Nomogram Model Research Articles

Myeloperoxidase and its derivative hypochlorous acid combined clinical indicators predict new-onset atrial fibrillation in sepsis: a case-control study

BackgroudNew-onset atrial fibrillation (NOAF) is a common complication of sepsis and linked to higher death rates in affected patients. The lack of effective predictive tools hampers early risk assessment for the development of NOAF. This study aims to develop practical and effective predictive tools for identifying the risk of NOAF.MethodsThis case-control study retrospectively analyzed patients with sepsis admitted to the emergency department of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine from September 2017 to January 2023. Based on electrocardiographic reports and electrocardiogram monitoring records, patients were categorized into NOAF and non-NOAF groups. Laboratory tests, including myeloperoxidase (MPO) and hypochlorous acid (HOCl), were collected, along with demographic data and comorbidities. Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were employed to identify predictors. The area under the curve (AUC) was used to evaluate the predictive model’s performance in identifying NOAF.ResultsA total of 389 patients with sepsis were included in the study, of which 63 developed NOAF. MPO and HOCl levels were significantly higher in the NOAF group compared to the non-NOAF group. Multivariate logistic regression analysis identified MPO, HOCl, tumor necrosis factor-α (TNF-α), white blood cells (WBC), and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score as independent risk factors for NOAF in sepsis. Additionally, a nomogram model developed using these independent risk factors achieved an AUC of 0.897.ConclusionThe combination of MPO and its derivative HOCl with clinical indicators improves the prediction of NOAF in sepsis. The nomogram model can serve as a practical predictive tool for the early identification of NOAF in patients with sepsis.

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression network analysis (WGCNA) for identifying sepsis-related modules and hub genes, and functional enrichment analyses to explore the roles of hub genes. Machine learning algorithms identify 3 diagnostic genes - CD177, LDHA, and MCEMP1 - consistently highly expressed in sepsis patients. The nomogram model effectively predicts sepsis risk, supported by receiver operator characteristic (ROC) curves. Correlations between diagnostic genes and immune cell infiltration are observed. MR analysis reveals a positive causal relationship between MCEMP1 and sepsis risk. In conclusion, this study presents potential sepsis diagnostic biomarkers, highlighting the genetic association of MCEMP1 with sepsis for insights into early diagnosis.

Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke

ObjectiveTo establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment.MethodsA retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4–6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsA total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801–0.919) and 0.775 (95%CI 0.605–0.945), respectively.ConclusionThe radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.

Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data

BackgroundThe risk assessment for survival in heart failure (HF) remains one of the key focuses of research. This study aims to develop a simple and feasible nomogram model for survival in HF based on the Heart Failure-A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) to support clinical decision-making.MethodsThe HF patients were extracted from the HF-ACTION database and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate Cox regression was used to identify and integrate significant prognostic factors to form a nomogram, which was displayed in the form of a static nomogram. Bootstrap resampling (resampling = 1000) and cross-validation was used to internally validate the model. The prognostic performance of the model was measured by the concordance index (C-index), calibration curve, and the decision curve analysis.ResultsThere were 1394 patients with HF in the overall analysis. Seven prognostic factors, which included age, body mass index (BMI), sex, diastolic blood pressure (DBP), exercise duration, peak exercise oxygen consumption (peak VO2), and loop diuretic, were identified and applied to the nomogram construction based on the training cohort. The C-index of this model in the training cohort was 0.715 (95% confidence interval (CI): 0.700, 0.766) and 0.662 (95% CI: 0.646, 0.752) in the validation cohort. The area under the ROC curve (AUC) value of 365- and 730-day survival is (0.731, 0.734) and (0.640, 0.693) respectively in the training cohort and validation cohort. The calibration curve showed good consistency between nomogram-predicted survival and actual observed survival. The decision curve analysis (DCA) revealed net benefit is higher than the reference line in a narrow range of cutoff probabilities and the result of cross-validation indicates that the model performance is relatively robust.ConclusionsThis study created a nomogram prognostic model for survival in HF based on a large American population, which can provide additional decision information for the risk prediction of HF.

Association of diabetes with cardiovascular calcification and all-cause mortality in end-stage renal disease in the early stages of hemodialysis: a retrospective cohort study

BackgroundThe main goal of this study was to examine how diabetes, cardiovascular calcification characteristics and other risk factors affect mortality in end-stage renal disease (ESRD) patients in the early stages of hemodialysis.MethodsA total of 285 ESRD patients in the early stages of hemodialysis were enrolled in this research, including 101 patients with diabetes. Survival time was monitored, and general data, biochemical results, cardiac ultrasound calcification of valvular tissue, and thoracic CT calcification of the coronary artery and thoracic aorta were recorded. Subgroup analysis and logistic regression were applied to investigate the association between diabetes and calcification. Cox regression analysis and survival between calcification, diabetes, and all-cause mortality. Additionally, the nomogram model was used to estimate the probability of survival for these individuals, and its performance was evaluated using risk stratification, receiver operating characteristic, decision, and calibration curves.ResultsCardiovascular calcification was found in 81.2% of diabetic patients (82/101) and 33.7% of nondiabetic patients (62/184). Diabetic patients had lower phosphorus, calcium, calcium-phosphorus product, plasma PTH levels and lower albumin levels (p < 0.001). People with diabetes were more likely to have calcification than people without diabetes (OR 5.66, 95% CI 1.96–16.36; p < 0.001). The overall mortality rate was 14.7% (42/285). The risk of death was notably greater in patients with both diabetes and calcification (29.27%, 24/82). Diabetes and calcification, along with other factors, collectively predict the risk of death in these patients. The nomogram model demonstrated excellent discriminatory power (area under the curve (AUC) = 0.975 at 5 years), outstanding calibration at low to high-risk levels and provided the greatest net benefit across a wide range of clinical decision thresholds.ConclusionsIn patients with ESRD during the early period of haemodialysis, diabetes significantly increases the risk of cardiovascular calcification, particularly multisite calcification, which is correlated with a higher mortality rate. The risk scores and nomograms developed in this study can assist clinicians in predicting the risk of death and providing individualised treatment plans to lower mortality rates in the early stages of hemodialysis.

Development of a nomogram to predict recurrence scores obtained using Oncotype DX in Japanese patients with breast cancer

BackgroundChemotherapy is crucial for hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, and its survival benefits may outweigh adverse events. Oncotype DX (ODX) assesses this balance; however, it is expensive. Using nomograms to identify cases requiring ODX may be economically beneficial. We aimed to identify clinicopathological variables that correlated with the recurrence score (RS) and develop a nomogram that predicted the RS.MethodsWe included 457 patients with estrogen receptor-positive, HER2-negative breast cancer with metastases in fewer than four axillary lymph nodes who underwent surgery and ODX at our hospital between 2007 and 2023. We developed nomograms and internally validated them in 310 patients who underwent surgery between 2007 and 2021 and validated the model’s performance in 147 patients who underwent surgery between 2022 and 2023.ResultsLogistic regression analysis revealed that progesterone receptor (PgR) level, histological grade (HG), and Ki67 index independently predicted the RS. A nomogram was developed using these variables to predict the RS (area under the curve [AUC], 0.870; 95% confidence interval [CI], 0.82–0.92). The nomogram was applied to the model validation group (AUC, 0.877; 95% CI, 0.80–0.95). When the sensitivity of the nomogram was 90%, the model was able to identify 52.3% low-RS and 41.2% high-RS cases not requiring ODX.ConclusionsThis was the first nomogram model developed based on data from a cohort of Japanese women. It may help determine the indications for ODX and the use of nomogram to identify cases requiring ODX may be economically beneficial.

Insights into inflammation and implications for the pathogenesis and long-term outcomes of endometrial cancer: genome-wide surveys and a clinical cohort study

BackgroundDespite evidence showing a connection between inflammation and endometrial cancer (EC) risk, the surveys on genetic correlation and cohort studies investigating the impact on long-term outcomes have yet to be refined. We aimed to address the impact of inflammation factors on the pathogenesis, progression and consequences of EC.MethodsFor the genetic correlation analyses, a two-sample of Mendelian randomization (MR) study was applied to investigate inflammation-related single-nucleotide polymorphisms involved with endometrial cancer from GWAS databases. The observational retrospective study included consecutive patients diagnosed with EC (stage I to IV) with surgeries between January 2010 and October 2020 at the Cancer Hospital of Shantou University Medical College.ResultsThe 2-sample MR surveys indicated no causal relationship between inflammatory cytokines and endometrial cancer. 780 cases (median age, 55.0 years ) diagnosed with EC were included in the cohort and followed up for an average of 6.8 years. Increased inflammatory parameters at baseline were associated with a higher FIGO stage and invasive EC risk (odds ratios [OR] 1.01 to 4.20). Multivariate-cox regression suggested that multiple inflammatory indicators were significantly associated with overall survival (OS) and progression-free survival (PFS) (P < 0.05). Nomogram models based on inflammatory risk and clinical factors were developed for OS and PFS with C-index of 0.811 and 0.789, respectively. LASSO regression for the validation supported the predictive efficacy of inflammatory and clinical factors on the long-term outcomes of EC.ConclusionsDespite the fact that the genetic surveys did not show a detrimental impact of inflammatory cytokines on the endometrial cancer risk, our cohort study suggested that inflammatory level was associated with the progression and long-term outcomes of EC. This evidence may contribute to new strategies targeted at decreasing inflammation levels during EC therapy.

WWP1 inhibition increases SHP2 inhibitor efficacy in colorectal cancer

Protein tyrosine phosphatase SHP2 activates RAS signaling, which is a novel target for colorectal cancer (CRC) therapy. However, SHP2 inhibitor monotherapy is ineffective for metastatic CRC and a combination therapy is required. In this study, we aimed to improve the antitumor efficacy of SHP2 inhibition and try to explore the resistance mechanism of SHP2 inhibitor. Results showed that WWP1 promoted the proliferation of CRC cells. Genetic or pharmacological inhibition of WWP1 enhanced the effect of SHP2 inhibitor in suppressing tumor growth in vitro and in vivo. WWP1 may mediate feedback reactivation of AKT signaling following SHP2 inhibition. Furthermore, nomogram models constructed with IHC expression of WWP1 and SHP2 greatly improved the accuracy of prognosis prediction for patients with CRC. Our findings indicate that WWP1 inhibitor I3C can synergize with SHP2 inhibitor and is expected to be a new strategy for clinical trials in treating advanced CRC patients.

Model of predicting fear of cancer recurrence in patients with digestive tract cancer: A cross-sectional study.

To investigate the incidence of fear of cancer recurrence in patients with digestive tract cancers analyse its influencing factors, and further establish a visual risk prediction model. A cross-sectional study. A cross-sectional survey was conducted among 570 patients with digestive tract tumours admitted to a local hospital, from May 2023 to December 2023 by convenient sampling method. Univariate analysis and logistic analysis were performed on the influencing factors, and the risk prediction nomogram model of fear of cancer recurrence in patients with digestive tract cancer was constructed by using R 4.1.3 software. ROC curve was used to evaluate the differentiation of the nomogram model. The calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the consistency of the model. This study was reported using the TRIPOD checklist. In this study, 272 (47.7%) patients developed fear of recurrence. The risk prediction model of recurrence fear column chart for digestive tract cancer patients incorporated six variables of gender, therapy, alimentary tract haemorrhage, pain, depression and social support. The C-statistic was (.976), and the calibration curve showed that the predicted probability was more in line with the actual probability of occurrence, and the decision curve showed that the predictive model had better practicality. The column-line diagram prediction model constructed in this study is effective and facilitates timely intervention and management by healthcare professionals based on their risk factors. Nomogram is helpful to calculate the risk probability of FCR in patients with digestive tract cancer, identify FCR patients in time, and formulate comprehensive and personalized countermeasures, to provide a good quality of life and prolong the survival cycle of patients with digestive tract cancer. Participants were hospitalized patients or patients with digestive tract cancer undergoing follow-up. First of all, before the investigation and research, a team is formed to discuss the concept, research purpose, method, significance, etc., and determine the research tools. Second, by reasonably explaining the study to patients to seek informed consent from the patient and sign it, patients filled in the questionnaire independently. For patients with low education levels who could not fill in the questionnaire, the team members made objective explanations to help them choose reasonable options.

A novel nomogram for predicting the risk of persistent hyperparathyroidism after kidney transplantation.

Persistent hyperparathyroidism (PTHPT) in kidney transplant recipients is associated with bone loss, graft dysfunction and cardiovascular mortality. There is no clear consensus on the management of PTHPT. Accurate risk prediction of the disease is needed to support individualized treatment decisions. We aim to develop a useful predictive model to provide early intervention for hyperparathyroidism in these patients. We retrospectively analyzed 263 kidney transplantations in the urology department of China-Japan Friendship Hospital from January 2018 to December 2022. The overall cohort was randomly assigned 70% of the patients to the training cohort and 30% to the validation cohort. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for PTHPT and to construct the predictive model. This model was assessed regarding discrimination, consistency, and clinical benefit. The occurrence of PTHPT was 25.9% (68 out of 263 patients) in this study. Dialysis duration, postoperative 3-month intact parathyroid hormone (iPTH), 3-month corrected calcium (cCa), and 3-month phosphorus (P) are independent risk factors for the development of PTHPT. The nomogram showed good discrimination with the area under the curve (AUC) value of 0.926 in the training cohort and 0.903 in the validation cohort. The calibration curve and decision curve also showed that the model was well-evaluated. We developed a validated nomogram model to predict PTHPT after kidney transplantation. This can help the clinic prevent and control PTHPT early and improve patients' prognosis.

Development and validation of a diagnostic nomogram for frailty in cancer patients

BackgroundThe presence of frailty decreases the overall survival of cancer patients. An accurate and operational diagnostic method is needed to help clinicians choose the most appropriate treatment to improve patient outcomes. MethodsData were collected from 10 649 cancer patients who were prospectively enrolled in the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers (INSCOC) project in China from July 2013 to August 2022. The training cohort and validation cohort were randomly divided at a ratio of 7:3. The multivariable logistic regression analysis, multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO) method were used to develop the nomogram. The concordance index and calibration curve were used to assess the diagnostic utility of the nomogram model. ResultsThe 10 risk factors associated with frailty in cancer patients were age, AJCC stage, liver cancer, hemoglobin, radiotherapy, surgery, hand grip strength (HGS), calf circumference (CC), PG-SGA score and QOL from the QLQ-C30. The diagnostic nomogram model achieved a good C index of 0.847 (95% CI, 0.832–0.862, P < 0.001) in the training cohort and 0.853 (95% CI, 0.83–0.876, P < 0.001) in the validation cohort. The prediction nomogram showed 1-, 3-, and 5-year mortality C indices in the training cohort of 0.708 (95% CI, 0.686–0.731), 0.655 (95% CI, 0.627–0.683), and 0.623 (95% CI, 0.568–0.678). The 1-, 3-, and 5-year C indices in the validation cohort were similarly 0.743 (95% CI, 0.711–0.777), 0.680 (95% CI, 0.639–0.722), and 0.629 (95% CI, 0.558–0.700). In addition, the calibration curves and decision curve analysis (DCA) were well-fitted for both the diagnostic model and prediction model. ConclusionsThe nomogram model provides an accurate method to diagnose frailty in cancer patients. Using this model could lead to the selection of more appropriate therapy and a better prognosis for cancer patients.

Development and validation of a risk prediction model for motoric cognitive risk syndrome in older adults

ObjectiveThe objective of this study was to develop a risk prediction model for motoric cognitive risk syndrome (MCR) in older adults.MethodsParticipants were selected from the 2015 China Health and Retirement Longitudinal Study database and randomly assigned to the training group and the validation group, with proportions of 70% and 30%, respectively. LASSO regression analysis was used to screen the predictors. Then, identified predictors were included in multivariate logistic regression analysis and used to construct model nomogram. The performance of the model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curves and decision curve analysis (DCA).Results528 out of 3962 participants (13.3%) developed MCR. Multivariate logistic regression analysis showed that weakness, chronic pain, limb dysfunction score, visual acuity score and Five-Times-Sit-To-Stand test were predictors of MCR in older adults. Using these factors, a nomogram model was constructed. The AUC values for the training and validation sets of the predictive model were 0.735 (95% CI = 0.708–0.763) and 0.745 (95% CI = 0.705–0.785), respectively.ConclusionThe nomogram constructed in this study is a useful tool for assessing the risk of MCR in older adults, which can help clinicians identify individuals at high risk.

Development and validation of nomogram for predicting the risk of transferring to the ICU for children with influenza.

Development of a nomogram model for predicting the magnitude of risk of transferring hospitalized children with influenza to the ICU. In a single-center retrospective study, 318 children with influenza who were hospitalized in our hospital from January 2018 to August 2023 were collected as study subjects. Children with influenza were randomly assigned to the training set and validation set in a ratio of 4:1. In the training set, risk factors were identified using univariate and multivariate logistic regression analyses, and a nomogram model was created on this basis. The validation set was used to evaluate the predictive power of the model. Multifactorial logistic regression analysis revealed six independent risk factors for transfer to the ICU in hospitalized children with influenza, including elevated peripheral white blood cell counts, elevated large platelet ratios, reduced mean platelet width, reduced complement C3, elevated serum globulin levels, and reduced total immunoglobulin M levels. Using these six metrics as predictors to construct a nomogram graphical model, the C-index was 0.970 (95% Cl: 0.953-0.988). The areas under the curve for the training and validation sets were 0.966 (95%Cl 0.947-0.985) and 0.919 (95%Cl 0.851-0.986), respectively. A nomogram for predicting the risk of transferring to the ICU for children with influenza was developed and validated, which demonstrates good calibration and clinical benefits.

Investigating distinct clinical features and constructing a nomogram model for survival probability in adults with cerebellar high-grade gliomas

BackgroundThe clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model.MethodsPatient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model.ResultsThere were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p < 0.001). Multivariate Cox regression analyses revealed that race, WHO grade, surgical procedures, radiotherapy, and chemotherapy were independent prognostic factors for cHGGs. Based on these factors, a nomogram was developed to predict 1-, 3-, and 5-year survival probabilities, with AUC of 0.860, 0.837, and 0.810, respectively. The model’s accuracy was validated by machine learning approaches, demonstrating consistent predictive effectiveness.ConclusionsAdult cHGGs are distinguished by distinctive clinical features different from those of sHGGs and are associated with an inferior prognosis. Based on these risk factors affecting cHGGs prognosis, the nomogram prediction model serves as a crucial tool for clinical decision-making in patient care.

Development of a major amputation prediction model and nomogram in patients with diabetic foot.

Diabetes mellitus, as one of the world's fastest-growing diseases, is a chronic metabolic disease that has now become a public health problem worldwide. The purpose of this research was to develop a predictive nomogram model to demonstrate the risk of major amputation in patients with diabetic foot. A total of 634 Type 2 Diabetes Mellitus (T2DM) patients with diabetic foot ulcer hospitalized at the Air Force Medical Center between January 2018 and December 2023 were included in our retrospective study. There were 468 males (73.82%) and 166 females (26.18%) with an average age of 61.64 ± 11.27years and average body mass index of 24.45 ± 3.56kg/m2. The predictive factors were evaluated by single factor logistic regression and multiple logistic regression and the predictive nomogram was established with these features. Receiver operating characteristic (subject working characteristic curve) and their area under the curve, calibration curve, and decision curve analysis of this major amputation nomogram were assessed. Model validation was performed by the internal validation set, and the receiver operating characteristic curve, calibration curve, and decision curve analysis were used to further evaluate the nomogram model performance and clinical usefulness. Predictors contained in this predictive model included body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, blood uric acid (BUA), and ejection fraction. Good discrimination with a C-index of 0.957 (95% CI, 0.931-0.983) in the training group and a C-index of 0.987 (95% CI, 0.969-1.000) in the validation cohort were showed with this predictive model. Good calibration were displayed. The decision curve analysis showed that using the nomogram prediction model in the training cohort and validation cohort would respectively have clinical benefits. This new nomogram incorporating body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, BUA, and ejection fraction has good accuracy and good predictive value for predicting the risk of major amputation in patients with diabetic foot.

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma

ObjectivesThis study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).MethodsThe study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan–Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.ResultsThe PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).ConclusionThe CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.Critical relevance statementThe intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.Key PointsThe multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis.The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading.The nomogram and radiomics models have a broad range of clinical applications.Graphical

Analysis of Risk Factors of Postoperative Arrhythmia in Patients with Coronary Heart Disease and Establishment of Nomogram Risk Model

Background: To explore the risk factors of postoperative arrhythmia in patients with coronary heart disease (CHD) and to establish a Nomogram model for predicting the risk of postoperative arrhythmia in CHD patients. Methods: Retrospectively, the medical data of CHD patients (from January 2021 to January 2024, n = 390) were collected. According to whether arrhythmia occurred after percutaneous coronary intervention surgery, patients were divided into the arrhythmia group (n = 130) and non-arrhythmia group (n = 260). The risk factors of postoperative arrhythmia were obtained by multi-factor logistic regression analysis. A Nomogram model for predicting the risk of postoperative arrhythmia in CHD patients was established using R language and underwent verification. Results: The results of multi-factor logistic regression analysis showed that diastolic pressure, heart function grade at III–IV, creatinine, C-reactive protein (CRP), N-terminal pro-brain natriuretic peptide (NT-ProBNP) peak value, total bilirubin (TBIL) and red cell distribution width (RDW) were the risk factors inducing postoperative arrhythmia in CHD patients. Based on these risk factors, a Nomogram model was successfully established. The verification results revealed that the predicted values were basically consistent with the actual values, indicating that the Nomogram model had good prediction ability. The area under the curve (AUC) value was 0.974, suggesting the high prediction efficiency of Nomogram model. Conclusion: Diastolic pressure, heart function grade at III–IV, creatinine, CRP, NT-ProBNP peak value, TBIL and RDW are the risk factors of postoperative arrhythmia in CHD patients. Nomogram model based on these risk factors has good prediction efficiency and underlying clinical value.

Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features

BackgroundDistinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma.MethodsApproximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA).ResultsUltimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p < 0.05). The AUC values of the radiomics model were 0.85 (95% CI, 0.76 to 0.95) in the training sets, and the corresponding AUC values in the validation sets were 0.82 (95% CI, 0.65 to 0.98), which were far superior to the clinical model AUC values of 0.68 (95% CI, 0.58 to 0.79) in the training sets and 0.72 (95% CI, 0.57 to 0.87) in the validation sets. The nomogram demonstrated good performance in the preoperative distinction of chondrosarcoma. The DCA analysis revealed that the nomogram model had a markedly higher clinical usefulness in predicting chondrosarcoma grading preoperatively than either the rad-score or clinical model alone.ConclusionThe nomogram based on MRI radiomics combined with optimal independent factors had better performance for the preoperative differentiation between low-grade and high-grade chondrosarcoma and has potential as a noninvasive preoperative tool for personalizing clinical plans.

Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients.

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.