Advances in medical technology, electronic healthcare databases and computational capacity are generating big data in the field of medicine. With the increase in sources, volume and availability of big data, clinicians face new challenges in synthesizing information. For the past two decades, significant technical developments in artificial intelligence (AI) methodology have expanded its power to address health questions and use data. Methods developed in the context of clinical machine learning ought to, primarily, improve patient welfare by achieving accurate diagnosis and treatment, and assist doctors in their daily routine. Compared with the traditional data analysis methods, AI algorithm has obvious advantages in data processing and analysis, and the relevant research results have been closed to or have already applied in clinical practice.
Urinary tumors, including prostate, bladder, kidney, urothelial, urethral, penile and testicular cancer, seriously affect human health all over the world. These tumors are heterogeneous groups of diseases with high morbidity and mortality rates. Confronted with existing problems in the management of urinary tumors, AI shows great potential in areas including detection, grading, treatment response assessment and outcome prediction. However, this research field is still relatively new to many medical researchers and clinicians. It is then a considerable obstacle for many medical researchers to understand and adapt to this new tool.
The objective of applying AI technologies in urologic oncology is to achieve accurate diagnosis, treatment optimization and accurate prognosis prediction. The purpose of this Research Topic is to offer a perspective on AI in urologic oncology to contextualize advancements, to summarize the successful application areas, to identify the potential societal impact arising from the development and deployment of AI systems, and to shed light on future research directions. With the help of AI, we can achieve accurate diagnosis and treatment of urinary tumors in terms of imaging and pathological diagnosis, surgical scheme and drug optimization for patients with urologic cancers.
In this Research Topic, we aim at providing a forum for researchers to discuss the significant impact of AI on the diagnosis and treatment of urologic tumors. We welcome submissions of Original Research, Case Reports, Mini Reviews, Perspectives, Reviews, with focuses on, but is not limited to, the following aspects
·Creation and optimization of artificial intelligence algorithm in the field of urologic oncology
·The application of Radiomics in urologic tumors,such as prostate and renal cancer.
·Combinatorial optimization of radiotherapy and chemotherapy for urinary tumors based on AI
·Model building and validation for outcome prediction based on AI
Advances in medical technology, electronic healthcare databases and computational capacity are generating big data in the field of medicine. With the increase in sources, volume and availability of big data, clinicians face new challenges in synthesizing information. For the past two decades, significant technical developments in artificial intelligence (AI) methodology have expanded its power to address health questions and use data. Methods developed in the context of clinical machine learning ought to, primarily, improve patient welfare by achieving accurate diagnosis and treatment, and assist doctors in their daily routine. Compared with the traditional data analysis methods, AI algorithm has obvious advantages in data processing and analysis, and the relevant research results have been closed to or have already applied in clinical practice.
Urinary tumors, including prostate, bladder, kidney, urothelial, urethral, penile and testicular cancer, seriously affect human health all over the world. These tumors are heterogeneous groups of diseases with high morbidity and mortality rates. Confronted with existing problems in the management of urinary tumors, AI shows great potential in areas including detection, grading, treatment response assessment and outcome prediction. However, this research field is still relatively new to many medical researchers and clinicians. It is then a considerable obstacle for many medical researchers to understand and adapt to this new tool.
The objective of applying AI technologies in urologic oncology is to achieve accurate diagnosis, treatment optimization and accurate prognosis prediction. The purpose of this Research Topic is to offer a perspective on AI in urologic oncology to contextualize advancements, to summarize the successful application areas, to identify the potential societal impact arising from the development and deployment of AI systems, and to shed light on future research directions. With the help of AI, we can achieve accurate diagnosis and treatment of urinary tumors in terms of imaging and pathological diagnosis, surgical scheme and drug optimization for patients with urologic cancers.
In this Research Topic, we aim at providing a forum for researchers to discuss the significant impact of AI on the diagnosis and treatment of urologic tumors. We welcome submissions of Original Research, Case Reports, Mini Reviews, Perspectives, Reviews, with focuses on, but is not limited to, the following aspects
·Creation and optimization of artificial intelligence algorithm in the field of urologic oncology
·The application of Radiomics in urologic tumors,such as prostate and renal cancer.
·Combinatorial optimization of radiotherapy and chemotherapy for urinary tumors based on AI
·Model building and validation for outcome prediction based on AI