Regulatory T Cells in Tumor-Associated Tertiary Lymphoid Structures Suppress Anti-tumor T Cell Responses.

Infiltration of regulatory T (Treg) cells into many tumor types correlates with poor patient prognoses. However, mechanisms of intratumoral Treg cell function remain to be elucidated. We investigated Treg cell function in a genetically engineered mouse model of lung adenocarcinoma and found that Treg cells suppressed anti-tumor responses in tumor-associated tertiary lymphoid structures (TA-TLSs). TA-TLSs have been described in human lung cancers, but their function remains to be determined. TLSs in this model were spatially associated with >90% of tumors and facilitated interactions between T cells and tumor-antigen-presenting dendritic cells (DCs). Costimulatory ligand expression by DCs and T cell proliferation rates increased in TA-TLSs upon Treg cell depletion, leading to tumor destruction. Thus, we propose that Treg cells in TA-TLSs can inhibit endogenous immune responses against tumors, and targeting these cells might provide therapeutic benefit for cancer patients.

Quadriceps muscle quality and quantity following tibial plateau fracture repair.

PURPOSE: To investigate the qualitative and quantitative changes seen in quadriceps muscles [QM] following tibial plateau fracture and surgery. METHODS: A consecutive series of patients with an isolated tibial plateau fracture presenting to a single academic center were enrolled and prospectively followed. Bilateral knee MRIs were performed preoperatively and 3 and 12 months postoperatively to assess quantity and quality of the quadriceps muscles. All patients underwent tibial plateau operative repair and were made non-weight-bearing for 10 weeks postoperatively then advanced to weight-bearing as tolerated. Functional status assessed via the short musculoskeletal functional assessment (SMFA); knee range of motion [ROM]; vastus medialis oblique [VMO] and vastus lateralis [VL] muscle quantity (axial width, cross sectional area [CSA] and volume) on injured and contralateral limb; VMO, sartorius, semi-membranous and biceps femoris [BF] muscle quality (fat and water content, and proton density fat fraction). All muscle quantitative and qualitative measurements were compared across all time points. RESULTS: Ten patients were included in the final analysis, 6 males and 4 females, with average age of 43.62 ± 16.3 years. While the VMO and VL axial width and CSA were significantly decreased at 3 months preoperatively, this was not statistically significant. There was no significant difference between any QM quantitative measurements at any time points. There was no difference in fat content, water content or PDFF at any time point for the VMO, sartorius, semi-membranous and BF muscles. Regression analysis also showed no association between 12-month SMFA scores and knee ROM with VMO/VL CSA at 1 year. CONCLUSIONS: QM quantity and quality do not significantly change at 3 months and 1 year postoperatively following tibial plateau fracture surgery. LEVEL OF EVIDENCE: Prognostic Level II.

Age-Dependent Changes in Knee Cartilage T1 , T2 , and T1p Simultaneously Measured Using MRI Fingerprinting.

BACKGROUND: Magnetic resonance fingerprinting (MRF) techniques have been recently described for simultaneous multiparameter cartilage mapping of the knee although investigation of their ability to detect early cartilage degeneration remains limited. PURPOSE: To investigate age-dependent changes in knee cartilage T1 , T2 , and T1p relaxation times measured using a three-dimensional (3D) MRF sequence in healthy volunteers. STUDY TYPE: Prospective. SUBJECTS: The study group consisted of 24 healthy asymptomatic human volunteers (15 males with mean age 34.9 ± 14.4 years and 9 females with mean age 44.5 ± 13.1 years). FIELD STRENGTH/SEQUENCE: A 3.0 T gradient-echo-based 3D-MRF sequence was used to simultaneously create proton density-weighted images and T1 , T2 , and T1p maps of knee cartilage. ASSESSMENT: Mean global cartilage and regional cartilage (lateral femur, lateral tibia, medial femur, medial tibia, and patella) T1 , T2 , and T1ρ relaxation times of the knee were measured. STATISTICAL TESTS: Kruskal-Wallis tests were used to compared cartilage T1 , T2 , and T1ρ relaxation times between different age groups, while Spearman correlation coefficients was used to determine the association between age and cartilage T1 , T2 , and T1ρ relaxation times. The value of P < 0.05 was considered statistically significant. RESULTS: Higher age groups showed higher global and regional cartilage T1 , T2 , and T1ρ . There was a significant difference between age groups in global cartilage T2 and T1ρ but no significant difference (P = 0.13) in global cartilage T1. Significant difference was also present between age groups in cartilage T2 and T1ρ for medial femur cartilage and medial tibia cartilage. There were significant moderate correlations between age and T2 and T1ρ for global cartilage (R2  = 0.63-0.64), medial femur cartilage (R2  = 0.50-0.56), and medial tibia cartilage (R2  = 0.54-0.66). CONCLUSION: Cartilage T2 and T1p relaxation times simultaneously measured using a 3D-MRF sequence in healthy volunteers showed age-dependent changes in knee cartilage, primarily within the medial compartment.

3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T.

Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1 , T2, T1ρ relaxation times, and B1 + were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.

Simultaneous bilateral T1 , T2 , and T1ρ relaxation mapping of the hip joint with magnetic resonance fingerprinting.

Quantitative MRI can detect early biochemical changes in cartilage, but its bilateral use in clinical routines is challenging. The aim of this prospective study was to demonstrate the feasibility of magnetic resonance fingerprinting for bilateral simultaneous T1 , T2 , and T1ρ mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T1 , T2 , and T1ρ relaxation components were assessed in femoral lateral, superolateral, and superomedial, and inferior, as well as acetabular, superolateral, and superomedial subregions in left and right hip cartilage. Aligned ranked nonparametric factorial analysis was used to assess the side's impact on the subregions. Kruskal-Wallis and Wilcoxon tests were used to compare subregions, and coefficient of variation to assess repeatability. Global averages of T1 (676.0 ± 45.4 and 687.6 ± 44.5 ms), T2 (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T1ρ (38.2 ± 5.5 and 38.2 ± 5.5 ms) were measured in the left and right hip, and articular cartilage, respectively. The Kruskal-Wallis test showed a significant difference between different subregions' relaxation times regardless of the hip side (p < 0.001 for T1 , p = 0.012 for T2 , and p < 0.001 for T1ρ ). The Wilcoxon test showed that T1 of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CVrms of 1%, 2%, and 4% for T1 , T2 , and T1ρ, respectively. It was concluded that bilateral T1 , T2 , and T1ρ relaxation times, as well as B1+ maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.

Fatty Acid Composition of Proximal Femur Bone Marrow Adipose Tissue in Subjects With Systemic Lupus Erythematous Using 3 T Magnetic Resonance Spectroscopy.

BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic, inflammatory disease with common musculoskeletal manifestations, notably reductions in bone quality. Bone marrow adipose tissue composition and quantity has been previously linked to bone quality and may play a role in SLE pathophysiology but has not been thoroughly studied. PURPOSE: To use magnetic resonance spectroscopy (MRS) to investigate bone marrow adipose tissue quantity and composition in proximal femur subregions of untreated SLE patients compared to controls and treated patients. STUDY TYPE: Prospective. SUBJECTS: A total of 64 female subjects: 28 SLE, 15 glucocorticoid (GC)-treated SLE and 21 matched controls. FIELD STRENGTH/SEQUENCE: Stimulated echo acquisition mode (STEAM) sequence at 3 T. ASSESSMENT: MRS was performed at multiple echo times in the femoral neck and trochanter regions and fatty acids (FA) composition was computed. STATISTICAL TESTS: Intergroup comparisons were carried out using ANOVA. A P value < 0.05 was considered statistically significant. RESULTS: SLE patients had significantly higher saturated FA compared to controls in both the femoral neck (+0.12) and trochanter (+0.11), significantly lower monounsaturated FA in the trochanter compared to controls (-0.05), and significantly lower polyunsaturated FA in the femoral neck compared to both controls (-0.07) and SLE patients on GC therapy (-0.05). DATA CONCLUSION: SLE patients have altered proximal femur marrow fat metabolism, which may reflect a manifestation of, or play a role in, the altered inflammatory response of these patients. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Methods for conducting trends analysis: roadmap for comparing outcomes from three national HIV Population-based household surveys in Kenya (2007, 2012, and 2018).

BACKGROUND: For assessing the HIV epidemic in Kenya, a series of independent HIV indicator household-based surveys of similar design can be used to investigate the trends in key indicators relevant to HIV prevention and control and to describe geographic and sociodemographic disparities, assess the impact of interventions, and develop strategies. We developed methods and tools to facilitate a robust analysis of trends across three national household-based surveys conducted in Kenya in 2007, 2012, and 2018. METHODS: We used data from the 2007 and 2012 Kenya AIDS Indicator surveys (KAIS 2007 and KAIS 2012) and the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA 2018). To assess the design and other variables of interest from each study, variables were recoded to ensure that they had equivalent meanings across the three surveys. After assessing weighting procedures for comparability, we used the KAIS 2012 nonresponse weighting procedure to revise normalized KENPHIA weights. Analyses were restricted to geographic areas covered by all three surveys. The revised analysis files were then merged into a single file for pooled analysis. We assessed distributions of age, sex, household wealth, and urban/rural status to identify unexpected changes between surveys. To demonstrate how a trend analysis can be carried out, we used continuous, binary, and time-to-event variables as examples. Specifically, temporal trends in age at first sex and having received an HIV test in the last 12 months were used to demonstrate the proposed analytical approach. These were assessed with respondent-specific variables (age, sex, level of education, and marital status) and household variables (place of residence and wealth index). All analyses were conducted in SAS 9.4, but analysis files were created in Stata and R format to support additional analyses. RESULTS: This study demonstrates trends in selected indicators to illustrate the approach that can be used in similar settings. The incidence of early sexual debut decreased from 11.63 (95% CI: 10.95-12.34) per 1,000 person-years at risk in 2007 to 10.45 (95% CI: 9.75-11.2) per 1,000 person-years at risk in 2012 and to 9.58 (95% CI: 9.08-10.1) per 1,000 person-years at risk in 2018. HIV-testing rates increased from 12.6% (95% CI: 11.6%-13.6%) in 2007 to 56.1% (95% CI: 54.6%-57.6%) in 2012 but decreased slightly to 55.6% [95% CI: 54.6%-56.6%) in 2018. The decrease in incidence of early sexual debut could be convincingly demonstrated between 2007 and 2012 but not between 2012 and 2018. Similarly, there was virtually no difference between HIV Testing rates in 2012 and 2018. CONCLUSIONS: Our approach can be used to support trend comparisons for variables in HIV surveys in low-income settings. Independent national household surveys can be assessed for comparability, adjusted as appropriate, and used to estimate trends in key indicators. Analyzing trends over time can not only provide insights into Kenya's progress toward HIV epidemic control but also identify gaps.

Clinical and Laboratory Findings in Patients With Potential Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reinfection, May-July 2020.

BACKGROUND: We investigated patients with potential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection in the United States during May-July 2020. METHODS: We conducted case finding for patients with potential SARS-CoV-2 reinfection through the Emerging Infections Network. Cases reported were screened for laboratory and clinical findings of potential reinfection followed by requests for medical records and laboratory specimens. Available medical records were abstracted to characterize patient demographics, comorbidities, clinical course, and laboratory test results. Submitted specimens underwent further testing, including reverse transcription polymerase chain reaction (RT-PCR), viral culture, whole genome sequencing, subgenomic RNA PCR, and testing for anti-SARS-CoV-2 total antibody. RESULTS: Among 73 potential reinfection patients with available records, 30 patients had recurrent coronavirus disease 2019 (COVID-19) symptoms explained by alternative diagnoses with concurrent SARS-CoV-2 positive RT-PCR, 24 patients remained asymptomatic after recovery but had recurrent or persistent RT-PCR, and 19 patients had recurrent COVID-19 symptoms with concurrent SARS-CoV-2 positive RT-PCR but no alternative diagnoses. These 19 patients had symptom recurrence a median of 57 days after initial symptom onset (interquartile range: 47-76). Six of these patients had paired specimens available for further testing, but none had laboratory findings confirming reinfections. Testing of an additional 3 patients with recurrent symptoms and alternative diagnoses also did not confirm reinfection. CONCLUSIONS: We did not confirm SARS-CoV-2 reinfection within 90 days of the initial infection based on the clinical and laboratory characteristics of cases in this investigation. Our findings support current Centers for Disease Control and Prevention (CDC) guidance around quarantine and testing for patients who have recovered from COVID-19.

A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium.

Identification of chemicals that affect hormone-regulated systems will help to predict endocrine disruption. In our previous study, a 46 gene biomarker was found to be an accurate predictor of estrogen receptor (ER) α modulation in chemically treated MCF-7 cells. Here, potential ERα modulators were identified using the biomarker by screening a microarray compendium consisting of ∼1600 gene expression comparisons representing exposure to ∼1200 chemicals. A total of ∼170 chemicals were identified as potential ERα modulators. In the Connectivity Map 2.0 collection, 75 and 39 chemicals were predicted to activate or suppress ERα, and they included 12 and six known ERα agonists and antagonists/selective ERα modulators, respectively. Nineteen and eight of the total number were also identified as active in an ERα transactivation assay carried out in an MCF-7-derived cell line used to screen the Tox21 10K chemical library in agonist or antagonist modes, respectively. Chemicals predicted to modulate ERα in MCF-7 cells were examined further using global and targeted gene expression in wild-type and ERα-null cells, transactivation assays, and cell-free ERα coregulator interaction assays. Environmental chemicals classified as weak and very weak agonists were confirmed to activate ERα including apigenin, kaempferol, and oxybenzone. Novel activators included digoxin, nabumetone, ivermectin, and six progestins. Novel suppressors included emetine, mifepristone, niclosamide, and proscillaridin. Our strategy will be useful to identify environmentally relevant ERα modulators in future high-throughput transcriptomic screens.

Countries with delayed COVID-19 introduction - characteristics, drivers, gaps, and opportunities.

BACKGROUND: Three months after the first reported cases, COVID-19 had spread to nearly 90% of World Health Organization (WHO) member states and only 24 countries had not reported cases as of 30 March 2020. This analysis aimed to 1) assess characteristics, capability to detect and monitor COVID-19, and disease control measures in these 24 countries, 2) understand potential factors for the reported delayed COVID-19 introduction, and 3) identify gaps and opportunities for outbreak preparedness, particularly in low and middle-income countries (LMICs). We collected and analyzed publicly available information on country characteristics, COVID-19 testing, influenza surveillance, border measures, and preparedness activities in these countries. We also assessed the association between the temporal spread of COVID-19 in all countries with reported cases with globalization indicator and geographic location. RESULTS: Temporal spreading of COVID-19 was strongly associated with countries' globalization indicator and geographic location. Most of the 24 countries with delayed COVID-19 introduction were LMICs; 88% were small island or landlocked developing countries. As of 30 March 2020, only 38% of these countries reported in-country COVID-19 testing capability, and 71% reported conducting influenza surveillance during the past year. All had implemented two or more border measures, (e.g., travel restrictions and border closures) and multiple preparedness activities (e.g., national preparedness plans and school closing). CONCLUSIONS: Limited testing capacity suggests that most of the 24 delayed countries may have lacked the capability to detect and identify cases early through sentinel and case-based surveillance. Low global connectedness, geographic isolation, and border measures were common among these countries and may have contributed to the delayed introduction of COVID-19 into these countries. This paper contributes to identifying opportunities for pandemic preparedness, such as increasing disease detection, surveillance, and international collaborations. As the global situation continues to evolve, it is essential for countries to improve and prioritize their capacities to rapidly prevent, detect, and respond, not only for COVID-19, but also for future outbreaks.

Metal artifacts of hip arthroplasty implants at 1.5-T and 3.0-T: a closer look into the B1 effects.

OBJECTIVE: To evaluate the effect of circular polarization (CP) and elliptical polarization (EP) of the B1 field on metal implant-induced artifacts of titanium (Ti) and cobalt-chromium (CoCr) hip arthroplasty implants at 1.5-T and 3.0-T field strengths. MATERIAL AND METHODS: In vitro Ti and CoCr total hip arthroplasty implants were evaluated using high transmit and receive bandwidth turbo spin echo (HBW-TSE) and slice encoding for metal artifact correction (SEMAC) metal artifact reduction techniques. Each technique was implemented at 1.5-T, which only allows for CP of B1 field as the system default, as well as 3.0-T, which permitted CP and EP. Manual segmentation quantified the size of the metal artifacts at the level of the acetabular cup, femoral neck, and femoral shaft. RESULTS: In the acetabular cup and femoral neck, 1.5-T CP achieved smaller artifact sizes than 3.0-T CP (28-29% on HBW-TSE, p = 0.002-0.005; 17-34% on SEMAC, p = 0.019-0.102) and 3.0-T EP (25-28% on HBW-TSE, p = 0.010-0.011; 14-36% on SEMAC, p = 0.058-0.135) techniques. In the femoral stem region, 3.0-T EP achieved more efficient artifact suppression than 3.0-T CP (HBW-TSE 44-45%, p < 0.001-0.022; SEMAC 76-104%, p < 0.001-0.022) and 1.5-T CP (HBW-TSE 76-96%, p < 0.001-0.003; SEMAC 138-173%, p = 0.003-0.005) techniques. CONCLUSION: Despite slightly superior metal reduction ability of the 1.5-T in the region of the acetabular cup and prosthesis neck, 3.0-T MRI of hip arthroplasty implants using elliptically polarized RF pulses may overall be more effective in reducing metal artifacts than the current standard 1.5-T MRI techniques, which by default implements circularly polarized RF pulses.

Current status of functional MRI of osteoarthritis for diagnosis and prognosis.

PURPOSE OF REVIEW: Osteoarthritis is a major source of disability, pain and socioeconomic cost worldwide. The epidemiology of the disorder is multifactorial including genetic, biological and biomechanical components, some of them detectable by MRI. This review provides the most recent update on MRI biomarkers which can provide functional information of the joint structures for diagnosis, prognosis and treatment response monitoring in osteoarthritis trials. RECENT FINDINGS: Compositional or functional MRI can provide clinicians with valuable information on glycosaminoglycan content (chemical exchange saturation transfer, sodium MRI, T1ρ) and collagen organization (T2, T2, apparent diffusion coefficient, magnetization transfer) in joint structures. Other parameters may also provide useful information, such as volumetric measurements of joint structures or advanced image data postprocessing and analysis. Automated tools seem to have a great potential to be included in these efforts providing standardization and acceleration of the image data analysis process. SUMMARY: Functional or compositional MRI has great potential to provide noninvasive imaging biomarkers for osteoarthritis. Osteoarthritis as a whole joint condition needs to be diagnosed in early stages to facilitate selection of patients into clinical trials and/or to measure treatment effectiveness. Advanced evaluation including machine learning, neural networks and multidimensional data analysis allow for wall-to-wall understanding of parameter interactions and their role in clinical evaluation of osteoarthritis.

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative.

Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.

MR fingerprinting for rapid simultaneous T1 , T2 , and T1ρ relaxation mapping of the human articular cartilage at 3T.

PURPOSE: To implement a novel technique for simultaneous, quantitative multiparametric mapping of the knee articular cartilage. METHODS: A novel MR fingerprinting pulse sequence is proposed and implemented for simultaneous measurements of proton density, T1 , T2, and T1ρ relaxation times at 3T. The repeatability and reproducibility of the proposed technique were assessed in model phantoms. Institutional review board-approved MR fingerprinting imaging sequence was performed on healthy volunteers and patients with mild knee osteoarthritis. The Wilcoxon test was used to compare healthy controls and patients. The intra- and intersubject repeatability were assessed with coefficient of variation and the RMS coefficient of variation, respectively RESULTS: The Bland-Altman plots demonstrated an average difference of 4.67 ms, -0.09 ms, and 0.05 ms between 2 scans in the same scanner; and 9.68 ms, 0.29 ms, and -0.72 ms between the scans acquired on 2 different scanners for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with RMS coefficient of variation less than 3%, 6%, and 5% for T1 , T2 , and T1ρ , respectively. The Wilcoxon test showed a significant difference between control and mild osteoarthritis patients for T1 (P = .04), T2 (P = .01), and T1ρ (P = .02) relaxation time in medial tibial cartilage, as well as for T2 relaxation time (P = .02) in medial femoral cartilage. CONCLUSION: The proposed MRF sequence is fast and can simultaneously measure the T1 , T2 , T1ρ , and B1+ maps in a single scan. It is able to discriminate between mild osteoarthritis patients and healthy volunteers.

Artificial intelligence, osteoporosis and fragility fractures.

PURPOSE OF REVIEW: Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images. RECENT FINDINGS: Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that has been published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays. SUMMARY: New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.

3D-T1ρ prepared zero echo time-based PETRA sequence for in vivo biexponential relaxation mapping of semisolid short-T2 tissues at 3 T.

BACKGROUND: In addition to the articular cartilage, osteoarthritis (OA) affects several other tissues such as tendons, ligaments, and subchondral bone. T1ρ relaxation study of these short T2 tissues may provide a more comprehensive evaluation of OA. PURPOSE: To develop a 3D spin-lattice relaxation in the rotating frame (T1ρ ) prepared zero echo time (ZTE)-based pointwise encoding time reduction with radial acquisition (3D-T1ρ -PETRA) sequence for relaxation mapping of semisolid short-T2 tissues on a clinical 3 T scanner. STUDY TYPE: Prospective. POPULATION: Phantom, two bovine whole knee joint and Achilles tendon specimens, 10 healthy volunteers with no known inflammation, trauma or pain in the knee or ankle. FIELD STRENGTH/SEQUENCE: A customized PETRA sequence to acquire fat-suppressed 3D T1ρ -weighted images tissues with semisolid short T2 / T2* relaxation times in the knee and ankle joints at 3 T. ASSESSMENT: Mono- and biexponential T1ρ relaxation components were assessed in the patellar tendon (PT), anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and Achilles tendon (AT). STATISTICAL TESTS: Kruskal-Wallis with post-hoc Dunn's test for multiple pairwise comparisons. RESULTS: Phantom and ex vivo studies showed the feasibility of T1ρ relaxation mapping using the proposed 3D-T1ρ -PETRA sequence. The in vivo study demonstrated an averaged mono-T1ρ relaxation of (median [IQR]) 15.9 [14.5] msec, 23.6 [9.4] msec, 17.4 [7.4] msec, and 5.8 [10.2] msec in the PT, ACL, PCL, and AT, respectively. The bicomponent analysis showed the short and long components (with their relative fractions) of 0.65 [1.0] msec (46.9 [15.3]%) and 37.3 [18.4] msec (53.1 [15.3]%) for PT, 1.7 [2.1] msec (42.5 [12.5]%) and 43.7 [17.8] msec (57.5 [12.5]%) for ACL, and 1.2 [1.9] msec (42.6 [14.0]%) and 27.7 [14.7] msec (57.3 [14.0]%) for PCL and 0.4 [0.02] msec (58.8 [13.3]%/) and 31.3 [10.8] msec (41.2 [13.3]%) for AT. Statistically significant (P ≤ 0.05) differences were observed in the mono- and biexponential relaxation between several regions. DATA CONCLUSION: The 3D-T1ρ -PETRA sequence allows volumetric, isotropic (0.78 × 0.78 × 0.78 mm), biexponential T1ρ assessment with corresponding fractions of the tissues with semisolid short T2 / T2* . LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1207-1218.

Biexponential T1ρ relaxation mapping of human knee menisci.

BACKGROUND: Measuring T1ρ in the knee menisci can potentially be used as noninvasive biomarkers in detecting early-stage osteoarthritis (OA). PURPOSE: To demonstrate the feasibility of biexponential T1ρ relaxation mapping of human knee menisci. STUDY TYPE: Prospective. POPULATION: Eight healthy volunteers with no known inflammation, trauma, or pain in the knee and three symptomatic subjects with early knee OA. FIELD STRENGTH/SEQUENCE: Customized Turbo-FLASH sequence to acquire 3D-T1ρ -weighted images on a 3 T MRI scanner. ASSESSMENT: T1ρ relaxation values were assessed in 11 meniscal regions of interest (ROIs) using monoexponential and biexponential models. STATISTICAL TESTS: Nonparametric rank-sum tests, Kruskal-Wallis test, and coefficient of variation. RESULTS: The mean monoexponential T1ρ relaxation in the lateral menisci were 28.05 ± 4.2 msec and 37.06 ± 10.64 msec for healthy subjects and early knee OA patients, respectively, while the short and long components were 8.07 ± 0.5 msec and 72.35 ± 3.2 msec for healthy subjects and 2.63 ± 2.99 msec and 55.27 ± 24.76 msec for early knee OA patients, respectively. The mean monoexponential T1ρ relaxation in the medial menisci were 34.30 ± 3.8 msec and 37.26 ± 11.38 msec for healthy and OA patients, respectively, while the short and long components were 7.76 ± 0.7 msec and 72.19 ± 4.2 msec for healthy subjects and 3.06 ± 3.24 msec and 55.27 ± 24.59 msec for OA patients, respectively. Statistically significant (P ≤ 0.05) differences were observed in the monoexponential relaxation between some of the ROIs. The T1ρ,short was significantly lower (P = 0.02) in the patients than controls. The rmsCV% ranges were 1.51-16.6%, 3.59-14.3%, and 4.91-15.6% for T1ρ -mono, T1ρ -short, and T1ρ -long, respectively. DATA CONCLUSION: Our results showed that in all ROIs, T1ρ relaxation times of outer zones (red zones) were less than inner zones (white zones). Monoexponential T1ρ was increased in medial, lateral, and body menisci of early OA while the biexponential numbers were decreased in early OA patients. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:824-835.

Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data.

BACKGROUND: A current challenge in osteoporosis is identifying patients at risk of bone fracture. PURPOSE: To identify the machine learning classifiers that predict best osteoporotic bone fractures and, from the data, to highlight the imaging features and the anatomical regions that contribute most to prediction performance. STUDY TYPE: Prospective (cross-sectional) case-control study. POPULATION: Thirty-two women with prior fragility bone fractures, of mean age = 61.6 and body mass index (BMI) = 22.7 kg/m2 , and 60 women without fractures, of mean age = 62.3 and BMI = 21.4 kg/m2 . Field Strength/ Sequence: 3D FLASH at 3T. ASSESSMENT: Quantitative MRI outcomes by software algorithms. Mechanical and topological microstructural parameters of the trabecular bone were calculated for five femoral regions, and added to the vector of features together with bone mineral density measurement, fracture risk assessment tool (FRAX) score, and personal characteristics such as age, weight, and height. We fitted 15 classifiers using 200 randomized cross-validation datasets. Statistical Tests: Data: Kolmogorov-Smirnov test for normality. Model Performance: sensitivity, specificity, precision, accuracy, F1-test, receiver operating characteristic curve (ROC). Two-sided t-test, with P < 0.05 for statistical significance. RESULTS: The top three performing classifiers are RUS-boosted trees (in particular, performing best with head data, F1 = 0.64 ± 0.03), the logistic regression and the linear discriminant (both best with trochanteric datasets, F1 = 0.65 ± 0.03 and F1 = 0.67 ± 0.03, respectively). A permutation of these classifiers comprised the best three performers for four out of five anatomical datasets. After averaging across all the anatomical datasets, the score for the best performer, the boosted trees, was F1 = 0.63 ± 0.03 for All-features dataset, F1 = 0.52 ± 0.05 for the no-MRI dataset, and F1 = 0.48 ± 0.06 for the no-FRAX dataset. Data Conclusion: Of many classifiers, the RUS-boosted trees, the logistic regression, and the linear discriminant are best for predicting osteoporotic fracture. Both MRI and FRAX independently add value in identifying osteoporotic fractures. The femoral head, greater trochanter, and inter-trochanter anatomical regions within the proximal femur yielded better F1-scores for the best three classifiers. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1029-1038.

3T chemical shift-encoded MRI: Detection of altered proximal femur marrow adipose tissue composition in glucocorticoid users and validation with magnetic resonance spectroscopy.

BACKGROUND: Osteoporosis (OP) results in weak bone and can ultimately lead to fracture. Drugs such as glucocorticoids can also induce OP (glucocorticoid-induced osteoporosis [GIO]). Bone marrow adipose tissue composition and quantity may play a role in OP pathophysiology, but has not been thoroughly studied in GIO compared to primary OP. PURPOSE/HYPOTHESIS: Chemical shift-encoded (CSE) MRI allows detection of subregional differences in bone marrow adipose tissue composition and quantity in the proximal femur of GIO compared to OP subjects and has high agreement with the reference standard of magnetic resonance spectroscopy (MRS). STUDY TYPE: Prospective. SUBJECTS: In all, 18 OP and 13 GIO subjects. FIELDS STRENGTH: 3T. SEQUENCE: Multiple gradient-echo, stimulated echo acquisition mode (STEAM). ASSESSMENT: Subjects underwent CSE-MRI in the proximal femurs, and for each parametric map regions of interest (ROIs) were assessed in the femoral head (fHEAD), femoral neck (fNECK), Ward's triangle (fTRIANGLE), and the greater trochanter (GTROCH). In addition, we compared CSE-MRI against the reference standard of MRS performed in the femoral neck and Ward's triangle. STATISTICAL TESTS: Differences between OP/GIO were investigated using the Mann-Whitney nonparametric test. Bland-Altman methodology was used to assess measurement agreement between CSE-MRI and MRS. RESULTS: GIO compared with OP subjects demonstrated: decreased monounsaturated fat fraction (MUFA) (-2.1%, P < 0.05) in fHEAD; decreased MUFA (-3.8%, P < 0.05), increased saturated fat fraction (SFA) (5.5%, P < 0.05), and decreased T2* (-3.8 msec, P < 0.05) in fNECK; decreased proton density fat fraction (PDFF) (-15.1%, P < 0.05), MUFA (-9.8%, P < 0.05), polyunsaturated fat fraction (PUFA) (-1.8%, P < 0.01), increased SFA (11.6%, P < 0.05), and decreased T2* (-5.4 msec, P < 0.05) in fTRIANGLE; and decreased T2* (-1.5 msec, P < 0.05) in GTROCH. There was high measurement agreement between MRI and MRS using the Bland-Altman test. DATA CONCLUSION: 3T CSE-MRI may allow reliable assessment of subregional bone marrow adipose tissue (bMAT) quantity and composition in the proximal femur in a clinically reasonable scan time. Glucocorticoids may alter the lipid profile of bMAT and potentially result in reduced bone quality. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:490-496.

Caspase-2-mediated cleavage of Mdm2 creates a p53-induced positive feedback loop.

Caspase-2 is an evolutionarily conserved caspase, yet its biological function and cleavage targets are poorly understood. Caspase-2 is activated by the p53 target gene product PIDD (also known as LRDD) in a complex called the Caspase-2-PIDDosome. We show that PIDD expression promotes growth arrest and chemotherapy resistance by a mechanism that depends on Caspase-2 and wild-type p53. PIDD-induced Caspase-2 directly cleaves the E3 ubiquitin ligase Mdm2 at Asp 367, leading to loss of the C-terminal RING domain responsible for p53 ubiquitination. As a consequence, N-terminally truncated Mdm2 binds p53 and promotes its stability. Upon DNA damage, p53 induction of the Caspase-2-PIDDosome creates a positive feedback loop that inhibits Mdm2 and reinforces p53 stability and activity, contributing to cell survival and drug resistance. These data establish Mdm2 as a cleavage target of Caspase-2 and provide insight into a mechanism of Mdm2 inhibition that impacts p53 dynamics upon genotoxic stress.