In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology.Brain metastasis is one of the most serious complications of breast cancer and causes h...In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology.Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality.Brain metastases may involve the brain parenchyma and/or leptomeninges.Symptomatic brain metastases develop in 10%-16%of newly recognized cases each year,and this rate increases to 30%in autopsy series.Depending on the size of the metastatic foci,it may be accompanied by extensive vasogenic edema or may occur as small tumor foci.Since brain metastases are a significant cause of morbidity and mortality,early diagnosis can have significant effects on survival and quality of life.The risk of developing brain metastases emerges progressively due to various patient and tumor characteristics.Patient variability may be particularly important in the susceptibility and distribution of brain metastases because malignant blood must cross the brain barrier and move within the brain parenchyma.Some characteristics of the tumor,such as gene expression,may increase the risk of brain metastasis.Clinical growth,tumor stage,tumor grade,growth receptor positivity,HER2 positivity,molecular subtype(such as triple negative status,luminal/nonluminal feature)increase the risk of developing breast cancer metastasis.Factors related to survival due to breast cancer brain metastasis include both tumor/patient characteristics and treatment characteristics,such as patient age,lung metastasis,surgery for brain metastasis,and HER2 positivity.If cases with a high risk of developing brain metastasis can be identified with the help of clinical procedures and artificial intelligence,survival and quality of life can be increased with early diagnosis and treatment.At the same time,it is important to predict the formation of this group in order to develop new treatment methods in cases with low survival expectancy with brain metastases.展开更多
In this editorial we comment on the article by Zhang et al published in the recent issue of the World Journal of Clinical Oncology.Pancreatic cancer is the fourth most common cause of cancer-related mortality and has ...In this editorial we comment on the article by Zhang et al published in the recent issue of the World Journal of Clinical Oncology.Pancreatic cancer is the fourth most common cause of cancer-related mortality and has the lowest survival rate among all solid cancers.It causes 227000 deaths annually worldwide,and the 5-year survival rate is very low due to early metastasis,which is 4.6%.Cancer survival increases with better knowledge of risk factors and early and accurate diagnosis.Circulating tumor cells(CTCs)are tumor cells that intravasate from the primary tumor or metastasis foci into the peripheral blood circulation system spontan-eously or during surgical operations.Detection of CTC in blood is promising for early diagnosis.In addition,studies have associated high CTC levels with a more advanced stage,and more intensive treatments should be considered in cases with high CTC.In tumors that are considered radiologically resectable,it may be of critical importance in detecting occult metastases and preventing unnecessary surgeries.展开更多
Artificial intelligence(AI)is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input.Machine learning(...Artificial intelligence(AI)is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input.Machine learning(ML)is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience.Deep learning is an ML technique that uses deep neural networks to create a model.The growth and sharing of data,increasing computing power,and developments in AI have initiated a transformation in healthcare.Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging,dosimetry,and imaging performed before each fraction.Of the many algorithms used in radiation oncology,has advantages and limitations with different computational power requirements.The aim of this review is to summarize the radiotherapy(RT)process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML.The RT stage is divided into seven stages:patient evaluation,simulation,contouring,planning,quality control,treatment application,and patient follow-up.A systematic evaluation of the applicability,limitations,and advantages of AI algorithms has been done for each stage.展开更多
Accurate and rapid diagnosis is essential for correct treatment in rectal cancer.Determining the optimal treatment plan for a patient with rectal cancer is a complex process,and the oncological results and toxicity ar...Accurate and rapid diagnosis is essential for correct treatment in rectal cancer.Determining the optimal treatment plan for a patient with rectal cancer is a complex process,and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage.In recent years,the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology.Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer.It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician.Prediction algorithms can be standardized by sharing data between centers,diversifying data,and creating big data.展开更多
文摘In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology.Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality.Brain metastases may involve the brain parenchyma and/or leptomeninges.Symptomatic brain metastases develop in 10%-16%of newly recognized cases each year,and this rate increases to 30%in autopsy series.Depending on the size of the metastatic foci,it may be accompanied by extensive vasogenic edema or may occur as small tumor foci.Since brain metastases are a significant cause of morbidity and mortality,early diagnosis can have significant effects on survival and quality of life.The risk of developing brain metastases emerges progressively due to various patient and tumor characteristics.Patient variability may be particularly important in the susceptibility and distribution of brain metastases because malignant blood must cross the brain barrier and move within the brain parenchyma.Some characteristics of the tumor,such as gene expression,may increase the risk of brain metastasis.Clinical growth,tumor stage,tumor grade,growth receptor positivity,HER2 positivity,molecular subtype(such as triple negative status,luminal/nonluminal feature)increase the risk of developing breast cancer metastasis.Factors related to survival due to breast cancer brain metastasis include both tumor/patient characteristics and treatment characteristics,such as patient age,lung metastasis,surgery for brain metastasis,and HER2 positivity.If cases with a high risk of developing brain metastasis can be identified with the help of clinical procedures and artificial intelligence,survival and quality of life can be increased with early diagnosis and treatment.At the same time,it is important to predict the formation of this group in order to develop new treatment methods in cases with low survival expectancy with brain metastases.
文摘In this editorial we comment on the article by Zhang et al published in the recent issue of the World Journal of Clinical Oncology.Pancreatic cancer is the fourth most common cause of cancer-related mortality and has the lowest survival rate among all solid cancers.It causes 227000 deaths annually worldwide,and the 5-year survival rate is very low due to early metastasis,which is 4.6%.Cancer survival increases with better knowledge of risk factors and early and accurate diagnosis.Circulating tumor cells(CTCs)are tumor cells that intravasate from the primary tumor or metastasis foci into the peripheral blood circulation system spontan-eously or during surgical operations.Detection of CTC in blood is promising for early diagnosis.In addition,studies have associated high CTC levels with a more advanced stage,and more intensive treatments should be considered in cases with high CTC.In tumors that are considered radiologically resectable,it may be of critical importance in detecting occult metastases and preventing unnecessary surgeries.
文摘Artificial intelligence(AI)is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input.Machine learning(ML)is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience.Deep learning is an ML technique that uses deep neural networks to create a model.The growth and sharing of data,increasing computing power,and developments in AI have initiated a transformation in healthcare.Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging,dosimetry,and imaging performed before each fraction.Of the many algorithms used in radiation oncology,has advantages and limitations with different computational power requirements.The aim of this review is to summarize the radiotherapy(RT)process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML.The RT stage is divided into seven stages:patient evaluation,simulation,contouring,planning,quality control,treatment application,and patient follow-up.A systematic evaluation of the applicability,limitations,and advantages of AI algorithms has been done for each stage.
文摘Accurate and rapid diagnosis is essential for correct treatment in rectal cancer.Determining the optimal treatment plan for a patient with rectal cancer is a complex process,and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage.In recent years,the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology.Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer.It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician.Prediction algorithms can be standardized by sharing data between centers,diversifying data,and creating big data.
