Nomogram Model Research Articles

Construction and validation of an innovative prognostic nomogram for overall survival in cervical cancer patients with lung metastasis: an analysis utilizing the SEER database.

To facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years. We extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). We gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts. By utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.

Correlation study of serum lipid levels and lipid metabolism-related genes in cervical cancer.

Lipid metabolism plays an important role in cancer. The aim of this study was to investigate the relationship between lipid metabolism and the development of cervical cancer, and to explore the prognostic significance of lipid metabolism-related genes in patients with cervical cancer. Initially, we retrospectively collected data from 1589 cervical cancer patients treated at the Affiliated Hospital of Qingdao University, with 1589 healthy individuals from the physical examination center serving as the control group. The correlation between their serum lipid levels and cervical cancer was analyzed. Subsequently, leveraging public databases, we conducted comprehensive studies on lipid metabolism-related genes. Additionally, we analyzed RNA expression profiling and clinical information sourced from TCGA and GTEx databases. Finally, we established a prognostic model integrating 9 genes associated with lipid metabolism and generated a nomogram model using R. GO and KEGG were performed to explore the functions and pathways of lipid metabolism-related genes. Our findings revealed that patients with cervical cancer exhibited dyslipidemia, characterized by elevated levels of TC, TG, and LDL-C, alongside reduced HDL-C levels compared to controls (P<0.05). Interestingly, compared with early-stage patients, advanced patients had lower HDL-C level and higher LDL-C level. Regression analysis further highlighted high TC, TG, and LDL-C as significant risk factors for cervical cancer. Then a total of 188 lipid metabolism-related genes were identified and a prognostic signature based on 9 genes was established and validated. The results of the GO and KEGG functional analysis indicated that the lipid metabolism-related genes are primarily concentrated on pathways associated with fatty acid metabolism. Our study underscores the varying degrees of dyslipidemia observed in patients with cervical cancer, emphasizing the relevance of serum lipids in disease development. Our prognostic riskScore model predicted the overall survival time of patients based on 9 genes associated with lipid metabolism. These 9 genes may be tumor biomarkers and new targets for the treatment of cervical cancer.

The diagnostic value of image-enhanced endoscopy system in sinonasal inverted papilloma.

This study aimed to evaluate the diagnostic value of image-enhanced endoscopy (IEE) in detecting sinonasal inverted papilloma (SNIP). Overall, 86 patients with unilateral nasal papillary or lobulated neoplasms were included between July 2018 and June 2019. All patients underwent IEE examinations, and the diagnosis of all neoplasms was confirmed through postoperative pathology. Logistic regression analysis was conducted to screen for independent predictors of various types of vascular patterns of SNIP. Furthermore, a prognostic nomogram was constructed using the independent predictors screened by logistic regression analysis to evaluate its usefulness in distinguishing SNIP from nasal polyp (NP) and papillary mucosa folds (PMF). In total, 86 consecutive cases were observed, including 37 with SNIP, 40 with NP, and 9 with PMF. Logistic regression analysis showed that spot, corkscrew, and multilayered vascular patterns were independent predictors of SNIP diagnosis. Furthermore, a nomogram comprising the three independent risk factors was constructed with scores of 5, 2, and 3. The area under the receiver operating characteristic curve for predicting SNIP was 0.954, 0.66, 0.71, and 0.76 for the nomogram model, spot vascular pattern, corkscrew vascular pattern, and multilayered vascular pattern, respectively. The nomogram model based on spot, corkscrew, and multilayered vascular patterns in SNIP observed using IEE can be a useful diagnostic tool for predicting and distinguishing between NP and PMF.

Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics

Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent on endoscopists’ experience. We aim to establish a more accurate predictive model for infiltration depth of early gastric cancer including a standardized colorimetric system, which demonstrates promising clinical implication. A retrospective study of 718 EGC cases was performed. Clinical and pathological characteristics were included, and Commission Internationale de l’Eclariage (CIE) standard colorimetric system was used to evaluate the chromaticity of lesions. The predicting models were established in the derivation set using multivariate backward stepwise logistic regression, decision tree model, and random forest model. Logistic regression shows location, macroscopic type, length, marked margin elevation, WLI color difference and histological type are factors significantly independently associated with infiltration depth. In the decision tree model, margin elevation, lesion located in the lower 1/3 part, WLI a*color value, b*color value, and abnormal thickness in enhanced CT were selected, which achieved an AUROC of 0.810. A random forest model was established presenting the importance of each feature with an accuracy of 0.80, and an AUROC of 0.844. Quantified color metrics can improve the diagnostic precision in the invasion depth of EGC. We have developed a nomogram model using logistic regression and machine learning algorithms were also explored, which turned out to be helpful in decision-making progress.

Risk Factors of Chemotherapy-Induced Thrombocytopenia After Oxaliplatin-Containing Chemotherapy for Gastrointestinal Malignancies

PurposeThrombocytopenia is among the most common chemotherapy-related hematologic toxicities. We aim to determine the predictors of oxaliplatin chemotherapy-induced thrombocytopenia in patients with gastrointestinal tumors to guide the clinic.MethodsClinical data of 750 patients with a malignant gastrointestinal tumor were included as the primary cohort. Basic clinical data, serological indices, and anthropometric indices of these patients were collected. According to the presence or absence of CIT, univariate analysis was performed to identify significant factors for multivariate analysis. In R language software, nomogram was constructed based on the results of multi-factor analysis, and the calibration curve and ROC curve were drawn.ResultsUnivariate analysis identified 17 factors as closely related to CIT occurrence, namely age, lymph node metastasis (N) stage, metastasis (M) stage, lung metastasis, other site metastasis, chemotherapy regimen, course of treatment, total dose of oxaliplatin, AST, albumin, neutrophils, monocytes, baseline platelets, transferrin, natural killer (NK) cell, phase angle, and SMI (P < 0.10). The binary logistic multivariate regression analysis revealed five independent risk factors for developing CIT (P < 0.05), including the M stage, total dose of oxaliplatin, albumin, baseline thrombocyte count, and NK cell. Based on the results of multivariate logistic regression analysis, R software was used to establish a nomogram model. The calibration curve shows that the combined predictor has good consistency. The area under the ROC curve was 0.877 and the best cut-off value was 0.3579613 (sensitivity, 78.9%; specificity, 81.8%), which showed the better prediction efficiency.ConclusionThe total dose of oxaliplatin, M stage, albumin, baseline platelet count, and NK cell was independent risk factors for CIT. The sequentially constructed histogram model had a good predictive effect on the risk of thrombocytopenia caused by oxaliplatin chemotherapy in patients with gastrointestinal malignancies.

A Visualized Nomogram for Predicting Prognosis in Elderly Patients after Percutaneous Coronary Intervention.

Revascularized patients still experience adverse cardiovascular events. This is particularly true for elderly patients over the age of 65, as they often have more co-morbid vascular conditions. It is important to develop a tool to assist clinicians in comprehensively assessing these patients' prognosis. The objective of this study is to create a comprehensive visual nomogram model combining clinical and physiological assessments to predict outcomes in elderly patients undergoing percutaneous coronary intervention (PCI). This study is a retrospective investigation of patients who underwent PCI between January 2016 and December 2017. A total of 691 patients with 1461 vessels were randomly divided into a training (n = 483) and a validation set (n = 208). A multivariate Cox regression model was employed using the training set to select variables for constructing a nomogram. The performance of the nomogram was assessed through the receiver operating characteristic curve (ROC) and calibration curves to evaluate its discrimination and predictive accuracy. To further assess the clinical usefulness, Kaplan-Meier curve analysis and landmark analysis were conducted. Independent risk factors, including diabetes mellitus (DM), post-PCI quantitative flow ratio (QFR), previous myocardial infarction (MI), and previous PCI, were contained in the nomogram. The nomogram exhibited a good area under the curve (AUC) ranging from 0.742 to 0.789 in the training set, 0.783 to 0.837 in the validation set, and 0.764 to 0.786 in the entire population. Calibration curves demonstrated a well-fitted curve in all three sets. The Kaplan-Meier curves showed clear separation and the patients with higher scores in the nomogram model exhibited a higher incidence of target vessel revascularization (TVR) (7.99% vs. 1.24% for 2-year, p 0.001 and 13.54% vs. 2.23% for 5-years, p 0.001, respectively). This study has developed the visually intuitive nomogram to predict the 2-year and 5-year TVR rates for elderly patients who underwent PCI. This tool provides more accurate and comprehensive healthcare guidance for patients and their physicians.

LncRNA FOXD2-AS1 promotes the growth, invasion and migration of OSCC cells by regulating the MiR-185–5p/PLOD1/Akt/mTOR pathway

Although lncRNAs are recognized to contribute to the development of oral squamous-cell carcinoma (OSCC), their exact function in invasion and cell migration is not clear. In this research, we explored the molecular and cellular mechanisms of FOXD2-AS1 in OSCC. Prognostic and bioinformatics analyses were used to test for the differential expression of FOXD2-AS1-PLOD1. Following FOXD2-AS1 suppression or overexpression, changes in cell viability were measured using the CCK-8 test; changes in cell migration and invasion abilities were measured using the migration and the Transwell assay. The expression of associated genes and proteins was found using Western blot and RT-qPCR. Analysis of luciferase reporter genes was done to look for regulatory connections between various molecules. The FOXD2-AS1-PLOD1 pair, which was highly expressed in OSCC, was analyzed and experimentally verified to be closely related to the prognosis of OSCC, and a nomogram model and correction curve were constructed. The inhibition of FOXD2-AS1 resulted in the reduction of cell activity, migration, invasion ability and changes in genes related to invasion and migration. In vivo validation showed that inhibition of FOXD2-AS1 expression slowed tumor growth, and related proteins changed accordingly. The experiments verified that FOXD2-AS1 negatively regulated miR-185–5 p and that miR-185–5 p negatively regulated PLOD1. In addition, it was found that the expression of PLOD1, p-Akt and p-mTOR proteins in OSCC cells was reduced by the inhibition of FOXD2-AS1, and FOXD2-AS1 and PLOD1 were closely related to the Akt/mTOR pathway. Increased expression of FOXD2-AS1 promotes OSCC growth, invasion and migration, which is important in part by targeting miR-185–5 p/PLOD1/Akt/mTOR pathway activity.

A novel nomogram for the prediction of perforation during endoscopic submucosal dissection for colorectal neoplasms.

High perforation risk hinders the widespread adoption of ESD for colorectal neoplasms. This study was performed to determine the risk factors of colorectal endoscopic submucosal dissection (ESD)-induced perforation and develop a predictive model. A total of 1046 colorectal neoplasms in 1011 patients were retrospectively enrolled from January 2011 to December 2021, in a single tertiary center as the derivation cohort. We identified independent risk factors for perforation using univariate analysis and multi-variate logistic regression. A nomogram was developed based on the logistic regression model and prospectively applied to 266 colorectal neoplasms as the validation cohort. The performance of the predictive model was evaluated with the receiver operating characteristic curve, calibration plot, and decision curve analysis. Independent pre-operative factors for colorectal ESD-induced perforation were tumor located in the left colon [odds ratio (OR) 2.39, P = 0.040], size ≥ 40 mm (OR 3.36, P < 0.001), ≥2/3 circumference (OR 7.55, P = 0.004), located across folds (OR 6.26, P < 0.001), and laterally spreading tumor (non-granular type, OR 2.34, P = 0.029; granular type, OR 2.46, P = 0.021). The nomogram model incorporating the pre-operative factors performed well in both the derivation and validation cohorts (areas under the curve of 0.750 and 0.806, respectively). Decision curve analysis demonstrated that the clinical benefit of the nomogram was favorable. The novel nomogram, developed and prospectively validated, incorporating tumor size, location, and morphology can successfully predict perforation during ESD for colorectal neoplasms.

Conditional survival of male breast cancer

Background The incidence of male breast cancer has been increasing in recent years; however, the long-term survival outcomes of diagnosed patients remain uncertain. This study was designed to evaluate the conditional survival of male breast cancer patients and to predict the future survival of patients through the conditional nomogram, to provide important suggestions for clinical decision-making. Methods Retrospective data from the SEER database included 3600 male breast cancer patients, divided into training and validation groups (7 : 3 ratio). Overall survival rates were calculated using Kaplan–Meier analysis. Conditional survival analysis described survival at specific years. Time-dependent multivariate Cox analysis identified prognostic factors’ impact. The conditional survival nomogram model predicted real-time survival rates. Results Over time, the 5-year real-time survival rate of patients gradually improved, increasing from 70.5 to 74.8, 79.4, 85.8, and 92.9% (respectively, representing 5-year survival rates of 1–4 years after diagnosis). In addition, the improvement in conditional survival rate CS5 showed a nonlinear trend. After 5 years of diagnosis, age, tumor size, and tumor stage had a sustained impact on patient prognosis. Finally, a conditional survival nomogram was constructed to predict the 10-year survival rate in real time. Conclusion Five years after diagnosis, the conditional survival rate of male patients with breast cancer has improved, but it is not nonlinear. In the first 5 years after diagnosis, patients with older age, larger tumor size, poorer tumor stage, and distant metastasis should be actively followed up and treated to improve their long-term survival.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases.

Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes. To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment. Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort. A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit. Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Bioinformatics Analysis Reveals Prognostic Significance of the Macrophage Marker Gene Signature in Gastric Adenocarcinoma.

Gastric adenocarcinoma (GAC) is a malignant tumor with the highest incidence in the digestive system. Macrophages have been proven to play important roles in tumor microenvironment. Herein, single-cell RNA sequencing (scRNA-seq) profiles from the Gene Expression Omnibus (GEO) and bulk RNA-seq data from the Cancer Genome Atlas (TCGA) database were utilized to construct a macrophage marker gene signature (MMGS) to predict the prognosis of GAC patients. Subsequently, a risk score model based on the MMGS was built to predict the prognosis of GAC patients; further, this was validated in the GEO cohort. The risk score categorized patients into the high- and low-risk groups. A nomogram model based on the risk score and clinic-pathological characteristics was developed. Seven genes, ABCA1, CTHRC1, GADD45B, NPC2, PLTP, PRSS23, and RNASE1, were included in the risk score model. Patients with a low-risk score showed a better prognosis. The MMGS had good sensitivity and specificity for predicting the prognosis inGAC patients. The risk score was an independent prognostic factor. The constructed nomogram exhibited favorable predictability and reliability for predicting GAC prognosis. In conclusion, the risk score model based on the seven MMGSs performed well in the predicting prognosis of GAC patients. Our study may provide new insights into clinical decision-making for the personalized treatment of patients with gastric cancer (GC).

A nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) pattern and prognosis of hepatocellular carcinoma

BackgroundVessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. This study aimed to establish a nomogram model to predict VETC patterns based on preoperative CT imaging features. Patients and methodsPatients who underwent surgical resection between January 1, 2016 and August 31, 2022 were retrospectively included. Predictors associated with VETC pattern were determined by using logistic regression analyses, and a nomogram model was constructed. Prognostic factors associated with recurrence-free survival (RFS) after surgical resection were identified by using Cox regression analyses. ResultsA total of 84 patients were included for CT analysis. All patients underwent radical surgical resection. AST/ALT >1.07(odds ratio [OR], 4.91; 95 ​% CI: 1.11, 21.68; P ​< ​0.05), intratumoral necrosis (OR, 4.99; 95 ​% CI: 1.25, 19.99; P ​< ​0.05) and enhancing capsule (OR, 3.32; 95 ​% CI: 1.27, 8.94; P ​< ​0.05) were independent predictors of VETC pattern. These features were used for the construction of nomogram model, which showed comparable prediction performance, with AUC value of 0.767 (95%CI [0.662, 0.852]). CK19 status (Hazard ratio [HR], 2.02; 95 ​% CI: 1.06, 3.86; P ​< ​0.05), the number of tumors (HR, 3.31; 95 ​% CI: 1.47, 7.45; P ​< ​0.05) and VETC pattern (HR, 2.52; 95 ​% CI: 1.31, 4.86; P ​< ​0.05) were independent predictors of postoperative RFS. ConclusionA nomogram model based on preoperative CT imaging features could be used for the characterization of VETC pattern, and has prognostic significance for postoperative RFS in patients with HCC.

Predictive potential of preoperative Naples prognostic score-based nomogram model for the prognosis in surgical resected thoracic esophageal squamous cell carcinoma patients: A retrospective cohort study.

The present study aimed to establish an effective prognostic nomogram model based on the Naples prognostic score (NPS) for resectable thoracic esophageal squamous cell carcinoma (ESCC). A total of 277 patients with ESCC, who underwent standard curative esophagectomy and designated as study cohort, were retrospectively analyzed. The patients were divided into different groups, including NPS 0, NPS 1, NPS 2, and NPS 3 or 4 groups, for further analysis, and the results were validated in an external cohort of 122 ESCC patients, who underwent surgery at another cancer center. In our multivariate analysis of the study cohort showed that the tumor-node-metastasis (TNM) stage, systemic inflammation score, and NPS were the independent prognostic factors for the overall survival (OS) and progression-free survival (PFS) durations. In addition, the differential grade was also an independent prognostic factor for the OS in the patients with ESCC after surgery (all P < .05). The area under the curve of receiver operator characteristics for the PFS and OS prediction with systemic inflammation score and NPS were 0.735 (95% confidence interval [CI] 0.676-0.795, P < .001) and 0.835 (95% CI 0.786-0.884, P < .001), and 0.734 (95% CI 0.675-0.793, P < .001) and 0.851 (95% CI 0.805-0.896, P < .001), respectively. The above independent predictors for OS or PFS were all selected in the nomogram model. The concordance indices (C-indices) of the nomogram models for predicting OS and PFS were 0.718 (95% CI 0.681-0.755) and 0.669 (95% CI 0.633-0.705), respectively, which were higher than that of the 7th edition of American Joint Committee on Cancer TNM staging system [C-index 0.598 (95% CI 0.558-0.638) for OS and 0.586 (95% CI 0.546-0.626) for PFS]. The calibration curves for predicting the 5-year OS or PFS showed a good agreement between the prediction by nomogram and actual observation. In the external validation cohort, the nomogram discrimination for OS was better than that of the 7th edition of TNM staging systems [C-index: 0.697 (95% CI 0.639-0.755) vs 0.644 (95% CI 0.589-0.699)]. The calibration curves showed good consistency in predicting the 5-year survival between the actual observation and nomogram predictions. The decision curve also showed a higher potential of the clinical application of predicting the 5-years OS of the proposed nomogram model as compared to that of the 7th edition of TNM staging systems. The preoperative NPS-based nomogram model had a certain potential role for predicting the prognosis of ESCC patients.

A clinical and computed tomography-based nomogram to predict the outcome in patients with anterior circulation large vessel occlusion after endovascular mechanical thrombectomy.

To explore the positive predictors of the clinical outcome in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion (ACLVO) after endovascular mechanical thrombectomy (EMT) at a 90-day follow-up, and to establish a nomogram model to predict the clinical outcome. AIS patients with ACLVO detected by multimodal Computed Tomography imaging who underwent EMT were collected. Patients were divided into the favorable and the unfavorable groups according to the 90-day modified Rankin Scale (mRS) score. Univariate and multivariate analyses were performed to investigate predictors of the favorable outcome (mRS of 0-2). A nomogram model for predicting the clinical outcome after EMT was drawn, and the receiver operating characteristic (ROC) curve was used to evaluate its predictive value. Totally 105 patients including 65 patients in the favorable group and 40 in the unfavorable group were enrolled. Multivariate logistic regression analysis showed that admission National Institute of Health Stroke scale (NIHSS) score [0.858 (95% CI 0.778-0.947)], ACLVO at M2 [20.023 (95% CI 2.204-181.907)] and infarct core (IC) volume [0.943 (95% CI 0.917-0.969)] was positively correlated with favorable outcome. The accuracy of the nomogram model in predicting the outcome was 0.923 (95% CI 0.870-0.976), with a cutoff value of 119.6 points. The area under the ROC curve was 0.848 (95% CI 0.780-0.917; sensitivity, 79.7%; specificity, 90.0%). A low Admission NIHSS score, ACLVO at M2, and a small IC volume were positive predictors for favorable outcome. The nomogram model may well predict the outcome in AIS patients with ACLVO after EMT.

Bioinformatics analysis of PANoptosis regulators in the diagnosis and subtyping of steroid-induced osteonecrosis of the femoral head.

In this study, we aimed to investigate the involvement of PANoptosis, a form of regulated cell death, in the development of steroid-induced osteonecrosis of the femoral head (SONFH). The underlying pathogenesis of PANoptosis in SONFH remains unclear. To address this, we employed bioinformatics approaches to analyze the key genes associated with PANoptosis. Our analysis was based on the GSE123568 dataset, allowing us to investigate both the expression profiles of PANoptosis-related genes (PRGs) and the immune profiles in SONFHallowing us to investigate the expression profiles of PRGs as well as the immune profiles in SONFH. We conducted cluster classification based on PRGs and assessed immune cell infiltration. Additionally, we used the weighted gene co-expression network analysis (WGCNA) algorithm to identify cluster-specific hub genes. Furthermore, we developed an optimal machine learning model to identify the key predictive genes responsible for SONFH progression. We also constructed a nomogram model with high predictive accuracy for assessing risk factors in SONFH patients, and validated the model using external data (area under the curve; AUC = 1.000). Furthermore, we identified potential drug targets for SONFH through the Coremine medical database. Using the optimal machine learning model, we found that 2 PRGs, CASP1 and MLKL, were significantly correlated with the key predictive genes and exhibited higher expression levels in SONFH. Our analysis revealed the existence of 2 distinct PANoptosis molecular subtypes (C1 and C2) within SONFH. Importantly, we observed significant variations in the distribution of immune cells across these subtypes, with C2 displaying higher levels of immune cell infiltration. Gene set variation analysis indicated that C2 was closely associated with multiple immune responses. In conclusion, our study sheds light on the intricate relationship between PANoptosis and SONFH. We successfully developed a risk predictive model for SONFH patients and different SONFH subtypes. These findings enhance our understanding of the pathogenesis of SONFH and offer potential insights into therapeutic strategies.

Abstract PO3-02-08: Risk factors and a new predictive nomogram for predicting the Non-sentinel Lymph Node Metastasis in 708 early-stage Breast Cancer Patients with Positive Sentinel Nodes in China

Abstract Objective: The management of positive sentinel lymph node biopsies (SLNB) in patients with early-stage breast cancer remains a matter of controversy. Some studies have shown that not all breast cancer patients with positive sentinel lymph nodes (SLNs) benefit from further axillary lymph node dissection (ALND). Our study aims to analyse the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis in Chinese early-stage breast cancer patients with positive sentinel lymph nodes. Methods: The clinical data of 708 patients with early-stage breast cancer who attended The Fourth Hospital of Hebei Medical University for surgical treatment from January 2016 to December 2021 was retrospectively analyzed. All of them were female breast cancer patients with positive sentinel lymph nodes and underwent ALND. The patients were divided into modeling group and validation group according to the time of treatment. The patients in the modeling group were divided into a positive NSLN group and a negative NSLN group. The characteristics: age, maximum tumor diameter, the number of positive SLNs, proportion of positive SLNs, vessel carcinoma embolus, nerve invasion, status of ER, PR, HER-2 and Ki-67 were included in the analysis. And the independent risk factors of NSLN metastasis were screened by univariate (chi-square test) and multivariate (logistic regression) analysis. A nomogram model was established according to the screened independent risk factors, and the model was externally validated by the validation group. The ROC curve was plotted to calculate the area under the curve (AUC) and evaluate the predictive power of the nomogram model. A calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the model. Results: Among the 708 patients, (modeling group n=410, validation group n=298), the average age, average number of positive SLNs, and NSLN positive rate were 50.9±11.1, 1.55±0.87 and 25.71%. The univariate analysis revealed statistically significant differences in the number of positive SLNs (P&amp;lt; 0.001), the proportion of positive SLNs (P &amp;lt; 0.001), vessel carcinoma embolus (P=0.001), nerve invasion (P=0.012), and ER status (P=0.029) in the NSLN-positive group compared with the negative group. The multifactorial logistic regression analysis revealed that the number of positive SLN (P=0.002; OR: 1.700; 95% CI: 1.219-2.370), the proportion of positive SLN (P=0.048; OR: 3.214; 95% CI: 1.001-10.490), vessel carcinoma embolus (P=0.010; OR: 2.076; 95% CI: 1.194-3.612), nerve invasion (P=0.024; OR: 2.119; 95% CI: 1.221-3.678), and ER status (P=0.045; OR: 2.978; 95% CI: 1.022-8.675) were independent risk factors for NSLN positivity. Furthermore, the nomogram was built using these 5 factors and validated on 298 patients in the validation group. The AUC was 0.728 (P&amp;lt; 0.001; 95%CI: 0.673-0.784) in the modeling group and 0.810 (P&amp;lt;0.001; 95%CI: 0.735-0.886) in the validation group. The calibration curves of the modeling and validation groups were close to the ideal curve, and DCA also showed that the model could be applied in clinical practice. Conclusion: For early-stage breast cancer with positive SLNs, these factors as the positive number of SLNs &amp;gt; 2, the ratio of positive SLNs &amp;gt; 50%, vessel carcinoma embolus, nerve invasion and ER-positive could predict NSLN metastasis well. The proposed nomogram appears to accurately estimate the likelihood of positive NSLNs and may greatly help the surgeon to decide intraoperatively whether to perform further ALND and avoid non-essential ALND as well as postoperative complications. Citation Format: Zhenchuan Song, Xueyi Zhao, Liu Yang. Risk factors and a new predictive nomogram for predicting the Non-sentinel Lymph Node Metastasis in 708 early-stage Breast Cancer Patients with Positive Sentinel Nodes in China [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-02-08.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models.

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Development and validation of a novel nomogram model for identifying risk of prolonged length of stay among patients receiving free vascularized flap reconstruction of head and neck cancer.

The aim of the present study was to build and internally validate a nomogram model for predicting prolonged length of stay (PLOS) among patients receiving free vascularized flap reconstruction of head and neck cancer (HNC). A retrospective clinical study was performed at a single center, examining patients receiving free vascularized flap reconstruction of HNC from January 2011 to January 2019. The variables were obtained from the electronic information system. The primary outcome measure was PLOS. Univariate and multivariate analyses were used to find risk factors for predicting PLOS. A model was then built according to multivariate results. Internal validation was implemented via 1000 bootstrap samples. The study included 1047 patients, and the median length of stay (LOS) was 13.00 (11.00, 16.00) days. Multivariate analysis showed that flap types ((radial forearm free flap (odds ratio [OR] = 2.238; 95% CI, 1.403-3.569; P = 0.001), free fibula flap (OR = 3.319; 95% CI, 2.019-4.882; P < 0.001)), duration of surgery (OR = 1.002; 95% CI, 1.001-1.003; P = 0.004), postoperative complications (OR = 0.205; 95% CI, 0.129-0.325; P = P < 0.001) and unplanned reoperation (OR = 0.303; 95% CI, 0.140-0.653; P = 0.002) were associated with PLOS. In addition to these variables, blood transfusion was comprised in the model. The AUC of the model was 0.78 (95% CI, 0.711-0.849) and 0.725 (95% CI, 0.605-0.845) in the primary and internal validation cohorts, respectively. The DCA revealed the clinical utility of the current model when making intervention decisions within the PLOS possibility threshold range of 0.2-0.8. Our study developed a nomogram that exhibits a commendable level of accuracy, thereby aiding clinicians in assessing the risk of PLOS among patients receiving free vascularized flap reconstruction for HNC.

Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study.

This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients. We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation. We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort. Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.

A real-world study was conducted to develop a nomogram that predicts the occurrence of anastomotic leakage in patients with esophageal cancer following esophagectomy.

The incidence of anastomotic leakage (AL) following esophagectomy is regarded as a noteworthy complication. There is a need for biomarkers to facilitate early diagnosis of AL in high-risk esophageal cancer (EC) patients, thereby minimizing its morbidity and mortality. We assessed the predictive abilities of inflammatory biomarkers for AL in patients after esophagectomy. In order to ascertain the predictive efficacy of biomarkers for AL, Receiver Operating Characteristic (ROC) curves were generated. Furthermore, univariate, LASSO, and multivariate logistic regression analyses were conducted to discern the risk factors associated with AL. Based on these identified risk factors, a diagnostic nomogram model was formulated and subsequently assessed for its predictive performance. Among the 438 patients diagnosed with EC, a total of 25 patients encountered AL. Notably, elevated levels of interleukin-6 (IL-6), IL-10, C-reactive protein (CRP), and procalcitonin (PCT) were observed in the AL group as compared to the non-AL group, demonstrating statistical significance. Particularly, IL-6 exhibited the highest predictive capacity for early postoperative AL, exhibiting a sensitivity of 92.00% and specificity of 61.02% at a cut-off value of 132.13 pg/ml. Univariate, LASSO, and multivariate logistic regression analyses revealed that fasting blood glucose ≥7.0mmol/L and heightened levels of IL-10, IL-6, CRP, and PCT were associated with an augmented risk of AL. Consequently, a nomogram model was formulated based on the results of multivariate logistic analyses. The diagnostic nomogram model displayed a robust discriminatory ability in predicting AL, as indicated by a C-Index value of 0.940. Moreover, the decision curve analysis provided further evidence supporting the clinical utility of this diagnostic nomogram model. This predictive instrument can serve as a valuable resource for clinicians, empowering them to make informed clinical judgments aimed at averting the onset of AL.