Background:Hepatocellular carcinoma(HCC)is the most common cause of cancer-related death in Saudi Arabia.Our study aimed to investigate the patterns of HCC and the effect of TNM staging,Alfa-fetoprotein(AFP),and Child...Background:Hepatocellular carcinoma(HCC)is the most common cause of cancer-related death in Saudi Arabia.Our study aimed to investigate the patterns of HCC and the effect of TNM staging,Alfa-fetoprotein(AFP),and Child-Turcotte Pugh(CTP)on patients’overall survival(OS).Methods:A retrospective analysis was conducted on 43 HCC patients at a single oncology center in Saudi Arabia from 2015 to 2020.All patients had to fulfill one of the following criteria:(a)a liver lesion reported as definitive HCC on dynamic imaging and/or(b)a biopsy-confirmed diagnosis.Results:The mean patient age of all HCC cases was 66.8 with a male-to-female ratio of 3.3:1.All patients were stratified into two groups:viral HCC(n=22,51%)and non-viral HCC(n=21,49%).Among viral-HCC patients,55%were due to HBV and 45%due to HCV.Cirrhosis was diagnosed in 79%of cases.Age and sex did not significantly statistically differ in OS among viral and non-viral HCC patients(p-value>0.05).About 65%of patients had tumor size>5 cm during the diagnosis,with a significant statistical difference in OS(p-value=0.027).AFP was>400 ng/ml in 45%of the patients.There was a statistically significant difference in the OS in terms of AFP levels(p-value=0.021).A statistically significant difference was also observed between the CTP score and OS(p-value=0.02).CTP class B had the longest survival.BSC was the most common treatment provided to HCC patients followed by sorafenib therapy.There was a significant statistical difference in OS among viral and non-viral HCC patients(p-value=0.008).Conclusions:The most common predictors for OS were the underlying cause of HCC,AFP,and tumor size.Being having non-viral etiology,a tumor size>5 cm,an AFP>400 ng/mL,and a CTP score class C were all negatively associated with OS.展开更多
Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In ...Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of challenges.In this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each output.Such models increase confidence if the results appear plausible and match the clinicians expectations.However,the absence of a plausible explanation does not imply an inaccurate model.Especially in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.For example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health information.Unlike centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health data.This narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.展开更多
The classification of central nervous system(CNS)glioma went through a sequence of developments,between 2006 and 2021,started with only histological approach then has been aided with a major emphasis on molecular sign...The classification of central nervous system(CNS)glioma went through a sequence of developments,between 2006 and 2021,started with only histological approach then has been aided with a major emphasis on molecular signatures in the 4^(th) and 5^(th) editions of the World Health Organization(WHO).The recent reformation in the 5th edition of the WHO classification has focused more on the molecularly defined entities with better characterized natural histories as well as new tumor types and subtypes in the adult and pediatric populations.These new subclassified entities have been incorporated in the 5^(th) edition after the continuous exploration of new genomic,epigenomic and transcriptomic discovery.Indeed,the current guidelines of 2021 WHO classification of CNS tumors and European Association of Neuro-Oncology(EANO)exploited the molecular signatures in the diagnostic approach of CNS gliomas.Our current review presents a practical diagnostic approach for diffuse CNS gliomas and circumscribed astrocytomas using histomolecular criteria adopted by the recent WHO classification.We also describe the treatment strategies for these tumors based on EANO guidelines.展开更多
Background:Lung cancer(LC)is one of the most common neoplastic diseases and a leading cause of death in Saudi Arabia.Its incidence in Saudi Arabia has increased by more than 3%within two decades.Our study aimed to des...Background:Lung cancer(LC)is one of the most common neoplastic diseases and a leading cause of death in Saudi Arabia.Its incidence in Saudi Arabia has increased by more than 3%within two decades.Our study aimed to describe the epidemiological and genetic landscapes of LC in Al-Madinah city in Saudi Arabia.Methods:A retrospective analysis was conducted on the medical records of 65 patients diagnosed with lung cancer between 2015 and 2021 at a single medical oncology center in Al-Madinah city of Saudi Arabia.Results:The mean patients’age was 59.2 years,with 50(76.9%)males and 15(23.1%)females;37(57%)smokers,and 28(43%)non-smokers.The number of cases per year has increased gradually over six years from 2015(n=3)to 2020(n=13).The most prevalent histopathological diagnosis was non-small cell lung cancer(NSCLC)(n=58,89%)followed by small cell lung cancer(SCLC)(n=5,7.8%).NSCLC was frequently more common in smokers while squamous cell carcinoma was more frequent in non-smokers.Around 89%(n=58)of the cases were diagnosed in late stage IV and the most common metastatic sites were to pleura and lymph nodes(n=32,49.2%).Program Death Legend-1(PDL-1)was fairly expressed in 7/10(70%)patients.Epidermal Growth Factor Receptor(EGFR)was mutated in 5/17(29%)patients.Other mutations detected include Anaplastic Lymphoma Kinase(ALK)and phosphatidylinositol 3-kinase(PIK3C)mutations in two patients.Conclusions:Our study revealed that lung cancer is a significant burden in AlMadinah city of Saudi Arabia.If the risk factors are not controlled,the number of cases may increase considerably.Health education about the risk factors and cancer prevention helps in early lung cancer detection.展开更多
文摘Background:Hepatocellular carcinoma(HCC)is the most common cause of cancer-related death in Saudi Arabia.Our study aimed to investigate the patterns of HCC and the effect of TNM staging,Alfa-fetoprotein(AFP),and Child-Turcotte Pugh(CTP)on patients’overall survival(OS).Methods:A retrospective analysis was conducted on 43 HCC patients at a single oncology center in Saudi Arabia from 2015 to 2020.All patients had to fulfill one of the following criteria:(a)a liver lesion reported as definitive HCC on dynamic imaging and/or(b)a biopsy-confirmed diagnosis.Results:The mean patient age of all HCC cases was 66.8 with a male-to-female ratio of 3.3:1.All patients were stratified into two groups:viral HCC(n=22,51%)and non-viral HCC(n=21,49%).Among viral-HCC patients,55%were due to HBV and 45%due to HCV.Cirrhosis was diagnosed in 79%of cases.Age and sex did not significantly statistically differ in OS among viral and non-viral HCC patients(p-value>0.05).About 65%of patients had tumor size>5 cm during the diagnosis,with a significant statistical difference in OS(p-value=0.027).AFP was>400 ng/ml in 45%of the patients.There was a statistically significant difference in the OS in terms of AFP levels(p-value=0.021).A statistically significant difference was also observed between the CTP score and OS(p-value=0.02).CTP class B had the longest survival.BSC was the most common treatment provided to HCC patients followed by sorafenib therapy.There was a significant statistical difference in OS among viral and non-viral HCC patients(p-value=0.008).Conclusions:The most common predictors for OS were the underlying cause of HCC,AFP,and tumor size.Being having non-viral etiology,a tumor size>5 cm,an AFP>400 ng/mL,and a CTP score class C were all negatively associated with OS.
基金This work was supported in part by the National Natural Science Foundation of China(82260360)the Foreign Young Talent Program(QN2021033002L).
文摘Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of challenges.In this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each output.Such models increase confidence if the results appear plausible and match the clinicians expectations.However,the absence of a plausible explanation does not imply an inaccurate model.Especially in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.For example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health information.Unlike centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health data.This narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.
文摘The classification of central nervous system(CNS)glioma went through a sequence of developments,between 2006 and 2021,started with only histological approach then has been aided with a major emphasis on molecular signatures in the 4^(th) and 5^(th) editions of the World Health Organization(WHO).The recent reformation in the 5th edition of the WHO classification has focused more on the molecularly defined entities with better characterized natural histories as well as new tumor types and subtypes in the adult and pediatric populations.These new subclassified entities have been incorporated in the 5^(th) edition after the continuous exploration of new genomic,epigenomic and transcriptomic discovery.Indeed,the current guidelines of 2021 WHO classification of CNS tumors and European Association of Neuro-Oncology(EANO)exploited the molecular signatures in the diagnostic approach of CNS gliomas.Our current review presents a practical diagnostic approach for diffuse CNS gliomas and circumscribed astrocytomas using histomolecular criteria adopted by the recent WHO classification.We also describe the treatment strategies for these tumors based on EANO guidelines.
文摘Background:Lung cancer(LC)is one of the most common neoplastic diseases and a leading cause of death in Saudi Arabia.Its incidence in Saudi Arabia has increased by more than 3%within two decades.Our study aimed to describe the epidemiological and genetic landscapes of LC in Al-Madinah city in Saudi Arabia.Methods:A retrospective analysis was conducted on the medical records of 65 patients diagnosed with lung cancer between 2015 and 2021 at a single medical oncology center in Al-Madinah city of Saudi Arabia.Results:The mean patients’age was 59.2 years,with 50(76.9%)males and 15(23.1%)females;37(57%)smokers,and 28(43%)non-smokers.The number of cases per year has increased gradually over six years from 2015(n=3)to 2020(n=13).The most prevalent histopathological diagnosis was non-small cell lung cancer(NSCLC)(n=58,89%)followed by small cell lung cancer(SCLC)(n=5,7.8%).NSCLC was frequently more common in smokers while squamous cell carcinoma was more frequent in non-smokers.Around 89%(n=58)of the cases were diagnosed in late stage IV and the most common metastatic sites were to pleura and lymph nodes(n=32,49.2%).Program Death Legend-1(PDL-1)was fairly expressed in 7/10(70%)patients.Epidermal Growth Factor Receptor(EGFR)was mutated in 5/17(29%)patients.Other mutations detected include Anaplastic Lymphoma Kinase(ALK)and phosphatidylinositol 3-kinase(PIK3C)mutations in two patients.Conclusions:Our study revealed that lung cancer is a significant burden in AlMadinah city of Saudi Arabia.If the risk factors are not controlled,the number of cases may increase considerably.Health education about the risk factors and cancer prevention helps in early lung cancer detection.
