DCE-MRI in Human Gliomas: A Surrogate for Assessment of Invasive Hypoxia Marker HIF-1Α Based on MRI-Neuronavigation Stereotactic Biopsies

Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC.

MATERIALS AND METHODS

A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves.

RESULTS

Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.

CONCLUSION

The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.

Prevalence, prognostic and clinicopathological value of HIF-1α in glioblastoma patients: a systematic review and meta-analysis

Several studies have investigated the role of HIF-1α in predicting the prognosis of patients with glioblastoma, yielding contradictory results. Therefore, we performed a meta-analysis to document the correlation between HIF-1α and glioblastoma in individuals diagnosed with glioblastoma. We searched the PubMed, Cochrane Library, EMBASE, and Web of Science by January 25, 2024. Hazard Ratio (HR) was used to evaluate the relationship between HIF-1α and survival outcome, and Odds Ratio (OR) was adopted for tumor features.There was incorporation of nine observational studies with 607 individuals. The total prevalence of HIF-1α (higher than cut-off values) among individuals with glioblastoma was 0.72 (95% confidence interval (CI) = 0.68-0.75, I2 = 95.1%). There is a strong association between increased levels of HIF-1α in tumour tissues and shorter Overall Survival (OS) (HR = 1.82, 95% CI = 1.41-2.34, I2 = 13.7%). Subgroup analysis also indicated a correlation between higher levels of HIF-1α and reduced OS, specifically in the Asian population (HR = 1.48, 95% CI = 1.13-1.83, I2 = 41.5%). In addition, there was a correlation between HIF-1α and age (older vs. younger, OR = 2.19, 95% CI = 1.25-3.86, P = 0.260). High levels of HIF-1α expression were associated with poorer survival outcomes and other clinicopathological characteristics of glioblastoma. Integrating HIF-1α into prognostic tools for glioblastoma aids in predicting survival, categorising risk, and advising patients on suitable treatment regimens.

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Dynamic contrast-enhanced magnetic resonance imaging in cervical cancer: correlation between quantitative parameters and molecular markers hypoxia-inducible factors-1-alpha, vascular endothelial growth factor, and Ki-67

AIM

To investigate whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to non-invasively detect microenvironmental condition by quantitatively measuring blood perfusion, vessel wall permeability, and vascularity, and to elucidate the possible correlations between DCE-MRI quantitative parameters and the expression level of hypoxia, vascularity, and cell proliferation related molecular biomarkers.

MATERIALS AND METHODS

In this prospective single center clinical study, 58 patients diagnosed with cervical cancer underwent DCE-MRI before anticancer treatment were enrolled. Ktrans, Kep, Ve, and Vp were generated from Extended Toft's model. Then patients conducted colposcopy biopsy within 1 week after DCE-MRI. Pretreatment expression levels of HIF-1α, VEGF and Ki-67 were assessed and scored by immunohistochemistry on colposcopy obtained tumor specimens.

RESULTS

In HIF-1α low-expression group, Ktrans (p=0.031) and Kep (p=0.012) values were significantly higher than the high-expression group. In VEGF high-expression group, Ktrans (p=0.044) and Ve values (p=0.021) were significantly higher than the low-expression group. In Ki-67 high-expression group, Ktrans (p=0.026) and Kep (p=0.033) were significantly higher than the low-expression group. Multiple linear regression analyses and Pearson correlation revealed that Ktrans independently negatively correlated with HIF-1α expression, Ve independently positively correlated with VEGF, and Kep independently positively correlated with Ki-67. The area under the ROC curves of Ktrans for HIF-1α, Ve for VEGF, and Kep for Ki-67 were 0.728, 0.743, 0.730, respectively.

CONCLUSION

Our results suggest that DCE-MRI quantitative parameters could be potentially used as imaging markers for non-invasively detecting microenvironmental hypoxia, vascularity and proliferation in cervical cancer patients.

The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC.

Glioma grade discrimination with dynamic contrast-enhanced MRI: An accurate analysis based on MRI guided stereotactic biopsy

PURPOSE

To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis.

METHODS

Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (K^trans), volume of extravascular-extracellular space (v_e), fractional plasma volume (f_pv), and reflux transfer rate (k_ep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve.

RESULTS

Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in K^trans and v_e were observed between grades except v_e between grade 2 and 3. K^trans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). V_e indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively).

CONCLUSION

Our study had identified K^trans, v_e and the combination of parameters to be an accurate predictor for grading glioma.

Whole-tumor histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for soft tissue sarcoma: correlation with HIF-1alpha expression

OBJECTIVE

To investigate the correlation of histogram metrics from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with HIF-1alpha expression in soft tissue sarcoma (STS).

METHODS

We enrolled 71 patients with STS who underwent 3.0-T MRI, including conventional MRI, DWI, and DCE-MRI sequences. Location, maximum tumor diameter, envelope, T2-weighted tumor heterogeneity, peritumoral edema, peritumoral enhancement, necrosis, tail-like pattern, bone invasion, and vessel/nerve invasion and/or encasement were determined using conventional MRI images. The whole-tumor histogram metrics were calculated on the apparent diffusion coefficient (ADC), K^trans, K_ep, and V_e maps. Independent-samples t test and one-way ANOVA were used for testing the differences between normally distributed categorical data with HIF-1alpha expression. Pearson and Spearman correlations and multiple linear regression analyses were performed to determine the correlations between histogram metrics and HIF-1alpha expression. Survival curves were plotted using the Kaplan-Meier method.

RESULTS

Regarding conventional MRI features, only highly heterogeneous on T2-weighted images (55.6 ± 19.9% vs. 45.4 ± 20.5%, p = 0.041) and more than 50% necrotic area (57.3 ± 20.4% vs. 43.9 ± 19.7%, p = 0.002) were prone to indicate STS with higher HIF-1alpha expression. Histogram metrics obtained from ADC (mean, median, 10th, and 25th percentile values), K^trans (mean, median, 75th, and 90th percentile values), and K_ep (90th percentile values) were significantly correlated with HIF-1alpha expression. Multiple linear regression analysis demonstrated that more than 50% necrosis, ADC_skewness, K^trans_90th, and grade III were independently associated with HIF-1alpha expression.

CONCLUSION

DWI and DCE-MRI histogram parameters were significantly correlated with HIF-1alpha expression in STS.

KEY POINTS

• DWI and DCE-MRI histogram parameters are correlated with HIF-1alpha expression in STS. • More than 50% necrosis, ADC_skewness, K^trans_90th, and grade III were independently associated with HIF-1alpha expression in STS.

DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1α, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to noninvasively detect expression of hypoxia inducible factor-1-alpha (HIF-1α), epidermal growth factor receptor (EGFR), and Ki-67 in nasopharyngeal carcinoma (NPC) by quantitatively measuring tumor blood flow, vascularity, and permeability.

PURPOSE

We aim to explore the utility of DCE-MRI in detecting HIF-1α, EGFR, and Ki-67 expression levels using traditional Kety's/Tofts' modeling and quantitative transport mapping (QTM).

MATERIALS AND METHODS

Eighty-nine NPC patients underwent DCE-MRI before treatment were enrolled. DCE-MRI was processed to generate the following kinetic parameters: |u| and D from the QTM model, tumor blood flow (TBF) from Kety's model, and K^trans, V_e, and K_ep from Tofts' model. Pretreatment levels of HIF-1α, EGFR, and Ki-67 were assessed by immunohistochemistry and classified into low and high expression groups.

RESULTS

|u| (p < 0.001) and TBF (p = 0.015) values were significantly higher in the HIF-1α high-expression group compared to low-expression group. Only K^trans (p = 0.016) was significantly higher in the EGFR high-expression group. Only |u| (p < 0.001) values were significantly higher in the Ki-67 high-expression group compared to low-expression group. Multiple linear regression analyses showed that |u| independently correlated with HIF-1α and Ki-67 expression, and K^trans independently correlated with EGFR. The areas under the ROC curves of |u| for HIF-1α and Ki-67, and K^trans for EGFR were 0.83, 0.74, and 0.70, respectively.

CONCLUSION

|u| and K^trans derived from DCE-MRI may be considered as noninvasive imaging markers for detecting hypoxia and proliferation in NPC patients.

Dynamic contrast-enhanced MRI predicts PTEN protein expression which can function as a prognostic measure of progression-free survival in NPC patients

OBJECTIVES

The objective of our study was to investigate whether a phosphatase and tensin homolog deleted on chromosome 10 (PTEN) expression was associated with dynamic contrast-enhanced MRI (DCE-MRI) parameters and prognosis in nasopharyngeal carcinoma (NPC).

METHODS

Two-hundred-and-forty-five (245) patients with NPC who underwent pretreatment biopsy, expression of PTEN detected by immunohistochemistry of biopsy, and radical intensity-modulated radiation therapy (IMRT) with or without chemotherapy were included. Tumor segmentations were delineated on pretreatment MRI manually. The pharmacokinetic parameters (K^trans, K_ep, V_e, and V_p) derived from dynamic contrast-enhanced MRI (DCE-MRI) using the extended Toft's model within the tumor segmentations were estimated. The following demographics and clinical features were assessed and correlated against each other: gender, age, TNM stage, clinical-stage, Epstein-Barr virus (EBV), pathological type, progression-free survival (PFS), and prognosis status. DCE parameter evaluation and clinical feature comparison between the PTEN positive and negative groups were performed and correlation between PTEN expression with the PFS and prognosis status using Cox regression for survival analysis were assessed.

RESULTS

A significantly lower K^trans and K_ep were found in NPC tumors in PTEN negative patients than in PTEN positive patients. K^trans performed better than K_ep in detecting PTEN expression with the ROC AUC of 0.752. PTEN negative was associated with later TNM stage, later clinical-stage, shorter PFS, and worse prognosis. Moreover, N stage, pathological type, K_ep, and prognostic status can be considered as independent variables in discrimination of PTEN negative expression in NPCs.

CONCLUSIONS

PTEN negative indicated a shorter PFS and worse prognosis than PTEN positive in NPC patients. K^trans and K_ep derived from DCE-MRI, which yielded reliable capability, may be considered as potential imaging markers that are correlated with PTEN expression and could be used to predict PTEN expression noninvasively. Combined radiological and clinical features can improve the performance of the classification of PTEN expression.

Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy

OBJECTIVE

To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy.

METHODS

A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test.

RESULTS

Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation.

CONCLUSIONS

Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.

Dynamic contrast-enhanced perfusion parameters in ovarian cancer: Good accuracy in identifying high HIF-1α expression

BACKGROUND

Hypoxia significantly influences treatment response and clinical outcome in solid tumors. A noninvasive marker for hypoxia will help physicians in treatment planning and encourage the efficient use of hypoxia targeted therapies. The purpose of this study was to investigate whether pharmacokinetic dynamic contrast-enhanced (DCE) perfusion parameters are associated with a specific marker of hypoxia, hypoxia-inducible factor 1 alpha (HIF-1α) in ovarian cancer (OC).

MATERIALS AND METHODS

Thirty-eight patients with primary OC were enrolled in this prospective study approved by the local ethical committee. Patients underwent dynamic gadolinium-enhanced 3.0 T MRI as part of their staging investigations. Pharmacokinetic perfusion parameters, including a rate constant for transfer of contrast agent from plasma to extravascular extracellular space (EES) (Ktrans) and a rate constant from EES to plasma (Kep), were measured by drawing two types of regions of interest (ROIs): a large solid lesion (L-ROI) and a solid, most enhancing small area (S-ROI) (NordicICE platform). Tissue samples for immunohistochemical analysis were collected during surgery. Kruskal-Wallis, Mann-Whitney U and Chi-square tests were used in statistical analyses. Receiver Operating Characteristic curve analyzes were done for DCE parameters to discriminate high HIF-1α expression.

RESULTS

Pharmacokinetic perfusion parameters Ktrans and Kep were inversely associated with HIF-1α expression (Ktrans L-ROI P = 0.021; Ktrans S-ROI P = 0.018 and Kep L-ROI P = 0.032; Kep S-ROI P = 0.033). Ktrans and Kep showed good accuracy in identifying high HIF-1α expression (AUC = 0.832 Ktrans L-ROI; 0.840 Ktrans S-ROI; 0.808 Kep L-ROI and 0.808 Kep L-ROI).

CONCLUSION

This preliminary study demonstrated that pharmacokinetic DCE-MRI perfusion parameters are associated with the hypoxia specific marker, HIF-1α in OC. DCE-MRI may be a useful supplementary tool in the characterization of OC tumors in a staging investigation.