Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis

Predicting the Risk of Postoperative Delirium in Elderly Patients Undergoing Hip Arthroplasty: Development and Assessment of a Novel Nomogram

OBJECTIVE

To construct and internally validate a nomogram that predicts the likelihood of postoperative delirium in a cohort of elderly individuals undergoing hip arthroplasty.

METHODS

Data for a total of 681 elderly patients underwent hip arthroplasty were retrospectively collected and divided into a model (n = 477) and a validation cohort (n = 204) according to the principle of 7:3 distribution temporally. The assessment of postoperative cognitive function was conducted through the utilization of The Confusion Assessment Method (CAM). The nomogram model for postoperative cognitive impairments was established by a combination of Lasso regression and logistic regression. The receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance.

RESULTS

The nomogram utilized various predictors, including age, body mass index (BMI), education, preoperative Barthel Index, preoperative hemoglobin level, history of diabetes, and history of cerebrovascular disease, to forecast the likelihood of postoperative delirium in patients. The area under the ROC curves (AUC) for the nomogram, incorporating the aforementioned predictors, was 0.836 (95% CI: 0.797-0.875) for the training set and 0.817 (95% CI: 0.755-0.880) for the validation set. The calibration curves for both sets indicated a good agreement between the nomogram's predictions and the actual probabilities.

CONCLUSION

The use of this novel nomogram can help clinicians predict the likelihood of delirium after hip arthroplasty in elderly patients and help prevent and manage it in advance.

A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer

Background

As a heterogeneous malignancy, breast cancer (BRCA) shows high incidence and mortality. Discovering novel molecular markers and developing reliable prognostic models may improve the survival of BCRA.

Methods

The RNA-seq data of BRCA patients were collected from the training set The Cancer Genome Atlas (TCGA)-BRCA and validation set GSE20685 in the Gene Expression Omnibus (GEO) databases. The "GSVA" R package was used to calculate the glycolysis score for each patient, based on which all the patients were divided into different glycolysis groups. The "limma" package was employed to perform differentially expression genes (DEGs) analysis. Key signature genes were selected by performing un/multivariate and least absolute shrinkage and selection operator (LASSO) C regression and used to develop a RiskScore model. The ESTIMATE and MCP-Counter algorithms were used for quantifying immune infiltration level. The functions of the genes were validated using Western blot, colony formation, transwell and wound-healing assay.

Results

The glycolysis score and prognostic analysis showed that high glycolysis score was related to tumorigenesis pathway and a poor prognosis in BRCA as overactive glycolysis inhibited the normal functions of immune cells. Subsequently, we screened five key prognostic genes using the LASSO Cox regression analysis and used them to establish a RiskScore with a high classification efficiency. Based on the results of the RiskScore, it was found that patients in the high-risk group had significantly unfavorable immune infiltration and prognostic outcomes. A nomogram integrating the RiskScore could well predict the prognosis for BRCA patients. Knockdown of PSCA suppressed cell proliferation, invasion and migration of BRCA cells.

Conclusion

This study developed a glycolysis-related signature with five genes to distinguish between high-risk and low-risk BRCA patients. A nomogram developed on the basis of the RiskScore was reliable to predict BRCA survival. Our model provided clinical guidance for the treatment of BRCA patients.

Establishment of a nomogram model for predicting distant metastasis in pancreatic ductal adenocarcinoma: a comparative analysis of different lymph node staging systems based on the SEER database

The purpose of this study was to compare the predictive value of different lymph node staging systems and to develop an optimal prognostic nomogram for predicting distant metastasis in pancreatic ductal adenocarcinoma (PDAC). Our study involved 6364 patients selected from the Surveillance, Epidemiology, and End Results (SEER) database and 126 patients from China. Independent risk factors for distant metastasis were screened by univariate and multivariate logistic regression analyses, and a model-based comparison of different lymph node staging systems was conducted. Furthermore, we developed a nomogram for predicting distant metastasis using the optimal performance lymph node staging system. The lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), age, primary site, grade, tumor size, American Joint Committee on Cancer (AJCC) 7th Edition T stage, and radiotherapy recipient status were significant predictors of distant metastasis in PDAC patients. The model with the LODDS was a better fit than the model with the LNR. We developed a nomogram model based on LODDS and six clinical parameters. The area under the curve (AUC) and concordance index (C-index) of 0.753 indicated that this model satisfied the discrimination criteria. Kaplan-Meier curves indicate a significant difference in OS among patients with different metastasis risks. LODDS seems to have a superior ability to predict distant metastasis in PDAC patients compared with the AJCC 8th Edition N stage, PLN and LNR staging systems. Moreover, we developed a nomogram model for predicting distant metastasis. Clinicians can use the model to detect patients at high risk of distant metastasis and to make further clinical decisions.

Meaningful nomograms based on systemic immune inflammation index predicted survival in metastatic pancreatic cancer patients receiving chemotherapy

OBJECTIVE

The purpose of the study is to construct meaningful nomogram models according to the independent prognostic factor for metastatic pancreatic cancer receiving chemotherapy.

METHODS

This study is retrospective and consecutively included 143 patients from January 2013 to June 2021. The receiver operating characteristic (ROC) curve with the area under the curve (AUC) is utilized to determine the optimal cut-off value. The Kaplan-Meier survival analysis, univariate and multivariable Cox regression analysis are exploited to identify the correlation of inflammatory biomarkers and clinicopathological features with survival. R software are run to construct nomograms based on independent risk factors to visualize survival. Nomogram model is examined using calibration curve and decision curve analysis (DCA).

RESULTS

The best cut-off values of 966.71, 0.257, and 2.54 for the systemic immunological inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) were obtained by ROC analysis. Cox proportional-hazards model revealed that baseline SII, history of drinking and metastasis sites were independent prognostic indices for survival. We established prognostic nomograms for primary endpoints of this study. The nomograms' predictive potential and clinical efficacy have been evaluated by calibration curves and DCA.

CONCLUSION

We constructed nomograms based on independent prognostic factors, these models have promising applications in clinical practice to assist clinicians in personalizing the management of patients.

Clinicopathological significance of cancer stem cell marker CD44/SOX2 in esophageal squamous cell carcinoma (ESCC) patients and construction of a nomogram to predict overall survival

Background

Esophageal squamous cell carcinoma (ESCC), a prevalent malignancy within the upper gastrointestinal system, is characterized by its unfavorable prognosis and the absence of specific indicators for outcome prediction and high-risk case identification. In our research, we examined the expression levels of cancer stem cells (CSCs), markers CD44/SOX2 in ESCC, scrutinized their association with clinicopathological parameters, and developed a predictive nomogram model. This model, which incorporates CD44/SOX2, aims to forecast the overall survival (OS) of patients afflicted with ESCC.

Methods

Immunohistochemistry was utilized to detect the expression levels of CD44 and SOX2 in both cancerous and paracancerous tissues of 68 patients with ESCC. The correlation between CD44/SOX2 expression and clinicopathological parameters was subsequently analyzed. Factors impacting the prognosis of ESCC patients were assessed through univariate and multivariate Cox regression analyses. Leveraging the results of these multivariate regression analyses, a nomogram prognostic model was established to provide individualized predictions of ESCC patient survival outcomes. The predictive accuracy of the nomogram prognostic model was evaluated using the consistency index (C-index) and calibration curves.

Results

The expression levels of CD44 were markedly elevated in the tumor tissues of ESCC patients. Similarly, SOX2 was significantly overexpressed in the tumor tissues of ESCC patients. The positive expression of SOX2 in ESCC demonstrated a strong correlation with both the pathological T-stage and the presence of carcinoembryonic antigen. CD44 and SOX2 co-positive expression was significantly associated with the pathological T-stage and tumor node metastasis (TNM) stage. Furthermore, ESCC patients exhibiting CD44-positive expression in their tumor tissue generally had a more adverse prognosis. The co-expression of CD44 and SOX2 resulted in a grimmer prognosis compared to patients with other combinations. Multivariate Cox regression analysis identified the co-expression of CD44 and SOX2, the pathological T-stage, and lymph node metastasis as independent prognostic indicators for ESCC patients. The three identified variables were subsequently incorporated into a nomogram for predicting OS. The C-index of the measurement model and the area under the curve of the subjects' work characteristics showed good individual prediction. This prognostic model stratified patients into low- and high-risk categories. Analysis revealed that the 5-year OS rate was significantly higher in the low-risk group compared to the high-risk group.

Conclusions

Elevated CD44 levels, indicative of CSC presence, are intimately linked with the oncogenesis of ESCC and are strongly predictive of unfavorable patient outcomes. Concurrently, the SOX2 gene exhibits a heightened expression in ESCC, markedly accelerating tumor progression and fostering more extensive disease infiltration. The co-expression of CD44 and SOX2 correlates significantly with ESCC patient prognosis, serving as a reliable, independent prognostic marker. Our constructed nomogram, incorporating CD44/SOX2 expression, enhances the prediction of OS and facilitates risk stratification in ESCC patients.

Development and external validation of a prognostic nomogram to predict survival in patients aged ≥60 years with pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis. Age is shown to be an independent factor affecting survival outcomes in patients with PDAC. Our study aimed to identify prognostic factors and construct a nomogram to predict survival in PDAC patients aged ≥60 years.

Methods

Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms.

Results

Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks.

Conclusions

We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.

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 Nomogram for Predicting Overall Survival of Patients With Primary Spinal Cord Glioblastoma

OBJECTIVE

Primary spinal cord glioblastoma (PSCGBM) is a rare malignancy with a poor prognosis. To date, no prognostic nomogram for this rare disease was established. Hence, we aimed to develop a nomogram to predict overall survival (OS) of PSCGBM.

METHODS

Clinical data of patients with PSCGBM was retrospectively collected from the neurosurgery department of Soochow University Affiliated Second Hospital and the Surveillance Epidemiology and End Results database. Information including age, sex, race, tumor extension, extent of resection, adjuvant treatment, marital status, income, year of diagnosis and months from diagnosis to treatment were recorded. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for PSCGBM. A nomogram was constructed to predict 1-year, 1.5-year, and 2-year OS of PSCGBM.

RESULTS

A total of 132 patients were included. The 1-year, 1.5-year, and 2-year OS were 45.5%, 29.5%, and 18.9%, respectively. Four variables: age groups, tumor extension, extent of resection, and adjuvant therapy, were identified as independent prognostic factors. The nomogram showed robust discrimination with a C-index value for the prediction of 1-year OS, 1.5-year OS, and 2-year of 0.71 (95% confidence interval [CI], 0.61-0.70), 0.72 (95% CI, 0.62-0.70), and 0.70 (95% CI, 0.61-0.70), respectively. The calibration curves exhibited high consistencies between the predicted and observed survival probability in this cohort.

CONCLUSION

We have developed and internally validated a nomogram for predicting the survival outcome of PSCGBM for the first time. The nomogram has the potential to assist clinicians in making individualized predictions of survival outcome of PSCGBM.

Survival analysis and prognostic model establishment of secondary osteosarcoma: a SEER-based study

Background

Surgical excision is considered one of the most effective treatments for secondary osteosarcoma (SO). It remains unclear whether the survival of patients with secondary osteosarcoma (SO) could be associated with their surgical willingness.

Materials and methods

The statistics of the patients diagnosed with SO between 1975 and 2008 were gathered from the surveillance epidemiology and end results (SEER) database. The patients were divided into three subgroups according to their surgical compliance. The authors used the multivariable Logistic regression analysis and cox regression method to reveal the influence of surgical compliance on prognosis and the risk factors of surgical compliance. Additionally, the authors formulated a nomogram model to predict the overall survival (OS) of patients. The concordance index (C-index) was used to evaluate the accuracy and practicability of the above prediction model.

Results

Sixty-three (9.2%) of the 688 patients with SO who were recommended for surgical treatment refused to undergo surgery. Lower surgical compliance can be ascribed to an earlier time of diagnosis and refusal of chemotherapy. The lower overall survival (OS) {[hazard ratio (HR)] 1.733, [CI] 1.205-2.494, P value [P]=0.003} of not surgical compliant patients was verified by the multivariate cox regression method, compared with surgical compliant patients. In addition, the discernibility of the nomogram model was proven to be relatively high (C-index=0.748), by which we can calibrate 3-year- and 5-year OS prediction plots to obtain good concordance to the actual situation.

Conclusions

Surgical compliance was proved to be an independent prognostic factor in the survival of patients with SO.

Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study

OBJECTIVE

This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI).

DESIGN

The study is a retrospective single-center study.

PARTICIPANTS

SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University.

SETTING

The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

METHODS

We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model.

RESULTS

A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85).

CONCLUSIONS

Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.

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.

Multi-omics analysis revealed the regulation mode of intratumor microorganisms and microbial signatures in gastrointestinal cancer

OBJECTIVE

Gastrointestinal cancer is one of the most common malignant tumors in the world, and its incidence rate is always high. In recent years, research has shown that microorganisms may play a broad role in the diagnosis, pathogenesis, and treatment of cancer.

METHODS

In this study, samples were first classified according to the microbial expression data of Gastrointestinal cancer, followed by functional enrichment and Immunoassay. In order to better understand the role of intratumor microorganisms in the prognosis, we screened gene signatures and constructed risk model through univariate cox and lasso regression and multivariable cox, then screened microbial signatures using zero-inflated model regression model and constructed risk index (RI), and finally predicted the immunotherapeutic effect of the risk model.

RESULTS

The results indicate that the composition of tumor microorganisms in the C3 subtype is closely related to tumor angiogenesis, and there is a significant difference in the proportion of innate and acquired immune cells between the C2 and C1 subtypes, as well as differences in the physiological functions of immune cells. There are significant differences in the expression of microbial signatures between high and low risk subtypes, with 9 microbial signatures upregulated in high risk subtypes and 15 microbial signatures upregulated in low risk subtypes. These microbial signatures were significantly correlated with the prognosis of patients. The results of immunotherapy indicate that immunotherapy for high-risk subtypes is more effective.

CONCLUSION

Overall, we analyze from the perspective of microorganisms within tumors, pointing out new directions for the diagnosis and treatment of cancer.

Reclassified the phenotypes of cancer types and construct a nomogram for predicting bone metastasis risk: A pan-cancer analysis

BACKGROUND

Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan-cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk.

METHODS

Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence-based cancer classification system (BM-CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM-CCS and the nomogram, respectively.

RESULTS

A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%-17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%-5.6%), and category C (1.2%; 95% CI: 1.1%-1.4%) (p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM-CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%-88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%-87.1%). The decision curve analysis also demonstrated the clinical usefulness.

CONCLUSION

The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM.

Predictive value of HTS grade in patients with intrahepatic cholangiocarcinoma undergoing radical resection: a multicenter study from China

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant tumor with a poor prognosis. This study aimed to investigate whether Hemoglobin, Albumin, Lymphocytes, and Platelets (HALP) score and Tumor Burden Score (TBS) serves as independent influencing factors following radical resection in patients with ICC. Furthermore, we sought to evaluate the predictive capacity of the combined HALP and TBS grade, referred to as HTS grade, and to develop a prognostic prediction model.

METHODS

Clinical data for ICC patients who underwent radical resection were retrospectively analyzed. Univariate and multivariate Cox regression analyses were first used to find influencing factors of prognosis for ICC. Receiver operating characteristic (ROC) curves were then used to find the optimal cut-off values for HALP score and TBS and to compare the predictive ability of HALP, TBS, and HTS grade using the area under these curves (AUC). Nomogram prediction models were constructed and validated based on the results of the multivariate analysis.

RESULTS

Among 423 patients, 234 (55.3%) were male and 202 (47.8) were aged ≥ 60 years. The cut-off value of HALP was found to be 37.1 and for TBS to be 6.3. Our univariate results showed that HALP, TBS, and HTS grade were prognostic factors of ICC patients (all P < 0.05), and ROC results showed that HTS had the best predictive value. The Kaplan-Meier curve showed that the prognosis of ICC patients was worse with increasing HTS grade. Additionally, multivariate regression analysis showed that HTS grade, carbohydrate antigen 19-9 (CA19-9), tumor differentiation, and vascular invasion were independent influencing factors for Overall survival (OS) and that HTS grade, CA19-9, CEA, vascular invasion and lymph node invasion were independent influencing factors for recurrence-free survival (RFS) (all P < 0.05). In the first, second, and third years of the training group, the AUCs for OS were 0.867, 0.902, and 0.881, and the AUCs for RFS were 0.849, 0.841, and 0.899, respectively. In the first, second, and third years of the validation group, the AUCs for OS were 0.727, 0.771, and 0.763, and the AUCs for RFS were 0.733, 0.746, and 0.801, respectively. Through the examination of calibration curves and using decision curve analysis (DCA), nomograms based on HTS grade showed excellent predictive performance.

CONCLUSIONS

Our nomograms based on HTS grade had excellent predictive effects and may thus be able to help clinicians provide individualized clinical decision for ICC patients.

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

Objective

This study aims to develop a predictive model for identifying lung cancer patients at elevated risk for bone metastases, utilizing the Unified Immunoinflammatory Index and various tumor markers. This model is expected to facilitate timely and effective therapeutic interventions, especially in the context of the growing significance of immunotherapy for lung cancer treatment.

Methods

A retrospective analysis was conducted on 324 lung cancer patients treated between January 2019 and January 2021. After meeting the inclusion criteria, 241 patients were selected, with 56 exhibiting bone metastases. The cohort was divided into a training group (169 patients) and a validation group (72 patients) at a 7:3 ratio. Lasso regression was employed to identify critical variables, followed by logistic regression to construct a Nomogram model for predicting bone metastases. The model's validity was ascertained through internal and external evaluations using the Concordance Index (C-index) and Receiver Operating Characteristic (ROC) curve.

Results

The study identified several factors influencing bone metastasis in lung cancer, such as the Systemic Immune-Inflammatory Index (SII), Carcinoembryonic Antigen (CEA), Neuron Specific Enolase (NSE), Cyfra21-1, and Neutrophil-to-Lymphocyte Ratio (NLR). These factors were incorporated into the Nomogram model, demonstrating high validation accuracy with C-index scores of 0.936 for internal and 0.924 for external validation.

Conclusion

The research successfully developed an intuitive and accurate Nomogram prediction model utilizing clinical indicators to predict the risk of bone metastases in lung cancer patients. This tool can be instrumental in aiding clinicians in developing personalized treatment plans, thereby optimizing patient outcomes in lung cancer care.

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.

Development and Validation of Nomograms to Predict Risk and Prognosis in Salivary Gland Carcinoma Patient with Distant Metastases

Background: Salivary gland carcinoma (SGC) patients with distant metastasis (DM) are rare, and understanding this disease is insufficient. Nomograms can predict the prognostic probability of patients, while few studies have examined diagnostic and prognostic factors in SGC patients with DM. The purpose of this study was to establish and validate the risk and prognostic nomograms of SGC patients with DM. Methods: Based on the SEER database, we analyzed the data of SGC patients between 2004 and 2015. Logistic regression analyses and Cox proportional hazards regression analyses were used to identify risk and prognostic factors for DM in SGC patients. Based on the Akaike information criterion (AIC) value and likelihood ratio test, the best-fitting model was selected to build risk and prognostic nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Kaplan-Meier (K-M) survival curves. ROC curves were also used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system. Results: 7418 SGC patients were included in the study, and 307 (4.14%) of them were diagnosed with DM. This study identified that there are variables (age ≥ 80, no-parotid gland primary site, histologic type of mucoepidermoid carcinoma and squamous cell carcinoma, T stage ≥ T2, N staged ≥ N1, histologic grade ≥ III, and tumor size ≥ 41 mm) associated with the occurrence of DM in SGC patients. Therefore, we constructed diagnostic and prognostic nomograms after incorporating these variables. ROC curves illustrated the better predictive efficacy of 2 nomograms over the AJCC staging system. DCA curves, calibration curves, and K-M survival curves showed that 2 nomograms can accurately predict the occurrence and prognosis of DM among SGC patients in training and validation sets. Conclusion: It was shown that the nomograms were highly discriminative in predicting the diagnosis and prognosis of SGC patients with DM, and could identify high-risk patients, thereby providing SGC patients with individualized treatment plans.

Novel nomograms to predict risk and prognosis in hospitalized patients with severe fever with thrombocytopenia syndrome

Background

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging and life-threatening infectious disease caused by SFTS virus. Although recent studies have reported the use of nomograms based on demographic and laboratory data to predict the prognosis of SFTS, no study has included viral load, which is an important factor that influences the prognosis, when compared with other risk factors. Therefore, this study aimed to develop a model that predicts SFTS prognosis before it reaches the critical illness stage and to compare the predictive ability of groups with and without viral load.

Methods

Two hundred patients with SFTS were enrolled between June 2018 and August 2023. Data were sourced from the first laboratory results at admission, and two nomograms for mortality risk were developed using multivariate logistic regression to identify the risk variables for poor prognosis in these patients. We calculated the area under the receiver operating characteristic curve (AUC) for the two nomograms to assess their discrimination, and predictive abilities were compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results

The multivariate logistic regression analysis identified four independent risk factors: age, bleeding manifestations, prolonged activated partial thromboplastin time, and viral load. Based on these factors, a final nomogram predicting mortality risk in patients with SFTS was constructed; in addition, a simplified nomogram was constructed excluding the viral load. The AUC [0.926, 95% confidence interval (CI): 0.882-0.970 vs. 0.882, 95% CI: 35 0.823-0.942], NRI (0.143, 95% CI, 0.036-0.285), and IDI (0.124, 95% CI, 0.061-0.186) were calculated and compared between the two models. The calibration curves of the two models showed excellent concordance, and decision curve analysis was used to quantify the net benefit at different threshold probabilities.

Conclusion

Two critical risk nomograms were developed based on the indicators for early prediction of mortality risk in patients with SFTS, and enhanced predictive accuracy was observed in the model that incorporated the viral load. The models developed will provide frontline clinicians with a convenient tool for early identification of critically ill patients and initiation of a better personalized treatment in a timely manner.

Impact of Preoperative Tumor Size on Prognosis of Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinomas

BACKGROUND

Tumor size (TS) is a well-established prognostic factor of pancreatic ductal adenocarcinoma (PDAC). However, whether a uniform treatment strategy can be applied for all resectable PDACs (R-PDACs) and borderline resectable PDACs (BR-PDACs), regardless of TS, remains unclear. This study aimed to investigate the impact of preoperative TS on surgical outcomes of patients with R-PDACs and BR-PDACs.

METHODS

Chart data from three institutions were reviewed to select patients who underwent pancreatectomy for R-PDACs and BR-PDACs between January 2006 and December 2020. The patients were divided into TSsmall and TSlarge groups according to a TS cutoff value determined for each of R- and BR-PDAC using the minimum P value approach for the risk of R1 resection.

RESULTS

TS of 35 mm and 24 mm was the best cutoff value in R-PDAC and BR-PDAC, respectively. The R1 rate was higher in the TSlarge than TSsmall group, in both R- (n = 35, 37% versus n = 294, 19%; P = 0.011) and BR-PDAC (n = 89, 37% versus n = 27, 15%; P = 0.030). Overall survival was significantly better in the TSsmall than TSlarge group in R-PDAC (38.2 versus 12.1 months; P < 0.001), but comparable between the two groups in BR-DPAC (21.2 versus 22.7 months; P = 0.363). Multivariate analysis revealed TS > 35 mm as an independent predictor of worse survival in patients with R-PDAC.

CONCLUSION

Larger TS was associated with a higher R1 rate and is a worse prognostic factor in patients with R-PDAC.

Prognostic model for oversurvival and tumor-specific survival prediction in patients with advanced extrahepatic cholangiocarcinoma: a population-based analysis

BACKGROUND

The prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) must be determined with precision. However, the usual TNM staging system has the drawback of ignoring age, adjuvant therapy, and gender and lacks the ability to more correctly predict patient prognosis. Therefore, we determine the risk factors of survival for patients with advanced ECCA patients and developed brand-new nomograms to forecast patients with advanced ECCA's overall survival (OS) and cancer-specific survival (CSS).

METHOD

From the Epidemiology and End Results (SEER) database, patients with advanced ECCA were chosen and randomly assigned in a ratio of 6:4 to the training and validation subgroups. The cumulative incidence function (CIF) difference between groups was confirmed by applying Gray's and Fine test and competing risk analyses. Next, the cancer-specific survival (CSS) and overall survival (OS) nomograms for advanced ECCA were developed and validated.

RESULTS

In accordance with the selection criteria, 403 patients with advanced ECCA were acquired from the SEER database and then split at random into two groups: a training group (n = 241) and a validation group (n = 162). The 1-, 2-, and 3-year cancer-specific mortality rates were 58.7, 74.2, and 78.0%, respectively, while the matching mortality rates for the competition were 10.0, 13.8, and 15.0%. Nomograms were generated for estimating OS and CSS, and they were assessed using the ROC curve and the C-index. The calibration curves showed that there was a fair amount of agreement between the expected and actual probabilities of OS and CSS. Additionally, greater areas under the ROC curve were seen in the newly developed nomograms for OS and CSS when compared to the 7th AJCC staging system. The advanced ECCA patients were divided into groupings with an elevated risk and those with a low risk and the Kaplan-Meier method was used for the survival analysis, which showed that survival time was shorter in the high-risk group than in the low-risk group.

CONCLUSION

The proposed nomograms have good predictive ability. The nomograms may can help doctors determine the prognosis of patients with advanced ECCA as well as provide more precise treatment plans for them.

Predicting survival of patients with bone metastasis of unknown origin

Purpose

Bone metastasis of unknown origin is a rare and challenging situation, which is infrequently reported. Therefore, the current study was performed to analyze the clinicopathologic features and risk factors of survival among patients with bone metastasis of unknown origin.

Patients and methods

We retrospectively analyzed the clinical data for patients with bone metastasis of unknown origin between 2010 and 2016 based on the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were first analyzed by applying univariable Cox regression analysis. Then, we performed multivariable analysis to confirm independent survival predictors.

Results

In total, we identified 1224 patients with bone metastasis of unknown origin for survival analysis, of which 704 males (57.5%) and 520 females (42.5%). Patients with bone metastasis of unknown origin had a 1-year OS rate of 14.50% and CSS rate of 15.90%, respectively. Race, brain metastasis, liver metastasis, radiotherapy, and chemotherapy were significant risk factors of OS on both univariable and multivariable analyses (p <0.05). As for CSS, both univariable and multivariable analyses revealed that no brain metastasis, no liver metastasis, radiotherapy, and chemotherapy were associated with increased survival (p <0.05).

Conclusion

Patients with bone metastasis of unknown origin experienced an extremely poor prognosis. Radiotherapy and chemotherapy were beneficial for prolonging the survival of those patients.

Risk factors for distant metastasis of Chondrosarcoma in the middle-aged and elderly people

Chondrosarcoma is the second most common primary bone malignancy with the highest incidence in middle-aged and elderly people, where distant metastasis (DM) still leads to poor prognosis. The purpose of this study was to construct a nomogram for studying the diagnosis of DM in middle-aged and elderly patients with chondrosarcoma. Data on chondrosarcoma patients aged ≥ 40 years diagnosed from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The data were divided into a training set and an internal validation set according to a 7:3 ratio, and the training set data were screened for independent risk factors for DM in chondrosarcoma patients using univariate and multivariate logistic regression analysis. The screened independent risk factors were then used to build a nomogram. In addition, data from 144 patients with chondrosarcoma aged ≥ 40 years diagnosed in a tertiary hospital in China from 2012 to 2021 were collected as the external validation set. The results were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis in the training set, internal validation set, and external validation set. A total of 1462 middle-aged and elderly patients with chondrosarcoma were included, and 92 (6.29%) had DM at the time of diagnosis. Independent risk factors for DM in middle-aged and elderly patients with chondrosarcoma included being married (OR: 2.119, 95% CI: 1.094-4.105), histological type of dedifferentiated chondrosarcoma (OR: 1.290, 95% CI: 1.110-1.499), high-grade tumor (OR: 1.511, 95% CI: 1.079-2.115), T3 stage (OR: 4.184, 95% CI: 1.977- 8.858), and N1 staging (OR: 5.666, 95% CI: 1.964-16.342). The area under the receiver operating characteristic curve (AUC) was 0.857, 0.820, and 0.859 in the training set, internal validation set, and external validation set, respectively. The results of the calibration curve and decision curve analysis also confirmed that the established nomogram could accurately predict DM in middle-aged and elderly patients with chondrosarcoma. Married, histological type of dedifferentiated chondrosarcoma, high-grade tumor, T3 stage, and N1 stage are independent risk factors for DM in middle-aged and elderly chondrosarcoma patients, and clinicians should see more attention.

Predicting Prognosis and Immunotherapy Response in Multiple Cancers Based on the Association of PANoptosis-Related Genes with Tumor Heterogeneity

PANoptosis is a newly recognized inflammatory pathway for programmed cell death (PCD). It participates in regulating the internal environment, homeostasis, and disease process in various complex ways and plays a crucial role in tumor development, but its mechanism of action is still unclear. In this study, we comprehensively analyzed the expression of 14 PANoptosis-related genes (PANRGs) in 28 types of tumors. Most PANRGs are upregulated in tumors, including Z-DNA binding protein 1 (ZBP1), nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing 3 (NLRP3), caspase (CASP) 1, CASP6, CASP8, PYCARD, FADD, MAP3K7, RNF31, and RBCK1. PANRGs are highly expressed in GBM, LGG, and PAAD, while their levels in ACC are much lower than those in normal tissues. We found that both the CNV and SNV gene sets in BLCA are closely related to survival performance. Subsequently, we conducted clustering and LASSO analysis on each tumor and found that the inhibitory and the stimulating immune checkpoints positively correlate with ZBP1, NLRP3, CASP1, CASP8, and TNFAIP3. The immune infiltration results indicated that KIRC is associated with most infiltrating immune cells. According to the six tumor dryness indicators, PANRGs in LGG show the strongest tumor dryness but have a negative correlation with RNAss. In KIRC, LIHC, and TGCT, most PANRGs play an important role in tumor heterogeneity. Additionally, we analyzed the linear relationship between PANRGs and miRNA and found that MAP3K7 correlates to many miRNAs in most cancers. Finally, we predicted the possible drugs for targeted therapy of the cancers. These data greatly enhance our understanding of the components of cancer and may lead to the discovery of new biomarkers for predicting immunotherapy response and improving the prognosis of cancer patients.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis

BACKGROUND

The mechanisms of distant metastasis in pancreatic cancer (PC) have not been elucidated, and this study aimed to explore the risk factors affecting the metastasis and prognosis of metastatic patients and to develop a predictive model.

METHOD

Clinical data from patients meeting criteria from 1990 to 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and two machine learning methods, random forest and support vector machine, combined with logistic regression, were used to explore risk factors influencing distant metastasis and to create nomograms. The performance of the model was validated using calibration curves and ROC curves based on the Shaanxi Provincial People's Hospital cohort. LASSO regression and Cox regression models were used to explore the independent risk factors affecting the prognosis of patients with distant PC metastases.

RESULTS

We found that independent risk factors affecting PC distant metastasis were: age, radiotherapy, chemotherapy, T and N; the independent risk factors for patient prognosis were: age, grade, bone metastasis, brain metastasis, lung metastasis, radiotherapy and chemotherapy.

CONCLUSION

Together, our study provides a method for risk factors and prognostic assessment for patients with distant PC metastases. The nomogram we developed can be used as a convenient individualized tool to facilitate aid in clinical decision making.

A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study

OBJECTIVES

Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now.

METHODS

A multivariate model was established based on the multiview-guided two-stream convolution network (MTCN) radiomics algorithms, which focused on primary tumor and peri-tumor features. Regarding discriminative ability, survival fitting, and model accuracy, different models were compared.

RESULTS

Three hundred and sixty-three pancreatic cancer patients were divided in to train and test cohorts by 7:3. The modified MTCN (MTCN+) model was established based on age, CA125, MTCN scores, and radiologist judgement. The MTCN+ model outperformed the MTCN model and the artificial model in discriminative ability and model accuracy. [Train cohort area under curve (AUC): 0.823 vs. 0.793 vs. 0.592; train cohort accuracy (ACC): 76.1 vs. 74.4 vs. 56.7%; test cohort AUC: 0.815 vs. 0.749 vs. 0.640; test cohort ACC: 76.1 vs. 70.6 vs. 63.3%; external validation AUC: 0.854 vs. 0.792 vs. 0.542; external validation ACC: 71.4 vs. 67.9 vs. 53.5%]. The survivorship curves fitted well between actual LN status and predicted LN status regarding disease free survival and overall survival. Nevertheless, the MTCN+ model performed poorly in assessing the LN metastatic burden among the LN positive population. Notably, among the patients with small primary tumors, the MTCN+ model performed steadily as well (AUC: 0.823, ACC: 79.5%).

CONCLUSIONS

A novel MTCN+ preoperative LN status predictive model was established and outperformed the artificial judgement and deep-learning radiomics judgement. Around 40% misdiagnosed patients judged by radiologists could be corrected. And the model could help precisely predict the survival prognosis.

Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram

This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients' post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78-0.99) and the C index was 0.62 (95% CI: 0.48-0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.

Risk factors and survival prediction of young breast cancer patients with liver metastases: a population-based study

Background

The risk and prognosis of young breast cancer (YBC) with liver metastases (YBCLM) remain unclear. Thus, this study aimed to determine the risk and prognostic factors in these patients and construct predictive nomogram models.

Methods

This population-based retrospective study was conducted using data of YBCLM patients from the Surveillance, Epidemiology, and End Results database between 2010 and 2019. Multivariate logistic and Cox regression analyses were used to identify independent risk and prognostic factors, which were used to construct the diagnostic and prognostic nomograms. The concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the performances of the established nomogram models. Propensity score matching (PSM) analysis was used to balance the baseline characteristics between the YBCLM patients and non-young patients with BCLM when comparing overall survival (OS) and cancer-specific survival (CSS).

Results

A total of 18,275 YBC were identified, of whom 400 had LM. T stage, N stage, molecular subtypes, and bone, lung, and brain metastases were independent risk factors for LM developing in YBC. The established diagnostic nomogram showed that bone metastases contributed the most risk of LM developing, with a C-index of 0.895 (95% confidence interval 0.877-0.913) for this nomogram model. YBCLM had better survival than non-young patients with BCLM in unmatched and matched cohorts after propensity score matching analysis. The multivariate Cox analysis demonstrated that molecular subtypes, surgery and bone, lung, and brain metastases were independently associated with OS and CSS, chemotherapy was an independent prognostic factor for OS, and marital status and T stage were independent prognostic factors for CSS. The C-indices for the OS- and CSS-specific nomograms were 0.728 (0.69-0.766) and 0.74 (0.696-0.778), respectively. The ROC analysis indicated that these models had excellent discriminatory power. The calibration curve also showed that the observed results were consistent with the predicted results. DCA showed that the developed nomogram models would be effective in clinical practice.

Conclusion

The present study determined the risk and prognostic factors of YBCLM and further developed nomograms that can be used to effectively identify high-risk patients and predict survival outcomes.

Genetic polymorphism of the Dab2 gene and its association with Type 2 Diabetes Mellitus in the Chinese Uyghur population

Objective

The human Disabled-2 (Dab2) protein is an endocytic adaptor protein, which plays an essential role in endocytosis of transmembrane cargo, including low-density lipoprotein cholesterol (LDL-C). As a candidate gene for dyslipidemia, Dab2 is also involved in the development of type 2 diabetes mellitus(T2DM). The aim of this study was to investigate the effects of genetic variants of the Dab2 gene on the related risk of T2DM in the Uygur and Han populations of Xinjiang, China.

Methods

A total of 2,157 age- and sex-matched individuals (528 T2DM patients and 1,629 controls) were included in this case-control study. Four high frequency SNPs (rs1050903, rs2255280, rs2855512 and rs11959928) of the Dab2 gene were genotyped using an improved multiplex ligation detection reaction (iMLDR) genotyping assay, and the forecast value of the SNP for T2DM was assessed by statistical analysis of clinical data profiles and gene frequencies.

Results

We found that in the Uygur population studied, for both rs2255280 and rs2855512, there were significant differences in the distribution of genotypes (AA/CA/CC), and the recessive model (CC vs. CA + AA) between T2DM patients and the controls (P < 0.05). After adjusting for confounders, the recessive model (CC vs. CA + AA) of both rs2255280 and rs2855512 remained significantly associated with T2DM in this population (rs2255280: OR = 5.303, 95% CI [1.236 to -22.755], P = 0.025; rs2855512: OR = 4.892, 95% CI [1.136 to -21.013], P = 0.033). The genotypes (AA/CA/CC) and recessive models (CC vs. CA + AA) of rs2855512 and rs2255280 were also associated with the plasma glucose and HbA1c levels (all P < 0.05) in this population. There were no significant differences in genotypes, all genetic models, or allele frequencies between the T2DM and control group in the Han population group (all P > 0.05).

Conclusions

The present study suggests that the variation of the Dab2 gene loci rs2255280 and rs2855512 is related to the incidence of T2DM in the Uygur population, but not in the Han population. In this study, these variations in Dab2 were an independent predictor for T2DM in the Uygur population of Xinjiang, China.

Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study

BACKGROUND

Pancreatic neuroendocrine tumors (PNETs) are one of the most common endocrine tumors, and liver metastasis (LMs) are the most common location of metastasis from PNETS; However, there is no valid nomogram to predict the diagnosis and prognosis of liver metastasis (LMs) from PNETs. Therefore, we aimed to develop a valid predictive model to aid physicians in making better clinical decisions.

METHODS

We screened patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2010-2016. Feature selection was performed by machine learning algorithms and then models were constructed. Two nomograms were constructed based on the feature selection algorithm to predict the prognosis and risk of LMs from PNETs. We then used the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration plot and consistency index (C-index) to evaluate the discrimination and accuracy of the nomograms. Kaplan-Meier (K-M) survival curves and decision curve analysis (DCA) were also used further to validate the clinical efficacy of the nomograms. In the external validation set, the same validation is performed.

RESULTS

Of the 1998 patients screened from the SEER database with a pathological diagnosis of PNET, 343 (17.2%) had LMs at the time of diagnosis. The independent risk factors for the occurrence of LMs in PNET patients included histological grade, N stage, surgery, chemotherapy, tumor size and bone metastasis. According to Cox regression analysis, we found that histological subtype, histological grade, surgery, age, and brain metastasis were independent prognostic factors for PNET patients with LMs. Based on these factors, the two nomograms demonstrated good performance in model evaluation.

CONCLUSION

We developed two clinically significant predictive models to aid physicians in personalized clinical decision-makings.

Value of thyroid cancer history in the prognosis of pancreatic cancer: a SEER population-based study

Thyroid cancer patients have a good prognosis, and their long survival increases the likelihood of developing a second primary tumor. Meanwhile, pancreatic cancer (PC) has a poor prognosis and therapeutic efficacy. However, the association between prior thyroid cancer and the subsequent PC prognosis is unknown. Herein, we selected pathologically diagnosed PC patients older than 17 between 2010 and 2015 from the SEER database. We used propensity score matching (PSM) to reduce confounding factors between groups and matched each PC patient with a history of thyroid cancer with 10 PC patients without a history of thyroid cancer. Finally, we selected 103 PC patients with prior thyroid cancer and 1030 PC patients without prior thyroid cancer. Then, we analyzed the factors influencing the overall survival (OS) and the cancer-specific survival (CSS) of PC patients. The median overall survival of PC patients with and without a history of thyroid cancer was 12 and 9 months, respectively. The history of thyroid cancer in PC patients reduced the PC-specific mortality (p < 0.05). Prior thyroid cancer might be a favorable prognostic factor for PC-specific mortality in PC patients.

Risk factors and nomogram-based prediction of the risk of limb weakness in herpes zoster

Background

Limb weakness is a less common complication of herpes zoster (HZ). There has been comparatively little study of limb weakness. The aim of this study is to develop a risk nomogram for limb weakness in HZ patients.

Methods

Limb weakness was diagnosed using the Medical Research Council (MRC) muscle power scale. The entire cohort was assigned to a training set (from January 1, 2018 to December 30, 2019, n = 169) and a validation set (from October 1, 2020 to December 30, 2021, n = 145). The least absolute shrinkage and selection operator (LASSO) regression analysis method and multivariable logistic regression analysis were used to identify the risk factors of limb weakness. A nomogram was established based on the training set. The discriminative ability and calibration of the nomogram to predict limb weakness were tested using the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). A validation set was used to further assess the model by external validation.

Results

Three hundred and fourteen patients with HZ of the extremities were included in the study. Three significant risk factors: age (OR = 1.058, 95% CI: 1.021–1.100, P = 0.003), VAS (OR = 2.013, 95% CI: 1.101–3.790, P = 0.024), involving C6 or C7 nerve roots (OR = 3.218, 95% CI: 1.180–9.450, P = 0.027) were selected by the LASSO regression analysis and the multivariable logistic regression analysis. The nomogram to predict limb weakness was constructed based on the three predictors. The area under the ROC was 0.751 (95% CI: 0.673–0.829) in the training set and 0.705 (95% CI: 0.619–0.791) in the validation set. The DCA indicated that using the nomogram to predict the risk of limb weakness would be more accurate when the risk threshold probability was 10–68% in the training set and 15–57% in the validation set.

Conclusion

Age, VAS, and involving C6 or C7 nerve roots are potential risk factors for limb weakness in patients with HZ. Based on these three indicators, our model predicted the probability of limb weakness in patients with HZ with good accuracy.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

Construction and analysis of a nomogram prediction model for post-infectious bronchiolitis obliterans in children with adenovirus pneumonia after invasive mechanical ventilation

BACKGROUND

Post-infectious bronchiolitis obliterans (PIBO) is the most common sequelae in children with adenovirus pneumonia (ADVP). However, there are few studies on the risk factors for PIBO occurrence. This study aims to investigate the risk factors for PIBO in pediatric patients with severe ADVP, especially after invasive mechanical ventilation (IMV), as well as to build a nomogram prediction model.

METHODS

The clinical data, laboratory and imaging features, and treatment of 863 children with ADVP under 3 years old who were admitted to our hospital from January to December 2019 were retrospectively analyzed. Among them, 66 children with severe ADVP received IMV treatment. The situation and the influencing factors of PIBO in children with severe ADVP were explored, and a nomogram prediction model was constructed.

RESULTS

Among the 863 cases of ADVP, 46 cases (5.33%) developed PIBO. Duration of fever, IMV, complications, and neutrophil percentage were independent risk factors for PIBO in children with ADVP. Among the 66 patients with ADVP who underwent IMV, 33 patients (50.0%) developed PIBO. Gender, duration of fever, adenovirus (ADV) load, and mixed fungal coinfections were independent risk factors for PIBO. In the nomogram prediction model analysis, the area under the curve (AUC) was 0.857; in addition, Hosmer‒Lemeshow (H-L) detection reflected good alignment (χ2 = 68.75, P < 0.01).

CONCLUSIONS

A nomogram prediction model, which can be utilized to predict PIBO occurrence in pediatric patients with ADVP after IMV at an early time period, was successfully built.

Risk factors, prognostic predictors, and nomograms for pancreatic cancer patients with initially diagnosed synchronous liver metastasis

BACKGROUND

Liver metastasis (LM) remains a major cause of cancer-related death in patients with pancreatic cancer (PC) and is associated with a poor prognosis. Therefore, identifying the risk and prognostic factors in PC patients with LM (PCLM) is essential as it may aid in providing timely medical interventions to improve the prognosis of these patients. However, there are limited data on risk and prognostic factors in PCLM patients.

AIM

To investigate the risk and prognostic factors of PCLM and develop corresponding diagnostic and prognostic nomograms.

METHODS

Patients with primary PC diagnosed between 2010 and 2015 were reviewed from the Surveillance, Epidemiology, and Results Database. Risk factors were identified using multivariate logistic regression analysis to develop the diagnostic mode. The least absolute shrinkage and selection operator Cox regression model was used to determine the prognostic factors needed to develop the prognostic model. The performance of the two nomogram models was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and risk subgroup classification. The Kaplan-Meier method with a log-rank test was used for survival analysis.

RESULTS

We enrolled 33459 patients with PC in this study. Of them, 11458 (34.2%) patients had LM at initial diagnosis. Age at diagnosis, primary site, lymph node metastasis, pathological type, tumor size, and pathological grade were identified as independent risk factors for LM in patients with PC. Age > 70 years, adenocarcinoma, poor or anaplastic differentiation, lung metastases, no surgery, and no chemotherapy were the independently associated risk factors for poor prognosis in patients with PCLM. The C- index of diagnostic and prognostic nomograms were 0.731 and 0.753, respectively. The two nomograms could accurately predict the occurrence and prognosis of patients with PCLM based on the observed analysis results of ROC curves, calibration plots, and DCA curves. The prognostic nomogram could stratify patients into prognostic groups and perform well in internal validation.

CONCLUSION

Our study identified the risk and prognostic factors in patients with PCLM and developed corresponding diagnostic and prognostic nomograms to help clinicians in subsequent clinical evaluation and intervention. External validation is required to confirm these results.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

Background

Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.

Methods

The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.

Results

Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.

Conclusion

Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

Cancer-specific survival and metastasis in pancreatic mucinous cystadenocarcinoma: A SEER-based cohort study

Aims

This study aimed to investigate the prognostic value of clinical features for cancer-specific survival (CSS) and metastasis in patients with pancreatic mucinous cystadenocarcinoma (MCAC). We further constructed and validated an effective nomogram to predict CSS.

Methods

We screened patients diagnosed with pancreatic MCAC from Surveillance Epidemiology and End Results (SEER) database. Kaplan-Meier curves were used to determine the CSS time. Univariate and multivariate Cox and logistic regression analyses were conducted to identify the prognostic factors for CSS and metastasis. The nomogram was constructed to predict the prognosis of pancreatic MCAC based on the results from the multivariate analysis. We used the concordance index (C-index), the area under the curve (AUC), and the calibration plots to determine the predictive accuracy and discriminability of the nomogram.

Results

Multivariate Cox analysis revealed that age, primary site, grade, and radiotherapy were independent prognostic factors associated with CSS. Multivariate logistic regression analysis revealed that surgery and grade were independent risk factors associated with metastasis. The independent risk factors were included to construct a prognosis prediction model for predicting CSS in patients with pancreatic MCAC. The concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots of the training cohort and the validation cohort showed that the nomogram had an acceptable predictive performance.

Conclusion

We established a nomogram that could determine the 3- and 5-year CSS, which could evaluate individual clinical outcomes and provide individualized clinical decisions.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study

Simple Summary

Surgery is the main treatment to cure pancreatic cancer (PC). However, the 5-year survival rate of surgical resection is only 10–20%. The aim of our study was to develop a prediction model with the novel machine learning algorithm random survival forest (RSF) and to offer easy-to-use prediction tools, including risk stratification and individual prognosis. The study would benefit patients and physicians in postoperative management and facilitate personalized medicine.

Abstract

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model’s favorable calibration and the decision curve analysis illustrated the model’s value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.

Risk factors and survival prediction of pancreatic cancer with lung metastases: A population-based study

Background

The risk and prognosis of pancreatic cancer with lung metastasis (PCLM) are not well-defined. Thus, this study aimed to identify the risk and prognostic factors for these patients, and establish predictive nomogram models.

Methods

Patients diagnosed with PCLM between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified using logistic regression and Cox regression analyses. Nomograms were constructed to predict the risk and survival of PCLM, and the area under the curve (AUC), C-index, and calibration curve were used to determine the predictive accuracy and discriminability of the established nomogram, while the decision curve analysis was used to confirm the clinical effectiveness.

Results

A total of 11287 cases with complete information were included; 601 (5.3%) patients with PC had lung metastases. Multivariable logistic analysis demonstrated that primary site, histological subtype, and brain, bone, and liver metastases were independent risk factors for lung metastases. We constructed a risk prediction nomogram model for the development of lung metastases among PC patients. The c-index of the established diagnostic nomogram was 0.786 (95%CI 0.726-0.846). Multivariable Cox regression analysis demonstrated that primary site, liver metastases, surgery, and chemotherapy were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS), while bone metastases were independent prognostic factors for CSS. The C-indices for the OS and CSS prediction nomograms were 0.76 (95% CI 0.74-0.78) and 0.76 (95% CI 0.74-0.78), respectively. Based on the AUC of the receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance.

Conclusions

The present study identified the risk and prognostic factors of PCLM and further established nomograms, which can help clinicians effectively identify high-risk patients and predict their clinical outcomes.

An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma

Background: Gliomas are the most common and fatal malignant type of tumor of the central nervous system. RNA post-transcriptional modifications, as a frontier and hotspot in the field of epigenetics, have attracted increased attention in recent years. Among such modifications, methylation is most abundant, and encompasses N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1 methyladenosine (m1A), and 7-methylguanosine (m7G) methylation.

Methods: RNA-sequencing data from healthy tissue and low-grade glioma samples were downloaded from of The Cancer Genome Atlas database along with clinical information and mutation data from glioblastoma tumor samples. Forty-nine m6A/m5C/m1A/m7G-related genes were identified and an m6A/m5C/m1A/m7G-lncRNA signature of co-expressed long non-coding RNAs selected. Least absolute shrinkage and selection operator Cox regression analysis was used to identify 12 m6A/m5C/m1A/m7G-related lncRNAs associated with the prognostic characteristics of glioma and their correlation with immune function and drug sensitivity analyzed. Furthermore, the Chinese Glioma Genome Atlas dataset was used for model validation.

Results: A total of 12 m6A/m5C/m1A/m7G-related genes (AL080276.2, AC092111.1, SOX21-AS1, DNAJC9-AS1, AC025171.1, AL356019.2, AC017104.1, AC099850.3, UNC5B-AS1, AC006064.2, AC010319.4, and AC016822.1) were used to construct a survival and prognosis model, which had good independent prediction ability for patients with glioma. Patients were divided into low and high m6A/m5C/m1A/m7G-LS groups, the latter of which had poor prognosis. In addition, the m6A/m5C/m1A/m7G-LS enabled improved interpretation of the results of enrichment analysis, as well as informing immunotherapy response and drug sensitivity of patients with glioma in different subgroups.

Conclusion: In this study we constructed an m6A/m5C/m1A/m7G-LS and established a nomogram model, which can accurately predict the prognosis of patients with glioma and provides direction toward promising immunotherapy strategies for the future.