Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.

中文翻译：

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使用放射性组学特征分析和基于树的管道优化工具快速识别二次谐波生成图像中的人类卵巢癌。

卵巢癌目前是女性生殖器官最常见的癌症之一，其死亡率是所有类型的妇科癌症中最高的。卵巢癌的快速准确分类在确定治疗计划和预后中起着重要作用。尽管如此，最常用的分类方法是基于组织病理学标本检查，这既费时又费力。因此，在这项研究中，我们利用放射学特征提取方法和基于机器学习树的自动管道优化工具（TOPT）对良性，恶性和正常人卵巢组织的3D，二次谐波生成图像进行分析，以开发出高水平的高效的计算机辅助诊断模型。接收机工作特性曲线下面积为0.98、0。对于三种组织类型的分类，分别获得96和0.94。此外，该方法可以容易地应用于其他相关的组织和疾病，并且具有提高医学诊断过程的效率的巨大潜力。