Hard and soft tissue alterations using dual-zone concept versus connective tissue graft at maxillary immediate implant placement: A 1-year randomized clinical and volumetric trial.

OBJECTIVES: To evaluate the esthetic outcome, as well as clinical, radiographic, and volumetric tissue alterations 1 year after immediate implant placement (IIP) with connective tissue grafting (CTG) versus dual-zone concept (DZ) at sites with thin labial bone in the esthetic zone. MATERIALS AND METHODS: This randomized clinical trial included 30 patients treated with IIP simultaneous with either CTG or DZ (n = 15 each). Pink esthetic score (PES) was assessed 6 months after crown placement as the primary outcome. Amount of bone labial to the implant, labio-palatal ridge reduction, and crestal bone changes were measured via CBCT after 1 year. Volumetric analysis of linear labial soft tissue contour, interdental, and mid-facial soft tissue level changes, and total volume loss (mm3) were measured after 1 year. RESULTS: Similar PES was observed in the CTG (12.53 ± 1.13) and DZ (12.13 ± 1.55) groups, with no significant difference (p = 0.42). Likewise, there were no statistically significant differences found between the two groups in labio-palatal bone reduction (mm&%), interdental papillae, and mid-facial gingival levels (p > 0.05). However, the mean vertical crestal bone changes in the CTG and DZ groups were -1.1 ± 0.6 mm and 0.2 ± 1.0 mm, respectively, with a statistically significant difference (p = 0.0002). Moreover, CTG revealed less linear and total volume (mm3) loss in the labial soft tissue which was statistically significant compared to DZ (p = 0.007). CONCLUSION: Both groups demonstrated the same PES, nevertheless, volumetric analysis revealed twice total labial volume loss in DZ compared to CTG. It might be concluded that the use of CTG with IIP caused less horizontal reduction in the supra-implant complex compared to the DZ.

Key Contributors to Signal Generation in Frequency Mixing Magnetic Detection (FMMD): An In Silico Study.

Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel-Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dC>25 nm with narrow size distributions (σ<0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.

Ectopic PLAG1 induces muscular dystrophy in the mouse.

Even though various genetic mutations have been identified in muscular dystrophies (MD), there is still a need to understand the biology of MD in the absence of known mutations. Here we reported a new mouse model of MD driven by ectopic expression of PLAG1. This gene encodes a developmentally regulated transcription factor known to be expressed in developing skeletal muscle, and implicated as an oncogene in certain cancers including rhabdomyosarcoma (RMS), an aggressive soft tissue sarcoma composed of myoblast-like cells. By breeding loxP-STOP-loxP-PLAG1 (LSL-PLAG1) mice into the MCK-Cre line, we achieved ectopic PLAG1 expression in cardiac and skeletal muscle. The Cre/PLAG1 mice died before 6 weeks of age with evidence of cardiomyopathy significantly limiting left ventricle fractional shortening. Histology of skeletal muscle revealed dystrophic features, including myofiber necrosis, fiber size variation, frequent centralized nuclei, fatty infiltration, and fibrosis, all of which mimic human MD pathology. QRT-PCR and Western blot revealed modestly decreased Dmd mRNA and dystrophin protein in the dystrophic muscle, and immunofluorescence staining showed decreased dystrophin along the cell membrane. Repression of Dmd by ectopic PLAG1 was confirmed in dystrophic skeletal muscle and various cell culture models. In vitro studies showed that excess IGF2 expression, a transcriptional target of PLAG1, phenocopied PLAG1-mediated down-regulation of dystrophin. In summary, we developed a new mouse model of a lethal MD due to ectopic expression of PLAG1 in heart and skeletal muscle. Our data support the potential contribution of excess IGF2 in this model. Further studying these mice may provide new insights into the pathogenesis of MD and perhaps lead to new treatment strategies.

The immunogenomic landscape of resected intrahepatic cholangiocarcinoma.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA) is a deadly and highly therapy-refractory cancer of the bile ducts, with early results from immune checkpoint blockade trials showing limited responses. Whereas recent molecular assessments have made bulk characterizations of immune profiles and their genomic correlates, spatial assessments may reveal actionable insights. APPROACH AND RESULTS: Here, we have integrated immune checkpoint-directed immunohistochemistry with next-generation sequencing of resected intrahepatic CCA samples from 96 patients. We found that both T-cell and immune checkpoint markers are enriched at the tumor margins compared to the tumor center. Using two approaches, we identify high programmed cell death protein 1 or lymphocyte-activation gene 3 and low CD3/CD4/inducible T-cell costimulator specifically in the tumor center as associated with poor survival. Moreover, loss-of-function BRCA1-associated protein-1 mutations are associated with and cause elevated expression of the immunosuppressive checkpoint marker, B7 homolog 4. CONCLUSIONS: This study provides a foundation on which to rationally improve and tailor immunotherapy approaches for this difficult-to-treat disease.

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

ABSTRACT: Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

Special Issue "Computer Aided Diagnosis Sensors".

Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors [...].

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data.

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results.

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview-Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.

Precise Identification of Prostate Cancer from DWI Using Transfer Learning.

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI).

Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey.

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

Synthesis, Characterization, In Vitro Anticancer Potentiality, and Antimicrobial Activities of Novel Peptide-Glycyrrhetinic-Acid-Based Derivatives.

Glycyrrhetinic acid (GA) is one of many interesting pentacyclic triterpenoids showing significant anticancer activity by triggering apoptosis in tumor cell lines. This study deals with the design and synthesis of new glycyrrhetinic acid (GA)-amino acid peptides and peptide ester derivatives. The structures of the new derivatives were established through various spectral and microanalytical data. The novel compounds were screened for their in vitro cytotoxic activity. The evaluation results showed that the new peptides produced promising cytotoxic activity against the human breast MCF-7 cancer cell line while comparing to doxorubicin. On the other hand, only compounds 3, 5, and 7 produced potent activity against human colon HCT-116 cancer cell line. The human liver cancer (HepG-2) cell line represented a higher sensitivity to peptide 7 (IC50; 3.30 µg/mL), while it appeared insensitive to the rest of the tested peptides. Furthermore, compounds 1, 3, and 5 exhibited a promising safety profile against human normal skin fibroblasts cell line BJ-1. In order to investigate the mode of action, compound 5 was selected as a representative example to study its in vitro effect against the apoptotic parameters and Bax/BCL-2/p53/caspase-7/caspase-3/tubulin, and DNA fragmentation to investigate beta (TUBb). Additionally, all the new analogues were subjected to antimicrobial assay against a panel of Gram-positive and Gram-negative bacteria and the yeast candida Albicans. All the tested GA analogues 1-8 exhibited more antibacterial effect against Micrococcus Luteus than gentamicin, but they exhibited moderate antimicrobial activity against the tested bacterial and yeast strains. Molecular docking studies were also simulated for compound 5 to give better rationalization and put insight to the features of its structure.

Impact of Integrating Insulin-Like Growth Factor 1 Levels into Model for End-Stage Liver Disease Score for Survival Prediction in Hepatocellular Carcinoma Patients.

BACKGROUND: Liver reserve affects survival in hepatocellular carcinoma (HCC). Model for End-Stage Liver Disease (MELD) score is used to predict overall survival (OS) and to prioritize HCC patients on the transplantation waiting list, but more accurate models are needed. We hypothesized that integrating insulin-like growth factor 1 (IGF-1) levels into MELD score (MELD-IGF-1) improves OS prediction as compared to MELD. METHODS: We measured plasma IGF-1 levels in training (n = 310) and validation (n = 155) HCC cohorts and created MELD-IGF-1 score. Cox models were used to determine the association of MELD and MELD-IGF-1 with OS. Harrell's c-index was used to compare the predictive capacity. RESULTS: IGF-1 was significantly associated with OS in both cohorts. Patients with an IGF-1 level of ≤26 ng/mL in the training cohort and in the validation cohorts had significantly higher hazard ratios than patients with the same MELD but IGF-1 >26 ng/mL. In both cohorts, MELD-IGF-1 scores had higher c-indices (0.60 and 0.66) than MELD scores (0.58 and 0.60) (p < 0.001 in both cohorts). Overall, 26% of training and 52.9% of validation cohort patients were reclassified into different risk groups by MELD-IGF-1 (p < 0.001). CONCLUSIONS: After independent validation, the MELD-IGF-1 could be used to risk-stratify patients in clinical trials and for priority assignment for patients on liver transplantation waiting list.

Correction to: The G178A polymorphic variant of INSL3 may be linked to cryptorchidism among Egyptian pediatric cohort.

In the original publication.

The G178A polymorphic variant of INSL3 may be linked to cryptorchidism among Egyptian pediatric cohort.

Cryptorchidism (CO) is a genital disorder of multifactorial etiology, with serious remote complications. Mutations in insulin-like 3 hormones (INSL3) G/A variant remain a matter of inquiry. We aimed to investigate the association between G178A-INSL3 polymorphism and undescended testis in a cohort of Egyptian children. In this study, a total of 160 children, including 80 cases with primary non-syndromic undescended testes and 80 healthy children with normal external genitalia as controls, both, were analyzed after detailed history, physical examination and imaging for mutations of G178A polymorphism of INSL3 gene by restriction fragment length polymorphism (RFLP) technique. We found most of the undescended testes were inside the inguinal canal mainly on the left side. Genetic analysis revealed that the mutant A allele of G178A INSL3 variant was significantly detected in the patient group with a frequency of 26.2% against 12.5% for control subjects, especially among cases with an evident family history of similar cases as shown by p value = 0.001 and odd's ratio (CI95%) of 0.13 (0.04-0.723). In conclusion, G178A-INSL3 gene polymorphism could be a susceptibility factor for testicular maldescent in Egyptian children. Also, family history of similar cases was considered as significant predictive risk for cryptorchidism, added to the shared genetic links to consanguinity in our locality.

Automated framework for accurate segmentation of leaf images for plant health assessment.

Leaf segmentation is significantly important in assisting ecologists to automatically detect symptoms of disease and other stressors affecting trees. This paper employs state-of-the-art techniques in image processing to introduce an accurate framework for segmenting leaves and diseased leaf spots from images. The proposed framework integrates an appearance model that visually represents the current input image with the color prior information generated from RGB color images that were formerly saved in our database. Our framework consists of four main steps: (1) Enhancing the accuracy of the segmentation at minimum time by making use of contrast changes to automatically identify the region of interest (ROI) of the entire leaf, where the pixel-wise intensity relations are described by an electric field energy model. (2) Modeling the visual appearance of the input image using a linear combination of discrete Gaussians (LCDG) to predict the marginal probability distributions of the grayscale ROI main three classes. (3) Calculating the pixel-wise probabilities of these three classes for the color ROI based on the color prior information of database images that are segmented manually, where the current and prior pixel-wise probabilities are used to find the initial labels. (4) Refining the labels with the generalized Gauss-Markov random field model (GGMRF), which maintains the continuity. The proposed segmentation approach was applied to the leaves of mangrove trees in Abu Dhabi in the United Arab Emirates. Experimental validation showed high accuracy, with a Dice similarity coefficient 90% for distinguishing leaf spot from healthy leaf area.

Identification and functional analysis of a novel oculocerebrorenal syndrome of Lowe (OCRL) gene variant in two pedigrees with varying phenotypes including isolated congenital cataract.

Purpose: To identify the genetic variation in two unrelated probands with congenital cataract and to perform functional analysis of the detected variants. Methods: Clinical examination and phenotyping, segregation, and functional analysis were performed for the two studied pedigrees. Results: A novel OCRL gene variant (c.1964A>T, p. (Asp655Val)) was identified. This variant causes defects in OCRL protein folding and mislocalization to the cytoplasm. In addition, the variant's location close to the Rab binding site is likely to be associated with membrane targeting abnormalities. Conclusions: The results highlight the importance of early genetic diagnosis in infants with congenital cataract and show that mutations in the OCRL gene can present as apparently isolated congenital cataract.

Estrogen Replacement Reduces Risk and Increases Survival Times of Women With Hepatocellular Carcinoma.

BACKGROUND & AIMS: Environmental factors have been identified that affect risk of hepatocellular carcinoma (HCC), but little is known about the effects of sex hormones on liver cancer development or outcome. The authors investigated whether menopause hormone therapy (MHT) affects risk, age at onset, or outcome of HCC. METHODS: We performed a case-control study of 234 female patients treated for HCC at a tertiary medical center and with 282 healthy women (controls) from January 1, 2004 through May 31, 2015. We collected detailed information on environmental exposures, ages of menarche and menopause, hysterectomies, and uses of birth control and MHT. We performed multivariable logistic and Cox regression analyses to determine the independent effects of factors associated with women on risk and clinical outcome in HCC. The primary outcomes were effect of MHT on HCC risk, the relationship between MHT with hepatitis virus infection on HCC development, and effect of MHT on age at HCC onset or survival after diagnosis of HCC. RESULTS: The estimated adjusted odds ratio (AOR) for HCC in women who ever used estrogen was 0.53 (95% confidence interval [CI], 0.32-0.88). This association was supported by the older age of HCC onset among estrogen users (mean, 64.5 ± 0.9 years) vs nonusers (mean 59.2 ± 1.1 years; P = .001) and the reduced risk of HCC among long-term users (more than 5 years) (AOR, 0.36; 95% CI, 0.20-0.63). Users of estrogen also had a reduced risk for hepatitis-associated HCC: AOR for users, 4.37 (95% CI, 1.67-11.44) vs AOR for nonusers, 17.60 (95% CI, 3.88-79.83). Estrogen use reduced risk of death from HCC (hazard ratio, 0.55; 95% CI, 0.40-0.77; P = .01). Median overall survival times were 33.5 months for estrogen users (95% CI, 25.7-41.3 months) and 24.1 months for nonusers (95% CI, 19.02-29.30 months; P = .008). CONCLUSION: In a case-control study of women with HCC vs female control subjects at a single center, we associated use of estrogen MHT with reduced risk of HCC and increased overall survival times of patients with HCC. Further studies are needed to determine the benefits of estrogen therapy for women and patients with HCC, and effects of tumor expression of estrogen receptor.

Natural History of T1N0M0 Hepatocellular Carcinoma: Large-Scale Study in the United States.

BACKGROUND: Hepatocellular carcinoma (HCC) prognosis depends on clinicopathological features in addition to the treatment provided. We aimed to assess the natural history of TNM stage I HCC tumors which received different treatment over a period of 20 years. METHODS: Between 1992 and 2011, a total of 397 stage I HCC patients were included. Detailed information was retrieved from MD Anderson Cancer Center patients' medical records. The Kaplan-Meier method was used to calculate patients' overall survival (OS). Cox regression analysis was used to calculate the estimated hazard ratio and 95% confidence interval of different prognostic factors. RESULTS: Out of 397 patients, 67.5% were males, 42.8% had hepatitis-related HCC, and 59.7% had underlying cirrhosis. After adjustment for confounding factors, we found that all therapeutic modalities were associated with a significant mortality rate reduction with an OS of 63, 42.03, 34.3, and 22.1 months among patients treated with surgery, ablation, local, and systemic therapy, respectively. A restricted analysis of cirrhotic and noncirrhotic patients showed that ablative and local therapy were significantly associated with a longer OS compared to systemic therapy. CONCLUSION: TNM stage I HCC patients have a favorable prognosis regardless of the type of treatment. Notably, ablative and local therapy significantly improved OS compared to systemic therapy.

Obesity Early in Adulthood Increases Risk but Does Not Affect Outcomes of Hepatocellular Carcinoma.

BACKGROUND & AIMS: Despite the significant association between obesity and several cancers, it has been difficult to establish an association between obesity and hepatocellular carcinoma (HCC). Patients with HCC often have ascites, making it a challenge to determine body mass index (BMI) accurately, and many factors contribute to the development of HCC. We performed a case-control study to investigate whether obesity early in adulthood affects risk, age of onset, or outcomes of patients with HCC. METHODS: We interviewed 622 patients newly diagnosed with HCC from January 2004 through December 2013, along with 660 healthy controls (frequency-matched by age and sex) to determine weights, heights, and body sizes (self-reported) at various ages before HCC development or enrollment as controls. Multivariable logistic and Cox regression analyses were performed to determine the independent effects of early obesity on risk for HCC and patient outcomes, respectively. BMI was calculated, and patients with a BMI of 30 kg/m(2) or greater were considered obese. RESULTS: Obesity in early adulthood (age, mid-20s to mid-40s) is a significant risk factor for HCC. The estimated odds ratios were 2.6 (95% confidence interval [CI], 1.4-4.4), 2.3 (95% CI, 1.2-4.4), and 3.6 (95% CI, 1.5-8.9) for the entire population, for men, and for women, respectively. Each unit increase in BMI at early adulthood was associated with a 3.89-month decrease in age at HCC diagnosis (P < .001). Moreover, there was a synergistic interaction between obesity and hepatitis virus infection. However, we found no effect of obesity on the overall survival of patients with HCC. CONCLUSIONS: Early adulthood obesity is associated with an increased risk of developing HCC at a young age in the absence of major HCC risk factors, with no effect on outcomes of patients with HCC.