Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

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.

Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review

The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.

The development process of 'fit-for-purpose' imaging biomarkers to characterize the tumor microenvironment

Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, reliable biomarkers for the assessment of immune features in patients with cancer are of significant importance. Biomarkers (BM) that enable a comprehensive and repeatable assessment of the tumor microenvironment (TME), the lymphoid system, and the dynamics induced by drug treatment can fill this gap. Medical imaging, notably positron emission tomography (PET) and magnetic resonance imaging (MRI), providing whole-body imaging BMs, might deliver such BMs. However, those imaging BMs must be well characterized as being 'fit for purpose' for the intended use. This review provides an overview of the key steps involved in the development of 'fit-for-purpose' imaging BMs applicable in drug development, with a specific focus on pharmacodynamic biomarkers for assessing the TME and its modulation by immunotherapy. The importance of the qualification of imaging BMs according to their context of use (COU) as defined by the Food and Drug Administration (FDA) and National Institutes of Health Biomarkers, EndpointS, and other Tools (BEST) glossary is highlighted. We elaborate on how an imaging BM qualification for a specific COU can be achieved.

Microvascular permeability and texture analysis of bone marrow in diabetic rabbits with critical limb ischemia based on dynamic contrast-enhanced magnetic resonance imaging

BACKGROUND

Early on in the development of diabetes, skeletal muscles can exhibit microarchitectural changes that can be detected using texture analysis (TA) based on volume transfer constant (Ktrans) maps. Nevertheless, there have been few studies and thus we evaluated microvascular permeability and the TA of the bone marrow in diabetics with critical limb ischemia (CLI).

METHODS

Eighteen male rabbits were randomly assigned equally into an operation group with hindlimb ischemia and diabetes, a sham-operated group with diabetes only, and a control group. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) was performed on all rabbits at predetermined intervals (1, 5, 10, 15, 20, and 25 days post-surgery). The pharmacokinetic model was used to generate the permeability parameters, while the textural parameters were derived from the Ktrans map. Data analysis methods included the independent sample t-test, Mann-Whitney U test, repeated-measures analysis of variance, and Pearson correlation tests.

RESULTS

The Ktrans values reached a minimum on day 1 after ischemia induction, then gradually recovered, but remained lower than those of the sham-operated group. The volume fraction only showed a significant difference between the operation group and the sham-operated group on day 5 post-surgery, but not in the extravascular extracellular space volume fraction at all time points. A significantly reduced Ktrans on day 1, a decreased number of bone trabeculae (Tb.N), and the area of bone trabeculae (Tb.Ar), and an increased microvessel density on day 25 in the operation group compared with the sham-operated group were observed. At each time point, there was a discernible difference between the two groups in the mean value, mean of positive pixels, and sumAverage.

CONCLUSIONS

The early stages of diabetic bone marrow with CLI can be evaluated by DCE-MRI for microvascular permeability. Texture analysis based on DCE-MRI could act as an imaging discriminator and new radiological analysis tool for critical limb ischemia in diabetes mellitus.

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis

OBJECTIVE

This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis.

RESULTS

For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses.

CONCLUSION

MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings.

CLINICAL RELEVANCE STATEMENT

Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients.

KEY POINTS

• MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.

Influence of scan duration on dynamic contrast -enhanced magnetic resonance imaging pharmacokinetic parameters for brain lesions

OBJECTIVE

Gadolinium-based contrast agent needs time to leak into the extravascular-extracellular space, leak back into the vascular space, and reach an equilibrium state. For this reason, acquisition times of <10 min may cause inaccurate estimation of pharmacokinetic parameters. Since no studies have been conducted on the influence of long scan times on DCE-MRI parameters in brain tumors, the aim of this study is to investigate the variation of DCE-MRI-derived kinetic parameters as a function of acquisition time, from 5 to 10 min in brain tumors.

MATERIALS AND METHODS

Fifty-two patients with histologically confirmed brain tumors were enrolled in this retrospective study, and examination at 3 T, DCE-MRI, with scan duration of 10 min, was used for retrospective generation of 6 sets of quantitative DCE-MRI maps (Ktrans, Ve and Kep) from 5 to 10 min. Features were extracted from the DCE-MRI maps in contrast enhancement (CE) volumes. Kruskal-Wallis with post-hoc correction and coefficient of variation (CoV) were used as statistical test to compare DCE-MRI maps obtained from 6 data sets.

SIGNIFICANCE

p < 0.05.

RESULTS

No differences in Ktrans features in CE volumes between different scan durations. Ve, Kep features in CE volumes were influenced by different data length. The highest number of significantly different Ve and Kep features in CE volumes were between 5 min and 10 min (p < 0.013), 5 min and 9 min (p < 0.044), 6 min and 10 min (p < 0.040). CoV of Kep was reduced from 5 min to 10 min, going from highly variable (CoV = 0.70) to mildly variable (CoV = 0.42).

CONCLUSION

Kep and Ve were time-dependent in brain tumors, so a longer scan time is needed to obtain reliable parameter values. Ktrans was found to be time-independent, as it remains the same in all 6 acquisition times and is the only reliable parameter with short acquisition times.

AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer

BACKGROUND AND OBJECTIVES

Tumor microenvironment (TME) is a determining factor in decision-making and personalized treatment for breast cancer, which is highly intra-tumor heterogeneous (ITH). However, the noninvasive imaging phenotypes of TME are poorly understood, even invasive genotypes have been largely known in breast cancer.

METHODS

Here, we develop an artificial intelligence (AI)-driven approach for noninvasively characterizing TME by integrating the predictive power of deep learning with the explainability of human-interpretable imaging phenotypes (IMPs) derived from 4D dynamic imaging (DCE-MRI) of 342 breast tumors linked to genomic and clinical data, which connect cancer phenotypes to genotypes. An unsupervised dual-attention deep graph clustering model (DGCLM) is developed to divide bulk tumor into multiple spatially segregated and phenotypically consistent subclusters. The IMPs ranging from spatial heterogeneity to kinetic heterogeneity are leveraged to capture architecture, interaction, and proximity between intratumoral subclusters.

RESULTS

We demonstrate that our IMPs correlate with well-known markers of TME and also can predict distinct molecular signatures, including expression of hormone receptor, epithelial growth factor receptor and immune checkpoint proteins, with the performance of accuracy, reliability and transparency superior to recent state-of-the-art radiomics and 'black-box' deep learning methods. Moreover, prognostic value is confirmed by survival analysis accounting for IMPs.

CONCLUSIONS

Our approach provides an interpretable, quantitative, and comprehensive perspective to characterize TME in a noninvasive and clinically relevant manner.

Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.

Dynamic Contrast-Enhanced MRI Assessing Antifibrotic Therapeutic Effects of Pancreatic Fibrosis with Curcumin - An Experimental Study at 11.7 T

RATIONALE AND OBJECTIVES

Pancreatic fibrosis is the hallmark of chronic pancreatitis (CP), which is associated with microcirculatory disturbance. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess the perfusion and permeability of the pancreas by providing information about microcirculation. We hypothesize that DCE-MRI parameters can be utilized to assess pancreatic fibrosis and may furthermore provide an opportunity to evaluate response to antifibrotic treatment with curcumin. Our study was to evaluate the feasibility of quantitative DCE-MRI in assessing pancreatic fibrosis and the antifibrotic effect of curcumin in a rat model of CP.

MATERIALS AND METHODS

Pancreatic fibrosis was induced by injecting dibutyltin dichloride (DBTC). Seventy rats were randomized to five groups: the control group (n = 10); DBTC for 2 weeks (n = 15); DBTC for 4 weeks (n = 15); DBTC + curcumin for 2 weeks (n = 15); DBTC + curcumin for 4 weeks (n = 15). DCE-MRI was performed at an 11.7 T MR scanner. DCE-MRI quantitative parameters (Ktrans, Ve, and Vp) were derived from an extended Tofts model. Fibrosis content and DCE-MRI parameters were compared among the above groups (one-way analysis of variance). The correlations between DCE-MRI parameters and pancreatic fibrosis content as well as the expression of α-SMA were computed by Spearman correlation coefficients.

RESULTS

Fifty-three rats survived and underwent MR imaging. Ktrans in rats 4 weeks after DBTC injection was significantly lower than DBTC 2 weeks rats and control rats (0.30 ± 0.06 min vs 0.49 ± 0.09 vs 0.62 ± 0.09, respectively). Vp in DBTC 4 weeks rats was also significantly lower than control rats (0.048 ± 0.010 min-1 vs 0.065 ± 0.011 min-1, respectively). Ktrans and Vp significantly correlated with fibrosis content of pancreas (r = -0.619 and -0.450, all P < 0.001), and the expression of α-SMA (r = -0.688 and -0.402, all P < 0.01). Ktrans and Vp in rats with daily curcumin treatment for 4 weeks were significantly higher than DBTC 4 weeks rats (Ktrans, 0.51 ± 0.09 vs 0.30 ± 0.06; Vp, 0.064 ± 0.015 vs 0.048 ± 0.010).

CONCLUSION

DCE-MRI parameters (Ktrans and Vp) have the potential to noninvasively assess pancreatic fibrosis and the antifibrotic treatment response of curcumin.

First-in-human technique translation of oxygen-enhanced MRI to an MR Linac system in patients with head and neck cancer

BACKGROUND AND PURPOSE

Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system.

MATERIALS AND METHODS

MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T1) was measured alongside the change in 1/T1 (termed ΔR1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems.

RESULTS

Baseline T1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR1 significantly increased (p < 0.0001) in healthy participants demonstrating OE-MRI feasibility. ΔR1 repeatability coefficients (RC) were 0.023-0.040 s-1 across both MR systems. The tumour ΔR1 RC was 0.013 s-1 and the within-subject coefficient of variation (wCV) was 25% on the diagnostic MR. Tumour ΔR1 RC was 0.020 s-1 and wCV was 33% on the MR Linac. ΔR1 magnitude and time-course trends were similar on both systems.

CONCLUSION

We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy.

Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer

PURPOSE

We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that K^trans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known.

PROCEDURES

Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment.

RESULTS

Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, k_ep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when k_ep is obtained from fitting the DCE-MRI data using the SSM, k_ep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of k_ep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when k_ep is derived from the TM, only the use of individual AIF leads to a significant increase of k_ep after PEGPH20 treatment, whereas the %change of k_ep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of V_p (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of V_p is consistent with a large increase of mean capillary lumen area estimated from immunostaining.

CONCLUSION

Our results suggest that k_ep derived from SSM and V_p from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

Ferroptosis MRI for early detection of anticancer drug-induced acute cardiac/kidney injuries

Ferroptosis has been realized in anticancer drug-induced acute cardiac/kidney injuries (ACI/AKI); however, molecular imaging approach to detect ferroptosis in ACI/AKI is a challenge. We report an artemisinin-based probe (Art-Gd) for contrast-enhanced magnetic resonance imaging of ferroptosis (feMRI) by exploiting the redox-active Fe(II) as a vivid chemical target. In vivo, the Art-Gd probe showed great feasibility in early diagnosis of anticancer drug-induced ACI/AKI, which was at least 24 and 48 hours earlier than the standard clinical assays for assessing ACI and AKI, respectively. Furthermore, the feMRI was able to provide imaging evidence for the different mechanisms of action of ferroptosis-targeted agents, either by blocking lipid peroxidation or depleting iron ions. This study presents a feMRI strategy with simple chemistry and robust efficacy for early evaluation of anticancer drug-induced ACI/AKI, which may shed light on the theranostics of a variety of ferroptosis-related diseases.

Criteria for the translation of radiomics into clinically useful tests

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.

DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml

Background

This study investigated the diagnostic utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters for distinguishing between benign and malignant prostate tumors when serum prostate-specific antigen (PSA) level is ≥10 ng/ml.

Methods

Patients with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) with serum PSA ≥10 ng/ml before treatment were recruited. Transrectal ultrasound-guided biopsy or surgery was performed for tumor classification and patients were stratified accordingly into PCa and BPH groups. Patients underwent DCE-MRI and DWI scanning and the transfer constant (K_trans), rate constant (K_ep), fractional volume of the extravascular extracellular space, plasma volume (V_p), and Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) score were determined. The apparent diffusion coefficient (ADC) was calculated from DWI. The diagnostic performance of these parameters was assessed by receiver operating characteristic (ROC) curve analysis, and those showing a significant difference between the PCa and BPH groups were combined into a multivariate logistic regression model for PCa diagnosis. Spearman's correlation was used to analyze the relationship between Gleason score and imaging parameters.

Results

The study enrolled 65 patients including 32 with PCa and 33 with BPH. Ktrans (P=0.006), Kep (P=0.001), and Vp (P=0.009) from DCE-MRI and ADC (P<0.001) from DWI could distinguish between the 2 groups when PSA was ≥10 ng/ml. PI-RADS score (area under the ROC curve [AUC]=0.705), Ktrans (AUC=0.700), Kep (AUC=0.737), Vp (AUC=0.688), and ADC (AUC=0.999) showed high diagnostic performance for discriminating PCa from BPH. A combined model based on PI-RADS score, Ktrans, Kep, Vp, and ADC had a higher AUC (1.000), with a sensitivity of 0.998 and specificity of 0.999. Imaging markers showed no significant correlation with Gleason score in PCa.

Conclusion

DCE-MRI and DWI parameters can distinguish between benign and malignant prostate tumors in patients with serum PSA ≥10 ng/ml.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice

PURPOSE

This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice.

METHODS

We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK.

RESULTS

The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions.

CONCLUSIONS

We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.

DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer

Objective

Low-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients.

Methods

One hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups.

Results

Four radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models.

Conclusion

DCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning.

Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars Sampling and KWIC Reconstruction

Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol optimized for such applications. The method consists of three acquisitions: (1) actual flip-angle B₁ mapping, (2) variable flip-angle T₁ mapping and (3) acquisition of the DCE series using a motion-robust radial strategy with k-space weighted image contrast (KWIC) reconstruction. All three acquisitions employ spoiled radial imaging with stack-of-stars sampling (SoS) and golden-angle increments between the views. This scheme is shown to minimize artifacts due to respiratory motion while simultaneously facilitating view-sharing image reconstruction for the dynamic series. The method is demonstrated in a genetically engineered mouse model of pancreatic ductal adenocarcinoma and yielded mean perfusion parameters of K^trans = 0.23 ± 0.14 min⁻¹ and v_e = 0.31 ± 0.17 (n = 22) over a wide range of tumor sizes. The SoS-sampled DCE method is shown to produce artifact-free images with good SNR leading to robust estimation of DCE parameters.

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [K^trans ], fractional volume of the extravascular extracellular space [v_e ], and blood plasma volume fraction [v_p ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of K^trans and v_e , and less than 11% in the estimation of v_p . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI

Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOF_SSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test-retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOF_SSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOF_SSS derived semi-quantitative parameters was good to excellent (ICC = 0.717-0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOF_SSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.

Predictors of Outcome after (Chemo)Radiotherapy for Node-Positive Oropharyngeal Cancer: The Role of Functional MRI

The prognosis of a subset of patients with locally advanced oropharyngeal cancer (LA-OPC) is still poor despite improvements in patient selection and treatment. Identifying specific patient- and tumor-related factors can help to select those patients who need intensified treatment. We aimed to assess the role of historical risk factors and novel magnetic resonance imaging (MRI) biomarkers in predicting outcomes in these patients. Patients diagnosed with LA-OPC were studied with diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline and at the 10th radiotherapy (RT) fraction. Clinical information was collected as well. The endpoint of the study was the development of disease progression, locally or distantly. Of the 97 patients enrolled, 68 were eligible for analysis. Disease progression was recorded in 21 patients (11 had loco-regional progression, 10 developed distant metastases). We found a correlation between N diameter and disease control (p = 0.02); features such as p16 status and extranodal extension only showed a trend towards statistical significance. Among perfusion MRI features, higher median values of K_ep both in primary tumor (T, p = 0.016) and lymph node (N, p = 0.003) and lower median values of v_e (p = 0.018 in T, p = 0.004 in N) correlated with better disease control. K_ep P90 and N diameter were identified by MRMR algorithm as the best predictors of outcome. In conclusion, the association of non-invasive MRI biomarkers and patients and tumor characteristics may help in predicting disease behavior and patient outcomes in order to ensure a more customized treatment.

Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

To prospectively explore the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters of sacroiliitis in patients with axial spondyloarthritis (axSpA).

METHODS

Patients with initially diagnosed axSpA prospectively underwent on 3.0 T MRI of sacroiliac joint (SIJ). The IVIM parameters (D, f, D ^*) were calculated using biexponential analysis. K ^trans, K _ep, V _e, and V _p from DCE-MRI were obtained in SIJ. The uni-variable and multi-variable linear regression analyses were used to evaluate the correlation between the parameters from these two imaging methods after controlling confounders, such as bone marrow edema (BME), age, agenda, scopes, and localization of lesions, and course of the disease. Then, their correlations were measured by calculating the Pearson's correlation coefficient (r).

RESULTS

The study eventually enrolled 234 patients (178 men, 56 women; mean age, 28.51 ± 9.50 years) with axSpA. With controlling confounders, D was independently related to K ^trans (regression coefficient [b] = 27.593, p < 0.001), K _ep (b = -6.707, p = 0.021), and V _e (b = 131.074, p = 0.003), whereas f and D ^* had no independent correlation with the parameters from DCE MRI. The correlations above were exhibited with Pearson's correlation coefficients (r) (r = 0.662, -0.408, and 0.396, respectively, all p < 0.001).

CONCLUSION

There were independent correlations between D derived from IVIM DWI and K ^trans, K _ep, and V _e derived from DCE-MRI. The factors which affect their correlations mainly included BME, gender, and scopes of lesions.

Blood-Brain Barrier Repair of Bevacizumab and Corticosteroid as Prediction of Clinical Improvement and Relapse Risk in Radiation-Induced Brain Necrosis: A Retrospective Observational Study

Background

Blood-brain barrier (BBB) disruption after endothelial damage is a crucial part of radiation-induced brain necrosis (RN), but little is known of BBB disruption quantification and its role in the evaluation of therapeutic effect and prognosis for drug treatment. In this retrospective study, BBB repair by bevacizumab and corticosteroid and the correlation between BBB permeability and treatment response and relapse were evaluated by dynamic contrast-enhanced MRI (DCE-MRI).

Methods

Forty-one patients with RN after radiotherapy for nasopharyngeal carcinoma (NPC) (28 treated with bevacizumab and 13 with corticosteroid), 12 patients with no RN after NPC radiotherapy, and 12 patients with no radiotherapy history were included as RN, non-RN, and normal groups, respectively. DCE-MRI assessed BBB permeability in white matter of bilateral temporal lobe. DCE parameters were compared at baseline among the three groups. DCE parameters after treatment were compared and correlated with RN volume decrease, neurological improvement, and relapse.

Results

The extent of BBB leakage at baseline increased from the normal group and non-RN group and to RN necrosis lesions, especially K ^trans (Kruskal-Wallis test, P < 0.001). In the RN group, bevacizumab-induced K ^trans and v _e decrease in radiation necrosis lesions (both P < 0.001), while corticosteroid showed no obvious effect on BBB. The treatment response rate of bevacizumab was significantly higher than that of corticosteroid [30/34 (88.2%) vs. 10/22 (45.4%), P < 0.001]. Spearman analysis showed baseline K ^trans, K _ep, and v _p positively correlated with RN volume decrease and improvement of cognition and quality of life in bevacizumab treatment. After a 6-month follow-up for treatment response cases, the relapse rate of bevacizumab and corticosteroid was 10/30 (33.3%) and 2/9 (22.2%), respectively, with no statistical difference. Post-bevacizumab K ^trans level predicted relapse in 6 months with AUC 0.745 (P < 0.05, 95% CI 0.546-0.943, sensitivity = 0.800, specificity = 0.631).

Conclusions

Bevacizumab improved BBB leakage in RN necrosis. DCE parameters may be useful to predict therapeutic effect and relapse after bevacizumab.

Assessment of Intratumor Heterogeneity in Parametric Dynamic Contrast-Enhanced MR Images: A Comparative Study of Novel and Established Methods

Intratumor heterogeneity is associated with aggressive disease and poor survival rates in several types of cancer. A novel method for assessing intratumor heterogeneity in medical images, named the spatial gradient method, has been developed in our laboratory. In this study, we measure intratumor heterogeneity in K^trans maps derived by dynamic contrast-enhanced magnetic resonance imaging using the spatial gradient method, and we compare the performance of the novel method with that of histogram analyses and texture analyses using the Haralick method. K^trans maps of 58 untreated and sunitinib-treated pancreatic ductal adenocaricoma (PDAC) xenografts from two PDAC models were investigated. Intratumor heterogeneity parameters derived by the spatial gradient method were sensitive to tumor line differences as well as sunitinib-induced changes in intratumor heterogeneity. Furthermore, the parameters provided additional information to the median value and were not severely affected by imaging noise. The parameters derived by histogram analyses were insensitive to spatial heterogeneity and were strongly correlated to the median value, and the Haralick features were severely influenced by imaging noise and did not differentiate between untreated and sunitinib-treated tumors. The spatial gradient method was superior to histogram analyses and Haralick features for assessing intratumor heterogeneity in K^trans maps of untreated and sunitinib-treated PDAC xenografts, and can possibly be used to assess intratumor heterogeneity in other medical images and to evaluate effects of other treatments as well.

A model selection framework to quantify microvascular liver function in gadoxetate-enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma

PURPOSE

We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI).

METHODS

Our framework includes a hierarchy of nested models, from which physiological parameters are derived in 2 regimes, corresponding to the active transport and free diffusion of gadoxetate. We use simulations to show the sensitivity of model selection and parameter estimation to temporal resolution, time-series duration, and noise. We apply the framework in 8 healthy volunteers (time-series duration up to 24 minutes) and 10 patients with hepatocellular carcinoma (6 minutes).

RESULTS

The active transport regime is preferred in 98.6% of voxels in volunteers, 82.1% of patients' non-tumorous liver, and 32.2% of tumor voxels. Interpatient variations correspond to known co-morbidities. Simulations suggest both datasets have sufficient temporal resolution and signal-to-noise ratio, while patient data would be improved by using a time-series duration of at least 12 minutes.

CONCLUSIONS

In patient data, gadoxetate exhibits different kinetics: (a) between liver and tumor regions and (b) within regions due to liver disease and/or tumor heterogeneity. Our generalized framework selects a physiological interpretation at each voxel, without preselecting a model for each region or duplicating time-consuming optimizations for models with identical functional forms.

Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly

Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice.

Dynamic contrast-enhanced CT-derived blood flow measurements enable early prediction of long term outcome in metastatic renal cell cancer patients on antiangiogenic treatment

PURPOSE

To evaluate the role of dynamic contrast-enhanced CT (DCE-CT) as an independent non-invasive biomarker in predicting long term outcome in patients with metastatic renal cell carcinoma (mRCC) on antiangiogenic treatment.

MATERIAL AND METHODS

Eighty two mRCC patients were prospectively enrolled from 09/2011 to 04/2015, out of which 71 were included in the final data analysis; the population was observed until 12/2020 to obtain complete overall survival data. DCE-CT imaging was performed at baseline and 10 to 12 weeks after start of treatment with targeted therapy. DCE-CT included a dynamic acquisition after injection of 50 ml of nonionic contrast agent at 6 ml/s using a 4D spiral mode (10 cm z-axis coverage, acquisition time 43 sec, 100 kVp (abdomen), 80 kVp (chest), 80-100 mAs) on a dual source scanner (Definition FLASH, Siemens). Blood flow (BF) was calculated for target tumor volumes using a deconvolution model. Progression free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier statistics (SPSS version 24).

RESULTS

Patients were treated with either sunitinib, pazopanib, sorafenib, tivozanib, axitinib, or cabozantinib. A cut-off value of 50% blood flow reduction at follow-up allowed for identification of patients with favorable long-term outcome: Median OS in n = 42 patients with an average blood flow reduction of >50% (mean, 79%) was 34 (range, 14-54) months, while n = 21 patients with an average reduction of less than 50% (mean, 28%) showed a median OS of 12 (range, 6-18) months, and n = 8 patients with an increase in blood flow survived for a median of 7 (range, 3-11) months.

CONCLUSION

Blood flow in metastases measured with DCE-CT at first follow-up is a strong predictor of overall survival in mRCC patients on antiangiogenic treatment.

Hypoxia boosts aerobic glycolysis of carcinoma：a complex process for tumor development

Hypoxia, a common feature in malignant tumors, is mainly caused by insufficient oxygen supply. Hypoxia is closely related to cancer development, affecting cancer invasion and metastasis, energy metabolism and other pathological processes, and is not conducive to cancer treatment and prognosis. Tumor cells exacerbate metabolic abnormalities to adapt to the hypoxic microenvironment, especially to enhance aerobic glycolysis. Glycolysis leads to an acidic microenvironment in cancer tissues, enhancing cancer metastasis, deterioration and drug resistance. Therefore, hypoxia is a therapeutic target that cannot be ignored in cancer treatment. The adaptation of tumor cells to hypoxia is mainly regulated by hypoxia inducible factors (HIFs), and the stability of HIFs is improved under hypoxic conditions. HIFs can promote the glycolysis of tumors by regulating glycolytic enzymes, transporters, and participates in regulating the TCA (tricarboxylic acid) cycle. In addition, HIFs indirectly affect glycolysis through its interaction with non-coding RNAs. Therefore, targeting hypoxia and HIFs are important tumor therapies.

Feasibility Study on Using Dynamic Contrast Enhanced MRI to Assess the Effect of Tyrosine Kinase Inhibitor Therapy within the STAR Trial of Metastatic Renal Cell Cancer

Objective: To identify dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters predictive of early disease progression in patients with metastatic renal cell cancer (mRCC) treated with anti-angiogenic tyrosine kinase inhibitors (TKI). Methods: The study was linked to a phase II/III randomised control trial. Patients underwent DCE-MRI before, at 4- and 10-weeks after initiation of TKI. DCE-MRI parameters at each time-point were derived from a single-compartment tracer kinetic model, following semi-automated tumour segmentation by two independent readers. Primary endpoint was correlation of DCE-MRI parameters with disease progression at 6-months. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) values were calculated for parameters associated with disease progression at 6 months. Inter-observer agreement was assessed using the intraclass correlation coefficient (ICC). Results: 23 tumours in 14 patients were measurable. Three patients had disease progression at 6 months. The percentage (%) change in perfused tumour volume between baseline and 4-week DCE-MRI (p = 0.016), mean transfer constant K^trans change (p = 0.038), and % change in extracellular volume (p = 0.009) between 4- and 10-week MRI, correlated with early disease progression (AUC 0.879 for each parameter). Inter-observer agreement was excellent for perfused tumour volume, K^trans and extracellular volume (ICC: 0.928, 0.949, 0.910 respectively). Conclusions: Early measurement of DCE-MRI biomarkers of tumour perfusion at 4- and 10-weeks predicts disease progression at 6-months following TKI therapy in mRCC.

The diagnostic efficiency of the perfusion-related parameters in assessing the vascular disrupting agent (CA4P) response in a rabbit VX2 liver tumor model

BACKGROUND

There are inconsistencies when concomitantly using dynamic contrast enhancement (DCE) and intravoxel incoherent motion (IVIM) to evaluate diagnostic efficiency.

PURPOSE

To evaluate the diagnostic efficiency of perfusion-related parameters in assessing the effect of Combretastatin-A4-phosphate (CA4P) in a rabbit VX2 liver tumor model using DCE and IVIM.

MATERIAL AND METHODS

Twenty rabbits implanted with VX2 tumors were included in the study. The perfusion-parameters of DCE (K^trans and iAUC60) and IVIM (f and D*) were measured at baseline and 4 h after administration of CA4P. Subsequently, the rabbits were euthanized. Pre- and post-treatment perfusion parameters were analyzed using paired t-test. Correlation between the various perfusion parameters and correlation of perfusion parameters with microvascular density (MVD) were assessed using Pearson correlation analysis. The diagnostic efficiency was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

All perfusion parameters (K^trans, iAUC60, f and D*) showed significant decrease after 4 h of CA4P administration (all P < 0.001). Post-treatment perfusion parameters showed a moderate correlation with MVD (r = 0.663, r = 0.567, r = 0.685, r = 0.618, respectively; all P < 0.05). At baseline and after treatment, K^trans values and iAUC60 showed correlation with f and D* (all P < 0.05). Concomitant use of perfusion parameters of DCE and IVIM showed the best diagnostic performance, which was slightly greater than that observed with individual application of DCE or IVIM (AUC = 0.915, 0.880, and 0.895, respectively).

CONCLUSION

Although concomitant application of DCE and IVIM can slightly improve the diagnostic value in assessing the effect of CA4P, the values were relatively small.

Sparse precontrast T1 mapping for high-resolution whole-brain DCE-MRI

PURPOSE

To develop and evaluate an efficient precontrast T₁ mapping technique suitable for quantitative high-resolution whole-brain dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI).

METHODS

Variable flip angle (VFA) T₁ mapping was considered that provides 1 × 1 × 2 mm³ resolution to match a recent high-resolution whole-brain DCE-MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T₁ and M₀ maps were estimated using model-based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise-mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high-grade gliomas.

RESULTS

T₁ -mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T₁ bias was <25 ms in white matter (WM) and <11 ms in brain tumor (BT). T₁ standard deviation (SD) was <119.5 ms in WM (coefficient of variation [COV] ~11.0%) and <253.2 ms in BT (COV ~12.7%). In the noisy DRO, T₁ bias was <50 ms in WM and <30 ms in BT. For R ≤ 10, T₁ SD was <107.1 ms in WM (COV ~9.9%) and <240.9 ms in BT (COV ~12.1%). In the healthy subject, T₁ bias was <30 ms for R ≤ 16. At R = 4, T₁ SD was 171.4 ms (COV ~13.0%). In the prospective brain tumor study, T₁ values were consistent with literature values in WM and BT.

CONCLUSION

High-resolution whole-brain VFA T₁ mapping is feasible with sparse sampling, supporting its use for quantitative DCE-MRI.

The LEGATOS technique: A new tissue-validated dynamic contrast-enhanced MRI method for whole-brain, high-spatial resolution parametric mapping

PURPOSE

A DCE-MRI technique that can provide both high spatiotemporal resolution and whole-brain coverage for quantitative microvascular analysis is highly desirable but currently challenging to achieve. In this study, we sought to develop and validate a novel dual-temporal resolution (DTR) DCE-MRI-based methodology for deriving accurate, whole-brain high-spatial resolution microvascular parameters.

METHODS

Dual injection DTR DCE-MRI was performed and composite high-temporal and high-spatial resolution tissue gadolinium-based-contrast agent (GBCA) concentration curves were constructed. The high-temporal but low-spatial resolution first-pass GBCA concentration curves were then reconstructed pixel-by-pixel to higher spatial resolution using a process we call LEGATOS. The accuracy of kinetic parameters (K^trans , v_p , and v_e ) derived using LEGATOS was evaluated through simulations and in vivo studies in 17 patients with vestibular schwannoma (VS) and 13 patients with glioblastoma (GBM). Tissue from 15 tumors (VS) was examined with markers for microvessels (CD31) and cell density (hematoxylin and eosin [H&E]).

RESULTS

LEGATOS derived parameter maps offered superior spatial resolution and improved parameter accuracy compared to the use of high-temporal resolution data alone, provided superior discrimination of plasma volume and vascular leakage effects compared to other high-spatial resolution approaches, and correlated with tissue markers of vascularity (P ≤ 0.003) and cell density (P ≤ 0.006).

CONCLUSION

The LEGATOS method can be used to generate accurate, high-spatial resolution microvascular parameter estimates from DCE-MRI.

Non-Invasive Evaluation of Acute Effects of Tubulin Binding Agents: A Review of Imaging Vascular Disruption in Tumors

Tumor vasculature proliferates rapidly, generally lacks pericyte coverage, and is uniquely fragile making it an attractive therapeutic target. A subset of small-molecule tubulin binding agents cause disaggregation of the endothelial cytoskeleton leading to enhanced vascular permeability generating increased interstitial pressure. The resulting vascular collapse and ischemia cause downstream hypoxia, ultimately leading to cell death and necrosis. Thus, local damage generates massive amplification and tumor destruction. The tumor vasculature is readily accessed and potentially a common target irrespective of disease site in the body. Development of a therapeutic approach and particularly next generation agents benefits from effective non-invasive assays. Imaging technologies offer varying degrees of sophistication and ease of implementation. This review considers technological strengths and weaknesses with examples from our own laboratory. Methods reveal vascular extent and patency, as well as insights into tissue viability, proliferation and necrosis. Spatiotemporal resolution ranges from cellular microscopy to single slice tomography and full three-dimensional views of whole tumors and measurements can be sufficiently rapid to reveal acute changes or long-term outcomes. Since imaging is non-invasive, each tumor may serve as its own control making investigations particularly efficient and rigorous. The concept of tumor vascular disruption was proposed over 30 years ago and it remains an active area of research.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Assessing tumor angiogenesis using dynamic contrast-enhanced integrated magnetic resonance-positron emission tomography in patients with non-small-cell lung cancer

Background

Angiogenesis assessment is important for personalized therapeutic intervention in patients with non-small-cell lung cancer (NSCLC). This study investigated whether radiologic parameters obtained by dynamic contrast-enhanced (DCE)-integrated magnetic resonance-positron emission tomography (MR-PET) could be used to quantitatively assess tumor angiogenesis in NSCLC.

Methods

This prospective cohort study included 75 patients with NSCLC who underwent DCE-integrated MR-PET at diagnosis. The following parameters were analyzed: metabolic tumor volume (MTV), maximum standardized uptake value (SUV_max), reverse reflux rate constant (k_ep), volume transfer constant (K^trans), blood plasma volume fraction (v_p), extracellular extravascular volume fraction (v_e), apparent diffusion coefficient (ADC), and initial area under the time-to-signal intensity curve at 60 s post enhancement (iAUC₆₀). Serum biomarkers of tumor angiogenesis, including vascular endothelial growth factor-A (VEGF-A), angiogenin, and angiopoietin-1, were measured by enzyme-linked immunosorbent assays simultaneously.

Results

Serum VEGF-A (p = 0.002), angiogenin (p = 0.023), and Ang-1 (p <  0.001) concentrations were significantly elevated in NSCLC patients compared with healthy individuals. MR-PET parameters, including MTV, K^trans, and k_ep, showed strong linear correlations (p <  0.001) with serum angiogenesis-related biomarkers. Serum VEGF-A concentrations (p = 0.004), MTV values (p <  0.001), and k_ep values (p = 0.029) were significantly higher in patients with advanced-stage disease (stage III or IV) than in those with early-stage disease (stage I or II). Patients with initial higher values of angiogenesis-related MR-PET parameters, including MTV > 30 cm³ (p = 0.046), K^trans > 200 10^− 3/min (p = 0.069), and k_ep > 900 10^− 3/min (p = 0.048), may have benefited from angiogenesis inhibitor therapy, which thus led to significantly longer overall survival.

Conclusions

The present findings suggest that DCE-integrated MR-PET provides a reliable, non-invasive, quantitative assessment of tumor angiogenesis; can guide the use of angiogenesis inhibitors toward longer survival; and will play an important role in the personalized treatment of NSCLC.

Effect of oxaliplatin plus 5-fluorouracil or capecitabine on circulating and imaging biomarkers in patients with metastatic colorectal cancer: a prospective biomarker study

BACKGROUND

Patients with metastatic colorectal cancer are treated with cytotoxic chemotherapy supplemented by molecularly targeted therapies. There is a critical need to define biomarkers that can optimise the use of these therapies to maximise efficacy and avoid unnecessary toxicity. However, it is important to first define the changes in potential biomarkers following cytotoxic chemotherapy alone. This study reports the impact of standard cytotoxic chemotherapy across a range of circulating and imaging biomarkers.

METHODS

A single-centre, prospective, biomarker-driven study. Eligible patients included those diagnosed with colorectal cancer with liver metastases that were planned to receive first line oxaliplatin plus 5-fluorouracil or capecitabine. Patients underwent paired blood sampling and magnetic resonance imaging (MRI), and biomarkers were associated with progression-free survival (PFS) and overall survival (OS).

RESULTS

Twenty patients were recruited to the study. Data showed that chemotherapy significantly reduced the number of circulating tumour cells as well as the circulating concentrations of Ang1, Ang2, VEGF-A, VEGF-C and VEGF-D from pre-treatment to cycle 2 day 2. The changes in circulating concentrations were not associated with PFS or OS. On average, the MRI perfusion/permeability parameter, Ktrans, increased in response to cytotoxic chemotherapy from pre-treatment to cycle 2 day 2 and this increase was associated with worse OS (HR 1.099, 95%CI 1.01-1.20, p = 0.025).

CONCLUSIONS

In patients diagnosed with colorectal cancer with liver metastases, treatment with standard chemotherapy changes cell- and protein-based biomarkers, although these changes are not associated with survival outcomes. In contrast, the imaging biomarker, Ktrans, offers promise to direct molecularly targeted therapies such as anti-angiogenic agents.

Dynamic Contrast-Enhanced MRI of Prostate Lesions of Simultaneous [68Ga]Ga-PSMA-11 PET/MRI: Comparison between Intraprostatic Lesions and Correlation between Perfusion Parameters

We aimed to retrospectively compare the perfusion parameters measured from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of prostate benign lesions and malignant lesions to determine the relationship between perfusion parameters. DCE-MRI was performed in patients with PCa who underwent simultaneous [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 positron emission tomography (PET)/MRI. Six perfusion parameters (arrival time (AT), time to peak (TTP), wash-in slope (W-in), wash-out slope (W-out), peak enhancement intensity (PEI), and initial area under the 60-s curve (iAUC)), and a semi-quantitative parameter, standardized uptake values maximum (SUVmax) were calculated by placing regions of interest in the largest area of the lesions. The DCE-MRI parameters between prostate benign and malignant lesions were compared. The DCE-MRI parameters in both the benign and malignant lesions subgroup with SUVmax ≤ 3.0 and SUVmax > 3.0 were compared. The correlation of DCE-MRI parameters was investigated. Malignant lesions demonstrated significantly shorter TTP and higher SUVmax than did benign lesions. In the benign and malignant lesions subgroup, perfusion parameters of lesions with SUVmax ≤ 3.0 show no significant difference to those with SUVmax > 3.0. DCE-MRI perfusion parameters show a close correlation with each other. DCE-MRI parameters reflect the perfusion characteristics of intraprostatic lesions with malignant lesions, demonstrating significantly shorter TTP. There is a moderate to strong correlation between DCE-MRI parameters. Semi-quantitative analysis reflects that malignant lesions show a significantly higher SUVmax than benign lesions.

An Ultrahigh-Field-Tailored T1 -T2 Dual-Mode MRI Contrast Agent for High-Performance Vascular Imaging

The assessment of vascular anatomy and functions using magnetic resonance imaging (MRI) is critical for medical diagnosis, whereas the commonly used low-field MRI system (≤3 T) suffers from low spatial resolution. Ultrahigh field (UHF) MRI (≥7 T), with significantly improved resolution and signal-to-noise ratio, shows great potential to provide high-resolution vasculature images. However, practical applications of UHF MRI technology for vascular imaging are currently limited by the low sensitivity and accuracy of single-mode (T₁ or T₂ ) contrast agents. Herein, a UHF-tailored T₁ -T₂ dual-mode iron oxide nanoparticle-based contrast agent (UDIOC) with extremely small core size and ultracompact hydrophilic surface modification, exhibiting dually enhanced T₁ -T₂ contrast effect under the 7 T magnetic field, is reported. The UDIOC enables clear visualization of microvasculature as small as ≈140 µm in diameter under UHF MRI, extending the detection limit of the 7 T MR angiography. Moreover, by virtue of high-resolution UHF MRI and a simple double-checking process, UDIOC-based dual-mode dynamic contrast-enhanced MRI is successfully applied to detect tumor vascular permeability with extremely high sensitivity and accuracy, providing a novel paradigm for the precise medical diagnosis of vascular-related diseases.

Advanced magnetic resonance imaging to support clinical drug development for malignant glioma

Even though the treatment options and survival of patients with glioblastoma multiforme (GBM), the most common type of malignant glioma, have improved over the past decade, there is still a high unmet medical need to develop novel therapies. Complexity in pathology and therapy require biomarkers to characterize tumors, to define malignant and active areas, to assess disease prognosis, and to quantify and monitor therapy response. While conventional magnetic resonance imaging (MRI) techniques have improved these assessments, limitations remain. In this review, we evaluate the role of various non-invasive biomarkers based on advanced structural and functional MRI techniques in the context of GBM drug development over the past 5 years.

Soft tissue sarcoma: can dynamic contrast-enhanced (DCE) MRI be used to predict the histological grade?

OBJECTIVE

To determine if dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters reflect histological grade of soft tissue sarcoma (STS) MATERIALS AND METHODS: The medical records of 50 patients diagnosed with pathologically confirmed STS were retrospectively reviewed. Each STS was assessed with conventional contrast-enhanced MRI and DCE-MRI using a 3.0-T MRI system. The conventional MRI characteristics of low-grade (grade 1) and high-grade (grade 2 and grade 3) tumors were analyzed. Semi-quantitative parameters, including iAUC and TTP, and quantitative parameters, including K^trans, K_ep, and V_e, were derived from DCE-MRI. The diagnostic performances and optimal thresholds of various combinations of DCE-MRI parameters for predicting histological grades of STS were investigated using receiver operator characteristic (ROC) curves.

RESULTS

On conventional MRI, high-grade STSs were significantly larger (≥ 5 cm) and more likely to show a heterogeneous signal intensity on T₂WI (> 75%), peritumoral hyperintensity on T₂WI, or tumor necrosis (> 50%) compared with low-grade STS. On DCE-MRI, iAUC, TTP, K^trans, and K_ep were significant predictors of STS histological grade. K^trans had a high diagnostic value for differentiating between high-grade and low-grade STSs. The combination of iAUC, TTP, and K^trans yielded a higher AUC value (0.841) than the other models.

CONCLUSION

High-grade STSs were usually larger than low-grade STSs, had unclear boundaries, a heterogeneous signal intensity on T2-weighted image (T₂WI), and extensive necrosis. On DCE-MRI, iAUC, TTP, K^trans, and K_ep could differentiate between high-grade and low-grade STSs. The combination of iAUC, TTP, and K^trans had a high diagnostic performance for differentiating between STS histological grades.

Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis

Purpose: Developing an MRI-based radiomics model to effectively and accurately predict the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases (CRLMs). Materials and Methods: In this study, 182 resected and histopathological proven CRLMs of chemotherapy-naive patients from two institutions, including 123 replacement CRLMs and 59 desmoplastic CRLMs, were retrospectively analyzed. Radiomics analysis was performed on two regions of interest (ROI), the tumor zone and the tumor-liver interface (TLI) zone. Decision tree (DT) algorithm was used for radiomics modeling on each MR sequence, and fused radiomics model was constructed by combining the radiomics signature of each sequence. The clinical and combination models were developed through multivariate logistic regression method. The performance of the developed models was assessed by receiver operating characteristic (ROC) curves with indicators of area under curve (AUC), accuracy, sensitivity, and specificity. A nomogram was constructed to evaluate the discrimination, calibration, and usefulness. Results: The fused radiomics^tumor and radiomics^TLI models showed better performance than any single sequence and clinical model. In addition, the radiomics^TLI model exhibited better performance than radiomics^tumor model (AUC of 0.912 vs. 0.879) in internal validation cohort. The combination model showed good discrimination, and the AUC of nomogram was 0.971, 0.909, and 0.905 in the training, internal validation, and external validation cohorts, respectively. Conclusion: MRI-based radiomics method has high potential in predicting the predominant HGPs of CRLM. Preoperative non-invasive identification of predominant HGPs could further explore the ability of HGPs as a potential biomarker for clinical treatment strategy, reflecting different biological pathways.

An MRI-Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

BACKGROUND

Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection.

PURPOSE

To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors.

STUDY TYPE

Retrospective.

POPULATION

In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors.

FIELD STRENGTH/SEQUENCES

Fast-spin-echo (FSE) T₁ -weighted and fat-suppressed FSE T₂ -weighted imaging on a 1.5T and 3.0T MRI.

ASSESSMENT

T₁ and fat-suppressed T₂ -weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI).

STATISTICAL TESTS

Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test.

RESULTS

In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model.

DATA CONCLUSION

The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

[18F]Fluciclatide PET as a biomarker of response to combination therapy of pazopanib and paclitaxel in platinum-resistant/refractory ovarian cancer

BACKGROUND

Angiogenesis is a driver of platinum resistance in ovarian cancer. We assessed the effect of combination pazopanib and paclitaxel followed by maintenance pazopanib in patients with platinum-resistant/refractory ovarian cancer. Integrins αvβ3 and αvβ5 are both upregulated in tumor-associated vasculature. [18F]Fluciclatide is a novel PET tracer that has high affinity for integrins αvβ3/5, and was used to assess the anti-angiogenic effect of pazopanib.

PATIENTS AND METHODS

We conducted an open-label, phase Ib study in patients with platinum-resistant/refractory ovarian cancer. Patients received 1 week of single-agent pazopanib (800 mg daily) followed by combination therapy with weekly paclitaxel (80 mg/m2). Following completion of 18 weeks of combination therapy, patients continued with single-agent pazopanib until disease progression. Dynamic [18F]fluciclatide-PET imaging was conducted at baseline and after 1 week of pazopanib. Response (RECIST 1.1), toxicities, and survival outcomes were recorded. Circulating markers of angiogenesis were assessed with therapy.

RESULTS

Fourteen patients were included in the intention-to-treat analysis. Complete and partial responses were seen in seven patients (54%). Median progression-free survival (PFS) was 10.63 months, and overall survival (OS) was 18.5 months. Baseline [18F]fluciclatide uptake was predictive of long PFS. Elevated baseline circulating angiopoietin and fibroblast growth factor (FGF) were predictive of greater reduction in SUV60,mean following pazopanib. Kinetic modeling of PET data indicated a reduction in K1 and Ki following pazopanib indicating reduced radioligand delivery and retention.

CONCLUSIONS

Combination therapy followed by maintenance pazopanib is effective and tolerable in platinum-resistant/refractory ovarian cancer. [18F]Fluciclatide-PET uptake parameters predict clinical outcome with pazopanib therapy indicating an anti-angiogenic response.

Angiogenesis in Hepatocellular Carcinoma; Pathophysiology, Targeted Therapy, and Role of Imaging

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, usually occurring on a background of liver cirrhosis. HCC is a highly vascular tumor in which angiogenesis plays a major role in tumor growth and spread. Tumor-induced angiogenesis is usually related to a complex interplay between multiple factors and pathways, with vascular endothelial growth factor being a major player in angiogenesis. In the past decade, understanding of tumor-induced angiogenesis has led to the emergence of novel anti-angiogenic therapies, which act by reducing neo-angiogenesis, and improving patient survival. Currently, Sorafenib and Lenvatinib are being used as the first-line treatment for advanced unresectable HCC. However, a disadvantage of these agents is the presence of numerous side effects. A major challenge in the management of HCC patients being treated with anti-angiogenic therapy is effective monitoring of treatment response, which decides whether to continue treatment or to seek second-line treatment. Several criteria can be used to assess response to treatment, such as quantitative perfusion on cross-sectional imaging and novel/emerging MRI techniques, including a host of known and emerging biomarkers and radiogenomics. This review addresses the pathophysiology of angiogenesis in HCC, accurate imaging assessment of angiogenesis, monitoring effects of anti-angiogenic therapy to guide future treatment and assessing prognosis.

Diffusion kurtosis imaging in head and neck cancer: A correlation study with dynamic contrast enhanced MRI

PURPOSE

To investigate the biophysical meaning of Diffusion Kurtosis Imaging (DKI) parameters via correlations with the perfusion parameters obtained from a long Dynamic Contrast Enhanced MRI scan, in head and neck (HN) cancer.

METHODS

Twenty two patients with newly diagnosed HN tumor were included in the present retrospective study. Some patients had multiple lesions, therefore a total of 26 lesions were analyzed. DKI was acquired using 5b values at 0, 500, 1000,1500 and 2000 s/mm². DCE-MRI was obtained with 130 dynamic volumes, with a temporal resolution of 5 s, to achieve a long scan time (>10 min). The apparent diffusion coefficient D_app and apparent diffusional kurtosis K_app were calculated voxel-by-voxel, removing the point at b value = 0 to eliminate possible perfusion effects on the parameter estimations. The transfer constants K^trans and K_ep, v_e, and the histogram-based entropy (En) and interquartile range (IQR) of each DCE-MRI parameter were quantified. Correlations between all variables were investigated by the Spearman's Rho correlation test.

RESULTS

Moderate relationships emerged between D_app and K_ep (Rho =  - 0.510, p = 0.009), and between D_app and v_e (Rho = 0.418, p = 0.038). En(K_ep) was significantly related to K_app (Rho = 0.407, p = 0.043), while IQR(K_ep) showed an inverse association with D_app (Rho = -0.422, p = 0.035).

CONCLUSIONS

A weak to intermediate correlation was found between DKI parameters and both K_ep and v_e. The kurtosis was associated to the intratumoral heterogeneity and complexity of the capillary permeability, expressed by En(K_ep).