人工智能在卵巢癌医学影像中的应用进展

doi:10.24920/003963

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The article bring up the advances of AI in medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction of ovarian cancers.

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women’s cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.

Key words: artificial intelligence, machine learning, ovarian cancer, radiomics, algorithm, medical imaging

Chen Xu, Huo Xiaofei, Wu Zhe, Lu Jingjing. Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers[J].Chinese Medical Sciences Journal, 2021, 36(3): 196-203.

Figures/Tables 1

Table 1

Basic information of publications of AI associated medical imaging studies on ovarian cancers"

Reference	Year	Country	Study type	Imaging modality	Patients No. (n)	Mean/Median age(y/d)	Clinical orientation	Radiomics model	AI model	Validations	Radiomics extraction tools	AI tools
Song XL, et al. ^[38]	2021	China	P	MRI	89	52	M	Radiomics nomogram	LASSO regression	Yes (internal)	ITK-SNAP	ND
Wang X, et al. ^[5]	2021	China	R	PET/CT	261	57	PFS	Cox regression	ND	Yes (internal)	SlicerRadomics	ND
Beer L, et al. ^[31]	2021	UK	P	CT/US	6	65	TB	Radiomic habitats	GMM	No	CERR	MATLAB
Ai Y, et al. ^[39]	2021	China	R	CT	101	52	M	Radiomics features	Mann-Whitney U tests, LASSO, and Ridge regression	Yes (internal)	LIFEx package	“Glmnet” package
Qian L, et al. ^[25]	2020	China	R	MRI	61	52	DD	Traditional/Combined/Mixed radiomics	LASSO regression	Yes (internal)	3D Slicer, PyRadiomics	ND
Wang R, et al. ^[8]	2020	USA	R	MRI	451	48	DD	BAG, MRMR, TPOT	CNN (EfficientNet, ResNet)	Yes (internal)	“Radiomics-develop” package	Sklearn package in python
Akazawa M, et al. ^[22]	2020	Japan	R	MRI	202	51	DD	ND	SVM, Random forest, Naive bayes, Logistic regression and XGBoost	No	ITK-SNAP	Keras and Scikit-learn package in python
Lu H, et al. ^[44]	2019	UK	R	CT	364	62	OS, PFS	RPV	ND	Yes (internal and external)	ITK-SNAP	MATLAB
Meier A, et al. ^[45]	2019	USA	R	CT	88	75	OS, PFS	ND	ND	No	ITK-SNAP	MATLAB
Wang S, et al. ^[50]	2019	China	R	CT	245	56	RFS	Cox-PH	CNN	Yes (internal)	Lifelines package in Python	Keras package in Python
Zhang H, et al. ^[23]	2019	China	R	MRI	280	53	DD, PP	MRI-radiomics	SVM	Yes (internal)	ITK-SNAP	MATLAB
Martinez-Mas J, et al. ^[13]	2019	Spain	R	US	ND	ND	DD	ND	KNN, LD, SVM, ELM	Yes (internal)	Fourier descriptors	MATLAB
Zargari A, et al. ^[46]	2018	USA	R	CT	120	67	PFS	ND	PSO	Yes (internal)	ND	ND
Rizzo S, et al. ^[47]	2018	Italy	R	CT	101	53	PFS	ND	ND	No	IBEX	ND
Wei W, et al. ^[48]	2018	China	R	CT	142	50	PFS	ND	Logistic regression	Yes (internal and external)	ITK-SNAP	MATLAB
Vargas HA, et al. ^[49]	2017	USA	R	CT	38	ND	OS	ND	ND	No	3D Slicer	ND
Mimura R, et al. ^[19]	2016	Japan	R	MRI	32	45	DD	ADC histogram	ND	No	SPM8, MRIcron	ND
Aramendia-V V, et al. ^[15]	2016	Spain	R	US	145	43	DD	ND	MLPs	Yes (internal)	ND	MATLAB

Table 1

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