机器学习算法在辅助超声诊断附件肿块良恶性中的应用研究进展

doi:10.16150/j.1671-2870.2022.04.022

摘要/Abstract

摘要：

卵巢癌是主要的附件恶性肿瘤,是全球女性癌症相关死亡的第二大常见原因,约75%的患者被诊断时已属晚期,5年生存率低于45%。因此,对附件肿块进行准确的非侵入性良恶性鉴别,对于患者的预后及生存质量至关重要。近年来,人工智能领域进展迅速,机器学习作为人工智能领域的一个分支,具有从大量复杂数据中进行高效学习的能力,逻辑回归、人工神经网络、支持向量机、深度卷积神经网络等算法已被应用于辅助超声鉴别附件肿块良恶性诊断中,并具有良好的诊断效能。本文将对机器学习算法在辅助超声鉴别附件肿块良恶性中应用价值的研究进展进行综述。

关键词: 附件肿块, 卵巢恶性肿瘤, 超声评分体系, 诊断模型, 机器学习

Abstract:

Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. Around 75% of patients present with stage Ⅲ/Ⅳ disease at diagnosis, with five-year survival rates below 45%. Ovarian cancer is the main adnexal malignant mass. Thus, accurate non-invasive risk stratification of adnexal masses is essential for optimal management and outcomes. In recent years, the field of artificial intelligence is developing rapidly. As a branch of artificial intelligence, machine learning could learn efficiently from complex and large amounts of data, which has infinite potential to differentiate benign and malignant adnexal masses. Logistic regression(LR), artificial neural network (ANN), support vector machine (SVM), deep learning convolution neural networks (DCNN) have been widely applied in this field, and achieved good diagnostic performance. This paper will review the history and progress of machine learning in ultrasound diagnosis of benign and malignant adnexal masses.

Key words: Adnexal mass, Ovarian malignant tumor, Ultrasound scoring system, Diagnostic model, Machine learning

中图分类号:

R541.7

何新, 陈慧, 冯炜炜. 机器学习算法在辅助超声诊断附件肿块良恶性中的应用研究进展[J]. 诊断学理论与实践, 2022, 21(04): 541-546.

HE Xin, CHEN Hui, FENG Weiwei. Research progress on the application of machine learning in assisted ultrasound diagnosis of adnexal masses[J]. Journal of Diagnostics Concepts & Practice, 2022, 21(04): 541-546.

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	研究	年份	AUC	灵敏度	特异度	图像数量（恶性/良性）	患者例数（恶性/良性）	模型
LR	Timmerman^[7]	2005	0.936	93%	77.0%		1 066（266/800）	LR1
			0.916	92%	75.0%			LR2
ANN	Biagiotti^[23]	1999		96%	97.7%		226（51/175）
	Timmermann^[24]	1999	0.979	95.9%	93.5%		173(49/124)
	Moszynski^[25]	2006	0.968	85.7%	93.1%		686（255/431）
SVM	Acharya^[29]	2012		100.0%	99.8%	2 000（1 000/1 000）	20（10/10）	SVM, RBF
				99.6%	100%			SVM, linear
	Khazendar^[28]	2019		80%	77.0%	187（75/112）	177
CNN	Zhang^[33]	2019	0.997	99.73%	95.85%	428（357/71）
	Christiansen^[34]	2021	0.950	96.0%	86.7%	3 077	758（309/449）	Ovry-Dx1
			0.958	97.1%	93.7%			Ovry-Dx2
	Gao^[38]	2022	0.911	83.1%	86.8%	592 275（39 258 /553 017）	107 624（4 254/103 370）
	Chen^[39]	2022	0.930	92%	85.0%		422（118/304）	DL_feature
			0.900	92%	80.0%			DL_decision

算法	优点	缺点
逻辑回归算法	（1）便于理解和实现,可以观测样本的概率分数。（2）训练速度快	（1）容易欠拟合。（2）在一些非线性的数据上表现欠佳
人工神经网络	（1）分类的准确度高,学习能力强。（2）有较强的鲁棒性和容错能力。	（1）需要大量的训练样本及参数。（2）浅层神经网络对于特征学习的表达能力有限,深层神经网络的参数繁多,易导致过拟合,甚至可能因为梯度消失而导致不可学习。
支持向量机	（1）SVM 是一种小样本学习方法。（2）算法简单,而且具有较好的鲁棒性。	（1）常规SVM的分类结果是二分类的,用SVM解决多分类问题存在困难,且不能直接提供概率估计。（2）SVM算法只有当核函数与数据的分布较为吻合时才能得到好的效果
深度卷积神经网络	（1）具有自学习功能,可以直接从训练数据中提取特征。（2）可以通过神经网络的深层结构来表示特征之间的关系。（3）能够获取大量数据中包含的信息,同时实现特征提取、特征选择和分类3个核心步骤,并构建模型。	（1）具有黑盒性,目前研究无法确定DCNN网络内部是如何运行的。（2）网络模型复杂程度越高,其对计算设备的硬件要求就越高,对于数据质量的要求也更高