Bone Marrow Aspirate Concentrate Combined with Ultra-Purified Alginate Bioresorbable Gel Enhances Intervertebral Disc Repair in a Canine Model: A Preclinical Proof-of-Concept Study.

Although discectomy is commonly performed for lumbar intervertebral disc (IVD) herniation, the capacity for tissue repair after surgery is limited, resulting in residual lower back pain, recurrence of IVD herniation, and progression of IVD degeneration. Cell-based therapies, as one-step procedures, are desirable for enhancing IVD repair. This study aimed to investigate the therapeutic efficacy of a combination of newly developed ultra-purified alginate (UPAL) gel and bone marrow aspirate concentrate (BMAC) implantation for IVD repair after discectomy. Prior to an in vivo study, the cell concentration abilities of three commercially available preparation kits for creating the BMAC were compared by measuring the number of bone marrow mesenchymal stem cells harvested from the bone marrow of rabbits. Subsequently, canine-derived BMAC was tested in a canine model using a kit which had the highest concentration rate. At 24 weeks after implantation, we evaluated the changes in the magnetic resonance imaging (MRI) signals as well as histological degeneration grade and immunohistochemical analysis results for type II and type I collagen-positive cells in the treated IVDs. In all quantitative evaluations, such as MRI and histological and immunohistochemical analyses of IVD degeneration, BMAC-UPAL implantation significantly suppressed the progression of IVD degeneration compared to discectomy and UPAL alone. This preclinical proof-of-concept study demonstrated the potential efficacy of BMAC-UPAL gel as a therapeutic strategy for implementation after discectomy, which was superior to UPAL and discectomy alone in terms of tissue repair and regenerative potential.

Intracerebral transplantation of MRI-trackable autologous bone marrow stromal cells for patients with subacute ischemic stroke.

BACKGROUND: Ischemic stroke is one of the leading causes of death and neurological disability worldwide, and stem cell therapy is highly expected to reverse the sequelae. This phase 1/2, first-in-human study evaluated the safety, feasibility, and monitoring of an intracerebral-transplanted magnetic resonance imaging (MRI)-trackable autologous bone marrow stromal cell (HUNS001-01) for patients with subacute ischemic stroke. METHODS: The study included adults with severe disability due to ischemic stroke. HUNS001-01 cultured with human platelet lysates and labeled with superparamagnetic iron oxide was stereotactically transplanted into the peri-infarct area 47-64 days after ischemic stroke onset (dose: 2 or 5 × 107 cells). Neurological and radiographic evaluations were performed throughout 1 year after cell transplantation. The trial was registered at UMIN Clinical Trial Registry (number UMIN000026130). FINDINGS: All seven patients who met the inclusion criteria successfully achieved cell expansion, underwent intracerebral transplantation, and completed 1 year of follow-up. No product-related adverse events were observed. The median National Institutes of Health Stroke Scale and modified Rankin scale scores before transplantation were 13 and 4, which showed improvements of 1-8 and 0-2, respectively. Cell tracking proved that the engrafted cells migrated toward the infarction border area 1-6 months after transplantation, and the quantitative susceptibility mapping revealed that cell signals at the migrated area constantly increased throughout the follow-up period up to 34% of that of the initial transplanted site. CONCLUSIONS: Intracerebral transplantation of HUNS001-01 was safe and well tolerated. Cell tracking shed light on the therapeutic mechanisms of intracerebral transplantation. FUNDING: This work was supported by the Japan Agency for Medical Research and Development (AMED; JP17bk0104045 and JP20bk0104011).

Protocol for treating lumbar spinal canal stenosis with a combination of ultrapurified, allogenic bone marrow-derived mesenchymal stem cells and in situ-forming gel: a multicentre, prospective, double-blind randomised controlled trial.

INTRODUCTION: In patients with combined lumbar spinal canal stenosis (LSCS), a herniated intervertebral disc (IVD) that compresses the dura mater and nerve roots is surgically treated with discectomy after laminoplasty. However, defects in the IVD after discectomy may lead to inadequate tissue healing and predispose patients to the development of IVD degeneration. Ultrapurified stem cells (rapidly expanding clones (RECs)), combined with an in situ-forming bioresorbable gel (dMD-001), have been developed to fill IVD defects and prevent IVD degeneration after discectomy. We aim to investigate the safety and efficacy of a new treatment method in which a combination of REC and dMD-001 is implanted into the IVD of patients with combined LSCS. METHODS AND ANALYSIS: This is a multicentre, prospective, double-blind randomised controlled trial. Forty-five participants aged 20-75 years diagnosed with combined LSCS will be assessed for eligibility. After performing laminoplasty and discectomy, participants will be randomised 1:1:1 into the combination of REC and dMD-001 (REC-dMD-001) group, the dMD-001 group or the laminoplasty and discectomy alone (control) group. The primary outcomes of the trial will be the safety and effectiveness of the procedure. The effectiveness will be assessed using visual analogue scale scores of back pain and leg pain as well as MRI-based estimations of morphological and compositional quality of the IVD tissue. Secondary outcomes will include self-assessed clinical scores and other MRI-based estimations of compositional quality of the IVD tissue. All evaluations will be performed at baseline and at 1, 4, 12, 24 and 48 weeks after surgery. ETHICS AND DISSEMINATION: This study was approved by the ethics committees of the institutions involved. We plan to conduct dissemination of the outcome data by presenting our data at national and international conferences, as well as through formal publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: jRCT2013210076.

Intravenous transplantation of amnion-derived mesenchymal stem cells promotes functional recovery and alleviates intestinal dysfunction after spinal cord injury.

Spinal cord injury (SCI) is often accompanied by gastrointestinal dysfunction due to the disconnection of the spinal autonomic nervous system. Gastrointestinal dysfunction reportedly upregulates intestinal permeability, leading to bacterial translocation of the gut microbiome to the systemic circulation, which further activates systemic inflammation, exacerbating neuronal damage. Mesenchymal stem cells (MSC) reportedly ameliorate SCI. Here, we aimed to investigate their effect on the associated gastrointestinal dysfunction. Human amnion-derived MSC (AMSCs) were intravenously transplanted one day after a rat model of midthoracic SCI. Biodistribution of transplanted cells, behavioral assessment, and histological evaluations of the spinal cord and intestine were conducted to elucidate the therapeutic effect of AMSCs. Bacterial translocation of the gut microbiome was examined by in situ hybridization and bacterial culture of the liver. Systemic inflammations were examined by blood cytokines, infiltrating immune cells in the spinal cord, and the size of the peripheral immune tissue. AMSCs released various neurotrophic factors and were mainly distributed in the liver and lung after transplantation. AMSC-transplanted animals showed smaller spinal damage and better neurological recovery with preserved neuronal tract. AMSCs transplantation ameliorated intestinal dysfunction both morphologically and functionally, which prevented translocation of the gut microbiome to the systemic circulation. Systemic inflammations were decreased in animals receiving AMSCs in the chronic phase. Intravenous AMSC administration during the acute phase of SCI rescues both spinal damage and intestinal dysfunction. Reducing bacterial translocation may contribute to decreasing systemic inflammation.

Prediction of Hypoxia in Brain Tumors Using a Multivariate Model Built from MR Imaging and 18F-Fluorodeoxyglucose Accumulation Data.

PURPOSE: The aim of this study was to generate a multivariate model using various MRI markers of blood flow and vascular permeability and accumulation of 18F-fluorodeoxyglucose (FDG) to predict the extent of hypoxia in an 18F-fluoromisonidazole (FMISO)-positive region. METHODS: Fifteen patients aged 27-74 years with brain tumors (glioma, n = 13; lymphoma, n = 1; germinoma, n = 1) were included. MRI scans were performed using a 3T scanner, and dynamic contrast-enhanced (DCE) perfusion and arterial spin labeling images were obtained. Ktrans and Vp maps were generated using the DCE images. FDG and FMISO positron emission tomography scans were also obtained. A model for predicting FMISO positivity was generated on a voxel-by-voxel basis by a multivariate logistic regression model using all the MRI parameters with and without FDG. Receiver-operating characteristic curve analysis was used to detect FMISO positivity with multivariate and univariate analysis of each parameter. Cross-validation was performed using the leave-one-out method. RESULTS: The area under the curve (AUC) was highest for the multivariate prediction model with FDG (0.892) followed by the multivariate model without FDG and univariate analysis with FDG and Ktrans (0.844 for all). In cross-validation, the multivariate model with FDG had the highest AUC (0.857 ± 0.08) followed by the multivariate model without FDG (0.834 ± 0.119). CONCLUSION: A multivariate prediction model created using blood flow, vascular permeability, and glycometabolism parameters can predict the extent of hypoxia in FMISO-positive areas in patients with brain tumors.

Brain Structure, Connectivity, and Cognitive Changes Following Revascularization Surgery in Adult Moyamoya Disease.

BACKGROUND: The effect of the combined direct/indirect revascularization surgery in Moyamoya disease has not been evaluated sufficiently with regard to cognitive function, brain microstructure, and connectivity. OBJECTIVE: To investigate structural and functional changes following revascularization surgery in patients with moyamoya disease (MMD) through a combined analysis of brain morphology, microstructure, connectivity, and neurobehavioral data. METHODS: Neurobehavioral and neuroimaging examinations were performed in 25 adults with MMD prior to and >12 mo after revascularization surgery. Cognitive function was investigated using the Wechsler Adult Intelligence Scale-III, Trail-Making Test, Wisconsin Card Sorting Test, Continuous Performance Test, Stroop test, and Wechsler Memory Scale. We assessed white matter integrity using diffusion tensor imaging, brain morphometry using magnetization-prepared rapid gradient-echo sequences, and brain connectivity using resting-state functional magnetic resonance imaging (MRI). RESULTS: Cognitive examinations revealed significant changes in the full-scale intelligence quotient (IQ), performance IQ (PIQ), perceptual organization (PO), processing speed, and Stroop test scores after surgery (P < .05). Enlargement of the lateral ventricle, volume reductions in the corpus callosum and subcortical nuclei, and cortical thinning in the prefrontal cortex were also observed (P < .05). Fractional anisotropy in the white matter tracts, including the superior longitudinal fasciculus, increased 2 to 4 yr after surgery, relative to that observed in the presurgical state (P < .05). Resting-state brain connectivity was increased predominantly in the fronto-cerebellar circuit and was positively correlated with improvements in PIQ and PO (P < .05). CONCLUSION: Revascularization surgery may improve processing speed and attention in adult patients with MMD. Further, multimodal MRI may be useful for detecting subtle postsurgical brain structural changes, reorganization of white matter tracts, and brain connectivity alterations.

The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies.

BACKGROUND: To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML). METHODS: The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI). RESULTS: The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10- 1 vs. 2.53 ± 0.12 × 10- 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10- 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10- 1). CONCLUSIONS: Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Radiomics and radiogenomics for precision radiotherapy.

Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.

Noninvasive electrical conductivity measurement by MRI: a test of its validity and the electrical conductivity characteristics of glioma.

OBJECTIVES: This study noninvasively examined the electrical conductivity (σ) characteristics of diffuse gliomas using MRI and tested its validity. METHODS: MRI including a 3D steady-state free precession (3D SSFP) sequence was performed on 30 glioma patients. The σ maps were reconstructed from the phase images of the 3D SSFP sequence. The σ histogram metrics were extracted and compared among the contrast-enhanced (CET) and noncontrast-enhanced tumour components (NCET) and normal brain parenchyma (NP). Difference in tumour σ histogram metrics among tumour grades and correlation of σ metrics with tumour grades were tested. Validity of σ measurement using this technique was tested by correlating the mean tumour σ values measured using MRI with those measured ex vivo using a dielectric probe. RESULTS: Several σ histogram metrics of CET and NCET of diffuse gliomas were significantly higher than NP (Bonferroni-corrected p ≤ .045). The maximum σ of NCET showed a moderate positive correlation with tumour grade (r = .571, Bonferroni-corrected p = .018). The mean tumour σ measured using MRI showed a moderate positive correlation with the σ measured ex vivo (r = .518, p = .040). CONCLUSIONS: Tissue σ can be evaluated using MRI, incorporation of which may better characterise diffuse gliomas. KEY POINTS: • This study tested the validity of noninvasive electrical conductivity measurements by MRI. • This study also evaluated the electrical conductivity characteristics of diffuse glioma. • Gliomas have higher electrical conductivity values than the normal brain parenchyma. • Noninvasive electrical conductivity measurement can be helpful for better characterisation of glioma.

Mapping altered brain connectivity and its clinical associations in adult moyamoya disease: A resting-state functional MRI study.

Detection of subtle ischemic injuries in moyamoya disease may enable optimization of timing of revascularization surgery, and could potentially improve functional outcomes. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study functional organization of the brain, but it remains unclear whether rs-fMRI could elucidate distinct characteristics in moyamoya disease. Here, we aimed to determine changes in a conventional rs-fMRI measure and analyze any associations with clinical symptoms and cerebral hemodynamics. Thirty-one adults with moyamoya disease and 25 adult controls underwent rs-fMRI, in which we measured brain connectivity via temporal correlations of low-frequency BOLD signals. We identified the extent of between-group differences with multivoxel pattern analysis. Seed-based analysis was performed to determine associations with vascular lesions, symptoms, and regional cerebral blood flow (rCBF). There was significantly altered connectivity in the precentral gyrus, operculo-insular region, precuneus, cingulate cortex, and middle frontal gyrus in moyamoya disease. There was reduced connectivity in the left insula, left precuneus, right precentral, and right middle frontal regions, which form part of the salience, default mode, motor, and central executive networks, respectively. Patients with ischemic motor-related symptoms showed significantly decreased connectivity in precentral homotopic regions compared with those without, while there were no differences in vascular lesions or rCBF. Connectivity between the right occipital and left hippocampus was significantly associated with cognitive performance and posterior cerebral artery involvement. Our results demonstrate distinct alterations in the temporal correlations of low-frequency BOLD signals, predominantly in resting-state networks in moyamoya disease. Additionally, rs-fMRI measures were associated with ischemic motor-related symptoms and cognitive performance in the patients. Thus, rs-fMRI may offer a useful non-invasive method of acquiring additional information beyond cerebral perfusion as part of clinical investigations in patients with moyamoya disease.

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients.

We assessed parameters of advanced diffusion weighted imaging (DWI) models for the prediction of the tumor growth rate in 55 head and neck squamous cell carcinoma (HNSCC) patients. The DWI acquisition used single-shot spin-echo echo-planar imaging with 12 b-values (0-2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, tri-exponential, stretched exponential and diffusion kurtosis imaging models. We directly measured the tumor growth rate from two sets of different-date imaging data. We divided the patients into a discovery group (n = 40) and validation group (n = 15) based on their MR acquisition dates. In the discovery group, we performed univariate and multivariate regression analyses to establish the multiple regression equation for the prediction of the tumor growth rate using diffusion parameters. The equation obtained with the discovery group was applied to the validation group for the confirmation of the equation's accuracy. After the univariate and multivariate regression analyses in the discovery-group patients, the estimated tumor growth rate equation was established by using the significant parameters of intermediate diffusion coefficient D2 and slow diffusion coefficient D3 obtained by the tri-exponential model. The discovery group's correlation coefficient between the estimated and directly measured tumor growth rates was 0.74. In the validation group, the correlation coefficient (r = 0.66) and intra-class correlation coefficient (0.65) between the estimated and directly measured tumor growth rates were respectively good. In conclusion, advanced DWI model parameters can be a predictor for determining HNSCC patients' tumor growth rate.

Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma.

OBJECTIVE: To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics. METHODS: We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data. RESULTS: On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002). CONCLUSION: Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information. KEY POINTS: • High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.

Topographic changes in cerebral blood flow and reduced white matter integrity in the first 2 weeks following revascularization surgery in adult moyamoya disease.

OBJECTIVE After revascularization surgery, hyperperfusion and ischemia are associated with morbidity and mortality in adult moyamoya disease (MMD). However, structural changes within the brain following revascularization surgery, especially in the early postsurgical period, have not been thoroughly studied. Such knowledge may enable improved monitoring and clinical management of hyperperfusion and ischemia in MMD. Thus, the objective of this study was to investigate the topographic and temporal profiles of cerebral perfusion and related white matter microstructural changes following revascularization surgery in adult MMD. METHODS The authors analyzed 20 consecutive surgeries performed in 17 adults. Diffusion imaging in parallel with serial measurements of regional cerebral blood flow (rCBF) using SPECT was performed. Both voxel-based and region-of-interest analyses were performed, comparing neuroimaging parameters of postoperative hemispheres with those of preoperative hemispheres at 4 different time points within 2 weeks after surgery. RESULTS Voxel-based analysis showed a distinct topographic pattern of cerebral perfusion, characterized by increased rCBF in the basal ganglia for the first several days and gradually increased rCBF in the lateral prefrontal cortex over 1 week (p < 0.001). Decreased rCBF was also observed in the lateral prefrontal cortex, occipital lobe, and cerebellum contralateral to the surgical hemisphere (p < 0.001). Reduced fractional anisotropy (FA) and axial diffusivity (AD), as well as increased radial diffusivity (RD), were demonstrated in both the anterior and posterior limbs of the internal capsule (p < 0.001). Diffusion parameters demonstrated the greatest changes in both FA and RD on Days 1-2 and in AD on Days 3-6; FA, RD, and AD recovered to preoperative levels on Day 14. Patients with transient neurological deteriorations (TNDs), as compared with those without, demonstrated greater increases in rCBF in both the lateral prefrontal cortex and striatum as well as smaller FAs in the posterior limb of the internal capsule (p < 0.05). CONCLUSIONS The excessively increased rCBF and the recovery process were heterogeneous across brain regions, demonstrating a distinct topographic pattern during the initial 2 weeks following revascularization surgery in MMD. Temporary impairments in the deep white matter tract and immediate postoperative ischemia were also identified. The study results characterized postoperative brain perfusion as well as the impact of revascularization surgery on the brain microstructure. Notably, rCBF and white matter changes correlated to TNDs, suggesting that these changes represent potential neuroimaging markers for tracking tissue structural changes associated with hyperperfusion during the acute postoperative period following revascularization surgery for MMD.

Prediction of the treatment outcome using intravoxel incoherent motion and diffusional kurtosis imaging in nasal or sinonasal squamous cell carcinoma patients.

OBJECTIVES: To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) parameters in nasal or sinonasal squamous cell carcinoma (SCC) patients to determine local control/failure. METHODS: Twenty-eight patients were evaluated. MR acquisition used single-shot spin-echo EPI with 12 b-values. Quantitative parameters (mean value, 25th, 50th and 75th percentiles) of IVIM (perfusion fraction f, pseudo-diffusion coefficient D*, and true-diffusion coefficient D), DKI (kurtosis value K, kurtosis corrected diffusion coefficient Dk) and apparent diffusion coefficient (ADC) were calculated. Parameter values at both the pretreatment and early-treatment period, and the percentage change between these two periods were obtained. RESULTS: Multivariate logistic regression analysis: the percentage changes of D (mean, 25th, 50th, 75th), K (mean, 50th, 75th), Dk (mean, 25th, 50th), and ADC (mean, 25th, 50th) were predictors of local control. ROC curve analysis: the parameter with the highest accuracy = the percentage change of D value with the histogram 25th percentile (0.93 diagnostic accuracy). Multivariate Cox regression analyses: the percentage changes of D (mean, 25th, 50th), K (mean, 50th, 75th), Dk (mean, 25th, 50th) and ADC (mean, 25th, 50th) are predictors. CONCLUSIONS: IVIM and DKI parameters, especially the D-value's histogram 25th percentile, are useful for predicting local control. KEY POINTS: • Noninvasive assessment of treatment outcome in SCC patients was achieved using IVIM/DKI. • Several IVIM and DKI parameters can predict the local control. • Especially, the D-value's histogram 25th percentile has high diagnostic accuracy.

Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion.

PURPOSE: We assessed advanced fitting models of diffusion weighted imaging (DWI) in head/neck squamous cell carcinoma (HNSCC) patients to determine the best goodness of fit and correlations among diffusion parameters. We compared these results with those of dynamic contrast-enhanced (DCE) perfusion parameters. MATERIALS AND METHODS: We retrospectively evaluated 32 HNSCC patients (12 sinonasal, 20 pharynx/oral cavity). The DWI acquisition used single-shot spin-echo echo-planar imaging (EPI) with 12 b-values (0-2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, and tri-exponential models, stretched exponential model (SEM) and diffusion kurtosis imaging (DKI) models. We compared each model's goodness of fit using the residual sum of squares (RSS), Akaike Information Criterion (AIC) and Bayesian information criterion (BIC) value. We determined the correlation between each pair of DWI parameters and between each DWI parameter and DCE perfusion parameter. RESULTS: The tri-exponential fit's RSS, AIC and BIC values were significantly smaller than those for bi-exponential fit. The RSS, AIC and BIC values of the SEM fit and DKI fit were significantly smaller than mono-exponential model. Significant correlations were observed in 30 pairs (sinonasal cavity) and 31 (sinonasal cavity group) among 91 DWI parameter combinations. Significant correlations were also observed in nine pairs (both sinonasal cavity and pharynx/oral cavity group) among 64 DWI/DCE perfusion parameter pairs, in particular, high positive correlations between the tri-exponential model's intermediate diffusion fraction (f2) and the volume of the extracellular extravascular space per unit volume of tissue (ve) were observed in both patient groups. CONCLUSION: We identified several correlations between DWI parameters by advanced fitting models and correlations between DWI and DCE parameters. These will help determine HNSCC patients' detailed tissue structures.

Comparison between borderline ovarian tumors and carcinomas using semi-automated histogram analysis of diffusion-weighted imaging: focusing on solid components.

PURPOSE: This study aimed to evaluate whether histogram analysis of the apparent diffusion coefficient (ADC) of a solid tumor component could distinguish borderline ovarian tumors from ovarian carcinoma. MATERIALS AND METHODS: Sixteen pathologically proven borderline tumors and 21 carcinomas were retrospectively examined. Magnetic resonance (1.5-T) image data sets were coregistered, and the solid components of each tumor were semiautomatically segmented. ADC histograms of the solid components were extracted; modes, minimums, means, and 10th, 25th, 50th, 75th, and 90th percentiles of the histograms were compared between the two tumor types, and receiver-operating characteristic (ROC) analysis was performed. RESULTS: The mode, minimum, mean, 10th, 25th, 50th, and 75th percentile ADC values of solid components of borderline tumors were significantly larger than those of carcinomas. Among these, the 10th percentile values had the lowest p value (p = 0.0003). At ROC analysis, the area under the curve (AUC) in the 10th percentile was the greatest (0.854), and the best cutoff value in the 10th percentile provided the highest specificity (93.8 %). CONCLUSIONS: ADC histograms of solid tumor components facilitated the distinction between borderline ovarian tumors and carcinoma. The 10th percentile ADC values had the best diagnostic performance.

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

PURPOSE: To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis. MATERIALS AND METHODS: This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. RESULTS: The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409). CONCLUSION: The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma.

Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.

OBJECTIVES: To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers. MATERIALS AND METHODS: In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation. RESULTS: Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement. CONCLUSIONS: Considering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

Diagnostic value of tumor blood flow and its histogram analysis obtained with pCASL to differentiate sinonasal malignant lymphoma from squamous cell carcinoma.

OBJECTIVES: To investigate the diagnostic value of tumor blood flow (TBF) obtained with pseudo-continuous arterial spin labeling (pCASL) for the differentiation of squamous cell carcinoma (SCC) and malignant lymphoma (ML) in the nasal or sinonasal cavity. METHODS: Thirty-three patients with SCC and 6 patients with ML in the nasal or sinonasal cavity were retrospectively analyzed. Quantitative TBF values were obtained using whole-tumor region of interest (ROI) from pCASL data. The histogram analysis of TBF values within the tumor ROI was also performed by calculating the coefficient of variation (CV), kurtosis, and skewness. The mean TBF value, histogram CV, kurtosis and skewness of the patients with SCC were compared with those of the ML patients. The diagnostic accuracy to differentiate SCC from ML was also calculated by receiver operating characteristic (ROC) curve analysis. In addition, multiple logistic regression models were also performed to determine their independent predictive value, and diagnostic accuracy with the combined use of these parameters. RESULTS: Between the SCC and ML groups, significant differences were observed in mean TBF, CV, and kurtosis, but not in skewness. In ROC curve analysis, the diagnostic accuracy values for the differentiation of SCC from ML in mean TBF, CV, and kurtosis were all 0.87, respectively. Multiple logistic regression models revealed TBF and CV were respectively independent predictive value. With the combination of these parameters, the diagnostic accuracy was elevated to 0.97. CONCLUSIONS: The TBF value and its histogram analysis obtained with pCASL can help differentiate SCC and ML.