Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.

中文翻译：

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基于机器学习的二次谐波生成图像快速诊断人类边缘性卵巢癌

关于生长模式和细胞学特征，交界性卵巢肿瘤介于良性和恶性之间，但往往会发展为恶性。目前，使用常见的医学影像学方法难以准确诊断卵巢癌，通常通过组织病理学检查来获得明确诊断。然而，这种检查需要有经验的病理学家，劳动密集、耗时，并可能导致观察者间的偏见。通过使用二次谐波生成成像和 k 近邻分类器，结合基于机器学习树的自动化管道优化工具，我们开发了一种计算机辅助诊断方法，将卵巢组织分类为恶性、良性、边缘和正常，获得接受者操作特征曲线下的面积为 1.00, 0. 分别为 99、0.98 和 0.97。这些结果表明，基于二次谐波生成图像和机器学习的诊断可以支持在临床实践中快速准确地检测卵巢癌。