Diffusion kurtosis imaging combined with molecular markers as a comprehensive approach to predict overall survival in patients with gliomas

Prognostic Value of Pre-Treatment Diffusion Kurtosis Imaging for Progression-Free Survival Prediction in Advanced Nasopharyngeal Carcinoma

PURPOSE

This study aimed to evaluate the value of diffusion kurtosis imaging (DKI) for prognostic value for long-term PFS in nasopharyngeal carcinoma (NPC).

METHODS

A cohort of 295 NPC patients underwent pretreatment 3.0T MRI with DKI to derive mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC). Clinical parameters (Tumor stage, EBV-DNA, neoadjuvant chemotherapy regimens) were recorded. Follow-up extended to December 2023. Statistical analyses (R software v4.3.0) included univariate/multivariate Cox regression and Kaplan-Meier survival analysis. A prognostic nomogram integrating key predictors was developed.

RESULTS

Median 10-year follow-up revealed 2-, 5-, and 10-year PFS rates of 89%, 79%, and 74%, respectively. Univariate Cox regression analysis demonstrated that T stage, Clinical Stages, NAC regimens, ADC_Group, MK_Group, and MD_Group were significant prognostic factors for PFS in NPC (p < 0.05). Multivariate analysis identified Clinical Stage (HR = 2.230, 95% CI 1.44-3.66, p < 0.001), NAC (neoadjuvant chemotherapy) regimens (HR = 0.56, 95% CI 0.35-0.90, p = 0.017), and MK_Group (HR = 0.52, 95% CI 0.33-0.82, p = 0.003) as independent prognostic factors. The MK_Group high exhibited superior survival rates versus MK_Group low (2-year: 94% vs. 81%; 5-year: 85% vs. 66%; 10-year: 79% vs. 64%; all p < 0.05). The nomogram combining Clinical Stage, NAC, and MK_Group demonstrated moderate predictive accuracy for 2-, 5-, and 10-year PFS (AUC = 0.736, 0.718, 0.697).

CONCLUSION

Pretreatment MK serves as a robust noninvasive biomarker for long-term PFS in NPC. Integration with Clinical Stage and NAC regimens enhances prognostic stratification, supporting personalized therapeutic strategies.

The diagnostic and prediction performance of MR diffusion kurtosis imaging in the glioma molecular classification: a systematic review and meta-analysis

Background

Although diffusion magnetic resonance imaging (dMRI), particularly diffusion kurtosis imaging (DKI), has demonstrated efficacy in distinguishing between low- and high-grade gliomas, its predictive utility across various molecular genotypes remains unclear. Evaluating the accuracy of DKI and identifying sources of heterogeneity in its predictive performance could advance noninvasive molecular diagnostic methods and support the development of personalized treatment strategies.

Materials and methods

A literature search of the PubMed, Web of Science, Cochrane Library, Embase, and Medline databases was performed. The studies retrieved were screened by two researchers (HFZ and ZGH), and those fulfilling the inclusion criteria were subsequently included in the meta-analysis. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. The analyses summarized the mean differences in mean kurtosis (MK) and mean diffusivity (MD) in patients harboring various genotypes using suitable models, and explored heterogeneity. Finally, a bivariate restricted maximum likelihood estimation method and meta-regression analysis were performed to assess diagnostic potential and stability.

Results

Fourteen studies comprising 886 patients were included in this meta-analysis. Regarding MK and MD, the mean difference between isocitrate dehydrogenase (IDH) mutation and IDH wild type was -0.21 (95% confidence interval [CI] -0.27 to -0.15; I 2 = 93%) and 0.22 (95% CI 0.11 to 0.33; I 2 = 92%), respectively. This heterogeneity could be explained by imaging parameters such as repetition time, echo time, maximal b-value, and number of diffusion directions. However, the mean difference did not reflect the genetic status of 1p/19q, α-thalassemia/mental retardation syndrome-X-linked (ATRX) gene, or O6-methylguanine-DNA-methyltransferase (MGMT). Analysis of diagnostic accuracy revealed that the pooled areas under the curve for MK and MD, based on IDH status, were 0.96 (95% CI 0.93 to 0.97) and 0.76 (95% CI 0.71 to 0.81), respectively. Heterogeneity was not observed for these DKI parameters.

Conclusion

MK and MD exhibited potential diagnostic utility in the prediction of glioma molecular status and should be explored in medical practice. These parameters should be compared with other MRI models to develop a stable and suitable genetic molecular prediction method for patients with gliomas.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024568923, CRD42024568923.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer

Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.

Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas

PURPOSE

EphA2 is a key factor underlying invasive propensity of gliomas, and is associated with poor prognosis of tumors. We aimed to develop a radiomics-based imaging index for predicting EphA2 expression in diffuse gliomas, and further estimating its value for grading of tumors.

METHODS

A total of 182 patients with diffuse gliomas were included. All subjects underwent pre-operative MRI and post-operative pathological diagnosis. EphA2 expression of tumors was scored on pathological sections with immunohistochemical staining using monoclonal EphA2 antibody. MRI radiomics features were extracted from three-dimensional contrast-enhanced T1-weighted imaging and diffusion kurtosis imaging. Predictive models were constructed using machine learning-based radiomics features selection and three classifiers for predicting EphA2 expression and tumor grade. Features of best EphA2 expression model were subsequently used to construct another model of tumor grading. For each model, 146 cases (80%) were randomly picked as training and the rest 36 (20%) were testing cohorts. EphA2 expression was further correlated to the radiomics features in both grade models using Spearman's correlation.

RESULTS

Logistic regression model presented highest performance for predicting EphA2 expression (AUC: 0.836/0.724 in training/validation set). Tumor gradings model guided by features from EphA2 expression model demonstrated comparable performance (AUC: 0.930/0.983) to that constructed directly using imaging radiomics features (AUC: 0.960/0.977). Two radiomics features which included in both LR-grade models showed strong correlation (P < 0.05) with EphA2 expression.

CONCLUSION

The expression of EphA2 in gliomas could be predicted by radiomics features extracted from diffusion kurtosis MRI, which could also be used to assist tumor grading.