High Clinical Utility Research Articles

A nomogram for predicting neurogenic lower urinary tract dysfunction in patients with acute ischemic stroke: A retrospective study.

To investigate the risk factors for neurogenic lower urinary tract dysfunction (NLUTD) in patients with acute ischemic stroke (AIS), and develop an internally validated predictive nomogram. The study aims to offer insights for preventing AIS-NLUTD. We conducted a retrospective study on AIS patients in a Shenzhen Hospital from June 2021 to February 2023, categorizing them into non-NLUTD and NLUTD groups. The bivariate analysis identified factors for AIS-NLUTD (p < 0.05), integrated into a least absolute shrinkage and selection operator (LASSO) regression model. Significant variables from LASSO were used in a multivariate logistic regression for the predictive model, resulting in a nomogram. Nomogram performance and clinical utility were evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC). Internal validation used 1000 bootstrap resamplings. A total of 373 participants were included in this study, with an NLUTD incidence rate of 17.7% (66/373). NIHSS score (OR = 1.254), pneumonia (OR = 6.631), GLU (OR = 1.240), HGB (OR = 0.970), and hCRP (OR = 1.021) were used to construct a predictive model for NLUTD in AIS patients. The model exhibited good performance (AUC = 0.899, calibration curve p = 0.953). Internal validation of the model demonstrated strong discrimination and calibration abilities (AUC = 0.898). Results from DCA and CIC curves indicated that the prediction model had high clinical utility. We developed a predictive model for AIS-NLUTD and created a nomogram with strong predictive capabilities, assisting healthcare professionals in evaluating NLUTD risk among AIS patients and facilitating early intervention.

Prognostic evaluation of segmental ureterectomy combined with chemotherapy in high-grade non-metastatic ureteral cancer: a study based on the SEER database

This study evaluates the survival outcomes of segmental ureterectomy (SU) combined with chemotherapy in patients with high-grade non-metastatic ureteral cancer (UC) using data from the SEER database. A total of 1757 patients with Grade III-IV non-metastatic UC were analyzed. Overall survival (OS) was assessed through Kaplan–Meier analysis, and independent prognostic factors were identified via Cox regression. A Nomogram model was developed and evaluated using the concordance index, area under the time-dependent ROC curve, calibration curves, and decision curve analysis. The 1-, 3-, and 5-year OS rates were 82.8%, 55.6%, and 42.8%, respectively. Age, treatment protocol, T stage, and N stage were significant prognostic factors. Both SU + chemotherapy and radical nephroureterectomy (RNU) + chemotherapy demonstrated comparable survival outcomes, outperforming surgery alone, particularly in patients aged 70 and older. The Nomogram demonstrated high predictive accuracy and clinical utility. These findings suggest that SU + chemotherapy offers survival benefits similar to RNU + chemotherapy, making it a viable option, especially for elderly patients or those with impaired renal function.

Planning for complex inferior vena cava filter retrievals: the implementation and effectiveness of 3D printed models

BackgroundInferior vena cava filter (IVC) retrieval is most often routine but can be challenging with high morbidity in complex cases, especially those with an extended dwelling time. While risk of morbidity in complex retrievals is decreased with advanced filter retrieval techniques, deciding when and which to use these requires detailed pre-procedural planning. The purpose of our study was to evaluate patient-specific 3D printed anatomic IVC filter models for aiding complex IVC filter retrievals.MethodsAll IVC filter retrieval patients between June 2021 and September 2022 at one academic medical hospital were prospectively screened. Nine met criteria for complex retrieval, and their CT images were used to 3D print patient-specific IVC and filter models. Models were used in pre-procedural planning and clinical utility was assessed using the Anatomic Model Utility Likert Questionnaire and estimations of the procedural and fluoroscopy time saved.ResultsThe usage of 3D printed models in pre-procedural planning had high clinical utility based on the Likert questionnaire (Anatomic Model Utility Points 366.7 ± 103.1). Using a model significantly increased confidence in planning (p = 0.03) and modified the treatment plan in seven cases. It also led to cost-efficient use of resources in the procedure suite with estimated reduction in procedure and fluoroscopy time of 29.0 [20.3] (p = 0.003) and 10.2 [6.7] (p = 0.002) minutes, respectively.Conclusion3D printed anatomic models for patients who require complex IVC filter retrieval demonstrated Likert-based high clinical utility and led to estimated reductions of procedural and fluoroscopy time.

Prediction of feeding difficulties in neonates with hypoxic-ischemic encephalopathy using magnetic resonance imaging-derived radiomics features.

The mechanisms behind brain and spinal cord injuries in hypoxic-ischemic encephalopathy (HIE) and associated feeding difficulties are unclear, with previous magnetic resonance imaging (MRI) attempts yielding inconclusive results. We aim to evaluate an MRI radiomics model for predicting feeding difficulties in HIE infants. Additionally, we investigate changes in predictive capability after incorporating the duration of mechanical ventilation and the timing of MRI examination. Retrospective study with 151 HIE infants (January 2013 to December 2021), randomly divided into training and validation sets. Radiomics features extracted from basal ganglia-thalamus and brainstem in T1-weighted and T2-weighted MRI. Established single-modality, single-site, and multimodality/multisite models. Receiver operating characteristic analysis and area under the curve evaluated models. Decision curve analysis assessed changes in predictive capability. The combined radiomics model of the basal ganglia-thalamus and brainstem regions on the T2-weighted imaging demonstrated superior performance (area under the curve: 0.958 and 0.875 for training and validation, respectively). Combining scores with duration of mechanical ventilation and MRI examination time in a calibration plot model improved and stabilized performance, showing high fitting and clinical utility. Decision curve analysis favored the combined calibration plot model. The MRI-based radiomics model predicts feeding difficulties in HIE infants, with basal ganglia-thalamus and brainstem as relevant factors. The combined calibration plot model exhibits the highest clinical predictive efficacy.

Predictive Value of Current Perception Threshold for Prognosis of Pulsed Radiofrequency in Patients with Acute Herpetic Neuralgia.

This study aimed to evaluate the prognostic accuracy of the Current Perception Threshold (CPT) in Acute Herpetic Neuralgia (AHN) patients receiving Pulsed Radiofrequency (PRF) therapy and to develop a corresponding prognostic model. We retrospectively analyzed data from 106 AHN patients treated with PRF between January 2022 and May 2023. The occurrence of Postherpetic Neuralgia (PHN) after treatment categorized patients into non-PHN and PHN groups. The predictive role of CPT indices for PRF outcomes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC). Then the dataset was split into a training set (n=74) and a validation set (n=32). Factors associated with PHN development were identified using univariate and multivariate logistic regression. A nomogram model was developed using significant predictors and internal validation was performed using valid set data. Among the 106 patients, 45had a poor prognosis. Significant differences in age, preoperative Numerical Rating Scale (NRS) score, and 5Hz CPT ratio were observed between the groups (p<0.05). Logistic regression identified these factors as independent predictors for PRF prognosis (p<0.05). The 5Hz CPT ratio demonstrated predictive value (AUC= 0.764, 95% CI: 0.674-0.855). The nomogram model, incorporating these predictors, showed high AUC in both the training (0.863, 95% CI: 0.776-0.950) and validation sets (0.859, 95% CI: 0.721-0.998). Calibration curves indicated good model fit, and the Hosmer-Lemeshow test confirmed this (p>0.05). Decision Curve Analysis (DCA) highlighted the model's predictive advantage. The 5Hz CPT ratio can predict the prognosis of PRF in AHN patients. The nomogram model has high precision and clinical utility. It can help identify AHN patients with a poor PRF prognosis at an early stage and assist in clinical decision-making.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Relationship between EZH2 expression and prognosis of patients with hepatocellular carcinoma using a pathomics predictive model

BackgroundEnhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) is overexpressed in hepatocellular carcinoma, promoting tumorigenesis and correlating with poor prognosis. Traditional histopathological examinations are insufficient to accurately predict hepatocellular carcinoma (HCC) survival; however, pathomics models can predict EZH2 expression and HCC prognosis. This study aimed to investigate the relationship between pathomics features and EZH2 expression for predicting overall survival of patients with HCC. MethodsWe analyzed 267 patients with HCC from the Cancer Genome Atlas database, with available pathological images and gene expression data. RNA sequencing data were divided into high and low EZH2 expression groups for prognosis and survival analysis. Pathological image features were screened using mRMR_RFE. A pathological model was constructed using a gradient boosting machine (GBM) algorithm, and efficiency evaluation and survival analysis of the model were performed. The R package “survminer” took the pathomics score (PS) cutoff value of 0.4628 to divide the patients into two groups: high and low PS expression. Survival analyses included Kaplan–Meier curve analysis, univariate and multivariate Cox regression analyses, and interaction tests. Potential pathomechanisms were explored through enrichment, differential, immune cell infiltration abundance, and gene mutation analyses. ResultEZH2 was highly expressed in tumor samples but poorly expressed in normal tissue samples. Univariate and multivariate Cox regression analyses revealed that EZH2 was an independent risk factor for HCC (hazard ratio [HR], 2.792 and 3.042, respectively). Seven imaging features were selected to construct a pathomics model to predict EZH2. Decision curve analysis showed that the model had high clinical utility. Multivariate Cox regression analysis showed that high PS expression was an independent risk factor for HCC prognosis (HR, 2.446). The Kaplan–Meier curve showed that high PS expression was a risk factor for overall survival. ConclusionEZH2 expression can affect the prognosis of patients with liver cancer. Our pathological model could predict EZH2 expression and prognosis of patients with HCC with high accuracy and robustness, making it a new and potentially valuable tool.

Nomogram based on lymphocyte-associated inflammatory indexes predicts portal vein thrombosis after splenectomy with esophagogastric devascularization

ObjectiveThe relationship between lymphocyte-associated inflammatory indices and portal vein thrombosis (PVT) following splenectomy combined with esophagogastric devascularization (SED) is currently unclear. This study aims to investigate the association between these inflammatory indices and PVT, and to develop a nomogram based on these indices to predict the risk of PVT after SED, providing an early warning tool for clinical practice.MethodsWe conducted a retrospective analysis of clinical data from 131 cirrhotic patients who underwent SED at Lanzhou University’s Second Hospital between January 2014 and January 2024. Independent risk factors for PVT were identified through univariate and multivariate logistic regression analyses, and the best variables were selected using the Akaike Information Criterion (AIC) to construct the nomogram. The model’s predictive performance was assessed through receiver operating characteristic (ROC), calibration, decision, and clinical impact curves, with bootstrap resampling used for internal validation.ResultsThe final model incorporated five variables: splenic vein diameter (SVD), D-Dimer, platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and red cell distribution width-to-lymphocyte ratio (RLR), achieving an area under the curve (AUC) of 0.807, demonstrating high predictive accuracy. Calibration and decision curves demonstrated good calibration and significant clinical benefits. The model exhibited good stability through internal validation.ConclusionThe nomogram model based on lymphocyte-associated inflammatory indices effectively predicts the risk of portal vein thrombosis after SED, demonstrating high accuracy and clinical utility. Further validation in larger, multicenter studies is needed.

High Diagnostic Yield and Clinical Utility of Next-Generation Sequencing in Children with Epilepsy and Neurodevelopmental Delays: A Retrospective Study.

Advances in genetics led to the identification of hundreds of epilepsy-related genes, some of which are treatable with etiology-specific interventions. However, the diagnostic yield of next-generation sequencing (NGS) in unexplained epilepsy is highly variable (10-50%). We sought to determine the diagnostic yield and clinical utility of NGS in children with unexplained epilepsy that is accompanied by neurodevelopmental delays and/or is medically intractable. A 5-year retrospective review was conducted at the American University of Beirut Medical Center to identify children who underwent whole exome sequencing (WES) or whole genome sequencing (WGS). Data on patient demographics, neurodevelopment, seizures, and treatments were collected. Forty-nine children underwent NGS with an overall diagnostic rate of 68.9% (27/38 for WES, and 4/7 for WGS). Most children (42) had neurodevelopmental delays with (18) or without (24) refractory epilepsy, and only three had refractory epilepsy without delays. The diagnostic yield was 77.8% in consanguineous families (18), and 61.5% in non-consanguineous families (26); consanguinity information was not available for one family. Genetic test results led to anti-seizure medication optimization or dietary therapies in six children, with subsequent improvements in seizure control and neurodevelopmental trajectories. Not only is the diagnostic rate of NGS high in children with unexplained epilepsy and neurodevelopmental delays, but also genetic testing in this population may often lead to potentially life-altering interventions.

Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.

This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis. Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability. Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes. Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary. Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies. International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence

ObjectiveTo predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features and clinical data from pelvic magnetic resonance imaging (MRI).Methods52 vulvar...

Patient-centric thresholding of Cobas® EGFR mutation Test v2 for surveillance of EGFR-mutated metastatic non-small cell lung cancer

Cobas EGFR mutation Test v2 was FDA-approved as qualitative liquid biopsy for actionable EGFR variants in non-small cell lung cancer (NSCLC). It generates semiquantitative index (SQI) values that correlate with mutant allele levels, but decision thresholds for clinical use in NSCLC surveillance are lacking. We conducted long-term ctDNA monitoring in 20 subjects with EGFR-mutated NSCLC; resulting in a 155 on-treatment samples. We defined optimal SQI intervals to predict/rule-out progression within 12 weeks from sampling and performed orthogonal calibration versus deep-sequencing and digital PCR. SQI showed significant diagnostic power (AUC 0.848, 95% CI 0.782–0.901). SQI below 5 (63% of samples) had 93% (95% CI 87–96%) NPV, while SQI above 10 (25% of samples) had 69% (95% CI 56–80%) PPV. Cobas EGFR showed perfect agreement with sequencing (Kappa 0.860; 95% CI 0.674–1.00) and digital PCR. SQI values strongly (r: 0.910, 95% 0.821–0.956) correlated to mutant allele concentrations with SQI of 5 and 10 corresponding to 6–9 (0.2–0.3%) and 64–105 (1.1–1.6%) mutant allele copies/mL (VAF) respectively. Our dual-threshold classifier of SQI 0/5/10 yielded informative results in 88% of blood draws with high NPV and good overall clinical utility for patient-centric surveillance of metastatic NSCLC.

Feasibility of noninvasive near-infrared spectroscopy monitoring in predicting the prognosis of spontaneous intracerebral hemorrhage.

This study aimed to assess the impact of multimodal monitoring on predicting the prognosis of patients with spontaneous intracerebral hemorrhage (SICH) and to examine the feasibility of using noninvasive near-infrared spectroscopy (NIRS) for monitoring clinical prognosis. Clinical data of 38 patients with SICH who underwent surgery in the Department of Neurosurgery of Shaanxi Provincial People's Hospital from May 2022 to December 2022 were retrospectively analyzed. The patients were categorized into two groups based on the Glasgow Outcome Scale (GOS) 3 months after operation: poor outcome group (GOSI-III) and good outcome group (GOSIV and V). Multimodal monitoring included invasive intracranial pressure (ICP), brain temperature (BT), internal jugular venous oxygen saturation (SjvO2), and noninvasive NIRS. NIRS monitoring comprised the assessment of brain tissue oxygen saturation (StO2), blood volume index (BVI), and tissue hemoglobin index (THI). The prognostic differences between the two groups were compared. The predictive values were evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). ICP, BT, BVI, and THI in the good prognosis group were lower than those in the poor prognosis group. The SjvO2 and StO2 in the group with a good prognosis were higher than those in the group with a poor prognosis. The levels of ICP, BT, SjvO2, StO2, BVI, and THI reflect the changes in brain function and cerebral blood flow and significantly correlate with the prognosis of patients with SICH. NIRS monitoring has a high clinical utility in assessing the prognosis.

The clinical significance assessment of the transverse lymph node metastasis in gastric cancer: The establishment and validation of nomogram from a single clinical medical center

BackgroundLymph node metastasis is an important route for gastric cancer metastasis. The clinical significance of transverse lymph node metastasis (TLNM) is still unclear. AimsThis study investigates effects of TLNM on the prognosis of GC patients and establishes two nomograms for evaluating the prognosis of GC patients and for predicting the risk clinicopathological factors to TLNM based on a Chinese medical database. MethodsA total of 902 GC patients with lymph node metastasis (LNM) who underwent R0 gastrectomy was included in this study. According to results of Cox proportional hazards analyses and logistic regression analyses, the prognostic and the predictive nomograms were established and validated. ResultsThe overall survival of patients with TLNM was significantly worse than those without TLNM (P < 0.001) and similar to patients with extra-gastric LNM (P > 0.05). TLNM independently influenced prognosis of GC patients. Prognostic and predictive nomograms were established and validated. Both nomograms were proven that have high accuracy by calculating each AUC (Area Under Cure) value. Calibration curves aligned well with actual outcomes. DCA (Decision Curve Analyses) analyses indicated the high clinical utility. ConclusionThese nomograms offer precise survival and TLNM occurrence predictions, which may aid clinical decisions.

The diagnostic value of pleural effusion/serum ratio of carcinoembryonic antigen and pleural effusion/serum ratio of interferon-γ in classification of pleural effusion.

Distinguishing between different types of pleural effusions (PEs) is crucial for clinical diagnosis and treatment. This study evaluates the diagnostic value of carcinoembryonic antigen (CEA) and interferon-gamma (IFN-γ) levels in PE and serum, as well as the PE/serum ratios of these markers, in classifying PE. We retrospectively analyzed 99 patients with PE, categorizing them into malignant pleural effusion (MPE), tuberculous pleural effusion (TPE), and benign PE groups. Levels of CEA and IFN-γ in PE and serum were quantified and their ratios were calculated. Diagnostic performance was assessed using receiver operating characteristic analysis, focusing on the area under the curve (AUC) to determine the efficacy of these biomarkers. Significantly elevated levels of CEA in PE and serum were observed in the MPE group compared to the benign and TPE groups, with the PE/serum CEA ratio offering substantial diagnostic value (AUCs: PE = 0.843, serum = 0.744). Conversely, IFN-γ levels in PE and serum were markedly higher in the TPE group, demonstrating notable diagnostic accuracy (AUCs: PE = 0.970, serum = 0.917). Both CEA and IFN-γ demonstrate high clinical utility in differentiating between MPE and TPE. The PE/serum ratio of these biomarkers enhances diagnostic accuracy, potentially facilitating earlier and more accurate therapeutic interventions.

Development and validation of an intraoperative hypothermia nomograph model for patients undergoing video-assisted thoracoscopic lobectomy: a retrospective study

This study aimed to develop and internally validate a nomogram model for assessing the risk of intraoperative hypothermia in patients undergoing video-assisted thoracoscopic (VATS) lobectomy. This study is a retrospective study. A total of 530 patients who undergoing VATS lobectomy from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into hypothermia group (n = 346) and non-hypothermia group (n = 184) according to whether hypothermia occurred during the operation. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of hypothermia during operation, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. Calibration curve and Hosmer Lemeshow test were used to evaluate the accuracy of the model. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Intraoperative hypothermia occurred in 346 of 530 patients undergoing VATS lobectomy (65.28%). Logistic regression analysis showed that age, serum total bilirubin, inhaled desflurane, anesthesia duration, intraoperative infusion volume, intraoperative blood loss and body mass index were risk factors for intraoperative hypothermia in patients undergoing VATS lobectomy (P < 0.05). The area under ROC curve was 0.757, 95% CI (0.714–0.799). The optimal cutoff value was 0.635, the sensitivity was 0.717, and the specificity was 0.658. These results suggested that the model was well discriminated. Calibration curve has shown that the actual values are generally in agreement with the predicted values. Hosmer–Lemeshow test showed that χ2 = 5.588, P = 0.693, indicating that the model has a good accuracy. The DCA results confirmed that the model had high clinical utility. The nomogram model constructed in this study showed good discrimination, accuracy and clinical utility in predicting patients with intraoperative hypothermia, which can provide reference for medical staff to screen high-risk of intraoperative hypothermia in patients undergoing VATS lobectomy.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer (EC) is a common gynecological malignancy that typically requires prompt surgical intervention; however, the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes. Previous studies have highlighted the prognostic potential of circulating tumor DNA (ctDNA) monitoring for minimal residual disease in patients with EC. AIM To develop and validate an optimized ctDNA-based model for predicting short-term postoperative EC recurrence. METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model, which was validated on 143 EC patients operated between 2020 and 2021. Prognostic factors were identified using univariate Cox, Lasso, and multivariate Cox regressions. A nomogram was created to predict the 1, 1.5, and 2-year recurrence-free survival (RFS). Model performance was assessed via receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA), leading to a recurrence risk stratification system. RESULTS Based on the regression analysis and the nomogram created, patients with postoperative ctDNA-negativity, postoperative carcinoembryonic antigen 125 (CA125) levels of &lt; 19 U/mL, and grade G1 tumors had improved RFS after surgery. The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis, calibration curves, and DCA methods, highlighting its high accuracy and clinical utility. Furthermore, using the nomogram, the patients were successfully classified into three risk subgroups. CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1, 1.5, and 2 years. This model will help clinicians personalize treatments, stratify risks, and enhance clinical outcomes for patients with EC.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Epidemiology and prognostic nomogram for locally advanced gastric signet ring cell carcinoma: A population-based study.

Gastric signet ring cell carcinoma (GSRC) represents a specific subtype of gastric cancer renowned for its contentious epidemiological features, treatment principles, and prognostic factors. To investigate the epidemiology of GSRC and establish an improved model for predicting the prognosis of patients with locally advanced GSRC (LAGSRC) after surgery. The annual rates of GSRC incidence and mortality, covering the years 1975 to 2019, were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to explore the temporal trends in both disease incidence and mortality rates using Joinpoint software. The clinical data of 3793 postoperative LAGSRC patients were collected from the SEER database for the analysis of survival rates. The Cox regression model was used to explore the independent prognostic factors for overall survival (OS). The risk factors extracted were used to establish a prognostic nomogram. The overall incidence of GSRC increased dramatically between 1975 and 1998, followed by a significant downward trend in incidence after 1998. In recent years, there has been a similarly optimistic trend in GSRC mortality rates. The trend in GSRC showed discrepancies based on age and sex. Receiver operating characteristic curves, calibration curves, and decision curve analysis for 1-year, 3-year, and 5-year OS demonstrated the high discriminative ability and clinical utility of this nomogram. The area under the curve indicated that the performance of the new model outperformed that of the pathological staging system. The model we established can aid clinicians in the early prognostication of LAGSRC patients, resulting in improved clinical outcomes by modifying management strategies and patient health care.

Desktop 3D printed anatomic models for minimally invasive direct coronary artery bypass

BackgroundThree-dimensional (3D) printing technology has impacted many clinical applications across medicine. However, 3D printing for Minimally Invasive Direct Coronary Artery Bypass (MIDCAB) has not yet been reported in the peer-reviewed literature. The current observational cohort study aimed to evaluate the impact of half scaled (50% scale) 3D printed (3DP) anatomic models in the pre-procedural planning of MIDCAB.MethodsRetrospective analysis included 12 patients who underwent MIDCAB using 50% scale 3D printing between March and July 2020 (10 males, 2 females). Distances measured from CT scans and 3DP anatomic models were correlated with Operating Room (OR) measurements. The measurements were compared statistically using Tukey’s test. The correspondence between the predicted (3DP & CT) and observed best InterCostal Space (ICS) in the OR was recorded. Likert surveys from the 3D printing registry were provided to the surgeon to assess the utility of the model. The OR time saved by planning the procedure using 3DP anatomic models was estimated subjectively by the cardiothoracic surgeon.ResultsAll 12 patients were successfully grafted. The 3DP model predicted the optimal ICS in all cases (100%). The distances measured on the 3DP model corresponded well to the distances measured in the OR. The measurements were significantly different between the CT and 3DP (p < 0.05) as well as CT and OR (p < 0.05) groups, but not between the 3DP and OR group. The Likert responses suggested high clinical utility of 3D printing. The mean subjectively estimated OR time saved was 40 min.ConclusionThe 50% scaled 3DP anatomic models demonstrated high utility for MIDCAB and saved OR time while being resource efficient. The subjective benefits over routine care that used 3D visualization for surgical planning warrants further investigation.