri region provided by humans or computers instead of the tumor region. We also propose a geometric feature called LS to describe the irregularity of the tissue structure inside the corpus uteri triggered by EC, which has not been leveraged for the DMI prediction model in other studies. Texture features are extracted and then automatically selected by recursive feature elimination. Utilizing a feature fusion strategy of strong and weak features devised in this study, multiple probabilistic support vector machines incorporate LS and texture features, which are then merged to form the ensemble model EPSVM. The model performance is evaluated via leave-one-out cross-validation. We make the following comparisons, EPSVM versus the commonly used classifiers such as random forest, logistic regression, and naive Bayes; EPSVM versus the models using LS or texture features alone. The results show that EPSVM attains an accuracy, sensitivity, specificity, and F1 score of 93.7%, 94.7%, 93.3%, and 87.8%, all of which are higher than those of the commonly used classifiers and the models using LS or texture features alone. Compared with the methods in existing studies, EPSVM exhibits high performance in terms of both sensitivity and specificity. Moreover, LS can achieve an accuracy, sensitivity, and specificity of 89.9%, 89.5%, and 90.0%. Thus, the devised geometric feature LS is significant for DMI detection. The fusion of LS and texture features in the proposed EPSVM can provide more reliable prediction. The computer-aided classification based on the proposed method can assist radiologists in accurately identifying DMI on MRI.
摘要：
子宫肌层浸润深度影响子宫内膜癌(EC)患者的治疗和预后,常规评估使用MR成像（MRI）。然而,只有少数计算机辅助诊断方法被报道用于使用MRI识别深肌层侵犯(DMA).此外，这些现有方法表现出相对不令人满意的灵敏度和特异性。这项研究提出了一种新颖的计算机化方法，以促进在MRI上准确检测STI。该方法仅需要由人或计算机提供的子宫体区域而不是肿瘤区域。我们还提出了一种称为LS的几何特征来描述由EC触发的子宫内组织结构的不规则性，这在其他研究中还没有被用于MI预测模型。提取纹理特征，然后通过递归特征消除自动选择。利用本研究中设计的强特征和弱特征的特征融合策略，多个概率支持向量机结合了LS和纹理特征，然后将其合并以形成集成模型EPSVM。通过留一法交叉验证评估模型性能。我们做了以下比较，EPSVM与常用分类器，如随机森林，逻辑回归，和朴素贝叶斯；EPSVM与仅使用LS或纹理特征的模型。结果表明，EPSVM达到了一定的准确性，灵敏度,特异性，F1得分为93.7%，94.7%,93.3%,87.8%,所有这些都高于常用的分类器和仅使用LS或纹理特征的模型。与现有研究的方法相比，EPSVM在灵敏度和特异性方面都表现出高性能。此外，LS可以实现精度，灵敏度,特异性为89.9%，89.5%,90.0%。因此，所设计的几何特征LS对于MI检测具有重要意义。在提出的EPSVM中融合LS和纹理特征可以提供更可靠的预测。基于所提出的方法的计算机辅助分类可以帮助放射科医生准确地识别MRI上的DMI。
