Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Secreted MicroRNA to Predict Embryo Implantation Outcome: From Research to Clinical Diagnostic Application

Embryo implantation failure is considered a leading cause of infertility and a significant bottleneck for in vitro fertilization (IVF) treatment. Confirmed factors that lead to implantation failure involve unhealthy embryos, unreceptive endometrium, and asynchronous development and communication between the two. The quality of embryos is further dependent on sperm parameters, oocyte quality, and early embryo development after fertilization. The extensive involvement of such different factors contributes to the variability of implantation potential across different menstrual cycles. An ideal approach to predict the implantation outcome should not compromise embryo implantation. The use of clinical material, including follicular fluid, cumulus cells, sperm, seminal exosomes, spent blastocyst culture medium, blood, and uterine fluid, that can be collected relatively non-invasively without compromising embryo implantation in a transfer cycle opens new perspectives for the diagnosis of embryo implantation potential. Compositional comparison of these samples between fertile women and women or couples with implantation failure has identified both quantitative and qualitative differences in the expression of microRNAs (miRs) that hold diagnostic potential for implantation failure. Here, we review current findings of secreted miRs that have been identified to potentially be useful in predicting implantation outcome using material that can be collected relatively non-invasively. Developing non-invasive biomarkers of implantation potential would have a major impact on implantation failure and infertility.

Non-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study

Objective

To predict fetal growth restriction (FGR) by whole‐genome promoter profiling of maternal plasma.

Design

Nested case–control study.

Setting

Hospital‐based.

Population or Sample

810 pregnancies: 162 FGR cases and 648 controls.

Methods

We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell‐free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models.

Main outcome measures

Genes with differential coverages in promoter regions through the low‐coverage whole‐genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers.

Results

Through the low‐coverage whole‐genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (−1000 to +1000 bp of transcription start sites) were identified. The non‐invasive ‘FGR classifier 1’ (C_FGR1) had the highest classification performance (AUC, 0.803; 95% CI 0.767–0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine.

Conclusions

A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling.

Tweetable abstract

A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.

A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.

Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction

INTRODUCTION

Liver parameters are associated with cardiovascular disease risk and severity of stenosis. It is unclear whether liver parameters could predict the long-term outcome of patients after acute myocardial infarction (AMI). We performed an unbiased analysis of the predictive value of serum parameters for long-term prognosis after AMI.

MATERIAL AND METHODS

In a retrospective, observational, single-center, cohort study, 569 patients after AMI were enrolled and followed up until 6 years for major adverse cardiovascular events, including cardiac death. Patients were classified into non-survivors (n = 156) and survivors (n = 413). Demographic and laboratory data were analyzed using ensemble feature selection (EFS) and logistic regression. Correlations were performed for serum parameters.

RESULTS

Age (73; 64; p < 0.01), alanine aminotransferase (ALT; 93 U/l; 40 U/l; p < 0.01), aspartate aminotransferase (AST; 162 U/l; 66 U/l; p < 0.01), C-reactive protein (CRP; 4.7 U/l; 1.6 U/l; p < 0.01), creatinine (1.6; 1.3; p < 0.01), γ-glutamyltransferase (GGT; 71 U/l; 46 U/l; p < 0.01), urea (29.5; 20.5; p < 0.01), estimated glomerular filtration rate (eGFR; 49.6; 61.4; p < 0.01), troponin (13.3; 7.6; p < 0.01), myoglobin (639; 302; p < 0.01), and cardiovascular risk factors (hypercholesterolemia p < 0.02, family history p < 0.01, and smoking p < 0.01) differed significantly between non-survivors and survivors. Age, AST, CRP, eGFR, myoglobin, sodium, urea, creatinine, and troponin correlated significantly with death (r = -0.29; 0.14; 0.31; -0.27; 0.20; -0.13; 0.33; 0.24; 0.12). A prediction model was built including age, CRP, eGFR, myoglobin, and urea, achieving an AUROC of 77.6% to predict long-term survival after AMI.

CONCLUSIONS

Non-invasive parameters, including liver and renal markers, can predict long-term outcome of patients after AMI.

A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma

Purpose: We aimed to analyze ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) images via the radiomic method to develop a model and validate the potential value of features reflecting glioma metabolism for predicting isocitrate dehydrogenase (IDH) genotype and prognosis.

Methods: PET images of 127 patients were retrospectively analyzed. A series of quantitative features reflecting the metabolic heterogeneity of the tumors were extracted, and a radiomic signature was generated using the support vector machine method. A combined model that included clinical characteristics and the radiomic signature was then constructed by multivariate logistic regression to predict the IDH genotype status, and the model was evaluated and verified by receiver operating characteristic (ROC) curves and calibration curves. Finally, Kaplan-Meier curves and log-rank tests were used to analyze overall survival (OS) according to the predicted result.

Results: The generated radiomic signature was significantly associated with IDH genotype (p < 0.05) and could achieve large areas under the ROC curve of 0.911 and 0.900 on the training and validation cohorts, respectively, with the incorporation of age and type of tumor metabolism. The good agreement of the calibration curves in the validation cohort further validated the efficacy of the constructed model. Moreover, the predicted results showed a significant difference in OS between high- and low-risk groups (p < 0.001).

Conclusions: Our results indicate that the ¹⁸F-FDG metabolism-related features could effectively predict the IDH genotype of gliomas and stratify the OS of patients with different prognoses.

AST-to-platelet ratio index in non-invasive assessment of long-term graft fibrosis following pediatric liver transplantation

Long-term graft fibrosis occurs in the majority of pediatric liver transplant recipients. Serial biopsies to monitor graft health are impractical and invasive. The APRI has been evaluated in pediatric liver disease, but not in the context of post-transplantation fibrosis. We aimed to investigate the validity of APRI as a predictor of long-term graft fibrosis in pediatric liver transplant recipients. This was a retrospective, observational study of a cohort of children who underwent liver transplantation at King's College Hospital between 1989 and 2003, with a relevant dataset available. Protocol liver biopsies were performed at 10-yr follow-up and fibrosis was graded using the Ishak scoring system, with S3-6 denoting "significant fibrosis." APRI was calculated concurrently with biopsy. A total of 39 asymptomatic patients (20 males; median age at transplant, 1.43 yr) underwent protocol liver biopsies at a median of 10.39 yr post-transplantation. APRI was associated with significant fibrosis (p = 0.012). AUROC for APRI as a predictor of significant fibrosis was 0.74 (p = 0.013). The optimal cutoff APRI value for significant fibrosis was 0.45 (sensitivity = 0.67; specificity = 0.79; PPV = 0.67; NPV = 0.79). APRI appears to be a useful non-invasive adjunct in the assessment of significant graft fibrosis in the long-term follow-up of pediatric liver transplant survivors.