Discrimination In Favour Research Articles

Interleukin-6 and thyroid-stimulating hormone index predict plaque stability in carotid artery stenosis: analyses by lasso-logistic regression

ObjectiveTo develop and validate a new prediction model based on the Lass-logistic regression with inflammatory serologic markers for the assessment of carotid plaque stability, providing clinicians with a reliable tool for risk stratification and decision-making in the management of carotid artery disease.MethodsIn this study, we retrospectively collected the data of the patients who underwent carotid endarterectomy (CEA) from 2019 to 2023 in Nanjing Drum Tower Hospital. Demographic characteristics, vascular risk factors, and the results of preoperative serum biochemistry were measured and collected. The risk factors for vulnerable carotid plaque were analyzed. A Lasso-logistic regression prediction model was developed and compared with traditional logistic regression models. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate the performance of three models.ResultsA total of 131 patients were collected in this study, including 66 (50.4%) in the vulnerable plaque group and 65 (49.6%) in the stable plaque group. The final Lasso-logistic regression model included 4 features:IL-6, TSH, TSHI, and TT4RI; AIC = 161.6376, BIC = 176.0136, both lower than the all-variable logistic regression model (AIC = 181.0881, BIC = 261.5936), and the BIC was smaller than the stepwise logistic regression model (AIC = 154.024, BIC = 179.9007). Finally, the prediction model was constructed based on the variables screened by the Lasso regression, and the model had favorable discrimination and calibration.ConclusionsThe noninvasive prediction model based on IL-6 and TSHI is a quantitative tool for predicting vulnerable carotid plaques. It has high diagnostic efficacy and is worth popularizing and applying.

Construction of OSA-related hypertension prediction model based on nomogram

Objective:This study aimed to construct a risk prediction model for obstructive sleep apnea（OSA） related hypertension based on the nomogram, and to explore the independent risk factors for OSA-related hypertension, so as to provide reference for clinical treatment decision-making. Methods:The clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 were collected retrospectively and randomly divided into training sets and validation sets. A total of 1 493 OSA patients with 27 variables were included. The least absolute shrinkage and selection operator（Lasso） logistic regression model was used to select potentially relevant features and establish a nomogram for OSA-related hypertension.The performance and clinical benefits of this nomogram were verified in terms of discrimination, calibration ability and clinical net benefit. Results:Multivariate logistic regression showed that body mass index（BMI）, family history of hypertension, lowest oxygen saturation（LSaO2）, age and cumulative percentage of total sleep time with oxygen saturation below 90% were independent risk factors for OSA-related hypertension. Lasso logistic regression identified BMI, family history of hypertension, LSaO2 and age as predictive factors for inclusion in the nomogram. The nomogram provided a favorable discrimination, with a C-indexes of 0.835（95% confidence interval[CI ]0.806-0.863） 0.865（95%CI 0.829-0.900） for the training and validation cohort, respectively, and well calibrated. The clinical decision curve analysis displayed that the nomogram was clinically useful. Conclusion:Compared with cumulative percentage of total sleep time with blood oxygen saturation below 90%, LSaO2 may have a greater impact on the incidence of OSA-related hypertension, and the effects of different times and degrees of hypoxia on OSA-related hypertension should be further explored in the future. Apnea hypopnea index involvement is weak in predicting OSA-related hypertension, and the blood oxygen index may be a better predictor variable. Furthermore, we established a risk prediction model for OSA-related hypertension patients using nomogram, and demonstrated that this prediction model was helpful to identify high-risk OSA-related hypertension patients. This model can provide early and individualized diagnosis and treatment plans, protect patients from the serious.

Establishment and Validation of Prognostic Nomograms for Nonmetastatic Melanoma of the Limbs—A SEER-Based Study

Background Malignant melanoma, a highly aggressive skin cancer, has remarkable incidence and mortality nowadays. This study aims to explore prognostic factors associated with nonmetastatic cutaneous melanoma of the limbs and to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS). Methods The study cohort was derived from the Surveillance, Epidemiology, and End Results database. Univariate Cox regression, Lasso regression, and multivariate Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The receiver operating characteristic (ROC) curve, time-dependent C-index, calibration curve, decision curve analysis (DCA) and Kaplan-Meier method were used to evaluate the accuracy and clinical applicability of the nomograms. Results A total of 15,606 patients were enrolled. Multivariate analysis identified several prognostic factors for OS and CSS including age, sex, histologic type, N stage, tumor thickness, depth of invasion, mitotic rate, ulceration, surgery of primary site, systemic therapy, race, and number of lymph nodes examined. A nomogram incorporating 12 independent predictors for OS was developed, with a C-index of 0.866 (95% confidence interval [CI]: 0.858–0.874) in the training cohort and 0.853 (95% CI: 0.839–0.867) in validation. For CSS, 10 independent predictors and one related factor were included, yielding a C-index of 0.913 (95% CI: 0.903–0.923) in the training cohort and 0.922 (95% CI: 0.908–0.936) in validation. The ROC curve, time-dependent C-index, calibration curve, DCA, and K-M plot demonstrated favorable discrimination, calibration, and clinical utility. Conclusion The developed nomograms provide a precise and personalized predictive tool for risk management of patients with nonmetastatic limb melanoma.

Dual-energy computed tomography-based radiomics for differentiating patients with and without gout flares.

There is a current lack of data pertaining to the potential link between gout flares and dual-energy computed tomography radiomic features. This study aimed to construct and validate a comprehensive dual-energy computed tomography-based radiomics model for differentiating patients with and without gout flares. The analysis included 200 patients, of whom 150 were confirmed to have experienced at least one flare in the past 12months; the remaining 50 patients did not experience flares. The radiomic features of the tophi at the bilateral first metatarsophalangeal joints were extracted and analyzed. Optimal radiomic features were selected using the least absolute shrinkage and selection operator method, and logistic regression analysis was used to screen clinical characteristics and establish a clinical model. The optimal radiomic features were then combined with the identified independent clinical variables to develop a comprehensive model. The performances of the radiomic, clinical, and comprehensive models were evaluated using receiver operating characteristic curve analysis, calibration curves, and decision curve analysis. Four radiomic features distinguished patients with at least one flare from those without flares and were used to establish the radiomic model. Disease duration and hypertension were independent factors that differentiated flare occurrences. The radiomic, clinical, and comprehensive models showed favorable discrimination, with areas under the receiver operating characteristic curves of 0.76 (95% CI, 0.69-0.83), 0.72(95% CI, 0.63-0.80), and 0.79(95% CI, 0.73-0.86), respectively. The calibration curves (P > 0.05) showed that the differentiated values of the comprehensive model agreed well with the actual values. Decision curve analysis demonstrated that the comprehensive model achieved higher net clinical benefits than the use of either the radiomic or clinical model alone. The results of this study suggest that a radiomics model can distinguish patients with and without gout flares. Our proposed clinical radiomics nomogram can increase the efficacy of differentiating flare occurrence, which may facilitate the clinical decision-making process.

A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients

BackgroundSmall cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME.MethodsWe retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 staining, and multiplex immunohistochemical staining for immune cells were performed on a subset of patients. A random survival forest (RSF) model encompassing clinical and pathomics features was established to predict overall survival. The function of putative genes was assessed via single-cell RNA sequencing.Results and conclusionDuring the median follow-up period of 12.12 months, 118 ES-SCLC patients receiving first-line immunotherapy were recruited. The RSF model utilizing three pathomics features and liver metastases, bone metastases, smoking status, and lactate dehydrogenase, could predict the survival of first-line chemoimmunotherapy in patients with ES-SCLC with favorable discrimination and calibration. Underlyingly, the higher RSF-Score potentially indicated more infiltration of CD8+ T cells in the stroma as well as a greater probability of MCL-1 amplification and EP300 mutation. At the single-cell level, MCL-1 was associated with TNFA-NFKB signaling and apoptosis-related processes. Hopefully, this noninvasive model could act as a biomarker for immunotherapy, potentially facilitating precision medicine in the management of ES-SCLC.

A DTI-based radiomics model for predicting epidermal growth factor receptor (EGFR) amplification in adult IDH1-wild glioblastomas.

Molecular alteration events are common in glioblastomas, the isocitrate dehydrogenase (IDH)-wild of which have had poor survival results so far. The progress of radiomics-based model provides novel sights for its preoperatively noninvasive prediction. To develop a radiomics-based model for predicting epidermal growth factor receptor (EGFR) amplification status in IDH1-wild glioblastomas of adults by pretreatment diffusion tensor imaging (DTI). A total of 124 patients with diagnosed glioblastomas were retrospectively collected. Six conventional magnetic resonance imaging (MRI) features of all the tumors were evaluated visually. Patients were divided into the training (n = 87) and the test set (n = 37) with a ratio of 7:3. Radiomics features were extracted from two regions of the glioblastomas, which were the total tumor (ROI_1) and the solid portion of tumor (ROI_2). The radiomics features extracted from the DTI and T1-contrast-enhanced (T1C) images were selected using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Logistic regression analysis was conducted to develop models for EGFR amplification prediction in the training set. The radiomics model based on ROI_1 demonstrated favorable discrimination in both the training (area under the curve [AUC] = 0.86) and the test set (AUC = 0.82) (P < 0.05). Combining the radiomics features and the conventional feature tumor location, no significant improvement of AUCs was achieved (AUC = 0.86 and 0.81). The radiomics model derived from pretreatment DTI may have potential in differentiating the EGFR mutation status in glioblastomas.

Prediction of Microinvasion in Breast Ductal Carcinoma in Situ Using Conventional Ultrasound Combined with Contrast-Enhanced Ultrasound Features: A Two-Center Study

BackgroundTo develop and validate a model based on conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features to preoperatively predict microinvasion in breast ductal carcinoma in situ (DCIS). Patients and MethodsData from 163 patients with DCIS who underwent CUS and CEUS from the internal hospital was retrospectively collected and randomly apportioned into training and internal validation sets in a ratio of 7:3. External validation set included 56 patients with DCIS from the external hospital. Univariate and multivariate logistic regression analysis were performed to determine the independent risk factors associated with microinvasion. These factors were used to develop predictive models. The performance was evaluated through calibration, discrimination, and clinical utility. ResultsMultivariate analysis indicated that centripetal enhancement direction (odds ratio [OR], 13.268; 95% confidence interval [CI], 3.687-47.746) and enhancement range enlarged on CEUS (OR, 4.876; 95% CI, 1.470-16.181), lesion size of ≥20 mm (OR, 3.265; 95% CI, 1.230-8.669) and calcification detected on CUS (OR, 5.174; 95% CI, 1.903-14.066) were independent risk factors associated with microinvasion. The nomogram incorporated the CUS and CEUS features achieved favorable discrimination (AUCs of 0.850, 0.848, and 0.879 for the training, internal and external validation datasets), with good calibration. The nomogram outperformed the CUS model and CEUS model (all P < .05). Decision curve analysis confirmed that the predictive nomogram was clinically useful. ConclusionThe nomogram based on CUS and CEUS features showed promising predictive value for the preoperative identification of microinvasion in patients with DCIS.

Predictive nomogram for hearing deficits after microvascular decompression treatment.

We explored the impact of brainstem auditory evoked potentials monitoring, as well as anatomical characteristics, in relation to their influence on hearing deficits. A total of 851 patients diagnosed with idiopathic hemifacial spasm underwent microvascular decompression treatment were recruited in our study. A nomogram was developed based on the regression analysis. Nomogram performance was evaluated through receiver operating characteristic (ROC), decision curve analyses and calibration curve. The rate of positive wave V change was also higher in the hearing deficit group (71.8% vs no hearing deficit group, p < 0.001). Furthermore, greater retraction depth (0.78 ± 0.25cm vs 0.55 ± 0.12cm, p < 0.001), duration (74.43 ± 15.74min vs 55.71 ± 7.01min, p < 0.001) and retraction distance (4.38 ± 0.38cm vs 4.17 ± 0.24cm, p = 0.001) were evident in the hearing deficit patients. Multivariate logistic regression showed that positive wave V change (OR 5.43), greater retraction depth (OR 55.57) and longer retraction duration (OR 1.14) emerged as significant independent predictors of postoperative hearing deficit. The external validation cohort exhibited a favorable discrimination with an AUC of 0.88. The calibration curves further confirmed the reliability of the predicted outcome in relation to the observed outcome in the external validation cohort (p = 0.89). The decision curves demonstrated that the nomogram outperformed the All or None scheme when the threshold probability ranged from > 2% to < 60% in the external validation cohort. We constructed a nomogram, including wave V, retraction depth, and retraction duration, which can effectively predict the occurrence of hearing deficits and has good clinical applicability.

Prediction modeling of postoperative pulmonary complications following lung resection based on random forest algorithm.

Postoperative pulmonary complications (PPCs) are a significant concern following lung resection due to prolonged hospital stays and increased morbidity and mortality among patients. This study aims to develop and validate a risk prediction model for PPCs after lung resection using the random forest (RF) algorithm to enhance early detection and intervention. Data from 180 patients who underwent lung resections at the Third Affiliated Hospital of the Naval Medical University between September 2022 and February 2024 were retrospectively analyzed. The patients were randomly allocated into a training set and a test set in an 8:2 ratio. An RF model was constructed using Python, with feature importance ranked based on the mean Gini index. The predictive performance of the model was evaluated through analyses of the receiver operating characteristic curve, calibration curve, and decision curve. Among the 180 patients included, 47 (26.1%) developed PPCs. The top 5 predictive factors identified by the RF model were blood loss, maximal length of resection, number of lymph nodes removed, forced expiratory volume in the first second as a percentage of predicted value, and age. The receiver operating characteristic curve and calibration curve analyses demonstrated favorable discrimination and calibration capabilities of the model, while decision curve analysis indicated its clinical applicability. The RF algorithm is effective in predicting PPCs following lung resection and holds promise for clinical application.

Multiparametric mri-based radiomics nomogram for predicting lymph-vascular space invasion in cervical cancer

PurposeTo develop and validate a multiparametric magnetic resonance imaging (mpMRI)-based radiomics model for predicting lymph-vascular space invasion (LVSI) of cervical cancer (CC).MethodsThe data of 177 CC patients were retrospectively collected and randomly divided into the training cohort (n=123) and testing cohort (n = 54). All patients received preoperative MRI. Feature selection and radiomics model construction were performed using max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) on the training cohort. The models were established based on the extracted features. The optimal model was selected and combined with clinical independent risk factors to establish the radiomics fusion model and the nomogram. The diagnostic performance of the model was assessed by the area under the curve.ResultsFeature selection extracted the thirteen most important features for model construction. These radiomics features and one clinical characteristic were selected showed favorable discrimination between LVSI and non-LVSI groups. The AUCs of the radiomics nomogram and the mpMRI radiomics model were 0.838 and 0.835 in the training cohort, and 0.837 and 0.817 in the testing cohort.ConclusionThe nomogram model based on mpMRI radiomics has high diagnostic performance for preoperative prediction of LVSI in patients with CC.

A Prognostic Model Incorporating Relevant Peripheral Blood Inflammation Indicator to Predict Postherpetic Neuralgia in Patients with Acute Herpes Zoster.

To determine the risk of postherpetic neuralgia (PHN) in patients with acute herpes zoster (HZ), this study developed and validated a novel clinical prediction model by incorporating a relevant peripheral blood inflammation indicator. Between January 2019 and June 2023, 209 patients with acute HZ were categorized into the PHN group (n = 62) and the non-PHN group (n = 147). Univariate and multivariate logistic regression analyses were conducted to identify risk factors serving as independent predictors of PHN development. Subsequently, a nomogram prediction model was established, and the discriminative ability and calibration were evaluated using the receiver operating characteristic curve, calibration plots, and decision curve analysis (DCA). The nomogram model was internally verified through the bootstrap test method. According to univariate logistic regression analyses, five variables, namely age, hypertension, acute phase Numeric Rating Scale (NRS-11) score, platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index, were significantly associated with PHN development. Multifactorial analysis further unveiled that age (odds ratio (OR) [95% confidence interval (CI)]: 2.309 [1.163-4.660]), acute phase NRS-11 score (OR [95% CI]: 2.837 [1.294-6.275]), and PLR (OR [95% CI]: 1.015 [1.010-1.022]) were independent risk factors for PHN. These three predictors were integrated to establish the prediction model and construct the nomogram. The area under the receiver operating characteristic curve (AUC) for predicting the PHN risk was 0.787, and the AUC of internal validation determined using the bootstrap method was 0.776. The DCA and calibration curve also indicated that the predictive performance of the nomogram model was commendable. In this study, a risk prediction model was developed and validated to accurately forecast the probability of PHN after HZ, thereby demonstrating favorable discrimination, calibration, and clinical applicability.

Discrimination of Degraded Pastures in the Brazilian Cerrado Using the PlanetScope SuperDove Satellite Constellation

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation.

A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database

PurposeThe objective of this investigation was to construct and validate a nomogram for prognosticating cancer-specific survival (CSS) in patients afflicted with gastrointestinal stromal tumor (GIST) at 3-, 5-, and 8-years post-diagnosis.MethodsData pertaining to patients diagnosed with GIST were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Through random selection, a training cohort (70%) and a validation cohort (30%) were established from the patient population. Employing a backward stepwise Cox regression model, independent prognostic factors were identified. Subsequently, these factors were incorporated into the nomogram to forecast CSS rates at 3-, 5-, and 8-years following diagnosis. The nomogram’s performance was assessed using indicators such as the consistency index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration curves, and decision-curve analysis (DCA).ResultsThis investigation encompassed a cohort of 3,062 GIST patients. By analyzing the Cox regression model within the training cohort, nine prognostic factors were identified: age, sex, race, marital status, AJCC (American Joint Committee on Cancer) stage, surgical status, chemotherapy status, radiation status, and income status. The nomogram was subsequently developed and subjected to both internal and external validation. The nomogram exhibited favorable discrimination abilities, as evidenced by notably high C-indices and AUC values. Calibration curves confirmed the nomogram’s reliability. Moreover, the nomogram outperformed the AJCC model, as demonstrated by enhanced NRI and IDI values. The DCA curves validated the clinical utility of the nomogram.ConclusionThe present study has successfully constructed and validated the initial nomogram for predicting prognosis in GIST patients. The nomogram’s performance and practicality suggest its potential utility in clinical settings. Nevertheless, further external validation is warranted.

Construction and Validation of a MRI‑Based Radiomic Nomogram to Predict Overall Survival in Patients with Local Advanced Cervical Cancer: A Multicenter Study

BackgroundCervical cancer is the fourth most common cancer among women. Radiomics has emerged as a new approach providing valuable information for cancer management. The aim of this study was to construct a radiomics nomogram to accurately predict survival outcomes in patients with locally advanced cervical cancer. MethodsThis retrospective study enrolled a total of 582 locally advanced cervical cancer patients from three center (training cohort: n=228; internal validation cohort: n=98; external validation cohort: n=256). Radiomic features were extracted from pretreatment MRI images. Least absolute shrinkage and selection operator logistic regression (LASSO) was applied to select radiomic features and calculated the radiomic scores. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic clinic-radiological factors for cervical cancer, which were incorporated into the nomogram. ResultsA total of 6 radiomic features were found to be associated with overall survival of locally advanced cervical cancer patients. The AUC of radiomic scores in the training cohort was 0.634-0.708 for the training cohort, 0.725-0.762 for internal validation cohort and 0.788-0.881 for the external validation cohort. Age, parametrial invasion, and radiomic score were the independent prognostic indicators for cervical cancer patients (Age: HR=1.041, 95% CI=1.012-1.071, p=0.006; Parametrial invasion: HR=4.755, 95% CI=1.493-15.144, p=0.008; HR=2.324, 95% CI=1.050-5.143, p=0.037). The nomogram model incorporating these factors showed favorable discrimination in predicting the overall survival rates of cervical cancer patients, with the AUC values of 0.809, 0.808, and 0.862 for 1-, 2-, and 3-year predictions. The decision curve analysis (DCA) indicated that the nomogram model achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities. ConclusionThe nomogram, incorporating clinicopathological factors and radiomic features derived from MRI images, showed satisfactory discrimination in predicting the overall survival rates of locally advanced cervical cancer patients.

Clinical calculator based on CT and clinicopathologic characteristics predicts short-term prognosis following resection of microsatellite-stabilized diffuse gastric cancer.

Although microsatellite stability/Epithelial-mesenchymal transition (MSS/EMT) subtypes have been reported in multiple cancer prognosis studies, strong confounding factors between MSS/EMT (usually with Lauren's diffuse phenotype) and diffuse gastric cancer (GC) may obscure the independent prognostic value of diffuse GC. Additionally, recent studies suggest a strong correlation between mural stratification based on CT and diffuse GC. This study aims to investigate potential prognostic factors of MSS diffuse GC using mural stratification and to develop a risk assessment model. This retrospective study included 131 patients with MSS diffuse GC who underwent radical surgery. Univariate and multivariate Cox proportional hazards regression analysis was used to identify model predictors and construct a nomogram for overall survival (OS) and recurrence-free survival (RFS) risks. The model's performance was evaluated using ROC, accuracy, and C-index. Internal validation of the model was conducted using the bootstrap resampling method. Among 131 cases, 60 cases (45.8%) exhibited grade 2 mural stratification, which correlated with a poorer tumor prognosis and a more invasive phenotype. Furthermore, a nomogram for predicting OS and RFS prognosis was established based on multivariate results (age, extranodal invasion, mural stratification, and/or P53). The nomogram demonstrated excellent performance, with an AUC of 0.859 (95% CI 0.794-0.924) for OS and 0.859 (95% CI 0.789-0.929) for RFS. Internal validation using 1000 bootstrap samples yielded AUC values of 0.845 and 0.846 for OS and RFS, respectively. Grade 2 mural stratification based on CT imaging revealed a more aggressive invasive phenotype, characterized by increased LN metastasis, higher rates of peritoneal metastasis, and a poorer short-term prognosis. Furthermore, the CT phenotype-based nomogram demonstrates favorable discrimination and calibration, enabling convenient individual short-term prognostic evaluation following resection of MSS diffuse GC.

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study

BackgroundAccurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methodsPatients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. ResultsA total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71–0.84; test: 0.77, 95%CI 0.68–0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. ConclusionsOur study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Rapid quantification models for assessing melamine adulteration in sport nutrition supplements via benchtop and portable NIRS instruments

Sport nutrition supplements (SNS) are vulnerable to adulteration with melamine, artificially augmenting their protein content as determined by conventional assay methodologies. Vibrational spectroscopy techniques are suitable for the detection of adulteration because they allow rapid analysis, require minimal sample preparation, and can perform numerous analyses in a short time. The aim of this study was to develop rapid quantification models for the determination of melamine adulteration in a variety of SNS matrices using NIRS (near-infrared spectroscopy) in combination with multivariate data processing. Moreover, a comparison of benchtop and portable NIR instruments was carried out. Employing a stepwise approach involving OPLS-DA and PLS analysis, matrix discrimination and prediction ability were investigated. The benchtop instrument effectively discriminated among matrices (R2Y = 0.964, Q2 = 0.933), while the portable device, although showing a slightly altered pattern, maintained favorable discrimination capability (R2Y = 0.966, Q2 = 0.931). The quantitative PLS models for each SNS matrix exhibited comparable statistical indicators for both instruments with reasonable errors for melamine content estimation and prediction (RMSEE: 0.3–2.4 %, RMSEP: 0.98–2.99 %). Higher estimation and prediction errors were observed for protein-containing samples in both acquisition modes, probably due to the tendency of protein agglomeration and adhesion to different surfaces, which affects the homogeneity of the powder. Despite data loss due to the narrower spectral range and lower resolution of the portable instrument, all models were found to be suitable for predicting melamine content in sport nutrition supplements.

Impact of magnetic resonance imaging-derived skeletal muscle index in locoregionally advanced nasopharyngeal carcinoma.

To evaluate the clinical implication of magnetic resonance imaging (MRI)-derived skeletal muscle index (SMI) in locoregionally advanced nasopharyngeal carcinoma (LANPC) patients undergoing induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) and further to develop a nomogram for predicting survival prognosis. SMI was determined through baseline MRI at the third cervical level. The nomogram was based on a training cohort involving 409 LANPC patients. We validated the prognostic accuracy of this prognostic model in an internal validation cohort (n = 204) and an external independent cohort (n = 272). SMI was an independent risk factor for OS. A prognostic model comprising age, TNM stage and SMI for individual survival prediction was developed and graphically represented as a nomogram. The model showed favorable discrimination (C-index: 0.686), predictive accuracy [time dependent area under the curve (tAUC) at 5years: 0.70], and calibration, and was further validated in the internal and external validation datasets. A risk stratification derived from the model stratified these patients into three prognostic subgroups with significantly different survival. Low SMI accessed by MRI was significantly associated with poor overall survival in LANPC patients undergoing IC + CCRT. Moreover, we established and validated a novel nomogram involving age, TNM stage and SMI that could provide accurate prognostic stratification among this population.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database

BackgroundMitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients. MethodsPatients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). ResultsThe study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems. ConclusionThis study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

Development and validation of a clinical and ultrasound features-based nomogram for preoperative differentiation of renal urothelial carcinoma and central renal cell carcinoma.

This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC). Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model's performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram. Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95% CI: 0.993-0.999) and validation (AUC = 0.995, 95% CI: 0.974, 1.000) cohorts, and good calibration (Brier scores: 0.019 and 0.016, respectively). DCA demonstrated the clinical usefulness of the US-based nomogram. A noninvasive clinical and US-based nomogram combining conventional US and CEUS features possesses good predictive value for differentiating rUC from c-RCC.