Dynamic contrast-enhanced MRI for oncology drug development

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.

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI)

PURPOSE

Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework.

METHODS

A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results.

RESULTS

The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions.

CONCLUSIONS

The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

The role of clinical imaging in oncology drug development: progress and new challenges

In 2008, the role of clinical imaging in oncology drug development was reviewed. The review outlined where imaging was being applied and considered the diverse demands across the phases of drug development. A limited set of imaging techniques was being used, largely based on structural measures of disease evaluated using established response criteria such as response evaluation criteria in solid tumours. Beyond structure, functional tissue imaging such as dynamic contrast-enhanced MRI and metabolic measures using [18F]flourodeoxyglucose positron emission tomography were being increasingly incorporated. Specific challenges related to the implementation of imaging were outlined including standardisation of scanning across study centres and consistency of analysis and reporting. More than a decade on the needs of modern drug development are reviewed, how imaging has evolved to support new drug development demands, the potential to translate state-of-the-art methods into routine tools and what is needed to enable the effective use of this broadening clinical trial toolset. In this review, we challenge the clinical and scientific imaging community to help refine existing clinical trial methods and innovate to deliver the next generation of techniques. Strong industry-academic partnerships and pre-competitive opportunities to co-ordinate efforts will ensure imaging technologies maintain a crucial role delivering innovative medicines to treat cancer.

Assessment of Meningeal Lymphatics in the Parasagittal Dural Space: A Prospective Feasibility Study Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

OBJECTIVE

Meningeal lymphatic vessels are predominantly located in the parasagittal dural space (PSD); these vessels drain interstitial fluids out of the brain and contribute to the glymphatic system. We aimed to investigate the ability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the dynamic changes in the meningeal lymphatic vessels in PSD.

MATERIALS AND METHODS

Eighteen participants (26-71 years; male:female, 10:8), without neurological or psychiatric diseases, were prospectively enrolled and underwent DCE-MRI. Three regions of interests (ROIs) were placed on the PSD, superior sagittal sinus (SSS), and cortical vein. Early and delayed enhancement patterns and six kinetic curve-derived parameters were obtained and compared between the three ROIs. Moreover, the participants were grouped into the young (< 65 years; n = 9) or older (≥ 65 years; n = 9) groups. Enhancement patterns and kinetic curve-derived parameters in the PSD were compared between the two groups.

RESULTS

The PSD showed different enhancement patterns than the SSS and cortical veins (P < 0.001 and P < 0.001, respectively) in the early and delayed phases. The PSD showed slow early enhancement and a delayed wash-out pattern. The six kinetic curve-derived parameters of PSD was significantly different than that of the SSS and cortical vein. The PSD wash-out rate of older participants was significantly lower (median, 0.09; interquartile range [IQR], 0.01-0.15) than that of younger participants (median, 0.32; IQR, 0.07-0.45) (P = 0.040).

CONCLUSION

This study shows that the dynamic changes of meningeal lymphatic vessels in PSD can be assessed with DCE-MRI, and the results are different from those of the venous structures. Our finding that delayed wash-out was more pronounced in the PSD of older participants suggests that aging may disturb the meningeal lymphatic drainage.

Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy

BACKGROUND

Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level.

METHODS

Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone.

RESULTS

All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours.

CONCLUSIONS

Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

Early stage T1-weighted perfusion magnetic resonance imaging: a factor that predicts local control response in patients with meningioma who underwent gamma-knife radiosurgery

BACKGROUND

Gamma-knife radiosurgery (GKRS) is an alternative treatment option for selected intracranial meningiomas. The study's aim is to demonstrate the advantages of T1-weighted perfusion magnetic resonance imaging (T1-PMRI) by measuring the volume transfer coefficient (K^trans) values in the prediction of local response for patients with meningioma who have undergone GKRS consecutively.

METHODS

The data of patients diagnosed radiologically with WHO grade 1 intracranial meningiomas was collected prospectively. The patients who were treated consecutively with GKRS at our institution (September 2017-September 2018) were included. After GKRS, the patients were followed up at the defined periods with routine contrast-enhanced MRI and T1-PMRI by measuring the K^trans. The comparison between the pre-treatment and third-month post-treatment (PO3M) K^trans was done using the Wilcoxon signed-rank test.

RESULTS

Thirty-one patients with 36 tumors have undergone GKRS. Twenty-two patients were female. The mean age was 55.3 years. The mean pre-GKRS volume was 7.67 ccs. The mean 50% radiation isodose was 12.2 Gy. The local tumor control rate was 100%. Fourteen tumors were regressed fully at the last MRI. PO3M K^trans decreased when compared with the pre-GKRS values (p < 0.0001). However, the numerical decrease in tumor volumes on contrast-enhanced MRI was not statistically significant (p = 0.117).

CONCLUSION

Changes between K^trans on PO3M and pre-GKRS T1-PMRI were more useful in determining the early response to GKRS in patients with meningioma than volumetric changes. Therefore, K^trans should be taken as a reference to predict the early response to GKRS in follow-up imaging scans.

Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer

Background

This study aimed to explore the predictive value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters for the response to neoadjuvant chemotherapy (NCT) in locally advanced gastric cancer (LAGC) patients, and the relationship between the prediction results and patients’ prognosis, so as to provide a basis for clinical individualized precision treatment.

Methods

One hundred twenty-nine newly diagnosed LAGC patients who underwent IVIM-DWI and DCE-MRI pretreatment were enrolled in this study. Pathological tumor regression grade (TRG) served as the reference standard of NCT response evaluation. The differences in DCE-MRI and IVIM-DWI parameters between pathological responders (pR) and pathological non-responders (pNR) groups were analyzed. Univariate and multivariate logistic regressions were used to identify independent predictive parameters for NCT response. Prediction models were built with statistically significant quantitative parameters and their combinations. The performance of these quantitative parameters and models was evaluated using receiver operating characteristic (ROC) analysis. Clinicopathological variables, DCE-MRI and IVIM-DWI derived parameters, as well as the prediction model were analyzed in relation to 2-year recurrence-free survival (RFS) by using Cox proportional hazards model. RFS was compared using the Kaplan–Meier method and the log-rank test.

Results

Sixty-nine patients were classified as pR and 60 were pNR. K^trans, k_ep, and v_e values in the pR group were significantly higher, while ADC_standard and D values were significantly lower than those in the pNR group. Multivariate logistic regression analysis demonstrated that K^trans, k_ep, v_e, and D values were independent predictors for NCT response. The combined predictive model, which consisted of DCE-MRI and IVIM-DWI, showed the best prediction performance with an area under the curve (AUC) of 0.922. Multivariate Cox regression analysis showed that ypStage III and NCT response predicted by the IVIM-DWI model were independent predictors of poor RFS. The IVIM-DWI model could significantly stratify median RFS (52 vs. 15 months) and 2-year RFS rate (72.3% vs. 21.8%) of LAGC.

Conclusion

Pretreatment DCE-MRI quantitative parameters K^trans, k_ep, v_e, and IVIM-DWI parameter D value were independent predictors of NCT response for LAGC patients. The regression model based on baseline DCE-MRI, IVIM-DWI, and their combination could help RFS stratification of LAGC patients.

Dynamic contrast-enhanced MRI in malignant pleural mesothelioma: prediction of outcome based on DCE-MRI measurements in patients undergoing cytotoxic chemotherapy

Background

The malignant pleural mesothelioma (MPM) response rate to chemotherapy is low. The identification of imaging biomarkers that could help guide the most effective therapy approach for individual patients is highly desirable. Our aim was to investigate the dynamic contrast-enhanced (DCE) MR parameters as predictors for progression-free (PFS) and overall survival (OS) in patients with MPM treated with cisplatin-based chemotherapy.

Methods

Thirty-two consecutive patients with MPM were enrolled in this prospective study. Pretreatment and intratreatment DCE-MRI were scheduled in each patient. The DCE parameters were analyzed using the extended Tofts (ET) and the adiabatic approximation tissue homogeneity (AATH) model. Comparison analysis, logistic regression and ROC analysis were used to identify the predictors for the patient’s outcome.

Results

Patients with higher pretreatment ET and AATH-calculated K^trans and v_e values had longer OS (P≤.006). Patients with a more prominent reduction in ET-calculated K^trans and k_ep values during the early phase of chemotherapy had longer PFS (P =.008). No parameter was identified to predict PFS. Pre-treatment ET-calculated K^trans was found to be an independent predictive marker for longer OS (P=.02) demonstrating the most favourable discrimination performance compared to other DCE parameters with an estimated sensitivity of 89% and specificity of 78% (AUC 0.9, 95% CI 0.74-0.98, cut off > 0.08 min^-1).

Conclusions

In the present study, higher pre-treatment ET-calculated K^trans values were associated with longer OS. The results suggest that DCE-MRI might provide additional information for identifying MPM patients that may respond to chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09277-x.

Early Changes in DCE-MRI Biomarkers May Predict Survival Outcomes in Patients with Advanced Hepatocellular Carcinoma after Sorafenib Failure: Two Prospective Phase II Trials

Simple Summary

In patients with advanced hepatocellular carcinoma, systemic therapy is recommended by most treatment guidelines. Sorafenib and lenvatinib are both 1st-line treatments for inoperable advanced HCC. Regorafenib, cabozantinib, and ramucirumab have been approved as 2nd-line targeted therapy in patients who show progression or do not tolerate sorafenib. However, there is a lack of imaging biomarkers for predicting survival outcomes in patients receiving 2nd-line targeted therapy after sorafenib failure. In this paper, we try to predict survival outcomes via early changes in the DCE-MRI biomarkers in participants with advanced HCC after 2nd-line targeted therapy following sorafenib failure, taking data from two different prospective clinical trials. We found that an early reduction in tumor perfusion detected by DCE-MRI biomarkers, especially on day 14, may predict survival outcomes in these participants. For the further clinical development of anti-angiogenic therapies, optimal participant selection with predictive biomarkers, such as DCE-MRI, is essential in order to improve treatment outcomes.

Abstract

In this paper, our main objective was to predict survival outcomes using DCE-MRI biomarkers in patients with advanced hepatocellular carcinoma (HCC) after progression from 1st-line sorafenib treatment in two prospective phase II trials. This study included 74 participants (men/women = 64/10, mean age 60 ± 11.8 years) with advanced HCC who received 2nd-line targeted therapy (n = 41 with lenalidomide in one clinical trial; n = 33 with axitinib in another clinical trial) after sorafenib failure from two prospective phase II studies. Among them, all patients underwent DCE-MRI at baseline, and on days 3 and 14 of treatment. The relative changes (Δ) in the DCE-MRI parameters, including ΔPeak, ΔAUC, and ΔK^trans, were derived from the largest hepatic tumor. The treatment response was evaluated using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1). The Cox model was used to investigate the associations of the clinical variables and DCE-MRI biomarkers with progression-free survival (PFS) and overall survival (OS). The objective response rate (ORR) was 10.8% (8/74) and the disease control rate (DCR) was 58.1% (43/74). The median PFS and OS values were 1.9 and 7.8 months, respectively. On day 3 (D3), participants with high reductions in ΔPeak_D3 (hazard ratio (HR) 0.4, 95% confidence interval (CI) 0.17–0.93, p = 0.017) or ΔAUC_D3 (HR 0.51, 95% CI 0.25–1.04, p = 0.043) were associated with better PFS. On day 14, participants with high reductions in ΔPeak_D14 (HR 0.51, 95% CI 0.26–1.01, p = 0.032), ΔAUC_D14 (HR 0.54, 95% CI 0.33–0.9, p = 0.009), or ΔK^trans_D14 (HR 0.26, 95% CI 0.12–0.56, p < 0.001) had a higher PFS than those with lower reduction values. In addition, high reductions in ΔAUC_D14 (HR 0.53, 95% CI 0.32–0.9, p = 0.016) or ΔK^trans_D14 (HR 0.47, 95% CI 0.23–0.98, p = 0.038) were associated with a better OS. Among the clinical variables, ORR was associated with both PFS (p = 0.001) and OS (p = 0.005). DCR was associated with PFS (p = 0.002), but not OS (p = 0.089). Cox multivariable analysis revealed that ΔK^trans_D14 (p = 0.002) remained an independent predictor of PFS after controlling for ORR and DCR. An early reduction in tumor perfusion detected by DCE-MRI biomarkers, especially on day 14, may predict favorable survival outcomes in participants with HCC receiving 2nd-line targeted therapy after sorafenib failure.

Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics

BACKGROUND

The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures.

METHOD

Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively.

RESULTS

Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics.

CONCLUSION

This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.

Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE

Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.

MATERIALS AND METHODS

We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.

RESULTS

The random forest methodology estimated cellular density with R 2 = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R2 = 0.52). Outputs were also reported as graphic maps.

CONCLUSIONS

Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

A Multicenter Phase II Study of Second-Line Axitinib for Patients with Advanced Hepatocellular Carcinoma Failing First-Line Sorafenib Monotherapy

LESSONS LEARNED

For patients with advanced hepatocellular carcinoma after failure of first-line sorafenib monotherapy, second-line axitinib provides modest efficacy with tolerable toxicity. The discrepant tumor responses and survival outcomes in trials using axitinib as salvage therapy highlight the importance of optimal patient selection with the aid of clinical biomarkers.

BACKGROUND

Multikinase inhibitors have been effective treatment for hepatocellular carcinoma (HCC). This multicenter phase II study explored the efficacy and safety of second-line axitinib for advanced HCC.

METHODS

Patients with advanced HCC and Child-Pugh A liver function, experiencing progression on first-line sorafenib monotherapy, were eligible. Axitinib 5 mg twice daily was given continuously with allowed dose escalation. Tumor assessment was performed according to RECIST version 1.1. The primary endpoint was rate of disease control.

RESULTS

From April 2011 to March 2016, 45 patients were enrolled. Thirty-seven patients (82%) tested positive for hepatitis B surface antigen. The disease control rate was 62.2%, and the response rate was 6.7%, according to RECIST criteria. Median progression-free survival (PFS) and overall survival (OS) were 2.2 months and 10.1 months, respectively. Treatment-related adverse events were compatible with previous reports of axitinib.

CONCLUSION

Second-line axitinib has moderate activity and acceptable toxicity for patients with advanced HCC after failing the first-line sorafenib monotherapy.

Multiparametric MRI for prediction of treatment response to neoadjuvant FOLFIRINOX therapy in borderline resectable or locally advanced pancreatic cancer

OBJECTIVES

To identify multiparametric MRI biomarkers to predict the tumor response to neoadjuvant FOLFIRINOX therapy in patients with borderline resectable (BR) or locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC).

METHODS

From May 2016 to March 2018, adult patients with BR or LA PDAC were prospectively enrolled in this study. They received eight cycles of FOLFIRINOX therapy and underwent multiparametric MRI twice (at baseline and after the second cycle). MRI evaluations included dynamic contrast-enhanced MRI, intravoxel incoherent motion diffusion-weighted imaging, and assessment of T2* relaxivity (R2*) and the change in T1 relaxivity (ΔR1, equilibrium phase R1 minus non-enhanced R1) of the tumors. Factors to predict the responders determined by the best overall response during FOLFIRINOX therapy and those to predict progression-free survival (PFS) and overall survival (OS) were evaluated using multivariable logistic regression and the Cox proportional hazard model.

RESULTS

Forty-one patients (mean age, 60.3 years ± 9.3; 24 men) were included. Among the clinical and MRI factors, the baseline ΔR1 (adjusted odds ratio, 31.07; p = 0.008) was the only independent predictor for tumor response. The baseline ΔR1 was also an independent predictor for PFS (adjusted hazard ratio, 0.40; p = 0.033) along with R0 resection. The use of a cutoff ΔR1 value of ≥ 1.31 s^-1 enabled prognostic stratification (median PFS, 16.0 months vs.10.0 months; p = 0.029; median OS, 34.9 months vs. 16.6 months; p = 0 .023, respectively).

CONCLUSIONS

The baseline tumor ΔR1 value may be useful to predict tumor response and survival in patients with BR or LA PDAC receiving FOLFIRINOX neoadjuvant therapy.

KEY POINTS

• Baseline ΔR1 was an independent predictor for tumor response (adjusted odds ratio, 31.07; p = 0.008) and progression-free survival (adjusted hazard ratio, 0.40; p = 0.033) in patients with borderline resectable or locally advanced pancreatic ductal adenocarcinoma receiving neoadjuvant FOLFIRINOX therapy. • The criterion of baseline ΔR1 value ≥ 1.31 s^-1 allowed for the prediction of favorable tumor response and survival outcome after neoadjuvant FOLFIRINOX therapy.

Effects of BMPER, CXCL10, and HOXA9 on Neovascularization During Early-Growth Stage of Primary High-Grade Glioma and Their Corresponding MRI Biomarkers

Neovascularization is required in high-grade glioma (HGG). The objective of this study was to explore neovascularization-related genes and their corresponding MRI biomarkers during the early-growth stage of HGG. Tumor tissues from 30 HGG patients underwent perfusion MRI scanning prior to surgery were used to establish orthotopic xenograft models, pathologically analyze the tumor vasculature and perform transcriptome sequencing. The cases were divided into two groups based on whether the xenograft was successfully established. Microvascular density and BMPER, CXCL10, and HOXA9 expression of surgical specimens in the xenograft-forming group was significantly elevated and the microvascular diameter was significantly reduced, in vitro inhibition of BMPER, CXCL10, or HOXA9 in the glioma stem cell significantly suppressed its tube formation abilities. The in vivo experiment showed that BMPER was highly expressed in the early tumor growth phase (20 days), CXCL10 and HOXA9 expression was elevated with tumor progress, and spatially associated with tumor vasculature. Perfusion weighted MRI (PWI-MRI) derived parameters, rCBV, rCBF, K_trans, and V_p, were also increased in the xenograft-forming group. In conclusion BMPER, CXCL10, and HOXA9 promote early tumor growth and progression by stimulating neovascularization of primary HGG. The rCBV, rCBF, K_trans, and V_p can be used as imaging biomarkers to predict the expression statuses of these genes.

Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging

BACKGROUND

Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard.

METHODS

MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain.

RESULTS

Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, Ktrans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R2 = 0.75). A less accurate predictive result (RMS error 5.4%, R2 = 0.50) was found using conventional imaging only.

CONCLUSION

Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.

Gadoxetate-enhanced dynamic contrast-enhanced MRI for evaluation of liver function and liver fibrosis in preclinical trials

Background

To facilitate translational drug development for liver fibrosis, preclinical trials need to be run in parallel with clinical research. Liver function estimation by gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI) is being established in clinical research, but still rarely used in preclinical trials. We aimed to evaluate feasibility of DCE-MRI indices as translatable biomarkers in a liver fibrosis animal model.

Methods

Liver fibrosis was induced in Sprague-Dawley rats by thioacetamide (200 mg, 150 mg, and saline for the high-dose, low-dose, and control groups, respectively). Subsequently, DCE-MRI was performed to measure: relative liver enhancement at 3-min (RLE-3), RLE-15, initial area-under-the-curve until 3-min (iAUC-3), iAUC-15, and maximum-enhancement (Emax). The correlation coefficients between these MRI indices and the histologic collagen area, indocyanine green retention at 15-min (ICG-R15), and shear wave elastography (SWE) were calculated. Diagnostic performance to diagnose liver fibrosis was also evaluated by receiver-operating-characteristic (ROC) analysis.

Results

Animal model was successful in that the collagen area of the liver was the largest in the high-dose group, followed by the low-dose group and control group. The correlation between the DCE-MRI indices and collagen area was high for iAUC-15, Emax, iAUC-3, and RLE-3 but moderate for RLE-15 (r, − 0.81, − 0.81, − 0.78, − 0.80, and − 0.51, respectively). The DCE-MRI indices showed moderate correlation with ICG-R15: the highest for iAUC-15, followed by iAUC-3, RLE-3, Emax, and RLE-15 (r, − 0.65, − 0.63, − 0.62, − 0.58, and − 0.56, respectively). The correlation coefficients between DCE-MRI indices and SWE ranged from − 0.59 to − 0.28. The diagnostic accuracy of RLE-3, iAUC-3, iAUC-15, and Emax was 100% (AUROC 1.000), whereas those of RLE-15 and SWE were relatively low (AUROC 0.777, 0.848, respectively).

Conclusion

Among the gadoxetate-enhanced DCE-MRI indices, iAUC-15 and iAUC-3 might be bidirectional translatable biomarkers between preclinical and clinical research for evaluating histopathologic liver fibrosis and physiologic liver functions in a non-invasive manner.

Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI

RATIONALE AND OBJECTIVES

Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters.

MATERIALS AND METHODS

Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters K_trans, v_e, and k_ep was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology.

RESULTS

Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for k_ep, 28% for v_e, and 83% for K_trans . The best p values for discrimination between tissues were p <10^-5 for k_ep and K_trans, and p = 0.11 for v_e. Addition of the BAT correction to the models did not improve repeatability.

CONCLUSION

The parameter k_ep, using an arterial input functions directly measured from blood signals, was more repeatable than K_trans. Both K_trans and k_ep values were highly discriminatory between healthy and diseased tissues in all cases. The parameter v_e had high repeatability but could not distinguish the two tissue types.

Medullary Thyroid Carcinoma: An Update on Imaging

Medullary thyroid carcinoma (MTC), arising from the parafollicular C cells of the thyroid, accounts for 1–2% of thyroid cancers. MTC is frequently aggressive and metastasizes to cervical and mediastinal lymph nodes, lungs, liver, and bones. Although a number of new imaging modalities for directing the management of oncologic patients evolved over the last two decades, the clinical application of these novel techniques is limited in MTC. In this article, we review the biology and molecular aspects of MTC as an important background for the use of current imaging modalities and approaches for this tumor. We discuss the modern and currently available imaging techniques—advanced magnetic resonance imaging (MRI)-based techniques such as whole-body MRI, dynamic contrast-enhanced (DCE) technique, diffusion-weighted imaging (DWI), positron emission tomography/computed tomography (PET/CT) with 18F-FDOPA and 18F-FDG, and integrated positron emission tomography/magnetic resonance (PET/MR) hybrid imaging—for primary as well as metastatic MTC tumor, including its metastatic spread to lymph nodes and the most common sites of distant metastases: lungs, liver, and bones.

The antitumour growth and antiangiogenesis effects of xanthatin in murine glioma dynamically evaluated by dynamic contrast-enhanced magnetic resonance imaging

To investigate the suppressive effects of xanthatin on glioma growth in a nude mouse xenograft model and rat orthotopic implantation model using magnetic resonance imaging (MRI) to dynamically monitor the antitumour growth and antiangiogenesis effects of xanthatin. The nude mouse xenograft tumour model and rat orthotopic implantation model were established to observe the antitumour effects of xanthatin in vivo. In the rat orthotopic implanted tumour model, MRI scanning was used to dynamically monitor the antitumour growth effect and evaluate the antiangiogenesis effect of xanthatin. We found that xanthatin at a dose of 0.4 mg/10 g dramatically decreased the growth of xenograft tumours in nude mice. The antiangiogenesis effect of xanthatin C6 glioma was evaluated by dynamic contrast-enhanced (DCE) MRI via comparison of the volume transfer constant (K^trans ) value, a parameter that reflects vessel permeability. We found that xanthatin at the doses of 8 and 16 mg/kg significantly decreased the K^trans value, which suggests that xanthatin has antiangiogenesis effects. These data demonstrate the suppressive effects of xanthatin on C6 glioma occur via antiangiogenesis. Meanwhile, this study also provides evidence for the application of quantitative parameters of DCE-MRI for dynamically evaluating the growth and angiogenesis of intracranial tumours and for experimental and clinical research.

Imaging biomarkers in oncology: Basics and application to MRI

Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called "precision cancer medicine." Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2018;48:13-26.

Fast magnetic resonance fingerprinting for dynamic contrast-enhanced studies in mice

PURPOSE

The goal of this study was to develop a fast MR fingerprinting (MRF) method for simultaneous T₁ and T₂ mapping in DCE-MRI studies in mice.

METHODS

The MRF sequences based on balanced SSFP and fast imaging with steady-state precession were implemented and evaluated on a 7T preclinical scanner. The readout used a zeroth-moment-compensated variable-density spiral trajectory that fully sampled the entire k-space and the inner 10 × 10 k-space with 48 and 4 interleaves, respectively. In vitro and in vivo studies of mouse brain were performed to evaluate the accuracy of MRF measurements with both fully sampled and undersampled data. The application of MRF to dynamic T₁ and T₂ mapping in DCE-MRI studies were demonstrated in a mouse model of heterotopic glioblastoma using gadolinium-based and dysprosium-based contrast agents.

RESULTS

The T₁ and T₂ measurements in phantom showed strong agreement between the MRF and the conventional methods. The MRF with spiral encoding allowed up to 8-fold undersampling without loss of measurement accuracy. This enabled simultaneous T₁ and T₂ mapping with 2-minute temporal resolution in DCE-MRI studies.

CONCLUSION

Magnetic resonance fingerprinting provides the opportunity for dynamic quantification of contrast agent distribution in preclinical tumor models on high-field MRI scanners.

Portable perfusion phantom for quantitative DCE-MRI of the abdomen

PURPOSE

The aim of this study was to develop a portable perfusion phantom and validate its utility in quantitative dynamic contrast-enhanced magnetic resonance imaging of the abdomen.

METHODS

A portable perfusion phantom yielding a reproducible contrast enhancement curve (CEC) was developed. A phantom package including perfusion and static phantoms were imaged simultaneously with each of three healthy human volunteers in two different 3T MR scanners. Look-up tables correlating reference (known) contrast concentrations with measured ones were created using either the static or perfusion phantom. Contrast maps of image slices showing four organs (liver, spleen, pancreas, and paravertebral muscle) were generated before and after data correction using the look-up tables. The contrast concentrations at 4.5 min after dosing in each of the four organs were averaged for each volunteer. The mean contrast concentrations (4 organs × 3 volunteers = 12) were compared for the two scanners, and the intra-class correlation coefficient (ICC) was calculated. Also, the ICC of the mean K^trans values between the two scanners was calculated before and after data correction.

RESULTS

The repeatability coefficient of CECs of perfusion phantom was higher than 0.997 in all measurements. The ICC of the tissue contrast concentrations between the two scanners was 0.693 before correction, but increased to 0.974 after correction using the look-up tables (LUTs) of perfusion phantom. However, the ICC was not increased after correction using static phantom (ICC: 0.617). Similarly, the ICC of the K^trans values was 0.899 before correction, but increased to 0.996 after correction using perfusion phantom LUTs. The ICC of the K^trans values, however, was not increased when static phantom LUTs were used (ICC: 0.866).

CONCLUSIONS

The perfusion phantom reduced variability in quantitating contrast concentration and K^trans values of human abdominal tissues across different MR units, but static phantom did not. The perfusion phantom has the potential to facilitate multi-institutional clinical trials employing quantitative DCE-MRI to evaluate various abdominal malignancies.

Influence of B1-Inhomogeneity on Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI: A Simulation Study

OBJECTIVE

To simulate the B₁-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

B₁-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R₁, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (K^trans, v_e, and v_p).

RESULTS

B₁-inhomogeneity resulted in -23-5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: -16.7-61.8% and -16.7-61.8% for the pre-contrast R₁, -1.0-0.3% and -5.2-1.3% for the contrast-enhancement ratio, and -14.2-58.1% and -14.1-57.8% for the Gd-concentration, respectively. These resulted in -43.1-48.4% error for K^trans, -32.3-48.6% error for the v_e, and -43.2-48.6% error for v_p. The pre-contrast R₁ was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a -10% FA error led to a 23.6% deviation in the pre-contrast R₁, -0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a -10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were -23.7% for K^trans, -23.7% for v_e, and -23.7% for v_p.

CONCLUSION

Even a small degree of B₁-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R₁ calculations to FA deviations is a significant cause of the miscalculation.

Anti-angiogenic therapy affects the relationship between tumor vascular structure and function: A correlation study between micro-computed tomography angiography and dynamic contrast enhanced MRI

PURPOSE

To compare the effects of two anti-angiogenic drugs, bevacizumab and a cytosolic phospholipase A2-α inhibitor (AVX235), on the relationship between vascular structure and dynamic contrast enhanced (DCE)-MRI measurements in a patient-derived breast cancer xenograft model.

METHODS

Mice bearing MAS98.12 tumors were randomized into three groups: bevacizumab-treated (n = 9), AVX235-treated (n = 9), and control (n = 8). DCE-MRI was performed pre- and post-treatment. Median initial area under the concentration-time curve (IAUC₆₀ ) and volume transfer constant (K^trans ) were computed for each tumor. Tumors were excised for ex vivo micro-CT (computed tomography) angiography, from which the vascular surface area (VSA) and fractional blood volume (FBV) were computed. Spearman correlation coefficients (ρ) were computed to evaluate the associations between the DCE-MRI and micro-CT parameters.

RESULTS

With the groups pooled, IAUC₆₀ and K^trans correlated significantly with VSA (ρ = 0.475 and 0.527; P = 0.019 and 0.008). There were no significant correlations within the control group. There were various significant correlations within the treatment groups, but the correlations in the bevacizumab group were of opposite sign, for example, K^trans versus FBV: AVX235, ρ = 0.800 (P = 0.014); bevacizumab, ρ = -0.786 (P = 0.023).

CONCLUSION

DCE-MRI measurements can highly depend on vascular structure. The relationship between vascular structure and function changed markedly after anti-angiogenic treatment. Magn Reson Med 78:1513-1522, 2017. © 2016 International Society for Magnetic Resonance in Medicine.