A novel nomogram for predicting the decision to delayed extubation after thoracoscopic lung cancer surgery

Development and validation of a CT-based radiomics nomogram for predicting progression-free survival in patients with small cell lung cancer

PURPOSE

Small cell lung cancer (SCLC) is a highly aggressive form of lung cancer, representing about 15% of cases worldwide. Despite advances in imaging, such as low-dose CT, which have increased diagnostic rates, survival outcomes for SCLC patients have remained stagnant. Recent studies have only focused on radiomics, which extracts detailed quantitative features from imaging, with clinical risk factors to improve prognostic models. Therefore, this study aimed to develop a clinical-radiomics fusion nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients diagnosed with SCLC. By integrating radiomics features extracted from CT with clinical data, this model provides personalized prognostic assessment for clinicians. Its clinical utility lies in aiding treatment decision-making by offering more accurate prognostic evaluation, optimizing therapeutic strategies, and identifying high-risk patients at an early stage, ultimately improving overall survival and quality of life.

METHODS

To develop the nomogram model, 95 patients diagnosed with pathologically confirmed SCLC between January 1, 2013, and December 31, 2023, were included in the study cohort. Participants were randomly divided into training and validation cohorts in a 7:3 ratio. Radiomics features associated with PFS were generated using the least absolute shrinkage and selection operator (LASSO) along with univariate and multivariate analyses. Additionally, in the training cohort, both univariate and multivariate analyses using Cox regression were conducted to identify the significant clinical risk factors influencing PFS. The predictive performance of the clinical and clinical-radiomics fusion nomogram were evaluated using the concordance index, calibration plots, and decision curve analysis (DCA).

RESULTS

Five radiomics features were selected and used to calculate the radiomics score (Rad-score). The radiomics features were significantly associated with PFS (hazard ratio: 0.5765, 95% confidence interval: 0.3641-0.9128, p < 0.05). Three clinical risk factors significantly associated with PFS were identified: neuron-specific enolase (NSE), carbohydrate antigen 125 levels (CA125), and treatment type, such as surgery. The clinical-radiomics fusion nomogram model (C-index:0.744) demonstrated superior performance compared to the clinical nomogram model (C-index: 0.718) in the training cohort. DCA indicated that the clinical-radiomics fusion nomogram outperformed the clinical nomogram in terms of clinical usefulness.

CONCLUSIONS

A CT-based clinical-radiomics fusion nomogram was developed to predict PFS in patients with SCLC, which is useful in providing individualized information.

ADVANCES IN KNOWLEDGE

A clinical-radiomics fusion nomogram was constructed to estimate the probability of PFS based on clinical risk factors and the rad-score.

Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection

BACKGROUND

Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer. Endoscopic mucosal resection (EMR) is a common polypectomy procedure in clinical practice, but it has a high postoperative recurrence rate. Currently, there is no predictive model for the recurrence of colorectal polyps after EMR.

AIM

To construct and validate a machine learning (ML) model for predicting the risk of colorectal polyp recurrence one year after EMR.

METHODS

This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou. Additionally, a total of 166 patients were collected to form a prospective validation set. Feature variable screening was conducted using univariate and multivariate logistic regression analyses, and five ML algorithms were used to construct the predictive models. The optimal models were evaluated based on different performance metrics. Decision curve analysis (DCA) and SHapley Additive exPlanation (SHAP) analysis were performed to assess clinical applicability and predictor importance.

RESULTS

Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR (P < 0.05). Among the models, eXtreme Gradient Boosting (XGBoost) demonstrated the highest area under the curve (AUC) in the training set, internal validation set, and prospective validation set, with AUCs of 0.909 (95%CI: 0.89-0.92), 0.921 (95%CI: 0.90-0.94), and 0.963 (95%CI: 0.94-0.99), respectively. DCA indicated favorable clinical utility for the XGBoost model. SHAP analysis identified smoking history, family history, and age as the top three most important predictors in the model.

CONCLUSION

The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.

Developing predictive nomogram models using quantitative electroencephalography for brain function in type a aortic dissection: a prospective observational study

BACKGROUND

Type A aortic dissection (TAAD) remains a significant challenge in cardiac surgery, presenting high risks of adverse outcomes such as permanent neurological dysfunction and mortality despite advances in medical technology and surgical techniques. This study investigates the use of quantitative electroencephalography (QEEG) to monitor and predict neurological outcomes during the perioperative period in TAAD patients.

METHODS

This prospective observational study was conducted at the hospital, involving patients undergoing TAAD surgery from February 2022 to January 2023. QEEG parameters, including the dynamic amplitude-integrated electroencephalography (aEEG) grade, which assesses changes in brain function over time, alongside aEEG and relative band power (RBP), were monitored and analyzed to assess brain function preoperatively, intraoperatively, and within 2 hours postoperatively. A predictive nomogram model was developed using these QEEG metrics along with other clinical variables to forecast neurological outcomes.

RESULTS

In this study, we analyzed the factors contributing to adverse outcomes (AO) and transient neurological dysfunction (TND) following TAAD surgery. For AO, multivariable analysis identified pre-mental status (odds ratio [OR] = 4.652, 95% confidence interval [CI] = 2.316-10.074, P < 0.001), cardiopulmonary bypass time (OR = 1.014, 95% CI = 1.006-1.023, P = 0.001), and dynamic aEEG grade (OR = 9.926, 95% CI = 4.493-25.268, P < 0.001) as independent risk factors. The AO model showed high discriminative ability with an area under the curve of 0.888 (95% CI = 0.818-0.960) and good calibration (Brier score = 0.0728). For TND, significant preoperative differences included dynamic aEEG grade ( P < 0.001) and Log(Post-RBP Alpha%) (6.00 vs. 4.00, P < 0.001). Multivariable analysis identified cardiopulmonary bypass time (OR = 1.014, 95% CI = 1.006-1.023, P = 0.001), Post-RBP Alpha% (OR = 0.263, 95% CI = 0.121-0.532, P < 0.001), and dynamic aEEG grade (OR = 12.444, 95% CI = 5.337-30.814, P < 0.001) as independent risk factors. The TND model had an area under the curve of 0.893 (95% CI = 0.844-0.941) and good calibration (Brier score = 0.125). These findings highlight the role of QEEG in predicting postoperative neurological dysfunction in TAAD patients.

CONCLUSION

Through perioperative QEEG monitoring of TAAD patients, combined with clinical indicators such as cardiopulmonary bypass time and preoperative mental status, we developed clinical predictive models for AO and TND after surgery. These models allow for early detection of postoperative brain function impairment, as assessed by QEEG parameters monitored intraoperatively and during the first 2 hours after surgery, a period chosen based on clinical definitions of delayed awakening and supported by the findings of this study. This study provides evidence supporting postoperative brain function monitoring in TAAD patients, with potential clinical implications for improved outcomes.

Development and validation of a nomogram for predicting immune-mediated colitis in lung cancer patients treated with immune checkpoint inhibitors: a retrospective cohort study in China

Background

The increasing utilization of immune checkpoint inhibitors (ICIs) has led to a concomitant rise in the incidence of immune-related adverse events (irAEs), notably immune-mediated colitis (IMC). This study aimed to identify the clinical risk factors associated with IMC development in patients with lung cancer and to develop a risk prediction model to facilitate personalized treatment and care strategies.

Methods

The data collected included 21 variables, including sociodemographic characteristics, cancer-related factors, and routine blood markers. The dataset was randomly partitioned into a training set (70%) and a validation set (30%). Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of IMC development. On the basis of the results of the multivariate analysis, a nomogram prediction model was developed. Model performance was assessed via the area under the receiver operating characteristic curve (AUC), calibration curve analysis, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

Among the 2103 patients, 66 (3.14%) developed IMCs. Multivariate logistic regression analysis revealed female sex, small cell lung cancer (SCLC), elevated β2 microglobulin (β2-MG) and globulin (GLB) levels, and an increased neutrophil-lymphocyte ratio (NLR) as independent predictors of IMC development (all P < 0.05). Conversely, a higher white blood cell (WBC) count, CD4/CD8 ratio, and platelet-lymphocyte ratio (PLR) were identified as factors associated with a reduced risk of IMC development (all P < 0.05). The nomogram prediction model demonstrated good discrimination, achieving an AUC of 0.830 (95% CI: 0.774-0.887) in the training set and 0.827 (95% CI: 0.709-0.944) in the validation set. Analysis of the calibration curve, DCA, and CIC indicated good predictive accuracy and clinical utility of the developed model.

Conclusion

This study identified eight independent predictors of IMC development in patients with lung cancer and subsequently developed a nomogram-based prediction model to assess IMC risk. Utilization of this model has the potential to assist clinicians in implementing appropriate preventive and therapeutic strategies, ultimately contributing to a reduction in the incidence of IMC among this patient population.

Relationship between clinical features and distant metastases in rectal cancer predicted based on a nomogram: a retrospective cohort study

Rectal cancer is a prevalent global malignancy. Recurrence and metastasis significantly impact patient survival over the long term. This study aims to identify independent risk factors associated with distant metastases in rectal cancer (RC) patients and develop a prognostic columnar-line diagram. This retrospective analysis encompasses data from 1,118 RC patients treated at the Department of Anorectal Surgery, Chifeng Municipal Hospital, between December 2015 and October 2023. These patients were diagnosed with stage I-IV RC. Univariate and multivariate Cox proportional hazard regression models identified risk factors for distant metastases development. The median follow-up duration was 61.3 months (range 2.24-96.33 months). The identified factors linked to distant metastases in RC included hemoglobin levels, body mass index (BMI), leukocyte neutrophil percentage, tumour diameter, pathology type, differentiation degree, number of detected lymph nodes, and T and N stages. These factors are significant risk indicators for distant metastases in RC patients. Incorporating these identified risk factors into a columnar-line diagram effectively predicts the likelihood of distant metastasis in RC patients. This approach aids in devising precise treatment strategies during the initial patient consultation.

Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms

BACKGROUND

Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND.

METHODS

Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models.

RESULTS

The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities.

CONCLUSION

The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.

Construction of a new tool for predicting cancer-specific survival in papillary thyroid cancer patients who have not received surgery

Background

The prevalence of papillary thyroid cancer is gradually increasing and the trend of youthfulness is obvious. Some patients may not be able to undergo surgery, which is the mainstay of treatment, due to physical or financial reasons. Therefore, the prediction of cancer-specific survival (CSS) in patients with non-operated papillary thyroid cancer is necessary.

Methods

Patients' demographic and clinical information was extracted from the Surveillance, Epidemiology, and End Results database. SPSS software was used to perform Cox regression analyses as well as propensity score matching analyses. R software was used to construct and validate the nomogram. X-tile software was used to select the best cutoff point for patient risk stratification.

Results

A total of 1319 patients were included in this retrospective study. After Cox regression analysis, age, grade, T stage, M stage, radiotherapy, and chemotherapy were used to construct the nomogram. C-index, calibration curves, and receiver operating characteristic curves all verified the high predictive accuracy of the nomogram. The decision curve analysis demonstrated that patients could gain clinical benefit from this predictive model. Survival curve analysis after propensity score matching demonstrated the positive effects of radiotherapy on CSS in non-operated patients.

Conclusion

Our retrospective study successfully established a nomogram that accurately predicts CSS in patients with non-operated papillary thyroid cancer and demonstrated that radiotherapy for operated patients can still help improve prognosis. These findings can help clinicians make better choices.

Prognosis and immunotherapy efficacy in dMMR&MSS colorectal cancer patients and an MSI status predicting model

The inconsistency between mismatch repair (MMR) protein immunohistochemistry (IHC) and microsatellite instability PCR (MSI-PCR) methods has been widely reported. We aim to investigate the prognosis and the effect of immunotherapy in dMMR by IHC but MSS by MSI-PCR (dMMR&MSS) colorectal cancer (CRC) patients. A microsatellite instability (MSI) predicting model was established to help find dMMR&MSS patients. MMR and MSI states were detected by the IHC and MSI-PCR in 1622 CRC patients (ZS6Y-1 cohort). Logistic regression analysis was used to screen clinical features to construct an MSI-predicting nomogram. We propose a new nomogram-based assay to find patients with dMMR&MSS, in which the MSI-PCR assay only detects dMMR patients with MSS predictive results. We applied the new strategy to a random cohort of 248 CRC patients (ZS6Y-2 cohort). The consistency of MMR IHC and MSI-PCR in the ZS6Y-1 cohort was 95.7% (1553/1622). Both pMMR&MSS and dMMR&MSS groups experienced significantly shorter overall survival (OS) than those in dMMR by IHC and MSI-H by MSI-PCR (dMMR&MSI-H) group (hazard ratio [HR] = 2.429, 95% confidence interval [CI]: 1.89-3.116, p < .01; HR = 21.96, 95% CI: 7.24-66.61, p < .01). The dMMR&MSS group experienced shorter OS than the pMMR&MSS group, but the difference did not reach significance (log rank test, p = .0686). In the immunotherapy group, the progression-free survival of dMMR&MSS patients was significantly shorter than that of dMMR&MSI-H patients (HR = 13.83, 95% CI: 1.508-126.8, p < .05). The ZS6Y-MSI-Pre nomogram (C-index = 0.816, 95% CI: 0.792-0.841, already online) found 66% (2/3) dMMR&MSS patients in the ZS6Y-2 cohort. There are significant differences in OS and immunotherapy effect between dMMR&MSI-H and dMMR&MSS patients. Our prediction model provides an economical way to screen dMMR&MSS patients.

Nomogram for Predicting Efficacy and Prognosis After Chemotherapy for Advanced NSCLC

PURPOSE

One major issue is the therapeutic effect following chemotherapy for non-small cell lung cancer (NSCLC). Although numerous risk factors have been identified and novel therapies have been developed, improving patient overall survival (OS) remains a crucial postoperative issue. This study aimed to develop a nomogram for accurately predicting the OS of patients with Stage III-IV NSCLC treated with chemotherapy.

METHODS

The Department of Respiration at Tangdu Hospital, Air Force Medical University, prospectively collected data on 321 patients between January 2018 and December 2023. A week before treatment, the platelet-to-lymphocyte ratio (PLR), the neutrophil-to-lymphocyte ratio (NLR), and seven autoantibodies were measured using Youden's index, which was obtained using the ROC curve. The formula was used to compute the values of PLR and NLR. After using multifactor Cox regression analysis to identify risk factors, a nomogram was produced regarding the therapeutic effect following chemotherapy. The performance of the nomogram was assessed using a bootstrapped-concordance index and calibration plots.

RESULT

It was determined that NLR, sex-determining region Y-box 2 (SOX2), adenosine triphosphate binding RNA deconjugase 4-5 (GBU4-5), and MAGE family member A1 (MAGEA1) were significantly associated factors that could be combined to accurately predict the therapeutic effect following chemotherapy. Utilizing these risk indicators, we were able to develop a nomogram that predicted the patients' survival at 1, 3, and 5 years. At 3 years, the area under the curve representing the expected survival probability was 0.762 (95% confidence interval 0.66-0.87). With a bootstrapped-concordance index of 0.762, the nomogram demonstrated good calibration.

CONCLUSIONS

Our nomogram proved to be a valuable instrument in accurately predicting the overall survival of patients.

Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan-Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859-0.956) for the overall dataset, 0.926 (95% CI: 0.883-0.968) for the training set, and 0.862 (95% CI: 0.728-0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BACKGROUND

This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.

METHODS

Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.

RESULTS

A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients.

CONCLUSIONS

The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Application of a nomogram from coagulation-related biomarkers and C1q and total bile acids in distinguishing advanced and early-stage lung cancer

BACKGROUND

This study aimed to establish a nomogram to distinguish advanced- and early-stage lung cancer based on coagulation-related biomarkers and liver-related biomarkers.

METHODS

A total of 306 patients with lung cancer and 172 patients with benign pulmonary disease were enrolled. Subgroup analyses based on histologic type, clinical stage, and neoplasm metastasis status were carried out and multivariable logistic regression analysis was applied. Furthermore, a nomogram model was developed and validated with bootstrap resampling.

RESULTS

The concentrations of complement C1q, fibrinogen, and D-dimers, fibronectin, inorganic phosphate, and prealbumin were significantly changed in lung cancer patients compared to benign pulmonary disease patients. Multiple regression analysis based on subgroup analysis of clinical stage showed that compared with early-stage lung cancer, female (P < 0.001), asymptomatic admission (P = 0.001), and total bile acids (P = 0.011) were negatively related to advanced lung cancer, while C1q (P = 0.038), fibrinogen (P < 0.001), and D-dimers (P = 0.001) were positively related. A nomogram model based on gender, symptom, and the levels of total bile acids, C1q, fibrinogen, and D-dimers was constructed for distinguishing advanced lung cancer and early-stage lung cancer, with an area under the receiver operating characteristic curve of 0.919. The calibration curve for this nomogram revealed good predictive accuracy (P-Hosmer-Lemeshow = 0.697) between the predicted probability and the actual probability.

CONCLUSIONS

We developed a nomogram based on gender, symptom, and the levels of fibrinogen, D-dimers, total bile acids, and C1q that can individually distinguish early- and advanced-stage lung cancer.

Developing a Nomogram for Predicting Surgical Intervention in Pediatric Intussusception After Pneumatic Reduction: A Multicenter Study from China

Purpose

The objective of this study was to develop and validate a nomogram for predicting the need for surgical intervention in pediatric intussusception after pneumatic reduction.

Patients and Methods

This retrospective study analyzed the clinical data of children with acute intussusception admitted to four hospitals in China from January 2019 to January 2022. Based on the results of pneumatic reduction, the patients were divided into two groups: the successful reduction group (control group) and the failed reduction group (operation group). The total sample was randomly divided into a training set and a validation set in a 7:3 ratio. Logistic regression analysis was performed to establish a predictive model for surgical risk.

Results

A total of 1041 samples were included in this study, with 852 in the control group and 189 in the operation group. Among the total sample, 728 cases were used for training and 313 cases were used for validation. Logistic regression analysis of the training set identified age, time of abdominal pain, presence or absence of hematostoecia, C-reactive protein value from blood test on admission, and nested position indicated by B-ultrasound as independent predictors of intussusception intervention. Based on the five independent risk factors identified through multivariate logistic regression, a nomogram was successfully constructed to predict the failure of resetting by air enema under X-ray.

Conclusion

A nomogram was developed to predict the need for surgical intervention after intussusception pneumatic reduction in children. The nomogram was based on clinical risk factors including age, time of abdominal pain, presence or absence of blood in stool, value of C-reactive protein in blood test on admission, and nested position indicated by B-ultrasound. Our internal validation demonstrated that this nomogram can serve as a useful tool for identifying risk factors associated with failure of air enema in children with intussusception.

Relative band power in assessing temporary neurological dysfunction post- type A aortic dissection surgery: a prospective study

Temporary neurological dysfunction (TND), a common complication following surgical repair of Type A Aortic Dissection (TAAD), is closely associated with increased mortality and long-term cognitive impairment. Currently, effective treatment options for TND remain elusive. Therefore, we sought to investigate the potential of postoperative relative band power (RBP) in predicting the occurrence of postoperative TND, with the aim of identifying high-risk patients prior to the onset of TND. We conducted a prospective observational study between February and December 2022, involving 165 patients who underwent surgical repair for TAAD at our institution. Bedside Quantitative electroencephalography (QEEG) was utilized to monitor the post-operative brain electrical activity of each participant, recording changes in RBP (RBP Delta, RBP Theta, RBP Beta and RBP Alpha), and analyzing their correlation with TND. Univariate and multivariate analyses were employed to identify independent risk factors for TND. Subsequently, line graphs were generated to estimate the incidence of TND. The primary outcome of interest was the development of TND, while secondary outcomes included intensive care unit (ICU) admission and length of hospital stay. A total of 165 patients were included in the study, among whom 68 (41.2%) experienced TND. To further investigate the independent risk factors for postoperative TND, we conducted both univariate and multivariate logistic regression analyses on all variables. In the univariate regression analysis, we identified age (Odds Ratio [OR], 1.025; 95% CI, 1.002-1.049), age ≥ 60 years (OR, 2.588; 95% CI, 1.250-5.475), hemopericardium (OR, 2.767; 95% CI, 1.150-7.009), cardiopulmonary bypass (CPB) (OR, 1.007; 95% CI, 1.001-1.014), RBP Delta (OR, 1.047; 95% CI, 1.020-1.077), RBP Alpha (OR, 0.853; 95% CI, 0.794-0.907), and Beta (OR, 0.755; 95% CI, 0.649-0.855) as independent risk factors for postoperative TND. Further multivariate regression analyses, we discovered that CPB time ≥ 180 min (OR, 1.021; 95% CI, 1.011-1.032), RBP Delta (OR, 1.168; 95% CI, 1.105-1.245), and RBP Theta (OR, 1.227; 95% CI, 1.135-1.342) emerged as independent risk factors. TND patients had significantly longer ICU stays (p < 0.001), and hospital stays (p = 0.002). We obtained the simplest predictive model for TND, consisting of three variables (CPB time ≥ 180 min, RBP Delta, RBP Theta, upon which we constructed column charts. The areas under the receiver operating characteristic (AUROC) were 0.821 (0.755, 0.887). Our study demonstrates that postoperative RBP monitoring can detect changes in brain function in patients with TAAD during the perioperative period, providing clinicians with an effective predictive method that can help improve postoperative TND in TAAD patients. These findings have important implications for improving clinical care in this population.Trial registration ChiCTR2200055980. Registered 30th Jan. 2022. This trial was registered before the first participant was enrolled.

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BACKGROUND

The recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.

METHODS

We extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan-Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.

RESULTS

Seven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675-0.690), 0.689 (95% CI: 0.677-0.701), and 0.823 (95% CI: 0.786-0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.

CONCLUSION

The nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

Risk prediction of poor wound healing in patients with thoracoscopic lung cancer resection with drainage tube

This work established a risk prediction (RP) model for poor wound healing (PWH) in patients with thoracoscopic lung cancer (LC) resection (TLCR) after drainage tube placement to explore its application effect. 359 patients with TLCR were categorized into a good wound healing group (GWH group, 275 cases) and a poor wound healing group (PWH group, 84 cases) based on incision healing condition. The independent prediction risk factors (IPRFs) of PWH were analyzed and a RP model was constructed. 70% of the patients were classified as the model group (Mod group) and 30% were in the validation group (Val group). Resolution of the RP model was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). The Hosmer-Lemeshow goodness of fit (HLGF) test was employed to evaluate the calibration of RP model. Results from the multivariate logistic regression analysis (MLRA) showed that age, preoperative albumin levels, diabetes history, dressing change frequency, and type of wound cleaning fluid were independent risk factors (IRFs) for postoperative PWH (P<0.05). In the Mod group, AUC=0.758 (P<0.05, 95% CI=0.712-0.806), and HLGF test showed P=0.493. In the Val group, AUC=0.783 (P<0.05, 95% CI=0.675-0.834), and HLGF test showed P=0.189. In conclusion, the constructed model was convenient, feasible, and demonstrates good predictive performance for postoperative incision healing issue, holding practical value and applicability.

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Objective

To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm).

Methods

This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors).

Results

Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful.

Conclusion

T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.