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
The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal c...The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal cancer biological variables must be evaluated.Currently,medical imaging plays a crucial role in the characterization of this disease,and it often requires a multimodal approach.Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors.Computed tomography is widely adopted for the detection of distant metastases.However,conventional imaging has recognized limitations,and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation.There is a growing interest in artificial intelligence applications in medicine,and imaging is by no means an exception.The introduction of radiomics,which allows the extraction of quantitative features that reflect tumor heterogeneity,allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers.To manage such a huge amount of data,the use of machine learning algorithms has been proposed.Indeed,without prior explicit programming,they can be employed to build prediction models to support clinical decision making.In this review,current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented,with an imaging modality-based approach and a keen eye on unsolved issues.The results are promising,but the road ahead for translation in clinical practice is rather long.展开更多
目的研究肺腺癌表皮生长因子受体(EGFR)基因突变状态与放射组学特征的相关性。方法采用放射组学软件提取病理诊断为肺腺癌的411例患者的术前薄层CT的94个CT放射组学特征,包括19个一阶统计(First Order Statistics)特征,27个灰度共生矩阵...目的研究肺腺癌表皮生长因子受体(EGFR)基因突变状态与放射组学特征的相关性。方法采用放射组学软件提取病理诊断为肺腺癌的411例患者的术前薄层CT的94个CT放射组学特征,包括19个一阶统计(First Order Statistics)特征,27个灰度共生矩阵(GLCM)特征,16个灰度运行长度矩阵(GLRLM)特征,16个灰度区域大小矩阵(GLSZM)特征,16个形态基础(Shap-based)特征。由于是回顾性研究,医院伦理委员会同意免除获取患者知情同意书的需要。采用单因素分析比较94个纹理特征和三个临床特征(年龄、性别和非吸烟者)与EGFR突变状态的关系,选出差异有统计学意义的变量进行Logistic回归分析得出独立危险因素。结果 411例肺腺癌患者中,EGFR突变型209例(50.9%),野生型202例(49.1%)。EGFR野生型和突变型女性患者和非吸烟者之间差异有统计学意义(P<0.001)。单因素分析显示四个放射组学特征Energy,Large Area Low Gray Level Emphasis,Size Zone Non Uniformity Normalized和Small Area Emphasis在EGFR野生型和突变型肺腺癌间差异有统计学意义(P<0.001)。Logistic回归分析表明非吸烟者和Size Zone Non Uniformity Normalized为EGFR基因突变独立危险因素,OR值分别为0.294 (95%CI:0.183~0.470;P=0.000)和0.007 (95%CI:0.000~0.012;P=0.007),且非吸烟者、Size Zone Non Uniformity Normalized以及Size Zone Non Uniformity Normalized联合非吸烟者预测肺腺癌EGFR突变的曲线下面积(AUC)值分别为0.629,0.571和0.663。结论肺腺癌的一些放射组学特征与EGFR突变有相关性,放射组学结合临床特征有望成为预测肺腺癌EGFR突变的影像生物标记物。展开更多
BACKGROUND Esophageal cancer(ESCA)is the sixth most common malignancy in the world,and its incidence is rapidly increasing.Recently,several microRNAs(miRNAs)and messenger RNA(mRNA)targets were evaluated as potential b...BACKGROUND Esophageal cancer(ESCA)is the sixth most common malignancy in the world,and its incidence is rapidly increasing.Recently,several microRNAs(miRNAs)and messenger RNA(mRNA)targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in early diagnosis.In addition,computed tomography(CT)radiomic studies on ESCA improved the early stage identification and the prediction of response to treatment.Radiogenomics provides clinically useful prognostic predictions by linking molecular characteristics such as gene mutations and gene expression patterns of malignant tumors with medical images and could provide more opportunities in the management of patients with ESCA.AIM To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients.METHODS Of 15 patients with diagnosed ESCA were included in this study and their CT imaging and transcriptomic data were extracted from The Cancer Imaging Archive and gene expression data from The Cancer Genome Atlas,respectively.Cancer stage,history of significant alcohol consumption and body mass index(BMI)were considered as clinical outcomes.Radiomic analysis was performed on CT images acquired after injection of contrast medium.In total,1302 radiomics features were extracted from three-dimensional regions of interest by using PyRadiomics.Feature selection was performed using a correlation filter based on Spearman’s correlation(ρ)and Wilcoxon-rank sum test respect to clinical outcomes.Radiogenomic analysis involvedρanalysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting of eight N6-methyladenosine RNA methylation regulators and five up-regulated miRNA.The significance level was set at P<0.05.RESULTS Of 25,five and 29 radiomic features survived after feature selection,considering stage,alcohol history and BMI as clinical outcomes,respectively.Radiogenomic analysis with stage as clinical outcome revealed that six 展开更多
Radiomics is an emerging analytical approach in the medical field that extracts high-throughput quantitative features from multiple imaging data and builds models for cancer diagnosis,prog-nosis,and treatment by machi...Radiomics is an emerging analytical approach in the medical field that extracts high-throughput quantitative features from multiple imaging data and builds models for cancer diagnosis,prog-nosis,and treatment by machine learning or deep learning.Radiomics allows radiologists to ob-tain a more complete picture of the tumor in a noninvasive way than by reading radiographs.Radiogenomics incorporates genomics on top of radiomics to analyze the potential relationship between imaging features and tumor genetic status,enabling biological profiling of the causes of tumor heterogeneity,and its development of biomarkers will be of great help for personal-ized treatment.Breast cancer is the most prevalent cancer among women worldwide today,and this survey aims to summarize the progress on radiomics and radiogenomics,their applications in breast cancer,and discuss the issues that need to be addressed before radiomics and radio-genomics can be used in clinic.From the literature,it can be concluded that radiomics and ra-diogenomics have a high potential for differentiating malignant and benign breast lesions to as-sess breast cancer types and lymph node status,as well as to predict neoadjuvant chemotherapy response,risk of recurrence and survival outcomes,especially in the context of the rapid devel-opment of artificial intelligence technologies,promising early realization of precision medicine.展开更多
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.
文摘The high incidence of rectal cancer in both sexes makes it one of the most common tumors,with significant morbidity and mortality rates.To define the best treatment option and optimize patient outcome,several rectal cancer biological variables must be evaluated.Currently,medical imaging plays a crucial role in the characterization of this disease,and it often requires a multimodal approach.Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors.Computed tomography is widely adopted for the detection of distant metastases.However,conventional imaging has recognized limitations,and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation.There is a growing interest in artificial intelligence applications in medicine,and imaging is by no means an exception.The introduction of radiomics,which allows the extraction of quantitative features that reflect tumor heterogeneity,allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers.To manage such a huge amount of data,the use of machine learning algorithms has been proposed.Indeed,without prior explicit programming,they can be employed to build prediction models to support clinical decision making.In this review,current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented,with an imaging modality-based approach and a keen eye on unsolved issues.The results are promising,but the road ahead for translation in clinical practice is rather long.
文摘目的研究肺腺癌表皮生长因子受体(EGFR)基因突变状态与放射组学特征的相关性。方法采用放射组学软件提取病理诊断为肺腺癌的411例患者的术前薄层CT的94个CT放射组学特征,包括19个一阶统计(First Order Statistics)特征,27个灰度共生矩阵(GLCM)特征,16个灰度运行长度矩阵(GLRLM)特征,16个灰度区域大小矩阵(GLSZM)特征,16个形态基础(Shap-based)特征。由于是回顾性研究,医院伦理委员会同意免除获取患者知情同意书的需要。采用单因素分析比较94个纹理特征和三个临床特征(年龄、性别和非吸烟者)与EGFR突变状态的关系,选出差异有统计学意义的变量进行Logistic回归分析得出独立危险因素。结果 411例肺腺癌患者中,EGFR突变型209例(50.9%),野生型202例(49.1%)。EGFR野生型和突变型女性患者和非吸烟者之间差异有统计学意义(P<0.001)。单因素分析显示四个放射组学特征Energy,Large Area Low Gray Level Emphasis,Size Zone Non Uniformity Normalized和Small Area Emphasis在EGFR野生型和突变型肺腺癌间差异有统计学意义(P<0.001)。Logistic回归分析表明非吸烟者和Size Zone Non Uniformity Normalized为EGFR基因突变独立危险因素,OR值分别为0.294 (95%CI:0.183~0.470;P=0.000)和0.007 (95%CI:0.000~0.012;P=0.007),且非吸烟者、Size Zone Non Uniformity Normalized以及Size Zone Non Uniformity Normalized联合非吸烟者预测肺腺癌EGFR突变的曲线下面积(AUC)值分别为0.629,0.571和0.663。结论肺腺癌的一些放射组学特征与EGFR突变有相关性,放射组学结合临床特征有望成为预测肺腺癌EGFR突变的影像生物标记物。
文摘BACKGROUND Esophageal cancer(ESCA)is the sixth most common malignancy in the world,and its incidence is rapidly increasing.Recently,several microRNAs(miRNAs)and messenger RNA(mRNA)targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in early diagnosis.In addition,computed tomography(CT)radiomic studies on ESCA improved the early stage identification and the prediction of response to treatment.Radiogenomics provides clinically useful prognostic predictions by linking molecular characteristics such as gene mutations and gene expression patterns of malignant tumors with medical images and could provide more opportunities in the management of patients with ESCA.AIM To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients.METHODS Of 15 patients with diagnosed ESCA were included in this study and their CT imaging and transcriptomic data were extracted from The Cancer Imaging Archive and gene expression data from The Cancer Genome Atlas,respectively.Cancer stage,history of significant alcohol consumption and body mass index(BMI)were considered as clinical outcomes.Radiomic analysis was performed on CT images acquired after injection of contrast medium.In total,1302 radiomics features were extracted from three-dimensional regions of interest by using PyRadiomics.Feature selection was performed using a correlation filter based on Spearman’s correlation(ρ)and Wilcoxon-rank sum test respect to clinical outcomes.Radiogenomic analysis involvedρanalysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting of eight N6-methyladenosine RNA methylation regulators and five up-regulated miRNA.The significance level was set at P<0.05.RESULTS Of 25,five and 29 radiomic features survived after feature selection,considering stage,alcohol history and BMI as clinical outcomes,respectively.Radiogenomic analysis with stage as clinical outcome revealed that six
文摘Radiomics is an emerging analytical approach in the medical field that extracts high-throughput quantitative features from multiple imaging data and builds models for cancer diagnosis,prog-nosis,and treatment by machine learning or deep learning.Radiomics allows radiologists to ob-tain a more complete picture of the tumor in a noninvasive way than by reading radiographs.Radiogenomics incorporates genomics on top of radiomics to analyze the potential relationship between imaging features and tumor genetic status,enabling biological profiling of the causes of tumor heterogeneity,and its development of biomarkers will be of great help for personal-ized treatment.Breast cancer is the most prevalent cancer among women worldwide today,and this survey aims to summarize the progress on radiomics and radiogenomics,their applications in breast cancer,and discuss the issues that need to be addressed before radiomics and radio-genomics can be used in clinic.From the literature,it can be concluded that radiomics and ra-diogenomics have a high potential for differentiating malignant and benign breast lesions to as-sess breast cancer types and lymph node status,as well as to predict neoadjuvant chemotherapy response,risk of recurrence and survival outcomes,especially in the context of the rapid devel-opment of artificial intelligence technologies,promising early realization of precision medicine.
