Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study

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 of the eight mRNA regulators and two of the five up-regulated miRNA were significantly correlated with ten and three of the 25 selected radiomic features,respectively(-0.61<ρ<-0.60 and 0.53<ρ<0.69,P<0.05).Assuming alcohol history as clinical outcome,no correlation was found between the five selected radiomic features and mRNA regulators,while a significant correlation was found between one radiomic feature and three up-regulated miRNAs(ρ=-0.56,ρ=-0.64 andρ=0.61,P<0.05).Radiogenomic analysis with BMI as clinical outcome revealed that four mRNA regulators and one up-regulated miRNA were significantly correlated with 10 and two radiomic features,respectively(-0.67<ρ<-0.54 and 0.53<ρ<0.71,P<0.05).CONCLUSION Our study revealed interesting relationships between the expression of eight N6-methyladenosine RNA regulators,as well as five up-regulated miRNAs,and CT radiomic features associated with clinical outcomes of ESCA patients.

Progress on radiomics and radiogenomics and their applications in breast cancer:A survey

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.

Hepatocellular carcinoma:State of the art diagnostic imaging

Primary liver cancer is the fourth most common malignancy worldwide,with hepatocellular carcinoma(HCC)comprising up to 90%of cases.Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines.Because early diagnosis can impact treatment approaches,utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis.Within this review manuscript,we provide an overview of imaging modalities used to screen and evaluate HCC.We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC character-ization,management,and treatment monitoring.

Radiomics and machine learning applications in rectal cancer:Current update and future perspectives

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.

Radiomics in pancreatic cancer for oncologist:Present and future

Radiomics is changing the world of medicine and more specifically the world of oncology.Early diagnosis and treatment improve the prognosis of patients with cancer.After treatment,the evaluation of the response will determine future treatments.In oncology,every change in treatment means a loss of therapeutic options and this is key in pancreatic cancer.Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions,in the evaluation of response,in the prediction of possible side effects,marking the risk of recurrence,survival and prognosis of the disease.Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity,and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters.In addition,these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy.This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.

Biomarkers in retinoblastoma

●Retinoblastoma(RB)is the most common intraocular malignancy of childhood caused by inactivation of the Rb genes.The prognosis of RB is better with an earlier diagnosis.Many diagnostic approaches and appropriate clinical treatments have been developed to improve clinical outcomes.However,limitations exist when utilizing current methods.Recently,many studies have identified identify new RB biomarkers which can be used in diagnosis,as prognostic indicators and may contribute to understanding the pathogenesis of RB and help determine specific treatment strategies.This review focuses on recent advances in the discovery of RB biomarkers and discusses their clinical utility and challenges from areas such as epigenetics,proteomics and radiogenomics.

Machine intelligence for precision oncology

Despite various advances in cancer research,the incidence and mortality rates of malignant diseases have remained high.Accurate risk assessment,prevention,detection,and treatment of cancer tailored to the individual are major challenges in clinical oncology.Artificial intelligence(AI),a field of applied computer science,has shown promising potential of accelerating evolution of healthcare towards precision oncology.This article focuses on highlights of the application of data-driven machine learning(ML)and deep learning(DL)in translational research for cancer diagnosis,prognosis,treatment,and clinical outcomes.MLbased algorithms in radiological and histological images have been demonstrated to improve detection and diagnosis of cancer.DL-based prediction models in molecular or multi-omics datasets of cancer for biomarkers and targets enable drug discovery and treatment.ML approaches combining radiomics with genomics and other omics data enhance the power of AI in improving diagnosis,prognostication,and treatment of cancer.Ethical and regulatory issues involving patient confidentiality and data security impose certain limitations on practical implementation of ML in clinical oncology.However,the ultimate goal of application of AI in cancer research is to develop and implement multi-modal machine intelligence for improving clinical decision on individualized management of patients.

Application of radiomics in hepatocellular carcinoma:A review

Hepatocellular carcinoma(HCC)is the most common form of primary liver cancer with low 5-year survival rate.The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC.However,current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers.The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semiquantitative analysis by radiologists.Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms.Using the radiomics method could quantify tumoral phenotypes and heterogeneity,which may provide benefits in clinical decision-making at a lower cost.Here,we review the workflow and application of radiomics in HCC.

Radiogenomic imaging-linking diagnostic imaging and molecular diagnostics

Radiogenomic imaging refers to the correlation between cancer imaging features and gene expression and is one of the most promising areas within science and medicine. High-throughput biological techniques have reshaped the perspective of biomedical research allowing for fast and efficient assessment of the entire molecular topography of a cell's physiology providing new insights into human cancers. The use of non-invasive imaging tools for gene expression profiling of solid tumors could serve as a means for linking specific imaging features with specific gene expression patterns thereby allowing for more accurate diagnosis and prognosis and obviating the need for high-risk invasive biopsy procedures. This review focuses on the medical imaging part as one of the main drivers for the development of radiogenomic imaging.

基于人工智能的影像组学在肝病中的应用现状

影像组学是指使用计算机从不同类型的影像图像中提取大量信息, 形成各种可定量的特征, 然后选取一些相关的特征通过人工智能方法构建模型, 用以预测一些临床问题(如诊断、治疗、预后)的结局。将影像组学应用于肝病研究将有助于肝病的早期诊断和早期治疗, 并改善患者生存和治愈率。影像组学的研究目前方兴未艾, 未来可能还会有巨大进展。因此, 我们总结了影像组学在肝病中的研究现状, 并指出相关不足及未来研究方向。

＂RADIOTRANSCRIPTOMICS＂： A synergy of imaging and transcriptomics in clinical assess- ment

Recent advances in quantitative imaging and ＂omics＂ technology have generated a wealth of mineable biological ＂big data＂. With the push towards a P4 ＂predictive, preventive, personalized, and participatory＂ approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose ＂radiotranscriptomics＂ as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment.We present a well-designed radiogenomic framework image–gene–gene set(IMAGGS),which detects multi-way associations in BC subtypes by integrating radiomic and genomic features.Our dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation data.To better characterize tumor traits,12 multi-angle US images are fused using two distinct strategies.Then,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous studies.Key radiomic and genomic features are screened using these associations.In addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes.We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases.Taken together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.