Search

Abstract Background To cope at their homes, community-dwelling older people surviving a hip fracture need a sufficient amount of functional ability and mobility. There is a lack of evidence on the best practices supporting recovery after hip fracture. The purpose of this article is to describe the design, intervention and demographic baseline results of a study investigating the effects of a rehabilitation program aiming to restore mobility and functional capacity among community-dwelling participants after hip fracture. Methods/Design Population-based sample of over 60-year-old community-dwelling men and women operated for hip fracture (n = 81, mean age 79 years, 78% were women) participated in this study and were randomly allocated into control (Standard Care) and ProMo intervention groups on average 10 weeks post fracture and 6 weeks after discharged to home. Standard Care included written home exercise program with 5-7 exercises for lower limbs. Of all participants, 12 got a referral to physiotherapy. After discharged to home, only 50% adhered to Standard Care. None of the participants were followed-up for Standard Care or mobility recovery. ProMo-intervention included Standard Care and a year-long program including evaluation/modification of environmental hazards, guidance for safe walking, pain management, progressive home exercise program and physical activity counseling. Measurements included a comprehensive battery of laboratory tests and self-report on mobility limitation, disability, physical functional capacity and health as well as assessments for the key prerequisites for mobility, disability and functional capacity. All assessments were performed blinded at the research laboratory. No significant differences were observed between intervention and control groups in any of the demographic variables. Discussion Ten weeks post hip fracture only half of the participants were compliant to Standard Care. No follow-up for Standard Care or mobility recovery occurred. There is a need for rehabilitation and follow-up for mobility recovery after hip fracture. However, the effectiveness of the ProMo program can only be assessed at the end of the study. Trial registration Current Controlled Trials ISRCTN53680197

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.

In pre-clinical models of brain gliomas, Relaxation Along a Fictitious Field in second rotating frame (TRAFF2), continues wave T1rho (T1ρcw), adiabatic T1rho (T1ρadiab), and adiabatic T2rho (T2ρadiab) relaxation time mappings have demonstrated potential to non-invasively characterize brain gliomas. Our aim was to evaluate the feasibility and potential of 4 different spin lock methods at 3T to characterize primary brain glioma. 22 patients (26-72 years) with suspected primary glioma. T1ρcw was performed using pulse peak amplitude of 500Hz and pulse train durations of 40 and 80 ms while the corresponding values for T1ρadiab, T2ρadiab, TRAFF2 were 500/500/500Hz and 48 and 96, 64 and 112, 45 and 90 ms, respectively. The parametric maps were calculated using a monoexponential model. Molecular profiles were evaluated from tissue specimens obtained during the resection. The lesion regions-of-interest were segmented from high intensity FLAIR using automatic segmentation with manual refinement. Statistical descriptors from the voxel intensity values inside each lesion and radiomic features (Pyrad MRC package) were calculated. From extracted radiomics, mRMRe R package version 2.1.0 was used to select 3 features in each modality for statistical comparisons. Of the 22 patients, 10 were found to have IDH-mutant gliomas and of those 5 patients had 1p/19q codeletion group comparisons. Following correction for effects of age and gender, at least one statistical descriptor was able to differentiate between IDH and 1p/19q codeletion status for all the parametric maps. In the radiomic analysis, corner-edge detector features with Harris-Stephens filtered signal showed significant group differences in IDH and 1p/19q codeletion groups. Spin lock imaging at 3T of human glioma was feasible and various qualitative parameters derived from the parametric maps were found to have potential to differentiate IDH and 1p19q codeletion status. Future larger prospective clinical trials are warranted to evaluate these methods further., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Jambor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.

The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a "neurofeedback" group, which performed motor imagery while receiving feedback, and a "control" group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual's ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer's disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigation.

Background: In preclinical models of multiple sclerosis (MS), both adiabatic T 1rho (T 1ρadiab ) and relaxation along a fictitious field (RAFF) imaging have demonstrated potential to noninvasively characterize MS., Purpose: To evaluate the feasibility of whole brain T 1ρadiab and RAFF imaging in healthy volunteers and patients with MS., Study Type: Single institutional clinical trial., Subjects: 38 healthy volunteers (24-69 years) and 21 patients (26-59 years) with MS. Five healthy volunteers underwent a second MR examination performed within 8 days. Clinical disease severity (The Expanded Disability Status Scale [EDSS] and The Multiple Sclerosis Severity Score [MSSS]) was evaluated at baseline and 1-year follow-up (FU)., Field Strength/sequence: RAFF in second rotating frame of reference (RAFF2) was performed at 3 T using 3D-fast-field echo with magnetization preparation, RF amplitude of 11.74 μT while the corresponding value for T 1ρadiab was 13.50 μT. T 1 -, T 2 -, and FLAIR-weighted images were acquired with reconstruction voxel size 1.0 × 1.0 × 1.0 mm 3 ., Assessment: The parametric maps of T 1ρadiab and RAFF2 (T RAFF2 ) were calculated using a monoexponential model. Semi-automatic segmentation of MS lesions, white matter (WM), and gray matter (GM), and WM tracks was performed using T 1 -, T 2 -, and FLAIR-weighted images., Statistical Tests: Regression analysis was used to evaluate correlation of T 1ρadiab and T RAFF2 with age and disease severity while a Friedman test followed by Wilcoxon Signed Rank test for differences between tissue types. Short-term repeatability was evaluated on voxel level., Results: Both T 1ρadiab and T RAFF2 demonstrated good short-term repeatability with relative differences on voxel level in the range of 6.1%-11.9%. Differences in T 1ρadiab and T RAFF2 between the tissue types in MS patients were significant (P < 0.05). T 1ρadiab and T RAFF2 correlated (P < 0.001) with baseline EDSS/MSSM and disease progression at FU (P < 0.001)., Data Conclusion: Whole brain T 1ρadiab and T RAFF2 at 3 T was feasible with significant differences in T 1ρadiab and T RAFF2 values between tissues types and correlation with disease severity., Evidence Level: 1 TECHNICAL EFFICACY: Stage 1., (© 2021 International Society for Magnetic Resonance in Medicine.)

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18 F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADC k , K), mono- (ADC m ), and biexponential functions (f, D p , D f ) while Logan plots were used to calculate volume of distribution (V T ). In total, 16 unique PET (V T , SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for V T was 0.85. T he best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18 F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18 F-FACBC PET derived parameters (V T , SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

Purpose: To evaluate repeatability of prostate DWI-derived radiomics and machine learning methods for prostate cancer (PCa) characterization., Methods: A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b-values (0-2000 s/mm 2 ), post-processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman's ρ, Wilcoxon rank-sum) were evaluated in terms of Scan1-Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split., Results: The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28-0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61-81%. Surface-to-volume ratio and corner detector-based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70., Conclusion: Surface-to-volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features., (© 2019 International Society for Magnetic Resonance in Medicine.)

Background and Purpose: Magnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC., Material and Methods: Pelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results., Results: The AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05)., Conclusion: Fully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Anna Kuisma and Iiro Ranta hold a research grant from Philips MR Therapy Oy with permission to publish. Marko Pesola was and Lizette Warner is employed by Philips MR Therapy Oy (Vantaa, Finland)., (© 2020 The Authors.)

Background: The aim of our prospective, multicenter, randomized, controlled trial (titled M2A-38 Ceramic-on-Metal RCT, NCT00754520) is to demonstrate noninferiority of a ceramic-on-metal (CoM) articulation compared with metal-on-metal (MoM) in total hip arthroplasty. The study arms are at 8 years since implantation, with metal ion and functional score analysis at 5 years., Methods: We recruited 211 patients between 2009 and 2011. The patients were randomized to ceramic or metal. A cohort of these patients had whole blood metal ions performed yearly, and all patients underwent annual radiographic and clinical outcome assessment. All revisions were recorded and some explants were analyzed. Recruitment ceased earlier than planned owing to concerns raised with failure of MoM implants., Results: No significant difference was seen in patient demographics, radiographic parameters, or functional outcomes at any time point. Lower cobalt ion levels were seen in the CoM group (P < .01) at all time points. Chromium levels were significantly lower in the CoM group up to 3 years, but raised at 5 years. There were slightly fewer revisions for adverse reaction to metal debris in the CoM group. Explant analysis suggested a different wear pattern to those seen in the MoM group., Conclusion: The results demonstrated that the CoM articulation behaved the same as the MoM in terms of functional outcome and radiographic parameters. The CoM coupling also demonstrates raised metal ions beyond 3 years and increasing revisions for adverse reaction to metal debris. It remains difficult to see a clinical application for CoM and further exploration or use is not warranted., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: Proximal tibial fractures are typically treated with osteosynthesis. In older patients, this method has been reported to be associated with a high complication rate, risk of post-traumatic osteoarthritis, and long partial or non-weight bearing during the recovery phase. To avoid these problems, primary total knee replacement (TKR) has become an increasingly common treatment option., Methods: Twenty-two patients (mean age 74 years, SD 12) underwent primary TKR due to tibial plateau fracture. Follow-up data were available for a mean of 19 (SD 16) months. Trauma mechanism, fracture classification, type of prosthesis used, complications, and re-operations during the follow-up were recorded. The Knee Society Score (KSS), Oxford Knee Score (OKS), range of flexion, and patient satisfaction were evaluated., Results: 13/22 of the fractures were due to a low-energy trauma. At final follow-up, mean KSS was 160 (SD 39) and mean OKS 27 (SD 11) points. Mean flexion was 109° (SD 16°). 14/17 of the patients were satisfied or highly satisfied with their post-operative knee and 11/17 reported their knee to be same or better than pre-trauma. 2/22 of the patients had complications requiring revision surgery., Conclusion: TKR as a primary definitive method seems to be a useful alternative to osteosynthesis, enabling immediate full weight bearing and rapid mobilization of patients. The risk of complications associated with primary TKR is higher than those reported after TKR due to primary osteoarthritis but lower than those reported after TKR due to secondary osteoarthritis.

Purpose: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2)., Methods: T2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS., Results: In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82-0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments., Conclusion: Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types., Competing Interests: The authors have declared that no competing interests exist.

Background and Purpose: The clinical feasibility of synthetic computed tomography (sCT) images derived from magnetic resonance imaging (MRI) images for external beam radiation therapy (EBRT) planning have been studied and adopted into clinical use recently. This paper evaluates the dosimetric and positioning performance of a sCT approach for different pelvic cancers., Materials and Methods: Seventy-five patients receiving EBRT at Turku University Hospital (Turku, Finland) were enrolled in the study. The sCT images were generated as part of a clinical MRI-simulation procedure. Dose calculation accuracy was assessed by comparing the sCT-based calculation with a CT-based calculation. In addition, we evaluated the patient position verification accuracy for both digitally reconstructed radiograph (DRR) and cone beam computed tomography (CBCT) -based image guidance using a subset of the cohort. Furthermore, the relevance of using continuous Hounsfield unit values was assessed., Results: The mean (standard deviation) relative dose difference in the planning target volume mean dose computed over various cancer groups was less than 0.2 (0.4)% between sCT and CT. Among all groups, the average minimum gamma-index pass-rates were better than 95% with a 2%/2mm gamma-criteria. The difference between sCT- and CT-DRR-based patient positioning was less than 0.3 (1.4) mm in all directions. The registrations of sCT to CBCT produced similar results as compared with CT to CBCT registrations., Conclusions: The use of sCT for clinical EBRT dose calculation and patient positioning in the investigated types of pelvic cancers was dosimetrically and geometrically accurate for clinical use., (© 2019 The Authors.)

The purpose of this study was to assess the effectiveness of the ultrasonography (US) on detecting osteoarthritis of the knee, and compare US and radiographic findings to intraoperative total knee arthroplasty (TKA) findings. Fifty-seven late-stage osteoarthritic knees undergoing TKA were evaluated with US and radiography. Standard knee US assessing femoral cartilage damage, osteophytes, effusion, synovitis, and meniscal extrusion was performed. On radiographs, osteophytes, joint space narrowing, and Kellgren-Lawrence grade were evaluated. Corresponding intra-operative findings were assessed during TKA as the gold standard. On the damage of the medial femoral condyle cartilage, the sensitivity of US was high (92%), whereas on the lateral condyle and sulcus area, sensitivities were 58% and 46%, respectively. On osteophytes, the detection rate of the US was remarkable especially on the medial side yielding sensitivities of 90-95%. The sensitivities for detecting effusion and synovitis were also excellent (97%). US detection rate of femoral cartilage damage was in concordance with the radiographic joint space narrowing. For the detection of osteophytes, US provided superior results to radiography particularly on the medial side. In conclusion, US can reliably assess the late-stage OA changes of the knee especially on the medial side of the knee joint.

Background and purpose - Fast-tracking shortens the length of the primary treatment period (length of stay, LOS) after total knee replacement (TKR). We evaluated the influence of the fast-track concept on the length of uninterrupted institutional care (LUIC) and other outcomes after TKR. Patients and methods - 4,256 TKRs performed in 4 hospitals between 2009-2010 and 2012-2013 were identified from the Finnish Hospital Discharge Register and the Finnish Arthroplasty Register. Hospitals were classified as fast track (Hospital A) and non-fast track (Hospitals B, C and D). We analyzed length of uninterrupted institutional care (LUIC), LOS, discharge destination, readmission, revision, manipulation under anesthesia (MUA) and mortality rate in each hospital. We compared these outcomes for TKRs performed in Hospital A before and after fast-track implementation and we also compared Hospital A outcomes with the corresponding outcomes for the other 3 hospitals. Results - After fast-track implementation, median LOS in Hospital A fell from 5 to 3 days (p < 0.001) and (median) LUIC from 7 to 3 (p < 0.001) days. These reductions in LOS and LUIC were accompanied by an increase in the discharge rate to home (p = 0.01). Fast-tracking in Hospital A led to no increase in 14- and 42-day readmissions, MUA, revision or mortality compared with the rates before fast-tracking, or with those in the other hospitals. Of the 4 hospitals, LOS and LUIC were most reduced in Hospital A. Interpretation - A fast-track protocol reduces LUIC and LOS after TKR without increasing readmission, complication or revision rates.

Purpose: The purpose of this study was to evaluate 18 F-FACBC PET/CT, PET/MRI, and multiparametric MRI (mpMRI) in detection of primary prostate cancer (PCa)., Methods: Twenty-six men with histologically confirmed PCa underwent PET/CT immediately after injection of 369 ± 10 MBq 18 F-FACBC (fluciclovine) followed by PET/MRI started 55 ± 7 min from injection. Maximum standardized uptake values (SUV max ) were measured for both hybrid PET acquisitions. A separate mpMRI was acquired within a week of the PET scans. Logan plots were used to calculate volume of distribution (V T ). The presence of PCa was estimated in 12 regions with radical prostatectomy findings as ground truth. For each imaging modality, area under the curve (AUC) for detection of PCa was determined to predict diagnostic performance. The clinical trial registration number is NCT02002455., Results: In the visual analysis, 164/312 (53%) regions contained PCa, and 41 tumor foci were identified. PET/CT demonstrated the highest sensitivity at 87% while its specificity was low at 56%. The AUC of both PET/MRI and mpMRI significantly (p < 0.01) outperformed that of PET/CT while no differences were detected between PET/MRI and mpMRI. SUV max and V T of Gleason score (GS) >3 + 4 tumors were significantly (p < 0.05) higher than those for GS 3 + 3 and benign hyperplasia. A total of 442 lymph nodes were evaluable for staging, and PET/CT and PET/MRI demonstrated true-positive findings in only 1/7 patients with metastatic lymph nodes., Conclusions: Quantitative 18 F-FACBC imaging significantly correlated with GS but failed to outperform MRI in lesion detection. 18 F-FACBC may assist in targeted biopsies in the setting of hybrid imaging with MRI.

Background and purpose - Fast-track protocols have been successfully implemented in many hospitals as they have been shown to result in shorter length of stay (LOS) without compromising results. We evaluated the effect of fast-track implementation on the use of institutional care and results after total hip replacement (THR). Patients and methods - 3,193 THRs performed in 4 hospitals between 2009-2010 and 2012-2013 were identified from the Finnish Hospital Discharge Register and the Finnish Arthroplasty Register. Hospitals were classified as fast-track (Hospital A) and non-fast-track (Hospitals B, C, and D). We analyzed LOS, length of uninterrupted institutional care (LUIC, including LOS), discharge destination, readmission, revision rate, and mortality in each hospital. We compared these outcomes for THRs performed in Hospital A before and after fast-track implementation and we also compared outcomes, excluding readmission rates, with the corresponding outcomes for the other hospitals. Results - After fast-track implementation, median LOS in Hospital A diminished from 5 to 2 days (p < 0.001) and (median) LUIC from 6 to 3 (p = 0.001) days. No statistically significant changes occurred in discharge destination. However, the reduction in LOS was combined with an increase in the 42-day readmission rate (3.1% to 8.3%) (p < 0.001). A higher proportion of patients were at home 1 week after THR (p < 0.001) in Hospital A after fast-tracking than before. Interpretation - The fast-track protocol reduces LUIC but needs careful implementation to maintain good quality of care throughout the treatment process.