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 ar...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.展开更多
Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are traine...Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations（phenotypes） to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype（to obtain genomic information） of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography（CT）, magnetic resonance imaging（MRI）, and positron-emission tomography（PET）, the challenges we are facing, and the potential contribution of radiomics towards precision medicine.展开更多
文摘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.
文摘Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations（phenotypes） to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype（to obtain genomic information） of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography（CT）, magnetic resonance imaging（MRI）, and positron-emission tomography（PET）, the challenges we are facing, and the potential contribution of radiomics towards precision medicine.
