Prediction of Microvascular Invasion and Its M2 Classification in Hepatocellular Carcinoma Based on Nomogram Analyses

Background and AimsAs a key pathological factor, microvascular invasion (MVI), especially its M2 grade, greatly affects the prognosis of liver cancer patients. Accurate preoperative prediction of MVI and its M2 classification can help clinicians to make the best treatment decision. Therefore, we aimed to establish effective nomograms to predict MVI and its M2 grade.MethodsA total of 111 patients who underwent radical resection of hepatocellular carcinoma (HCC) from January 2015 to September 2020 were retrospectively collected. We utilized logistic regression and least absolute shrinkage and selection operator (LASSO) regression to identify the independent predictive factors of MVI and its M2 classification. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. Nomograms for predicting MVI and its M2 grade were then developed by incorporating these factors. Area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were respectively used to evaluate the efficacy, accuracy, and clinical utility of the nomograms.ResultsCombined with the results of LASSO regression, logistic regression, and IDI and NRI analyses, we founded that clinical tumor-node-metastasis (TNM) stage, tumor size, Edmondson–Steiner classification, α-fetoprotein (AFP), tumor capsule, tumor margin, and tumor number were independent risk factors for MVI. Among the MVI-positive patients, only clinical TNM stage, tumor capsule, tumor margin, and tumor number were highly correlated with M2 grade. The nomograms established by incorporating the above variables had a good performance in predicting MVI (AUCMVI = 0.926) and its M2 classification (AUCM2 = 0.803). The calibration curve confirmed that predictions and actual observations were in good agreement. Significant clinical utility of our nomograms was demonstrated by DCA.ConclusionsThe nomograms of this study make it possible to do individualized predictions of MVI and its M2 classification, which may help us select an appropriate treatment plan.

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Hypopharyngeal Squamous Cell Carcinomas: A Population-Based Study

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

Construction and validation of a prognostic nomogram for predicting the survival of HIV/AIDS adults who received antiretroviral therapy: a cohort between 2003 and 2019 in Nanjing

Background Great achievements have been achieved by free antiretroviral therapy (ART). A rapid and accurate prediction of survival in people living with HIV/AIDS (PLHIV) is needed for effective management. We aimed to establish an effective prognostic model to forecast the survival of PLHIV after ART. Methods The participants were enrolled from a follow-up cohort over 2003-2019 in Nanjing AIDS Prevention and Control Information System. A nested case-control study was employed with HIV-related death, and a propensity-score matching (PSM) approach was applied in a ratio of 1:4 to allocate the patients. Univariable and multivariable Cox proportional hazards analyses were performed based on the training set to determine the risk factors. The discrimination was qualified using the area under the curve (AUC) and concordance index (C-Index). The nomogram was calibrated using the calibration curve. The clinical benefit of prognostic nomogram was assessed by decision curve analysis (DCA). Results Predictive factors including CD4 cell count (CD4), body mass index (BMI) and hemoglobin (HB) were determined and incorporated into the nomogram. In the training set, AUC and C-index (95% CI) were 0.831 and 0.798 (0.758, 0.839), respectively. The validation set revealed a good discrimination with an AUC of 0.802 and a C-index (95% CI) of 0.786 (0.681, 0.892). The calibration curve also exhibited a high consistency in the predictive power (especially in the first 3 years after ART initiation) of the nomogram. Moreover, DCA demonstrated that the nomogram was clinically beneficial. Conclusion The nomogram is effective and accurate in forecasting the survival of PLHIV, and beneficial for medical workers in health administration.

Construction and Validation of a Risk Prediction Model for Acute Kidney Injury in Patients Suffering from Septic Shock

Background. Acute kidney injury (AKI) is an important complication in critically ill patients, especially in sepsis and septic shock patients. Early prediction of AKI in septic shock can provide clinicians with sufficient information for timely intervention so that improve the patients’ survival rate and quality of life. The aim of this study was to establish a nomogram that predicts the risk of AKI in patients with septic shock in the intensive care unit (ICU). Methods. The data were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database between 2001 and 2012. The primary outcome was AKI in the 48 h following ICU admission. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AKI. The performance of the nomogram was evaluated according to the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis, and clinical impact curve. Results. A total of 2415 patients with septic shock were included in this study. In the training and validation cohort, 1091 (64.48%) of 1690 patients and 475 (65.52%) of 725 patients developed AKI, respectively. The predictive factors for nomogram construction were gender, ethnicity, congestive heart failure, diabetes, obesity, Simplified Acute Physiology Score II (SAPS II), angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs), bilirubin, creatinine, blood urea nitrogen (BUN), and mechanical ventilation. The model had a good discrimination with the area under the ROC curve of 0.756 and 0.760 in the training and validation cohorts, respectively. The calibration curve for probability of AKI in septic shock showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that the nomogram conferred high clinical net benefit. Conclusion. The proposed nomogram can quickly and effectively predict the risk of AKI at an early stage in patients with septic shock in ICU, which can provide information for timely and efficient intervention in patients with septic shock in the ICU setting.

Incidence, Risk Factors and Prognosis of T4a Gastric Cancer: A Population-Based Study

Background: T4a gastric cancer (GC) is a subtype of advanced GC (AGC), which urgently needs a comprehensive grade method for better treatment strategy choosing. The purpose of this study was to develop two nomograms for predicting the prognosis of patients with T4a GC.Methods: A total of 1,129 patients diagnosed as T4a GC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Result (SEER) program database. Univariate and multivariate Cox analyses were performed to explore the independent predictors and to establish nomogram for overall survival (OS) of the patients, whereas competing risk analyses were performed to find the independent predictors and to establish nomogram for cancer-specific survival (CSS) of the patients. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier analysis were performed to evaluate the nomograms.Results: Older age, larger tumor size, black race, signet ring cell carcinoma (SRCC), more lymph node involvement, the absence of surgery, the absence of radiotherapy, and the absence of chemotherapy were identified as independent prognostic factors for both OS and CSS. In the training cohort, the AUCs of the OS nomogram were 0.760, 0.743, and 0.723 for 1-, 3-, and 5-year OS, whereas the AUCs of the CSS nomogram were 0.724, 0.703, and 0.713 for 1-, 3-, and 5-year CSS, respectively. The calibration curve and DCA indicated that both nomograms can effectively predict OS and CSS, respectively. The abovementioned results were also confirmed in the validation cohort. Stratification of the patients into high- and low-risk groups highlighted the differences in prognosis between the two groups both in training and in validation cohorts.Conclusions: Age, tumor size, race, histologic type, N stage, surgery status, radiotherapy, and chemotherapy were confirmed as independent prognostic factors for both OS and CSS in patients with T4a GC. Two nomograms based on the abovementioned variables were constructed to provide more accurate individual survival predictions for them.

A simulation approach to predict the calibration curve of low LET radiations for biological dosimetry using the Geant4-DNA toolkit

Ionizing radiation is extensively utilized in various applications; however, it can lead to significant harm to living systems. In this regard, the radiation absorbed dose is usually evaluated by performing biological dosimetry and physical reconstruction of exposure scenarios. But, this is costly, time-consuming, and maybe impractical for a biodosimetry lab to perform biological dosimetry. This study aimed to assess the applicability and reliability of the Geant4-DNA toolkit as a simulation approach to construct a reliable dose-response curve for biodosimetry purposes as an appropriate substitution for experimental measurements. In this matter, the total number of double-strand breaks (DSBs), due to different doses of low LET radiation qualities on DNA molecules, was calculated and converted to the values of dicentric chromosomes using a mechanistic model of cellular response. Then, the number of dicentric chromosomes induced by 200 kVp X-rays were modified by using a semi-empirical scaling factor for compensating the restriction of simulation code to consider what can happen in a real cell. Next, the trend of dicentrics for 137Cs and 60Co were calculated and modified by the above scaling factor. Finally, the dose-response curves for these gamma sources compared to several published experiments. The suggested calibration curves for 137Cs and 60Co followed a linear quadratic equation: Ydic = 0.0054 (± 0.0133) - 0.0089 (± 0.0212) × D + 0.0568 (± 0.0051) × D2 and Ydic = 0.0052 (± 0.0128) - 0.00568 (± 0.0203) × D + 0.0525 (± 0.0049) × D2 respectively. They revealed a satisfactory agreement with the experimental data reported by others. The Geant4 program developed in this work could provide an appropriate tool for predicting the dose-response (calibration) curve for biodosimetry purposes.

Determination of the Detection Limit of the Detection of GMO in Food Using the Isothermal Solid-Phase Recombinase Polymerase Amplification on Microfluidic DVDs

A major drawback of the current literature on bioassay development is that these tests are not made using statistically robust methods for establishing the limit of detection. As an alternative, researchers often make use of simple detection-limit methods that are only roughly indicative of the actual detection limit. We can only assume that this is due to a practical need for simplified processes, in addition to the notion that the limit of detection theory has already been lowered to practice for bioassays. A DNA sensor based on light intensity of the scanning laser on a DVD drive with microfluidic layer etched onto the polycarbonate surface of an ordinary DVD has been previously developed for fast screening of genetically modified organisms (GMOs). The resultant calibration curve showed a sigmoidal calibration curve but was not modelled according to any of the sigmoidal models available. The objective of this study is the remodel the data using the standard 4-PL model and to determine the Limits of Detection (LOD) based on the standard method. The LOD value obtained through the 4PL modelling exercise based on a pooled standard deviation method yielded an LOD value of 62 mg/g (95% confidence interval of 17 to 158), which was quite similar to the classical three standard deviation of the blank method but was lower than the rough estimation employed in the original publication.

Detection Limit of the Four-Parameter Logistic Model for the Quantitative Detection of Serum Squamous Cell Carcinoma Antigenin Cervical Cancer Based on Surface Plasmon Resonance Biosensor

Biochemical diagnostic procedures, such as protein binding, rely on biomolecular interactions as its diagnostic modality, and as a consequence, their calibration curves are more complex. In addition, sigmoidal curves are often seen in these tests. In the event of asymmetry, a logistic (5PL) curve, or a logistic (4PL) curve, may be the best way to represent a distinctive sigmoidal relationship. It is possible that the linearization of an otherwise nonlinear connection by log transformation may result in a disruption of the error structure of the curve, and that this will have the opposite effect of decreasing or even eliminating error in the relationship. Previously, a surface plasmon resonance biosensor for the detection of squamous cell carcinoma antigen (SCCa) using nanoparticle technology was developed. However, based on the calibration curve, it conforms to the majority on sigmoidal shape curve for antibody-type sensing system. The resultant curve showed a sigmoidal calibration curve but was not modelled according to any of the sigmoidal models available. The LOD value obtained through the 4PL modelling exercise based on the classical method was 0.255 pM (95% confidence interval of 0.167 to 0.379) while the pooled standard deviation (PSD) method yielded an LOD value of 0.035 pM (95% confidence interval of 0.011 to 0.067), which indicates that the PSD method was superior.

Nomogram to Predict Symptomatic Intracranial Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke in an Asian Population.

Background: Symptomatic intracranial hemorrhage (sICH) is a serious hemorrhagic complication after intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) patients. Most existing predictive scoring systems were derived from Western countries Objective: To develop a nomogram to predict the possibility of sICH after IVT in an Asian population. Methods: This retrospective cohort study included AIS patients treated with recombinant tissue plasminogen activator (rt-PA) in a tertiary hospital in Shenzhen, China, from January 2014 to December 2020. The end point was sICH within 36 hours of IVT treatment. Multivariable logistic regression was used to identify risk factors of sICH, and a predictive nomogram was developed. Area under the curve of receiver operating characteristic curves (AUC), calibration curve, and decision curve analyses were performed. The nomogram was validated by bootstrap resampling Results: Data on a total of 462 patients were collected, of whom 20 patients (4.3%) developed sICH. In the multivariate logistic regression model, the National Institute of Health stroke scale scores (NIHSS) (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.06–1.23, P < 0.001), onset to treatment time (OTT) (OR, 1.02; 95% CI, 1.01–1.03, P < 0.001), neutrophil to lymphocyte ratio (NLR) (OR, 1.22; 95% CI, 1.09–1.35, P < 0.001), and cardioembolism (OR, 3.74; 95% CI, 1.23–11.39, P = 0.020) were independent predictors for sICH and were used to construct a nomogram. Our nomogram exhibited favorable discrimination ability [AUC, 0.878; specificity, 87.35%; and sensitivity, 73.81%]. Bootstrapping for 500 repetitions was performed to further validate the nomogram. The AUC of the bootstrap model was 0.877 (95% CI: 0.823–0.922). The calibration curve exhibited good fit and calibration. The decision curve revealed good positive net benefits and clinical effects Conclusion: The nomogram consisted of the predictors NIHSS, OTT, NLR, and cardioembolism could be used as an auxiliary tool to predict the individual risk of sICH in Chinese AIS patients after IVT. Further external verification among more diverse patient populations is needed to demonstrate the accuracy of the model’s predictions.

Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database

Background: This study aimed to develop and validate a nomogram for predicting mortality in patients with thoracic fractures without neurological compromise and hospitalized in the intensive care unit.Methods: A total of 298 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the study, and 35 clinical indicators were collected within 24 h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was established, and a nomogram was constructed. Internal validation was performed by the 1,000 bootstrap samples; a receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test (HL test). A decision curve analysis (DCA) was performed, and the nomogram was compared with scoring systems commonly used during clinical practice to assess the net clinical benefit.Results: Indicators included in the nomogram were age, OASIS score, SAPS II score, respiratory rate, partial thromboplastin time (PTT), cardiac arrhythmias, and fluid-electrolyte disorders. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.902 and 0.883 using the training set and on internal validation. The calibration curve and the Hosmer-Lemeshow goodness-of-fit (HL). The HL tests exhibited satisfactory concordance between predicted and actual outcomes (P = 0.648). The DCA showed a superior net clinical benefit of our model over previously reported scoring systems.Conclusion: In summary, we explored the incidence of mortality during the ICU stay of thoracic fracture patients without neurological compromise and developed a prediction model that facilitates clinical decision making. However, external validation will be needed in the future.