Nomogram for Predicting Hypoglycemia in Type 2 Diabetes Mellitus Patients Treated with Insulin Pump During Enteral Nutrition.

Purpose: To develop a prediction model for hypoglycemia in type 2 diabetes mellitus (T2DM) patients treated with an insulin pump during enteral nutrition. Methods: This retrospective study included T2DM patients treated with an insulin pump during enteral nutrition at the First Affiliated Hospital of Jinan University, Guangzhou Red Cross Hospital, Foshan First People's Hospital, and Guangdong Provincial Hospital of Traditional Chinese Medicine between January 2016 and December 2023. The patients were randomized 3:1 to the training and validation sets. The risk factors for hypoglycemia were analyzed. A prediction model was developed. Results: This study included 122 patients, and 57 patients had at least one hypoglycemic event during their hospitalization (46.72%). The multivariable logistic regression analysis showed that the time to reach the glycemic targets (odds ratio (OR)=1.408, 95% confidence interval (CI)=1.084-1.825, P=0.006), average glycemia (OR=0.387, 95% CI=0.233-0.643, P=0.010), coronary heart disease (OR=0.089, 95% CI=0.016-0.497, P<0.001), and the administration of hormone therapy (OR=6.807, 95% CI=1.128-41.081, P=0.037) were independently associated with hypoglycemia. A nomogram was built. The receiver operating characteristics analysis showed that the area under the curve of the model was 0.872 (95% CI=0.0.803-0.940) for the training set and 0.839 (95% CI=0.688-0.991) in the validation set. Conclusion: A nomogram was successfully built to predict hypoglycemia in T2DM patients treated with an insulin pump during enteral nutrition, based on the time to reach the glycemic targets, average glycemia, coronary heart disease, and the administration of hormone therapy.

A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis.

Background: The most serious manifestation of pulmonary cryptococcosis is complicated with cryptococcal meningitis, while its clinical manifestations lack specificity with delayed diagnosis and high mortality. The early prediction of this complication can assist doctors to carry out clinical interventions in time, thus improving the cure rate. This study aimed to construct a nomogram to predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis through a scoring system. Methods: The clinical data of 525 patients with pulmonary cryptococcosis were retrospectively analyzed, including 317 cases (60.38 %) with cryptococcal meningitis and 208 cases (39.62 %) without cryptococcal meningitis. The risk factors of cryptococcal meningitis were screened by univariate analysis, LASSO regression analysis and multivariate logistic regression analysis. Then the risk factors were incorporated into the nomogram scoring system to establish a prediction model. The model was validated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve. Results: Fourteen risk factors for cryptococcal meningitis in patients with pulmonary cryptococcosis were screened out by statistical method, including 6 clinical manifestations (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and 8 clinical indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells and B cells). The AUC value was 0.978 (CI 96.2 %∼98.9 %), indicating the nomogram was well verified. Conclusion: The nomogram scoring system constructed in this study can accurately predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis, which may provide a reference for clinical diagnosis and treatment of patients with cryptococcal meningitis.

Clinical implications of a mathematical-geometrical model to predict changes in the anterior arch dimension.

PURPOSE: Anterior arch length (AL) and the alterations in its dimension following incisor movements were shown to be predictable for an individual patient using a mathematical-geometrical model based on a third-degree parabola. Although the model has been validated previously, it is hard to apply in daily orthodontic routine. Thus, the aim of this study was to modify the model using different approaches to allow its establishment in daily routine. METHODS: This retrospective study was based on a study collective, which was described previously and consisted of 50 randomly chosen dental casts and lateral cephalograms taken before (T0) and after (T1) orthodontic treatment with fixed appliances. A JAVA computer program (Oracle, Austin, TX, USA) was developed to predict AL changes following therapeutic changes of arch width, depth or incisor inclination/position, taking the type of tooth movement into account. Performing exemplary AL calculations with the computer program, general rules and nomograms were set up, followed by multiple linear regression analyses to establish easy-to-use regression equations. RESULTS: The JAVA computer program is available for download. Sagittal changes showed more effect on AL than transverse modifications. Protruding incisors increased AL, but also reduced overbite. The extent of alteration in AL depended on the initial depth, width, incisor inclination, tooth movement type and distance between the incisal edge and the centre of rotation. CONCLUSIONS: The computer program precisely predicts individual changes in AL but is time-consuming. The presented regression equations and nomograms, considering metric variables, are easier to apply clinically and the differences compared to the AL calculated by the computer program are negligible.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

Prediction of Subsequent Vertebral Fracture After Acute Osteoporotic Fractures from Clinical and Paraspinal Muscle Features.

To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307 patients with acute OVCF between January 2013 and August 2022, and performed magnetic resonance imaging of the L3/4 and L4/5 intervertebral discs (IVDs) to estimate the cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles. We also collected clinical and radiographic data. We used univariable and multivariable Cox proportional hazards models to identify factors that should be included in the predictive nomogram. Post-OVCF vertebral fracture occurred within 3, 12, and 24 months in 33, 69, and 98 out of the 307 patients (10.8%, 22.5%, and 31.9%, respectively). Multivariate analysis revealed that this event was associated with percutaneous vertebroplasty treatment, higher FI at the L3/4 IVD levels of the psoas muscle, and lower relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. Area under the curve values for subsequent vertebral fracture at 3, 12, and 24 months were 0.711, 0.724, and 0.737, respectively, indicating remarkable accuracy of the nomogram. We developed a model for predicting post-OVCF vertebral fracture from diagnostic information about prescribed treatment, FI at the L3/4 IVD levels of the psoas muscle, and relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. This model could facilitate personalized predictions and preventive strategies.

Development and Validation of a Prognostic Nomogram for HR+ HER- Breast Cancer.

Purpose: We aimed to develop a nomogram to predict prognosis of HR+ HER2- breast cancer patients and guide the application of postoperative adjuvant chemotherapy. Methods: We identified 310 eligible HR+ HER- breast cancer patients and randomly divided the database into a training group and a validation group. The endpoint was disease free survival (DFS). Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluate predictive accuracy and discriminative ability of the nomogram. We also compared the predictive accuracy and discriminative ability of our nomogram with the eighth AJCC staging system using overall data. Results: According to the training group, platelet-to-lymphocyte ratio (PLR), tumor size, positive lymph nodes and Ki-67 index were used to construct the nomogram of DFS. The C-index of DFS was 0.708 (95% CI: 0.623-0.793) in the training group and 0.67 (95% CI: 0.544-0.796) in the validation group. The calibration curves revealed great consistencies in both groups. Conclusion: We have developed and validated a novel and practical nomogram that can provide individual prediction of DFS for patients with HR+ HER- breast cancer. This nomogram may help clinicians in risk consulting and guiding the application of postoperative adjuvant chemotherapy.

A novel anoikis-related gene signature can predict the prognosis of hepatocarcinoma patients.

Background: Hepatocellular carcinoma (HCC) is a major health problem with more than 850,000 cases per year worldwide. This cancer is now the third leading cause of cancer-related deaths worldwide, and the number is rising. Cancer cells develop anoikis resistance which is a vital step during cancer progression and metastatic colonization. However, there is not much research that specifically addresses the role of anoikis in HCC, especially in terms of prognosis. Methods: This study obtained gene expression data and clinical information from 371 HCC patients through The Cancer Genome Atlas (TCGA) Program and The Gene Expression Omnibus (GEO) databases. A total of 516 anoikis-related genes (ANRGs) were retrieved from GeneCard database and Harmonizome portal. Differential expression analysis identified 219 differentially expressed genes (DEGs), and univariate Cox regression analysis was utilized to select 99 ANRGs associated with the prognosis of HCC patients. A risk scoring model with seven genes was established using the least absolute shrinkage and selection operator (LASSO) regression model, and internal validation of the model was performed. Results: The identified 99 ANRGs are closely associated with the prognosis of HCC patients. The risk scoring model based on seven characteristic genes demonstrates excellent predictive performance, further validated by receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. The study reveals significant differences in immune cell infiltration, gene expression, and survival status among different risk groups. Conclusions: The prognosis of HCC patients can be predicted using a unique prognostic model built on ANRGs in HCC.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days.

Objectives: This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods: From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study.

OBJECTIVES: To create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass. RESEARCH METHODOLOGY/DESIGN: This prospective, observational study was conducted in the Cardiac Intensive Care Unit at a Children's Hospital, enrolling 501 pediatric patients from February 2022 to January 2023. Perioperative data were systematically collected through the hospital information system. Postoperative delirium was assessed using the Cornell Assessment of Pediatric Delirium (CAPD). For model development, Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify the most relevant predictors. These selected predictors were then incorporated into a multivariable logistic regression model to construct the predictive nomogram. The performance of the model was evaluated by Harrell's concordance index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. External validity of the model was confirmed through the C-index and calibration plots. RESULTS: Five independent predictors were identified: age, SpO2 levels, lymphocyte count, diuretic use, and midazolam administration, integrated into a predictive nomogram. This nomogram demonstrated strong predictive capacity (AUC 0.816, concordance index 0.815) with good model fit (Hosmer-Lemeshow test p = 0.826) and high accuracy. Decision curve analysis showed a significant net benefit, and external validation confirmed the nomogram's reliability. CONCLUSIONS: The study successfully developed a precise and effective nomogram for identifying pediatric patients at high risk of post-cardiopulmonary bypass delirium, incorporating age, SpO2 levels, lymphocyte counts, diuretic use, and midazolam medication. IMPLICATIONS FOR CLINICAL PRACTICE: This nomogram aids early delirium detection and prevention in critically ill children, improving clinical decisions and treatment optimization. It enables precise monitoring and tailored medication strategies, significantly contributes to reducing the incidence of delirium, thereby enhancing the overall quality of patient care.

Serum Anti-Mullerian hormone levels in Turkish girls aged 18 and younger for ovarian reserve determination.

Objective: Our aim was to show that AMH may be used as a quantitative marker of ovarian reserve in Turkish girls aged 18 and younger and establish the reference values for AMH in Turkish girls. Material and Methods: This retrospective study includes girls between ages of 8-18, without premature ovarian failure or without genetic factors resulting in ovarian dysgenesis. Blood specimens were collected after overnight fasting early in the morning during the early follicular phase. Measurement of serum levels of gonadotropins and AMH is done. Mean serum AMH levels of different age groups and best fitting curve representing AMH percentiles (10th, 25th, 50th, 75th, 90th) were calculated. Results: We identified 785 Turkish girls with mean age of 16.16 ± 1.90. The girls were divided into seven age groups. The mean serum AMH level for total cohort is 5.20 ± 4.19 ng/mL. There was statistically significant difference between the mean values of AMH in age groups as follows: £12 and 17-£18 (p=0.011). The best fitting curves for AMH percentiles were 4th order polynomial functions. There is statistically significant correlation of AMH with age and FSH levels (r=0.148, p=0.000 and r=-0.092, p=0.010). Conclusion: Our results reflect the real-life data for serum AMH values in Turkish girls. Our nomogram may be useful for counseling the adolescents about their ovarian reserve and diagnosing other gynecological diseases. A longitudinal study is necessary for improving the predictive value of AMH values in girls aged 18 and younger.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Introduction: Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Methods: Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. Results: The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. Conclusion: This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

Association between increased systemic immune-inflammation index and postoperative delirium in older intertrochanteric fracture patients.

BACKGROUND AND PURPOSE: The systemic immune-inflammation index (SII), a novel inflammation index derived from the counts of circulating platelets, neutrophils and lymphocytes, has been studied in the treatment of acute cancer and ischemic stroke (AIS). However, the clinical value of the SII in postoperative delirium patients has not been further investigated. The purpose of our research was to study the incidence and preoperative risk factors for postoperative delirium (POD) and verify whether the SII could serve as a potential marker for POD in older intertrochanteric fracture patients. Finally, we created a novel nomogram for predicting POD in older patients with intertrochanteric fractures. METHODS: We enrolled elderly patients with intertrochanteric fractures who underwent proximal femoral nail antirotation (PFNA) between February 2021 and April 2023. Univariate and multivariate logistic analyses were subsequently performed to confirm the risk factors and construct a nomogram model.Calibration curve and clinical decision curve analysis (DCA) were used to assess the model's fitting performance. The performance of the nomogram was evaluated for discrimination, calibration, and clinical utility. RESULTS: A total of 293 patients were eligible for inclusion in the study, 25.6% (75/293) of whom had POD. The POD patients had higher SII values than the non-POD patients. The SII was strongly correlated with POD in older intertrochanteric fracture patients, and the optimal cutoff value was 752.6 × 109. Multivariate analysis revealed that age, diabetes, total albumin, SII > 752.6 × 109 and a CRP > 20.25 mg/L were independent risk factors for POD patients. By incorporating these 5 factors, the model achieved a concordance index of 0.745 (95% CI, 0.683-0.808) and had a well-fitted calibration curve and good clinical application value. CONCLUSION: The SII is a simple and valuable biomarker for POD, and the new nomogram model can be used to accurately predict the occurrence of POD. They can be utilized in clinical practice to identify those at high risk of POD in older intertrochanteric fracture patients.

Development and validation of nomograms for predicting prognosis in patients with solitary HCC: A TRIPOD-Compliant study.

Objective: To develop and validate nomograms for predicting the OS and CSS of patients with Solitary Hepatocellular Carcinoma (HCC). Methods: Using the TRIPOD guidelines, this study identified 5206 patients in the Surveillance, Epidemiology, and End Results (SEER) 17 registry database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 3646) and a validation cohort (n = 1560), and the Chinese independent cohort (n = 307) constituted the external validation group. The prognosis-related risk factors were selected using univariate Cox regression analysis, and the independent prognostic factors of OS and CSS were identified using the Lasso-Cox regression model. The nomograms for predicting the OS and CSS of the patients were constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve in both the training and validation cohorts. Results: We identified factors that predict OS and CSS and constructed two nomograms based on the data. The ROC analysis, C-index analysis, and calibration analysis indicated that the two nomograms performed well over the 1, 3, and 5-year OS and CSS periods in both the training and validation cohorts. Additionally, these results were confirmed in the external validation group. Decision curve analysis (DCA) demonstrated that the two nomograms were clinically valuable and superior to the TNM stage system. Conclusion: We established and validated nomograms to predict 1,3, and 5-year OS and CSS in solitary HCC patients, and our results may also be helpful for clinical decision-making.

A New Risk Prediction Model for Detecting Endoscopic Activity of Ulcerative Colitis.

Background/Aims: : Ulcerative colitis (UC) is an incurable, relapsing-remitting inflammatory disease that increases steadily. Mucosal healing has become the primary therapeutic objective for UC. Nevertheless, endoscopic assessments are invasive, expensive, time-consuming, and inconvenient. Therefore, it is crucial to develop a noninvasive predictive model to monitor endoscopic activity in patients with UC. Methods: : Clinical data of 198 adult patients with UC were collected from January 2016 to August 2022 at Huadong Hospital, China. Results: : Patients with UC were randomly divided into the training cohort (70%, n=138) and the validation cohort (30%, n=60). The receiver operating characteristic curve value for the training group was 0.858 (95% confidence interval [CI], 0.781 to 0.936), whereas it was 0.845 (95% CI, 0.731 to 0.960) for the validation group. The calibration curve employed the Hosmer-Lemeshow test (p>0.05) to demonstrate the consistency between the predicted and the actual probabilities in the nomogram of these two groups. The decision curve analysis validated that the nomogram had clinical usefulness. Conclusions: : The nomogram, which incorporated activated partial thromboplastin time, fecal occult blood test, ß2-globulin level, and fibrinogen degradation products, served as a prospective tool for evaluating UC activity in clinical practices.

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Objective: To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM). Methods: The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020. Results: According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval [CI]: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models. Conclusion: The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

Establishment and validation of a nomogram for predicting overall survival of upper-tract urothelial carcinoma with bone metastasis: a population-based study.

BACKGROUND: Bone metastasis (BM) carries a poor prognosis for patients with upper-tract urothelial carcinoma (UTUC). This study aims to identify survival predictors and develop a prognostic nomogram for overall survival (OS) in UTUC patients with BM. METHODS: The Surveillance, Epidemiology, and End Results database was used to select patients with UTUC between 2010 and 2019. The chi-square test was used to assess the baseline differences between the groups. Kaplan-Meier analysis was employed to assess OS. Univariate and multivariate analyses were conducted to identify prognostic factors for nomogram establishment. An independent cohort was used for external validation of the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). All statistical analyses were performed using SPSS 23.0 and R software 4.2.2. RESULTS: The mean OS for UTUC patients with BM was 10 months (95% CI: 8.17 to 11.84), with 6-month OS, 1-year OS, and 3-year OS rates of 41%, 21%, and 3%, respectively. Multi-organ metastases (HR = 2.21, 95% CI: 1.66 to 2.95, P < 0.001), surgery (HR = 0.72, 95% CI: 0.56 to 0.91, P = 0.007), and chemotherapy (HR = 0.37, 95% CI: 0.3 to 0.46, P < 0.001) were identified as independent prognostic factors. The C-index was 0.725 for the training cohort and 0.854 for the validation cohort, and all AUC values were > 0.679. The calibration curve and DCA curve showed the accuracy and practicality of the nomogram. CONCLUSIONS: The OS of UTUC patients with BM was poor. Multi-organ metastases was a risk factor for OS, while surgery and chemotherapy were protective factors. Our nomogram was developed and validated to assist clinicians in evaluating the OS of UTUC patients with BM.

Using Apparent Diffusion Coefficient (ADC) of Endometrial Cancer MRI to Determine P53 Molecular Subtypes.

BACKGROUND: Endometrial Cancer (EC) is a highly heterogeneous cancer comprising both histological and molecular subtypes. Using a non-invasive modality method to trigger these subtypes as early as possible can aid clinicians in establishing individualized treatment. PURPOSE: The study aimed to clarify the value of the Apparent Diffusion Coefficient (ADC) of EC MRI in determining molecular subtypes. MATERIAL AND METHODS: We retrospectively recruited 109 patients with pathologically proven EC (78 endometrioid cancers and 31 non-endometrioid cancers) with available molecular classification from a tertiary centre. MRI was prospectively performed a month prior to surgery; images were blindly interpreted by two experienced radiologists with consensus reading. The ADC value was measured by an experienced radiologist on the commercially available processing workstation. Interoperator measurement consistency was calculated. RESULTS: Our sample comprised 17 PLOE, 32 MSI-H, 31 NSMP, and 29 P53abn ECs. Clinical information did not differ significantly among the groups. The maximum diameter and volume of the lesions differed among the groups. The ADC value in the maximal area (ADCarea) or region of interest (ROI, ADCroi) in the P53abn group was higher than that in the other groups (894.0 ±12.6 and 817.5 ± 83.3 x10-6 mm2/s). The ADC mean values were significantly different between the P53abn group and the other groups (P = 0.000). The nomogram showed the highest discriminative ability to distinguish P53abn EC from other types (AUC: 0.859). CONCLUSION: Our results have suggested the quantitative MR characteristics (ADC values) derived from preoperative EC MRI to provide useful information in preoperatively determining P53abn cancer.

A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer.

OBJECTIVES: To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. MATERIALS AND METHODS: A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications. RESULTS: The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model. CONCLUSION: A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.

A Nomogram for Predicting ADHD and ASD in Child and Adolescent Mental Health Services (CAMHS).

Objectives: To enhance the early detection of Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) by leveraging clinical variables collected at child and adolescent mental health services (CAMHS). Methods: This study included children diagnosed with ADHD and/or ASD (n = 857). Three logistic regression models were developed to predict the presence of ADHD, its subtypes, and ASD. The analysis began with univariate logistic regression, followed by a multicollinearity diagnostic. A backward logistic regression selection strategy was then employed to retain variables with p < 0.05. Ethical approval was obtained from the local ethics committee. The models' internal validity was evaluated based on their calibration and discriminative abilities. Results: The study produced models that are well-calibrated and validated for predicting ADHD (incorporating variables such as physical activity, history of bone fractures, and admissions to pediatric/psychiatric services) and ASD (including disability, gender, special education needs, and Axis V diagnoses, among others). Conclusions: Clinical variables can play a significant role in enhancing the early identification of ADHD and ASD.