Perfusion estimation using synthetic MRI-based measurements and a porous media flow model.

The measurement of perfusion and filtration of blood in biological tissue give rise to important clinical parameters used in diagnosis, follow-up, and therapy. In this paper, we address techniques for perfusion analysis using processed contrast agent concentration data from dynamic MRI acquisitions. A new methodology for analysis is evaluated and verified using synthetic data generated on a tissue geometry.

Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer.

BACKGROUND: In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI-based radiomic tumor profiling may aid in preoperative risk-stratification and support clinical treatment decisions in EC. PURPOSE: To develop MRI-based whole-volume tumor radiomic signatures for prediction of aggressive EC disease. STUDY TYPE: Retrospective. POPULATION: A total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30). FIELD STRENGTH/SEQUENCE: Axial oblique T1 -weighted gradient echo volumetric interpolated breath-hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection. ASSESSMENT: Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically-verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high-grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area. STATISTICAL TESTS: Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT ) and validation (AUCV ) cohorts. Progression-free survival was assessed using the Kaplan-Meier and Cox proportional hazard model. RESULTS: The whole-tumor radiomic signatures yielded AUCT /AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high-grade (E3) tumor. Single-slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUCT to the whole-tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUCT for E3 tumors (P < 0.05). All of the whole-tumor radiomic signatures significantly predicted poor progression-free survival with hazard ratios of 4.6-9.8 (P < 0.05 for all). DATA CONCLUSION: MRI-based whole-tumor radiomic signatures yield medium-to-high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model.

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.

Dynamic contrast-enhanced MRI measurement of renal function in healthy participants.

Background High repeatability, accuracy, and precision for renal function measurements need to be achieved to establish renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a clinically useful diagnostic tool. Purpose To investigate the repeatability, accuracy, and precision of DCE-MRI measured renal perfusion and glomerular filtration rate (GFR) using iohexol-GFR as the reference method. Material and Methods Twenty healthy non-smoking volunteers underwent repeated DCE-MRI and an iohexol-GFR within a period of 10 days. Single-kidney (SK) MRI measurements of perfusion (blood flow, Fb) and filtration (GFR) were derived from parenchymal intensity time curves fitted to a two-compartment filtration model. The repeatability of the SK-MRI measurements was assessed using coefficient of variation (CV). Using iohexol-GFR as reference method, the accuracy of total MR-GFR was determined by mean difference (MD) and precision by limits of agreement (LoA). Results SK-Fb (MR1, 345 ± 84; MR2, 371 ± 103 mL/100 mL/min) and SK-GFR (MR1, 52 ± 14; MR2, 54 ± 10 mL/min/1.73 m2) measurements achieved a repeatability (CV) in the range of 15-22%. With reference to iohexol-GFR, MR-GFR was determined with a low mean difference but high LoA (MR1, MD 1.5 mL/min/1.73 m2, LoA [-42, 45]; MR2, MD 6.1 mL/min/1.73 m2, LoA [-26, 38]). Eighty percent and 90% of MR-GFR measurements were determined within ± 30% of the iohexol-GFR for MR1 and MR2, respectively. Conclusion Good repeatability of SK-MRI measurements and good agreement between MR-GFR and iohexol-GFR provide a high clinical potential of DCE-MRI for renal function assessment. A moderate precision in MR-derived estimates indicates that the method cannot yet be used in clinical routine.

Intercellular transfer of transferrin receptor by a contact-, Rab8-dependent mechanism involving tunneling nanotubes.

Intercellular communication between cancer cells, especially between cancer and stromal cells, plays an important role in disease progression. We examined the intercellular transfer of organelles and proteins in vitro and in vivo and the role of tunneling nanotubes (TNTs) in this process. TNTs are membrane bridges that facilitate intercellular transfer of organelles of unclear origin. Using 3-dimensional quantitative and qualitative confocal microscopy, we showed that TNTs contain green fluorescent protein (GFP)-early endosome antigen (EEA) 1, GFP Rab5, GFP Rab11, GFP Rab8, transferrin (Tf), and Tf receptor (Tf-R) fused to mCherry (Tf-RmCherry). Tf-RmCherry was transferred between cancer cells by a contact-dependent but secretion-independent mechanism. Live cell imaging showed TNT formation preceding the transfer of Tf-RmCherry and involving the function of the small guanosine triphosphatase (GTPase) Rab8, which colocalized with Tf-RmCherry in the TNTs and was cotransferred to acceptor cells. Tf-RmCherry was transferred from cancer cells to fibroblasts, a noteworthy finding that suggests that this process occurs between tumor and stromal cells in vivo. We strengthened this hypothesis in a xenograft model of breast cancer using enhanced (e)GFP-expressing mice. Tf-RmCherry transferred from tumor to stromal cells and this process correlated with an increased opposite transfer of eGFP from stromal to tumor cells, together pointing toward complex intercellular communication at the tumor site.

Quantification of Single-Kidney Function and Volume in Living Kidney Donors Using Dynamic Contrast-Enhanced MRI.

OBJECTIVE: The objective of our study was to investigate whether dynamic contrast-enhanced MRI (DCE-MRI) can detect differences and potential adaption in single-kidney parenchymal volume, blood flow, glomerular filtration rate (GFR), and filtration fraction in the remaining kidney of healthy donors compared with nondonors. Further, we evaluated the agreement in donor GFRs measured using DCE-MRI versus serum clearance of iohexol. SUBJECTS AND METHODS: Twenty living kidney donors and 20 healthy control subjects underwent DCE-MRI and iohexol GFR. Renal parenchymal volume was assessed from maximum-signal-intensity maps. Single-kidney MRI measurements of blood flow and GFR were derived from parenchymal signal intensity-time curves fitted to a two-compartment filtration model. The Student t test, Pearson correlation coefficient, mean differences, and limits of agreement were applied to analyze MRI measurements between groups and agreement with iohexol GFR. RESULTS: MRI findings showed significantly higher blood flow (difference in mean values of donors vs control subjects, 54%; p = 0.001), GFR (78%, p < 0.0001), and renal parenchymal volume (65%, p < 0.0001) in the single kidney of donors compared with the single kidney of healthy control subjects. In the donors, a proportional increase in blood flow and GFR resulted in a comparable filtration fraction, as was observed in the control subjects. Significant correlations were found between MRI-derived GFR and parenchymal volume (p < 0.0016) as well as with iohexol GFR (p < 0.0001). The mean difference between MRI-derived GFR and iohexol GFR was 14.0 mL/min, and the limits of agreement between MRI-derived GFR and iohexol GFR were -24.1 and 52.1 mL/min. CONCLUSION: DCE-MRI-derived values for single-kidney function and volume in kidney donors were significantly higher than those in control subjects and suggest a future potential benefit of DCE-MRI for diagnostic and prognostic structural and functional assessments in living kidney donors.

Use of 3D DCE-MRI for the estimation of renal perfusion and glomerular filtration rate: an intrasubject comparison of FLASH and KWIC with a comprehensive framework for evaluation.

OBJECTIVE. The purpose of this article is to compare two 3D dynamic contrast-enhanced (DCE) MRI measurement techniques for MR renography, a radial k-space weighted image contrast (KWIC) sequence and a cartesian FLASH sequence, in terms of intrasubject differences in estimates of renal functional parameters and image quality characteristics. SUBJECTS AND METHODS. Ten healthy volunteers underwent repeated breath-hold KWIC and FLASH sequence examinations with temporal resolutions of 2.5 and 2.8 seconds, respectively. A two-compartment model was used to estimate MRI-derived perfusion parameters and glomerular filtration rate (GFR). The latter was compared with the iohexol GFR and the estimated GFR. Image quality was assessed using a visual grading characteristic analysis of relevant image quality criteria and signal-to-noise ratio calculations. RESULTS. Perfusion estimates from FLASH were closer to literature reference values than were the KWIC sequences. In relation to the iohexol GFR (mean [± SD], 103 ± 11 mL/min/1.73 m(2)), KWIC produced significant underestimations and larger bias in GFR values (mean, 70 ± 30 mL/min/1.73 m(2); bias = -33.2 mL/min/1.73 m(2)) compared with the FLASH GFR (110 ± 29 mL/min/1.73 m(2); bias = 6.4 mL/min/1.73 m(2)). KWIC was statistically significantly (p < 0.005) more impaired by artifacts than was FLASH (AUC = 0.18). The average signal-enhancement ratio (delta ratio) in the cortex was significantly lower for KWIC (delta ratio = 0.99) than for FLASH (delta ratio = 1.40). Other visually graded image quality characteristics and signal-to-noise ratio measurements were not statistically significantly different. CONCLUSION. Using the same postprocessing scheme and pharmacokinetic model, FLASH produced more accurate perfusion and filtration parameters than did KWIC compared with clinical reference methods. Our data suggest an apparent relationship between image quality characteristics and the degree of stability in the numeric model-based renal function estimates.

Flow cytometry-based functional selection of RNA interference triggers for efficient epi-allelic analysis of therapeutic targets.

BACKGROUND: The dose-response relationship is a fundamental pharmacological parameter necessary to determine therapeutic thresholds. Epi-allelic hypomorphic analysis using RNA interference (RNAi) can similarly correlate target gene dosage with cellular phenotypes. This however requires a set of RNAi triggers empirically determined to attenuate target gene expression to different levels. RESULTS: In order to improve our ability to incorporate epi-allelic analysis into target validation studies, we developed a novel flow cytometry-based functional screening approach (CellSelectRNAi) to achieve unbiased selection of shRNAs from high-coverage libraries that knockdown target gene expression to predetermined levels. Employing a Gaussian probability model we calculated that knockdown efficiency is inferred from shRNA sequence frequency profiles derived from sorted hypomorphic cell populations. We used this approach to generate a hypomorphic epi-allelic cell series of shRNAs to reveal a functional threshold for the tumor suppressor p53 in normal and transformed cells. CONCLUSION: The unbiased CellSelectRNAi flow cytometry-based functional screening approach readily provides an epi-allelic series of shRNAs for graded reduction of target gene expression and improved phenotypic validation.

Quantitative lung ventilation using Fourier decomposition MRI; comparison and initial study.

OBJECTIVE: The Fourier decomposition (FD) method is a noninvasive method for assessing ventilation and perfusion-related information in the lungs, but the lack of quantifiable values is a drawback. We demonstrate a novel technique for quantification of the FD ventilation maps, compare it to two published methods, and show results from both healthy volunteers and patients diagnosed with lung cancer. MATERIALS AND METHODS: We quantified the standard FD ventilation images by utilizing additional information, i.e., the zero-frequency component image, which is also obtained from the Fourier analysis. This image acts as a baseline for the changes recorded in the FD ventilation image and can therefore be used to calculate the ventilation. Using this technique, we compared the ventilation values from ten healthy volunteers and ten patients to two previously published methods for quantitative ventilation assessment. RESULTS: All methods showed good overall agreement (mean difference between the methods was 14-38 ml/min). The mean minute ventilation for the FD method was calculated to be 693 ml/min for a 2D slice, which is in the expected range. CONCLUSION: The zero-frequency component image can be used as a baseline to quantify the FD ventilation maps. Our initial study showed good agreement with published methods in healthy volunteers, but less so in patients with lung cancer.

Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.

Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).

Registration of FA and T1-weighted MRI data of healthy human brain based on template matching and normalized cross-correlation.

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.

Assessing competence needs for doctors in the emergency department duty rosters: an observational study.

BACKGROUND: The purpose of our investigation is to analyze if emergency epidemiology is randomly variable or predictable. If emergency admissions show a predictable pattern, we can use it for multiple planning purposes, especially defining competence needs for duty roster personnel. METHOD: An observational study of consecutive emergency admissions at Haukeland University Hospital in Bergen over six years. We extracted the discharge diagnoses from our electronic patient record and sorted the patients by diagnoses and frequency. Data were loaded into a Jupyter notebook and presented in form of frequency diagrams. The study population, 213,801 patients, comprises all emergency admissions in need of secondary emergency care from the relevant specialities in the catchment area of our hospital in the western health region of Norway. Patients in need of tertiary care from the whole region are also included. RESULTS: Our analysis shows an annually reproducible distribution pattern regarding type and number of patients. The pattern adhere to an exponential curve that is stable from year to year. An exponential distribution pattern also applies when we sort patients according to the capital letters groups in the ICD 10 system. The same applies if patients are sorted adhering to primarily surgical or medical diagnoses. CONCLUSION: Analysis of the emergency epidemiology of all admitted emergency patients in a defined geographical area gives a solid basis for defining competence needs for duty roster work.

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

BACKGROUND: Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE: To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. STUDY TYPE: Retrospective. POPULATION: CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT: Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS: Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. RESULTS: The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION: Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

Distinct roles of myosin Va in membrane remodeling and exocytosis of secretory granules.

Hormone- and neuropeptide-containing secretory granules (SGs) of neuroendocrine PC12 cells are formed at the trans- Golgi network as immature SGs. These intermediates are converted to mature SGs in a complex maturation process, including matrix condensation, processing of cargo proteins and removal of proteins and membrane in clathrin-coated vesicles. The resulting mature SGs undergo Ca2+-dependent exocytosis upon an appropriate stimulus. We here show that the motor protein myosin Va is implicated in a maturation step of SGs, their binding to F-actin and their stimulated exocytosis. Interference with myosin Va function blocked the removal of the transmembrane protein furin from maturing SGs without affecting condensation and processing of proteins of the SG lumen. Furthermore, the ATP-inhibited binding of SGs to F-actin decreased with progressive maturation and upon interference with myosin Va function. Moreover, the expression of a dominant-negative myosin Va-tail or shRNA-based downregulation of myosin Va interfered with stimulated exocytosis of SGs. In summary,our data suggest an essential function of myosin Va in the membrane remodeling of SGs during maturation and a role in their exocytosis.

Episodic memory of APOE ε4 carriers is correlated with fractional anisotropy, but not cortical thickness, in the medial temporal lobe.

The ε4 allele of apolipoprotein E (apoE, protein; APOE, gene) is the most important genetic risk factor for the development of Alzheimer's disease (AD). Cortical structures in the medial temporal lobe (MTL) are important for memory function and are affected early in AD. Both gray matter (GM) and white matter (WM) structures in the MTL have been reported to display AD related changes in healthy APOE ε4 carriers, but the effects are relatively small and somewhat deviating. Still, there is a lack of studies directly linking structural measures with performance on psychometric tests in ε4+ individuals. We hypothesized that intact WM integrity in the MTL facilitates episodic memory, and predicted a higher correlation between WM integrity and memory performance in APOE ε4 carriers due to a possible limiting effect of WM microstructure. In the present study of 92 healthy (MMSE>27) participants we acquired T1 3D and DTI images from a 1.5T MRI scanner, and tested the participants with California Verbal Learning Test II (CVLT-II). The study had two main aims: 1) to relate verbal memory performance to entorhinal WM (EWM) integrity in APOE ε4 carriers and non-carriers, and 2) to investigate APOE ε4 effects on EWM and EC thickness. We observed a strong, positive correlation between FA in the EWM and memory performance, which was driven solely by APOE ε4 carriers. These effects were significant while controlling for age, sex, EWM volume and EC thickness. Although EC thickness was significantly reduced in ε4 carriers, we did not find a relationship between EC thickness and memory performance. Thus, increased susceptibility of the WM structures underpinning the entorhinal-hippocampal network, offers a plausible explanation for the earlier onset of cognitive decline previously reported in APOE ε4 carriers.

Modeling a 3-D multiscale blood-flow and heat-transfer framework for realistic vascular systems.

Modeling of biological domains and simulation of biophysical processes occurring in them can help inform medical procedures. However, when considering complex domains such as large regions of the human body, the complexities of blood vessel branching and variation of blood vessel dimensions present a major modeling challenge. Here, we present a Voxelized Multi-Physics Simulation (VoM-PhyS) framework to simulate coupled heat transfer and fluid flow using a multi-scale voxel mesh on a biological domain obtained. In this framework, flow in larger blood vessels is modeled using the Hagen-Poiseuille equation for a one-dimensional flow coupled with a three-dimensional two-compartment porous media model for capillary circulation in tissue. The Dirac distribution function is used as Sphere of Influence (SoI) parameter to couple the one-dimensional and three-dimensional flow. This blood flow system is coupled with a heat transfer solver to provide a complete thermo-physiological simulation. The framework is demonstrated on a frog tongue and further analysis is conducted to study the effect of convective heat exchange between blood vessels and tissue, and the effect of SoI on simulation results.

Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer.

Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.

Cortico-striatal connectivity and cognition in normal aging: a combined DTI and resting state fMRI study.

Resting state fMRI studies have found that cognitive decline in aging is associated with alterations in functional connectivity of distributed neural systems in the brain. While functional connections have been shown to rely on the underlying structural connectivity, direct structural connections have been studied in only a few distributed cortical systems so far. It is well known that subcortical nuclei have structural connections to the entire cortex. We hypothesized that structural subcortico-cortical connections may provide integral routes for communication between cortical resting state networks, and that changes in the integrity of these connections have a role in cognitive aging. We combined anatomical MRI, diffusion tensor MRI, and resting state fMRI in 100 healthy elderly to identify fiber bundles connecting cortical resting state networks to subcortical nuclei. In identified tracts, white matter fiber bundle integrity measures were compared to composite cognitive measures on executive function, processing speed, and memory performance. The integrity (FA values) in selected fiber bundles correlated strongly with cognitive measures on executive function and processing speed. Correlation was most pronounced between executive function and fiber bundles connecting the putamen to the dorsal attention network (r=0.73, p<0.001). Our findings show that unique cortico-subcortical fiber bundles can be identified for a range of cortical resting state networks, and indicate that these connections play an important role in cortical resting state network communication and cognition.

Automated segmentation of endometrial cancer on MR images using deep learning.

Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, [Formula: see text]). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, [Formula: see text], [Formula: see text], and [Formula: see text]). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

A radiogenomics application for prognostic profiling of endometrial cancer.

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.