Aberrant positivity for BCOR immunohistochemistry in merkel cell carcinoma - a potential diagnostic pitfall.

BACKRGOUND: Merkel cell carcinoma (MCC) is a rare, aggressive primary cutaneous neuroendocrine carcinoma, frequently associated with clonal Merkel cell polyomavirus integration. MCC can pose significant diagnostic challenges due to its diverse clinical presentation and its broad histological differential diagnosis. Histologically, MCC presents as a small-round-blue cell neoplasm, where the differential diagnosis includes basal cell carcinoma, melanoma, hematologic malignancies, round cell sarcoma and metastatic small cell carcinoma of any site. We here report strong aberrant immunoreactivity for BCOR in MCC, a marker commonly used to identify round cell sarcomas and other neoplasms with BCOR alterations. METHODS: Based on strong BCOR expression in three index cases of MCC, clinically mistaken as sarcoma, a retrospective analysis of three patient cohorts, comprising 31 MCC, 19 small cell lung carcinoma (SCLC) and 5 cases of neoplasms with molecularly confirmed BCOR alteration was conducted. Immunohistochemical staining intensity and localization for BCOR was semi-quantitatively analyzed. RESULTS: Three cases, clinically and radiologically mimicking a sarcoma were analyzed in our soft tissue and bone pathology service. Histologically, the cases showed sheets of a small round blue cell neoplasm. A broad panel of immunohistochemistry was used for lineage classification. Positivity for synaptophysin, CK20 and Merkel cell polyoma virus large T-antigen lead to the diagnosis of a MCC. Interestingly, all cases showed strong positive nuclear staining for BCOR, which was included for the initial work-up with the clinical differential of a round cell sarcoma. We analyzed a larger retrospective MCC cohort and found aberrant weak to strong BCOR positivity (nuclear and/or cytoplasmic) in up to 90% of the cases. As a positive control, we compared the expression to a small group of BCOR-altered neoplasms. Furthermore, we investigated a cohort of SCLC as another neuroendocrine neoplasm and found in all cases a diffuse moderate to strong BCOR positivity. CONCLUSIONS: This study demonstrates that neuroendocrine neoplasms, such as MCC and SCLC can express strong aberrant BCOR. This might represent a diagnostic challenge or pitfall, in particular when MCC is clinically mistaken as a soft tissue or a bone sarcoma.

Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.

Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.

Reproducible MS/MS library cleaning pipeline in matchms.

Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening.

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.

Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods.

Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.

Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.

Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides.

Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.

Frequently Encountered Artifacts in the Application of Dual-Energy Computed Tomography to Cardiovascular Imaging for Urate Crystals in Gout: A Matched-Control Study.

OBJECTIVE: There is surging interest in using dual-energy computed tomography (DECT) to identify cardiovascular monosodium urate (MSU) deposits in patients with gout. We sought to examine the prevalence and characterization of cardiovascular DECT artifacts using non-electrocardiogram (EKG)-gated DECT pulmonary angiograms. METHODS: We retrospectively reviewed non-EKG-gated DECT pulmonary angiograms performed on patients with and without gout at a single academic center. We noted the presence and locations of vascular green colorization using the default postprocessing two-material decomposition algorithm for MSU. The high- and low-energy grayscale images and advanced DECT measurements were used to determine whether they were true findings or artifacts. We classified artifacts into five categories: streak, contrast medium mixing, misregistration due to motion, foreign body, and noise. RESULTS: Our study included CT scans from 48 patients with gout and 48 age- and sex-matched controls. The majority of patients were male with a mean age of 67 years. Two independent observers attributed all areas of vascular green colorization to artifacts. The most common types of artifacts were streak (56% vs 57% between patients and controls, respectively) and contrast medium mixing (51% vs 65%, respectively). Whereas some of the default DECT measurements of cardiovascular green colorization were consistent with values reported for subcutaneous tophi, advanced DECT measurements were not consistent with that of tophi. CONCLUSION: Artifacts that could be misconstrued as cardiovascular MSU deposits were commonly identified in patients with and without gout on non-EKG-gated DECT pulmonary angiograms. These artifacts can inform future vascular DECT studies on patients with gout to minimize false-positive findings.

A Cluster of Evolutionarily Recent KRAB Zinc Finger Proteins Protects Cancer Cells from Replicative Stress-Induced Inflammation.

Heterochromatin loss and genetic instability enhance cancer progression by favoring clonal diversity, yet uncontrolled replicative stress leads to mitotic catastrophe and inflammatory responses that promote immune rejection. KRAB domain-containing zinc finger proteins (KZFP) contribute to heterochromatin maintenance at transposable elements (TE). Here, we identified an association of upregulation of a cluster of primate-specific KZFPs with poor prognosis, increased copy-number alterations, and changes in the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL). Depleting two of these KZFPs targeting evolutionarily recent TEs, ZNF587 and ZNF417, impaired the proliferation of cells derived from DLBCL and several other tumor types. ZNF587 and ZNF417 depletion led to heterochromatin redistribution, replicative stress, and cGAS-STING-mediated induction of an interferon/inflammatory response, which enhanced susceptibility to macrophage-mediated phagocytosis and increased surface expression of HLA-I, together with presentation of a neoimmunopeptidome. Thus, cancer cells can exploit KZFPs to dampen TE-originating surveillance mechanisms, which likely facilitates clonal expansion, diversification, and immune evasion. SIGNIFICANCE: Upregulation of a cluster of primate-specific KRAB zinc finger proteins in cancer cells prevents replicative stress and inflammation by regulating heterochromatin maintenance, which could facilitate the development of improved biomarkers and treatments.

Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning.

Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.

Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma.

Adoptive cell therapy (ACT) using ex vivo-expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell-intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor-reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

Extensive thoracoabdominal aortic electro septotomy: A case report of a novel approach in the treatment of chronic aortic dissections.

Treatment of post-dissection arch and thoracoabdominal aortic aneurysms presents significant therapeutic challenges. True lumen collapse or take off of aortic branches from the false lumen makes endograft alignment difficult, if not impossible. We present herein the first successful case of an extensive thoracoabdominal electro aortic septotomy of the entire dissection membrane from the aortic arch down to the aortic bifurcation during an open redo aortic arch replacement employing the frozen elephant trunk technique. The procedure was performed on a 59 years old female patient presenting with a progressive post-dissection aortic aneurysm during follow-up with a maximum diameter of 6 cm 11 years after operating on an acute type A aortic dissection. Due to the extensive longitudinal aortic electric septotomy, we created a new "common lumen" for subsequent endovascular completion of the repair.

Decreased rotator cuff muscle cross-sectional areas in subjects with adhesive capsulitis: a study comparing male and female subjects.

OBJECTIVE: To compare rotator cuff (RC) muscle cross-sectional areas (CSA) in subjects with adhesive capsulitis (AC) to age- and sex-matched controls. MATERIALS AND METHODS: We retrospectively analyzed 97 shoulder MRIs or MR arthrography studies, of which 42 were clinically diagnosed with AC (27 female, 15 male) and 55 were age- and sex-matched controls (38 female, 17 male). All AC subjects underwent imaging ≥ 6 months after symptom onset. All imaging was examined to exclude RC full-thickness tears and prior surgery. A standardized T1 sagittal MR image was segmented in each subject to obtain the CSA of subscapularis (SSC), supraspinatus (SSP), and infraspinatus (ISP) muscles. Differences in CSAs between AC and control subjects were analyzed by sex (females and males separately) and all subjects combined. RESULTS: AC females had significantly decreased SSC (P = 0.002) and total (P = 0.006) CSAs compared to controls. Male AC subjects showed decreased SSC (P = 0.044), SSP (P = 0.001), and total (P = 0.005) CSAs. Across all subjects, male and female, the AC cohort had significantly decreased SSC (P = 0.019) and total (P = 0.029) CSAs compared to controls. CONCLUSION: Decreased RC muscle CSAs were present in AC subjects with ≥ 6 months of symptom duration, with decreased SSC and total CSAs in male and female subjects, and decreased SSP CSA in males.

Cross-sectional areas of rotator cuff muscles in males without tears on shoulder MRI.

OBJECTIVE: To establish reference values of rotator cuff (RC) cross sectional area (CSA) in males. MATERIALS AND METHODS: We retrospectively analyzed shoulder MRIs from 500 patients aged 13-78 years, grouped as follows (N=100 in each): <20, 20-30, 30-40, 40-50, >50 years. All examinations were reviewed to exclude prior surgery, tears, or significant RC pathology. We segmented a standardized T1 sagittal MR image in each case to obtain CSA of supraspinatus (SUP), infraspinatus/teres minor (INF), and subscapularis (SUB) muscles. Across age groups, we recorded individual and total muscle CSA. We also performed ratios between individual muscle CSA and total CSA to examine total muscle mass contribution over age groups. We tested for differences between age groups controlled for BMI. RESULTS: CSAs for SUP, INF, SUB, and total RC CSA were lower in subjects >50 years compared to all other groups (P<0.003 for all comparisons), persisting after controlling for BMI (P<0.03). Relative contribution of SUP CSA to total RC CSA was stable across age groups (P>0.32). INF CSA relative to total RC CSA increased with age, whereas SUB decreased (P<0.005). Subjects >50 years showed lower SUP (-15%), INF (-6%), and SUB (-21%) CSA, when compared to mean CSAs of all subjects <50 years. Total RC CSA significantly correlated with age (r=-0.34, P<0.001), persisting after controlling for BMI (r=-0.42, P<0.001). CONCLUSION: RC muscles in male subjects with no tears on MRI show decreasing CSA with age, independent of BMI.

Transapical electrosurgical laceration and stabilization of mitral clips followed by transcatheter mitral valve replacement-A one-stop shop.

Objective: Electrosurgical laceration and stabilization of mitral clips (ELASTA-CLIP) is a bail-out technique to recreate a single-orifice mitral valve after transcatheter edge-to-edge repair (TEER) with subsequent transcatheter mitral valve replacement (TMVR). This technique is a novel option for patients with significant residual mitral regurgitation after TEER with high risk for conventional surgery. The original ELASTA CLIP procedure features a transseptal approach, whereas the TMVR with the Tendyne bioprosthesis has a transapical access. Hereby we tested the hypothesis that a modified transapical ELASTA CLIP technique can be safely applied transapically allowing a straightforward one-stop shop access strategy. Methods: We developed the procedural steps in a porcine passive-beating heart model and applied the modified technique with subsequent TMVR in 2 consecutive patients with severe mitral regurgitation after previous TEER. Patients were followed up to 30 days. Results: The modified transapical ELASTA CLIP procedure was successful in both patients. The mean total procedure time was 118 minutes, and the mean fluoroscopy duration 22 minutes. At 30 days' follow-up, both patients were alive without bleeding complications, reintervention, or prosthetic valve dysfunction. Conclusions: The modified transapical ELASTA CLIP procedure is technically feasible and safe at 30 days. Procedure times are lower compared with previous reports of the original transseptal approach.

Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction.

The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Diagnostic Performance of the Darth Vader Sign for the Diagnosis of Lumbar Spondylolysis in Routinely Acquired Abdominal CT.

Spondylolysis is underdiagnosed and often missed in non-musculoskeletal abdominal CT imaging. Our aim was to assess the inter-reader agreement and diagnostic performance of a novel "Darth Vader sign" for the detection of spondylolysis in routine axial images. We performed a retrospective search in the institutional report archives through keyword strings for lumbar spondylolysis and spondylolisthesis. Abdominal CTs from 53 spondylolysis cases (41% female) and from controls (n = 6) without spine abnormalities were identified. A total of 139 single axial slices covering the lumbar spine (86 normal images, 40 with spondylolysis, 13 with degenerative spondylolisthesis without spondylolysis) were exported. Two radiology residents rated all images for the presence or absence of the "Darth Vader sign". The diagnostic accuracy for both readers, as well as the inter-reader agreement, was calculated. The "Darth Vader sign" showed an inter-reader agreement of 0.77. Using the "Darth Vader sign", spondylolysis was detected with a sensitivity and specificity of 65.0-88.2% and 96.2-99.0%, respectively. The "Darth Vader sign" shows excellent diagnostic performance at a substantial inter-reader agreement for the detection of spondylolysis. Using the "Darth Vader sign" in the CT reading routine may be an easy yet effective tool to improve the detection rate of spondylolysis in non-musculoskeletal cases and hence improve patient care.

Changes in Hepatic Fat Content by CT 1 Year After Sleeve Gastrectomy in Adolescents and Young Adults With Obesity.

CONTEXT: Obesity is associated with nonalcoholic fatty liver disease (NAFLD). Sleeve gastrectomy (SG) is an effective means of weight loss and improvement of NAFLD in adults; however, data regarding the efficacy of SG in the early stages of pediatric NAFLD are sparse. OBJECTIVE: To assess the impact of SG on hepatic fat content 1 year after SG in youth with obesity compared with nonsurgical controls with obesity (NS). DESIGN: A 12-month prospective study in 52 participants (mean age, 18.2 ± .36 years) with obesity, comprising 25 subjects who underwent SG (84% female; median body mass index [BMI], 44.6 [42.1-47.9] kg/m2) and 27 who were NS (70% female; median BMI, 42.2 [38.7-47.0] kg/m2). MAIN OUTCOME MEASURES: Hepatic fat content by computed tomography (liver/spleen ratio), abdominal fat by magnetic resonance imaging. RESULTS: Mean 12-month decrease in BMI was greater in SG vs NS (-12.5 ± .8 vs -.2 ± .5 kg/m2, P < .0001). There was a within-group increase in the liver-to-spleen (L/S) ratio in SG (.13 ± .05, P = .014) but not NS with a trend for a difference between groups (P = .055). All SG participants with an L/S ratio <1.0 (threshold for the diagnosis of NAFLD) before surgery had a ratio of >1.0 a year after surgery, consistent with resolution of NAFLD. Within SG, the 12-month change in L/S ratio was negatively associated with 12-month change in visceral fat (ρ = -.51 P = .016). CONCLUSIONS: Hepatic fat content as assessed by noncontrast computed tomography improved after SG over 1 year in youth with obesity with resolution of NAFLD in all subjects. This was associated with decreases in visceral adiposity.