Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.

PURPOSE: Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions. METHODS: A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects. RESULTS: The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar. CONCLUSIONS: The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.

MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee Osteoarthritis Incidence.

Background Infrapatellar fat pad (IPFP) quality has been implicated as a marker for predicting knee osteoarthritis (KOA); however, no valid quantification for subtle IPFP abnormalities has been established. Purpose To investigate whether MRI-based three-dimensional texture analysis of IPFP abnormalities could help predict incident radiographic KOA. Materials and Methods In this prospective nested case-control study, 690 participants whose knees were at risk for KOA were included from the Pivotal Osteoarthritis Initiative MRI Analyses incident osteoarthritis cohort. All knees had a Kellgren-Lawrence grade of 1 or less at baseline. During the 4-year follow-up, case participants were matched 1:1 to control participants, with incident radiographic KOA as the outcome. MRI scans were segmented at the incident time point of KOA (hereafter, P0), 1 year before P0 (hereafter, P-1), and baseline. MRI-based three-dimensional texture analysis was performed to extract IPFP texture features. Least absolute shrinkage and selection operator and multivariable logistic regressions were applied in the development cohort and evaluated in the test cohort. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative value of the clinical score, IPFP texture score, and MRI Osteoarthritis Knee Score. Results Participants were allocated to development (n = 500, 340 women; mean age, 60 years) and test (n = 190, 120 women; mean age, 61 years) cohorts. In both cohorts, IPFP texture scores (AUC ≥0.75 for all) showed greater discrimination than clinical scores (AUC ≤0.69 for all) at baseline, P-1, and P0, with significant differences in pairwise comparisons (P ≤ .002 for all). Greater predictive and concurrent validities of IPFP texture scores (AUC ≥0.75 for all) compared with MRI Osteoarthritis Knee Scores (AUC ≤0.66 for all) were also demonstrated (P < .001 for all). Conclusion MRI-based three-dimensional texture of the infrapatellar fat pad was associated with future development of knee osteoarthritis. ClinicalTrials.gov registration no.: NCT00080171 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Fischer in this issue.

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models.

Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.

Patellar Dislocation in Adolescent Patients: Influence on Cartilage Properties Based on T1ρ Relaxation Times.

BACKGROUND: Adolescents who experience a patellar dislocation have an elevated risk of patellofemoral posttraumatic osteoarthritis. Magnetic resonance imaging (MRI)-based T1ρ relaxation times were measured for adolescents to evaluate patellofemoral cartilage after patellar dislocation. Long T1ρ relaxation times are an indicator of cartilage degradation. HYPOTHESIS: The primary hypothesis is that patellofemoral cartilage T1ρ relaxation times will be elevated in the acute phase after patellar dislocation. The secondary hypothesis is that T1ρ relaxation times will be higher for knees with multiple rather than single dislocations due to repeated traumatic injury. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: In total, 23 adolescents being treated for a recent patellar dislocation, 13 for a first-time dislocation (47 ± 38 days since most recent dislocation) and 10 for multiple dislocations (55 ± 24 days since most recent dislocation), and 10 healthy controls participated in MRI-based T1ρ relaxation time mapping. For multiple regions of the patellofemoral joint, mean T1ρ values were compared between the 3 groups with multiple group comparisons and post hoc tests. T1ρ relaxation times were also correlated against measures of patellofemoral anatomy and alignment for single and multiple dislocations. Statistical significance was set at P < .05. RESULTS: T1ρ relaxation times were significantly longer for injured knees (single and multiple dislocations) than controls at the medial and central patella and central trochlear groove. For the regions on the patella, significant differences between injured and control knees exceeded 15%. No significant differences were identified between single and multiple dislocations. For the initial dislocation group, T1ρ relaxation times within multiple regions of the patellofemoral joint were significantly correlated with lateral patellar alignment or patellar height. CONCLUSION: Elevated patellofemoral cartilage T1ρ relaxation times are consistent with a high risk of long-term patellofemoral osteoarthritis for adolescents who experience patellar dislocations. T1ρ relaxation times were elevated for multiple regions of patellofemoral cartilage. T1ρ relaxation times were expected to increase with additional dislocation episodes, but relaxation times after single and multiple dislocations were similar. After a first dislocation, parameters related to patellar maltracking were correlated with cartilage degradation.

Baseline cartilage T1ρ and T2 predicted patellofemoral joint cartilage lesion progression and patient-reported outcomes after ACL reconstruction.

This study aims to determine if baseline T1ρ and T2 will predict cartilage morphological lesion progression in the patellofemoral joint (PFJ) and patient-reported outcomes at 2-year after anterior cruciate ligament (ACL) reconstruction (ACLR). Thirty-nine ACL-injured patients were studied at baseline and two-year after ACLR. 3 T MR T1ρ and T2 images and Knee Injury and Osteoarthritis Outcome Score (KOOS) were acquired at both time points. Voxel-based relaxometry (VBR) technique was used to detect local cartilage abnormalities. Patients were divided into progression and non-progression groups based on changes of the whole-organ magnetic resonance imaging scoring (WORMS) grading of cartilage in PFJ from baseline to 2-year, and into lower (more pain) and higher (less pain) KOOS pain groups based on 2-year KOOS pain scores, separately. Voxel-based analyses of covariance were used to compare T1ρ and T2 values at baseline between the defined groups. Using VBR analysis, the progression group at 2-year showed higher T1ρ and T2 compared with the non-progression group at baseline, with the medial femoral condyle showing the largest areas with significant differences. At two-year, 56% of patients were able to recover with respect to KOOS pain. The lower KOOS pain group at 2-year showed significantly elevated T1ρ and T2 in the patella at baseline compared with the higher KOOS pain group. In conclusion, baseline T1ρ and T2 mapping, combined with VBR analysis, may help identify ACLR patients at high risk of developing progressive PFJ cartilage lesions and worse clinical symptoms 2-year after surgery.

Traditional herbalists and cancer management in Kumasi, Ghana.

Cancer incidence rates are increasing in sub-Saharan Africa where traditional medical practitioners (TMPs) are involved in cancer management. Little is known about the specific role that TMPs play in cancer management in Ghana; we hypothesize that an understanding of the practices of TMPs with regard to cancer patients would help to enhance literacy about cancer amongst TMPs and would contribute to the diagnosis of cancer at earlier stages, by avoiding the detrimental delays while enlisting their help in certain activities that enhance cancer care. To elucidate the nature of the involvement of TMPs in cancer management, we conducted semi-structured interviews with 42 TMPs who practice in Kumasi, Ghana. The interviews elicited information about their knowledge and practices regarding cancer management and interactions with local hospitals. The results showed that TMPs tended to identify cancers as diseases of visible masses, fungating lesions, ulceration, and bleeding reflecting the advanced stages and types of cancers they usually encounter. TMPs identified certain causes of cancer and believed that they can treat and prevent cancer. These results indicate that TMPs are significant health service delivery resources in Ghana for patients potentially affected with cancer. Our work suggests that dedicated efforts to further integrate TMPs into the overall health care system would be beneficial to patients. Future research should examine the role of cancer education and training programs for TMPs to enhance their knowledge, strengthen their ability to complement allopathic practitioners, and increase early detection and treatment efforts through appropriate and timely referrals.

Elevated Patellofemoral and Tibiofemoral T1ρ Relaxation Times Following a First Time Patellar Dislocation.

OBJECTIVE: The study was performed to evaluate cartilage within the knee following a first-time patellar dislocation, using elevated MRI-based T1ρ relaxation times as an indicator of low proteoglycan concentration. The hypothesis is that MRI-based T1ρ relaxation times for patellofemoral and tibiofemoral cartilage are significantly longer for knees being treated for patellar dislocation than for healthy control knees. DESIGN: Twenty-one subjects being treated for a first-time, unilateral dislocation of the patella and 16 healthy controls participated in MRI-based T1ρ relaxation time mapping. Mean relaxation times were quantified for patellofemoral and tibiofemoral regions for injured knees, the contralateral knees, and healthy controls. T1ρ values for each region were compared between the 3 groups with generalized estimating equations. Linear regressions were also performed to correlate T1ρ relaxation times with time from injury. RESULTS: The knees with a disloction had longer T1ρ relaxation times than the contralateral knees and control group at the medial patella and longer relaxation times than the control group at the lateral tibia (P < 0.05). T1ρ relaxation times at the medial patella also decreased with time from injury (r2 = 0.21, P = 0.037). CONCLUSIONS: Compositional changes to cartilage on the medial patella are related to traumatic impact during a dislocation. Potential exists for cartilage properties at the medial patella to improve with time. Cartilage degradation at the lateral tibia is not directly related to traumatic impact. The current baseline data are a starting point to characterize the pathway from a first-time dislocation to progressive cartilage degradation and osteoarthritis.

Severe hyperglycemic hyperosmolar nonketotic coma in a nondiabetic patient receiving aripiprazole.

Aripiprazole is commonly used as an atypical antipsychotic. It has been associated with abnormalities in glucose metabolism. Severe diabetic complications associated with its use occur rarely. We present a case of a patient without a history of diabetes mellitus who presented to the emergency department in severe hyperosmolar nonketotic coma while using aripiprazole.