scholarly journals Radiomics Nomogram Based on Spectral CT Imaging to Diagnose Osteoporosis

2021 ◽  
Author(s):  
Qianqian Yao ◽  
Mengke Liu ◽  
Kemei Yuan ◽  
Yue Xin ◽  
Xiaoqian Qiu ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Area Under The Curve ◽  
Spectral Ct ◽  
Mineral Density ◽  
Marrow Fat ◽  
Clinical Factor ◽  
Training Cohort ◽  
Clinical Model ◽  
Normal Bone Mineral Density ◽  
Differential Ability

Abstract Background: Osteoporosis is associated with a decrease of bone mineralized component as well as a increase of bone marrow fat. At present, there are few studies using radiomics nomogram based fat-water material decomposition (MD) images of spectral CT as an evaluation method of osteoporosis. This study aims to establish and validate a radiomics nomogram based the fat-water imaging of spectral CT in diagnosing osteoporosis.Methods: 95 patients who underwent spectral CT included T11-L2 and dual x-ray absorptiometry (DXA) were collected. The patients were divided into two groups according to T-score, normal bone mineral density (BMD) (T≥-1) and abnormally low BMD (T<-1). Radiomic features were selected from fat-water imaging of the spectral CT. Radscore was calculated by summing the selected features weighted by their coefficients. A nomogram combining the radiomics signature and significant clinical variables was built. The ROC curve was performed to evaluate the performance of the model. Finally, we used decision curve analysis (DCA) to evaluate the clinical usefulness of the model.Results: Five radiomic features based on fat-water imaging of spectral CT were constructed to distinguish abnormally low BMD from normal BMD, and its differential performance was high with an area under the curve (AUC) of 0.95 (95% CI, 0.89-1.00) in the training cohort and 0.97 (95% CI, 0.91-1.00) in the test cohort. The radiomics nomogram showed excellent differential ability with AUC of 0.96 (95%CI, 0.91-1.00) in the training cohort and 0.98 (95%CI, 0.93-1.00) in the test cohort, which performed better than the radiomics model and clinics model only. The DCA showed that the radiomics nomogram had a higher benefit in differentiating abnormally low BMD from normal BMD than the clinical model alone.Conclusion: The radiomics nomogram incorporated radiomics features and clinical factor based the fat-water imaging of spectral CT may serve as an efficient tool to identify abnormally low BMD from normal BMD well.

scholarly journals Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuyan Chen ◽  
Zelong Liu ◽  
Yunxian Mo ◽  
Bin Li ◽  
Qian Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Failure ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Training Cohort ◽  
Clinical Model ◽  
Preoperative Prediction ◽  
Medical Centers ◽  
Appropriate Treatment ◽  
Magnetic Resonance Imaging Mri

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI).Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI).Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955–0.962) and 0.844 (95% CI: 0.833–0.886), respectively], compared with the radiomics model and the clinical model.Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

scholarly journals Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics –Based Nomogram

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Du ◽  
Yu Wang ◽  
Dongdong Li ◽  
Xueming Xia ◽  
Qiaoyue Tan ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Tumor Stage ◽  
Support Vector ◽  
Training Cohort ◽  
Clinical Model ◽  
Radiomics Signature

PurposeTo build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation.MethodThis study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models.ResultThe radiomics model performed much better than the clinical model in both training (AUCs 0.925 vs. 0.786, accuracies 87.5% vs. 70.5%, sensitivities 83.6% vs. 41.7% and specificities 90.9% vs. 94.7%) and testing (AUCs 0.911 vs. 0.706, accuracies 84.0% vs. 71.3%, sensitivities 81.1% vs. 43.4% and specificities 86.4% vs. 95.0%). The combined model based on the radiomics signature and tumor stage, tumor infiltration depth and tumor pathology yielded the best performance (training cohort, AUC = 0.943, accuracies 89.5%, sensitivities 85.4% and specificities 92.9%; testing cohort, AUC = 0.923, accuracies 84.6%, sensitivities 84.0% and specificities 85.1%).ConclusionRadiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.

scholarly journals POS1137 IDENTIFYING ADULT HYPOPHOSPHATASIA IN THE RHEUMATOLOGY UNIT

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 848.1-848
Author(s):  
J. Feurstein ◽  
M. Behanova ◽  
J. Haschka ◽  
K. Roetzer ◽  
G. Uyanik ◽  
...  
Keyword(s):  
Burden Of Illness ◽  
Trabecular Bone Score ◽  
Correct Diagnosis ◽  
Diagnostic Delay ◽  
Official Journal ◽  
Mineral Density ◽  
Mineral Research ◽  
Clinical Factor ◽  
Logistic Regression Models ◽  
Rheumatology Unit

Background:The most frequent manifestation in adult Hypophosphatasia (HPP) is musculoskeletal pain.1,2 The unspecific nature of its clinical presentation may prevent correct diagnosis.3Objectives:Identifying adult hypophosphatasia in the rheumatology unit.Methods:Over a period of 10 years 9,522 patients were screened in a rheumatological outpatient unit. Serum ALP levels ≤ 40 U/l were found in 524 patients. After screening for secondary causes, 73 patients were invited for clinical evaluation. Genetic testing was performed in 23 patients with suspected HPP. Logistic regression models were used to estimate the association of each clinical factor with HPP.Results:Mutations in the ALPL gene were observed in 57% of genetically screened patients. Arthralgia, fractures and pain were the leading symptoms in HPP patients. Chondrocalcinosis (OR 29.12; 95% CI 2.02-1593.52) and dental disease (OR 8.33; 95% CI 0.93-143.40) were associated with HPP independent of BMI. Onset of symptoms in HPP was at 35.1 (14.3) years, with a mean duration from symptoms to diagnosis of 14.4 (8.1) years. Bone mineral density (BMD) and trabecular bone score (TBS) as well as bone turnover markers were not indicative for HPP.Conclusion:HPP can mimic joint diseases.4 Thus, in patients with uncertain rheumatologic complaints and low ALP, HPP should be considered as potential diagnosis.References:[1]Durrough C, Colazo JM, Simmons J, et al. Characterization of physical, functional, and cognitive performance in 15 adults with hypophosphatasia. Bone 2021;142:115695.[2]Seefried L, Kishnani PS, Moseley S, et al. Pharmacodynamics of asfotase alfa in adults with pediatric-onset hypophosphatasia. Bone 2021;142:115664.[3]Högler W, Langman C, Gomes da Silva H, et al. Diagnostic delay is common among patients with hypophosphatasia: initial findings from a longitudinal, prospective, global registry. BMC musculoskeletal disorders 2019;20(1):80.[4]Seefried L, Dahir K, Petryk A, et al. Burden of Illness in Adults With Hypophosphatasia: Data From the Global Hypophosphatasia Patient Registry. Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research 2020;35(11):2171-78.Disclosure of Interests:None declared.

scholarly journals A Novel m6A Gene Signature Associated With Regulatory Immune Function for Prognosis Prediction in Clear-Cell Renal Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Siteng Chen ◽  
Ning Zhang ◽  
Encheng Zhang ◽  
Tao Wang ◽  
Liren Jiang ◽  
...  
Keyword(s):  
Risk Score ◽  
Clear Cell ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Cell Renal Cell Carcinoma ◽  
Training Cohort ◽  
Rna Methylation ◽  
Cancer Genome Atlas ◽  
Genome Atlas

The important role of N6-methyladenosine (m6A) RNA methylation regulator in carcinogenesis and progression of clear-cell renal cell carcinoma (ccRCC) is poorly understood by now. In this study, we performed comprehensive analyses of m6A RNA methylation regulators in 975 ccRCC samples and 332 adjacent normal tissues and identified ccRCC-related m6A regulators. Moreover, the m6A diagnostic score based on ccRCC-related m6A regulators could accurately distinguish ccRCC from normal tissue in the Meta-cohort, which was further validated in the independent GSE-cohort and The Cancer Genome Atlas-cohort, with an area under the curve of 0.924, 0.867, and 0.795, respectively. Effective survival prediction of ccRCC by m6A risk score was also identified in the Cancer Genome Atlas training cohort and verified in the testing cohort and the independent GSE22541 cohort, with hazard ratio values of 3.474, 1.679, and 2.101 in the survival prognosis, respectively. The m6A risk score was identified as a risk factor of overall survival in ccRCC patients by the univariate Cox regression analysis, which was further verified in both the training cohort and the independent validation cohort. The integrated nomogram combining m6A risk score and predictable clinicopathologic factors could accurately predict the survival status of the ccRCC patients, with an area under the curve values of 85.2, 82.4, and 78.3% for the overall survival prediction in 1-, 3- and 5-year, respectively. Weighted gene co-expression network analysis with functional enrichment analysis indicated that m6A RNA methylation might affect clinical prognosis through regulating immune functions in patients with ccRCC.

scholarly journals The Use of Mushrooms and Spirulina Algae as Supplements to Prevent Growth Inhibition in a Pre-Clinical Model for an Unbalanced Diet

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4316
Author(s):  
Roni Sides ◽  
Shelley Griess-Fishheimer ◽  
Janna Zaretsky ◽  
Astar Shitrit ◽  
Rotem Kalev-Altman ◽  
...  
Keyword(s):  
Dietary Supplements ◽  
Bone Growth ◽  
Positive Impact ◽  
Bone Development ◽  
Harmful Effect ◽  
Ct Scanning ◽  
Childhood And Adolescence ◽  
Mineral Density ◽  
Clinical Model ◽  
Microbiome Composition

Today’s eating patterns are characterized by the consumption of unbalanced diets (UBDs) resulting in a variety of health consequences on the one hand, and the consumption of dietary supplements in order to achieve overall health and wellness on the other. Balanced nutrition is especially crucial during childhood and adolescence as these time periods are characterized by rapid growth and development of the skeleton. We show the harmful effect of UBD on longitudinal bone growth, trabecular and cortical bone micro-architecture and bone mineral density; which were analyzed by micro-CT scanning. Three point bending tests demonstrate the negative effect of the diet on the mechanical properties of the bone material as well. Addition of Spirulina algae or Pleurotus eryngii or Agaricus bisporus mushrooms, to the UBD, was able to improve growth and impaired properties of the bone. 16SrRNA Sequencing identified dysbiosis in the UBD rats’ microbiota, with high levels of pro-inflammatory associated bacteria and low levels of bacteria associated with fermentation processes and bone related mechanisms. These results provide insight into the connection between diet, the skeletal system and the gut microbiota, and reveal the positive impact of three chosen dietary supplements on bone development and quality presumably through the microbiome composition.

scholarly journals Non-communicable disease pattern in adults seeking preventive general health checkup

2019 ◽  
Vol 1 (1) ◽  
pp. 21-25
Author(s):  
Bikash Shrestha ◽  
Bipin Nepal ◽  
Ravi Mahat ◽  
Abish Adhikari
Keyword(s):  
General Health ◽  
Communicable Disease ◽  
Tertiary Care ◽  
Communicable Diseases ◽  
Care Hospital ◽  
Mineral Density ◽  
Health Checkup ◽  
Middle Income ◽  
Normal Bone Mineral Density ◽  
The Government

Non Communicable diseases (NCDs) are now endemic in low and middle income countries. Nepal had a high burden of communicable diseases (CDs) which has now been overtaken by NCDs. Although prevention and control of NCDs is prioritized in national policies and strategies, there is no proper monitoring system. This study aims to review the morbidity pattern among the adults seeking preventive general health checkup in a major tertiary care hospital in Kathmandu. 3000 cases were evaluated. 53.6% were males and 46.4% were females. The mean age of cases was 44.9 yrs. Most of the cases ranged from 40 to 60 years of age. Almost half of them were from Kathmandu district. Nearly 78% participants live a sedentary life. Abdominal obesity was seen in 27.5% of females and 21.7% of males. Nearly 49% of cases were overweight and 24% were obese. Almost 21 % of the cases were smokers and about 36% of them consumed alcohol. Only 9% are vegetarians. 10% have diabetes and 20% have hypertension. 69% of females and 43% of males have less than normal bone mineral density. The government and private sectors must focus on strengthening preventive and curative services for early detection of risk factors and management of NCDs.

Amyotrophic lateral sclerosis diagnostic index

Neurology ◽  
2019 ◽  
Vol 92 (6) ◽  
pp. e536-e547 ◽  
Author(s):  
Nimeshan Geevasinga ◽  
James Howells ◽  
Parvathi Menon ◽  
Mehdi van den Bos ◽  
Kazumoto Shibuya ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Confidence Interval ◽  
Diagnostic Accuracy ◽  
Neuromuscular Disorders ◽  
Area Under The Curve ◽  
Disease Process ◽  
Diagnostic Utility ◽  
Training Cohort ◽  
Diagnostic Index ◽  
Lateral Sclerosis

ObjectiveThe aim of the study was to assess the utility of a novel amyotrophic lateral sclerosis (ALS) diagnostic index (ALSDI).MethodsA prospective multicenter study was undertaken on patients presenting with suspected ALS. The reference standard (Awaji criteria) was applied to all patients at recruitment. Patients were randomly assigned to a training (75%) and a test (25%) cohort. The ALSDI was developed in the training cohort and its diagnostic utility was subsequently assessed in the test cohort.ResultsA total of 407 patients were recruited, with 305 patients subsequently diagnosed with ALS and 102 with a non-ALS mimicking disorder. The ALSDI reliably differentiated ALS from neuromuscular disorders in the training cohort (area under the curve 0.92, 95% confidence interval 0.89–0.95), with ALSDI ≥4 exhibiting 81.6% sensitivity, 89.6% specificity, and 83.5% diagnostic accuracy. The ALSDI diagnostic utility was confirmed in the test cohort (area under the curve 0.90, 95% confidence interval 0.84–0.97), with ALSDI ≥4 exhibiting 83.3% sensitivity, 84% specificity, and 83.5% diagnostic accuracy. In addition, the diagnostic utility of the ALSDI was confirmed in patients who were Awaji negative at recruitment and in those exhibiting a predominantly lower motor neuron phenotype.ConclusionThe ALSDI reliably differentiates ALS from mimicking disorders at an early stage in the disease process.Classification of evidenceThis study provides Class I evidence that for patients with suspected ALS, the ALSDI distinguished ALS from neuromuscular mimicking disorders.

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