Background: To investigate whether magnetic resonance (MR) radiomic features combined with machine learning may aid in predicting extraprostatic extension (EPE) in high- and non-favorable intermediate-risk patients with prostate cancer. Purpose: To investigate the diagnostic performance of radiomics to detect EPE. Material and Methods: MR radiomic features were extracted from 228 patients, of whom 86 were diagnosed with EPE, using prostate and lesion segmentations. Prediction models were built using Random Forest. Further, EPE was also predicted using a clinical nomogram and routine radiological interpretation and diagnostic performance was assessed for individual and combined models. Results: The MR radiomic model with features extracted from the manually delineated lesions performed best among the radiomic models with an area under the curve (AUC) of 0.74. Radiology interpretation yielded an AUC of 0.75 and the clinical nomogram (MSKCC) an AUC of 0.67. A combination of the three prediction models gave the highest AUC of 0.79. Conclusion: Radiomic analysis combined with radiology interpretation aid the MSKCC nomogram in predicting EPE in high- and non-favorable intermediate-risk patients.

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.

Objective: Chronic kidney disease (CKD) is a serious medical condition characterized by gradual loss of kidney function. Early detection and diagnosis is mandatory for adequate therapy and prognostic improvement. Hence, in the current pilot study we explore the use of image registration methods for detecting renal morphologic changes in patients with CKD. Methods: Ten healthy volunteers and nine patients with presumed CKD underwent dynamic T1 weighted imaging without contrast agent. From real and simulated dynamic time series, kidney deformation fields were estimated using a poroelastic deformation model. From the deformation fields several quantitative parameters reflecting pressure gradients, and volumetric and shear deformations were computed. Eight of the patients also underwent a kidney biopsy as a gold standard. Results: We found that the absolute deformation, normalized volume changes, as well as pressure gradients correlated significantly with arteriosclerosis from biopsy assessments. Furthermore, our results indicate that current image registration methodologies are lacking sensitivity to recover mild changes in tissue stiffness. Conclusion: Image registration applied to dynamic time series correlated with structural renal changes and should be further explored as a tool for invasive measurements of arteriosclerosis. Significance: Under the assumption that the proposed framework can be further developed in terms of sensitivity and specificity, it can provide clinicians with a non-invasive tool of high spatial coverage available for characterization of arteriosclerosis and potentially other pathological changes observed in chronic kidney disease.

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, F_b) 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-F_b (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.

Background: It is uncertain whether T2-weighted Dixon water images (DixonT2w) and short tau inversion recovery (STIR) are interchangeable when evaluating vertebral bone edema, or if one method is superior or visualizes the edema differently. Purpose: To compare image quality and Modic change (MC)-related edema between DixonT2w and STIR and estimate inter-observer reliability for MC edema on DixonT2w. Material and Methods: Consecutive patients (n = 120) considered for the Antibiotics in Modic changes (AIM) trial underwent lumbar 1.5-T magnetic resonance imaging with two-point DixonT2w and STIR. Two radiologists assessed MC-related high-signal lesions on DixonT2w and compared image quality and lesion extent with STIR. Cohen's kappa and mean of differences ± limits of agreement were calculated. Results: Fat suppression and artefacts were similar on DixonT2w and STIR in 116 of 120 (97%) patients. Lesion conspicuity was similar in 88, better on STIR in 10, and better on DixonT2w in 9 of 107 patients with MC-related high-signal lesions. Contrast-to-noise ratio for STIR versus DixonT2w was 19.1 versus 17.1 (mean of differences 2.0 ± 8.2). Of 228 lesions L4-S1, 215 (94%) had similar extent on DixonT2w and STIR, 11 were smaller/undetected on STIR, and two were smaller/undetected on DixonT2w. Lesions missed on STIR (n = 9) or DixonT2w (n = 1) had a weak signal increase on the other sequence (≤17%; 0% = vertebral body, 100% = cerebrospinal fluid). Inter-observer reliability (mean kappa L4-S1) was very good for presence (0.87), moderate for height (0.44), and good for volume (0.63) of lesions on DixonT2w. Conclusion: DixonT2w provided similar visualization of MC-related vertebral edema as STIR.