Survival Prediction Model For Patients Research Articles

Prognostic nomogram in patients with right-sided colon cancer after colectomy: a surveillance, epidemiology, and end results-based study.

This study aimed to develop and validate a nomogram for predicting overall survival (OS) in patients undergoing surgery for right-sided colon cancer (RCC). We collected 25,203 patients with RCC from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided them into 7:3 training and internal validation set. Utilizing the Cox proportional hazards regression model, we constructed a nomogram based on prognostic risk factors. Furthermore, for external validation, we retrospectively followed up with 228 patients from Jiaxing First Hospital and assessed and calibrated the nomogram using the C-index and calibration curves. After identifying independent prognostic factors through univariate and multivariate analyses, a nomogram was developed. The c-index values of this nomogram differed as follows: 0.851 (95% CI: 0.845-0.857) in the training set, 0.860 (95% CI: 0.850-0.870) in the internal validation set, and 0.834 (95% CI: 0.780-0.888) in the external validation set, indicating the model's strong discriminative ability. Calibration curves for 1-year, 3-year, and 5-year overall survival (OS) probabilities exhibited a high level of consistency between predicted and actual survival rates. Furthermore, Decision Curve Analysis (DCA) demonstrated that the new model consistently outperformed the TNM staging system in terms of net benefit. We developed and validated a survival prediction model for patients with RCC. This novel nomogram outperforms the traditional TNM staging system and can guide clinical practitioners in making optimal clinical decisions.

Construction and efficacy test of a survival prediction model for locally advanced cervical cancer based on anti-angiogenesis

ObjectiveThis study aimed to develop and evaluate an anti-angiogenesis-based model for predicting the survival and the potential benefits of targeted therapy for patients with localized advanced cervical cancer. MethodsWe collected clinical data from 163 patients with cervical cancer who received paclitaxel and cisplatin (TP) or TP plus bevacizumab during or after radiotherapy from June 2017 to February 2023. We analyzed the clinical measures of recent efficacy and overall survival (OS) using univariate and logistic multivariate and Cox regression methods, respectively. We constructed a nomogram model and evaluated its efficacy using the c-index, the area under the curve (AUC), a calibration curve, and the clinical decision curve (DCA). ResultsWe found that targeted agents and hemoglobin were independent determinants of near-term efficacy (P < 0.05), while targeted agents and stage were independent factors of OS (P < 0.05). We developed a predictive model for an OS prognostic nomogram and performed internal validation 1000 times using the Bootstrap re-sampling method. The c-index was 0.81, and the AUC was 0.84 (P < 0.01).The calibration curves showed a good agreement between the projected and actual values. The DCA curve indicated that the model had a high positive predictive accuracy. ConclusionWe developed a novel anti-angiogenesis-based survival prediction model for patients with locally advanced cervical cancer. This model could estimate the benefit of targeted therapy before treatment, and it had good validation.

Construction of a prediction model for postoperative prognosis in patients with resectable cholangiocarcinoma based on silence information regulator 2 expression.

To develop a prediction model for postoperative prognosis in patients with cholangiocarcinoma (CCA) based on the expression of silence information regulator 2 (SIRT2). The differential expression of SIRT2 between CCA and normal tissues was analyzed using TCGA and GEO databases. Gene set enrichment analysis (GSEA) was used to explore potential mechanisms of SIRT2 in CCA. The expression of SIRT2 protein in CCA tissues and normal tissues (including 44 pairs of specimens) was also detected by immunohistochemistry (IHC) in 89 resectable CCA patients who underwent surgical treatment in the First Affiliated Hospital of Bengbu Medical College between January 2016 and December 2021. The relationship between SIRT2 expression and clinicopathological characteristics and prognosis of CCA patients was analyzed. A survival prediction model for patients with resectable CCA was constructed with COX regression results, the calibration curve and the time-dependent receiver operating characteristic curve (ROC) were used to evaluate the performance of the constructed model, and the predictive power between this model and the American Joint Committee on Cancer (AJCC)/TNM staging system (8th edition) was compared. SIRT2 mRNA was overexpressed in CCA tissues as shown in TCGA and GEO databases. IHC staining showed that SIRT2 protein expression in CCA tissues was significantly higher than that in adjacent non-tumor tissues. GSEA results showed that elevated SIRT2 expression may be involved in multiple metabolism-related signaling pathway, such as fatty acid metabolism, oxidative phosphorylation and amino acid metabolism. SIRT2 expression was related to serum triglycerides level, tumor size and lymph node metastasis (all P<0.05). The survival analysis results showed that patients with higher SIRT2 expression had a significantly lower overall survival (OS) than patients with lower SIRT2 expression (P<0.05). Univariate COX regression analysis suggested that pathological differentiation, clinical stage, postoperative treatment and SIRT2 expression level were associated with the prognosis of CCA patients (all P<0.05). Multivariate regression analysis confirmed that clinical stage and SIRT2 expression level were independent predictors of OS in postoperative CCA patients (both P<0.05). A nomogram based on SIRT2 for prediction of survival in postoperative CCA patients was constructed. The C-index of the model was 0.675, and the area under the time-dependent ROC curve (AUC) for predicting survival in the first, second, and third years was 0.879, 0.778, and 0.953, respectively, which were superior to those of AJCC/TNM staging system (8th Edition). SIRT2 is highly expressed in CCA tissues, which is associated with poor prognosis in patients with resectable CCA. The nomogram developed based on SIRT2 may have better predictive power than the AJCC/TNM staging system (8th edition) in prediction of survival of postoperative CCA patients.

Survival Prediction Model for Patients with Hepatocellular Carcinoma and Extrahepatic Metastasis Based on XGBoost Algorithm.

Accurate estimation of survival is of utmost importance in patients with hepatocellular carcinoma (HCC) and extrahepatic metastasis. This study aimed to develop a survival prediction model using real-world data. A total of 993 patients with treatment-naïve HCC and extrahepatic metastasis were included from 13 Korean hospitals between 2013 and 2018. Patients were randomly divided into a training set (70.0%) and a test set (30.0%). The eXtreme Gradient Boosting (XGBoost) algorithm was applied to predict survival at 3, 6, and 12 months. The mean age of the patients was 60.8 ± 12.3 years, and 85.4% were male. During the study period, 96.1% died, and median survival duration was 4.0 months. In multivariate analysis, Child-Pugh class, number and size of tumors, presence of vascular or bile duct invasion, lung or bone metastasis, serum AFP, and primary anti-HCC treatment were associated with survival. We constructed a model for survival prediction based on the relevant variables, which is available online (https://metastatic-hcc.onrender.com/form). Our model demonstrated high performance, with areas under the receiver operating characteristic curves of 0.778, 0.794, and 0.784 at 3, 6, and 12 months, respectively. Feature importance analysis indicated that the primary anti-HCC treatment had the highest importance. We developed a model to predict the survival of patients with HCC and extrahepatic metastasis, which demonstrated good discriminative ability. Our model would be helpful for personalized treatment and for improving the prognosis.

Prediction of 3-Year Survival in Patients with Cognitive Impairment Based on Demographics, Neuropsychological Data, and Comorbidities: A Prospective Cohort Study.

Based on readily available demographic data, neuropsychological assessment results, and comorbidity data, we aimed to develop and validate a 3-year survival prediction model for patients with cognitive impairment. In this prospective cohort study, 616 patients with cognitive impairment were included. Demographic information, data on comorbidities, and scores of the Mini-Mental State Examination (MMSE), Instrumental Activities of Daily Living (IADL) scale, and Neuropsychiatric Inventory Questionnaire were collected. Survival status was determined via telephone interviews and further verified in the official death register in the third year. A 7:3 ratio was used to divide patients into the training and validation sets. Variables with statistical significance (p < 0.05) in the single-factor analysis were incorporated into the binary logistic regression model. A nomogram was constructed according to multivariate analysis and validated. The final cohort included 587 patients, of whom 525 (89.44%) survived and 62 (10.56%) died. Younger age, higher MMSE score, lower IADL score, absence of disinhibition, and Charlson comorbidity index score ≤ 1 were all associated with 3-year survival. These predictors yielded good discrimination with C-indices of 0.80 (0.73-0.87) and 0.85 (0.77-0.94) in the training and validation cohorts, respectively. According to the Hosmer-Lemeshow test results, neither cohort displayed any statistical significance, and calibration curves displayed a good match between predictions and results. Our study provided further insight into the factors contributing to the survival of patients with cognitive impairment. Our model showed good accuracy and discrimination ability, and it can be used at community hospitals or primary care facilities that lack sophisticated equipment.

Prediction of 5-year overall survival of tongue cancer based machine learning

ObjectiveWe aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods.Subjects and methodsThe data were obtained from electronic medical records of 224 OTSCC patients at the PLA General Hospital. A five-year overall survival prediction model was constructed using logistic regression, Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. The output of the optimal model was explained using the Python package (SHapley Additive exPlanations, SHAP).ResultsAfter passing through the grid search and secondary modeling, the Light Gradient Boosting Machine was the best prediction model (AUC = 0.860). As explained by SHapley Additive exPlanations, N-stage, age, systemic inflammation response index, positive lymph nodes, plasma fibrinogen, lymphocyte-to-monocyte ratio, neutrophil percentage, and T-stage could perform a 5-year overall survival prediction for OTSCC. The 5-year survival rate was 42%.ConclusionThe Light Gradient Boosting Machine prediction model predicted 5-year overall survival in OTSCC patients, and this predictive tool has potential prognostic implications for patients with OTSCC.

Survival Prediction of Esophageal Squamous Cell Carcinoma Based on the Prognostic Index and Sparrow Search Algorithm-Support Vector Machine

Aim: Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world, and recent studies show that the incidence of ESCC is on the rise, and the mortality rate remains high. An effective survival prediction model can assist physicians in treatment decisions and improve the quality of patient survival. Introduction: In this study, ESCC prognostic index and survival prediction model based on blood indicators and TNM staging information are developed, and their effectiveness is analyzed. Methods: Kaplan-Meier survival analysis and COX regression analysis are used to find influencing factors that are significantly associated with patient survival. The binary logistic regression method is utilized to construct a prognostic index (PI) for esophageal squamous cell carcinoma (ESCC). Based on the sparrow search algorithm (SSA) and support vector machine (SVM), a survival prediction model for patients with ESCC is established. Results: Eight factors significantly associated with patient survival are selected by Kaplan-Meier survival analysis and COX regression analysis. PI is divided into four stages, and the stages can reasonably reflect the survival condition of diverse patients. Compared with the other four existing models, the sparrow search algorithm-support vector machine (SSA-SVM) proposed in this paper has higher prediction accuracy. Conclusion: In order to accurately and effectively predict the five-year survival rate of patients with ESCC, a survival prediction model based on Kaplan-Meier survival analysis, COX regression analysis, binary logistic regression and support vector machine is proposed in this paper. The results show that the method proposed in this paper can accurately predict the five-year survival rate of ESCC patients.

Construction and validation of a survival prognostic model for stage III hepatocellular carcinoma: a real-world, multicenter clinical study

ObjectiveTo construct a survival prediction model for patients with TNM stage III hepatocellular carcinoma (HCC) to guide the clinical diagnosis and treatment of HCC patients and improve prognosis.MethodsBased on data from patients with stage III (AJCC 7th TNM stage) recorded by the American Institute of Cancer Research from 2010 to 2013, risk factors affecting the prognosis were screened by Cox univariate and multivariate regression, line plots was constructed, and the credibility of the model was verified by Boostrap method. ROC operating curves, calibration curves and DCA clinical decision curves were used to evaluate the model, and Kaplan–Meier was used for survival analysis was used to evaluate the efficacy of the model. External survival data from patients newly diagnosed with stage III hepatocellular carcinoma during 2014–2015 were used to validate and fit the model and to optimize the model.ResultsAge > 75 years vs.18-53 years [HR = 1.502; 95%CI(1.134–1.990)], stage IIIC vs. Stage IIIA [HR = 1.930; 95%CI(1.509–2.470)], lobotomy vs. non-surgery [HR = 0.295; 95%CI(0.228–0.383)], radiotherapy vs. non-radiotherapy [HR = 0.481; 95%CI(0.373–0.619)], chemotherapy vs. Non-chemotherapy [HR = 0.443; 95%CI(0.381–0.515)], positive serum AFP before treatment vs. negative [HR = 1.667; 95%CI(1.356–2.049)], the above indicators are independent prognostic factors for patients with stage III hepatocellular carcinoma, and the P values for the above results were less than 0.05. A joint prediction model was constructed based on age, TNM stage, whether and how to operate, whether to receive radiotherapy, whether to receive chemotherapy, pre-treatment serum AFP status and liver fibrosis score. The consistency index of the improved prognosis model was 0.725.ConclusionsThe traditional TNM staging has limitations for clinical diagnosis and treatment, while the Nomogram model modified by TNM staging has good predictive efficacy and clinical significance.

Prognostication for Patients Receiving Palliative Radiation Therapy

Estimation of patient prognosis plays a central role in guiding decision making for the palliative management of metastatic disease, and a number of statistical models have been developed to provide survival estimates for patients in this context. In this review, we discuss several well-validated survival prediction models for patients receiving palliative radiotherapy to sites outside of the brain. Key considerations include the type of statistical model, model performance measures and validation procedures, studies' source populations, time points used for prognostication, and details of model output. We then briefly discuss underutilization of these models, the role of decision support aids, and the need to incorporate patient preference in shared decision making for patients with metastatic disease who are candidates for palliative radiotherapy.

Survival and prognostic factors in patients with synchronous multiple primary esophageal squamous cell carcinoma receiving definitive radiotherapy: A propensity score-matched analysis.

To compare the lesion characteristics and radiotherapy efficacy of patients with single and multiple esophageal squamous cell carcinoma (ESCC), to evaluate the effect of multiple lesions on ESCC, and establish a nomogram survival prediction model for patients with synchronous multiple primary esophageal squamous cell carcinoma (SMPESCC) who received definitive radiotherapy. The study enrolled 1,034 patients with ESCC who underwent definitive radiotherapy between 2010 and 2020. The efficacy of radiotherapy was compared between 101 patients with SMPESCC and 933 patients with single ESCC. Propensity score matching was used to control for potential confounders. For patients with SMPESCC, a nomogram prediction model was established based on the Cox regression model. The median OS was 30.00 (95% CI = 25.08-34.92) months for the single lesion group and 19.00 (95% CI = 15.51-22.48) months for the multiple cancer group respectively. Multivariate COX regression analysis showed that multiple cancer was an independent prognostic factor for ESCC patients (HR=1.89, 95%CI=1.49-2.38, P<0.001). Cox multivariate analysis of SMPESCC patients showed that T stage (P =0.002), chemotherapy (P =0.006), and lesion spacing (P =0.004) were independent prognostic factors associated with OS. The nomogram was established by combining T stage, chemotherapy, and lesion spacing, and Harrell's C index was 0.711 after internal cross-validation. The calibration curve and decision curve analysis confirmed that the nomogram survival prediction model had a good predictive value for individual survival. The survival rate of single esophageal cancer is significantly better than that of multiple lesions. Patients with SMPESCC exhibit worse survival than patients with single ESCC. Multiple lesions have a significant impact on the survival of patients with ESCC. The nomogram model established for SMPESCC patients can well predict the individual survival of patients.

Development a survival prediction model for patients with Paget disease of the breast based on the SEER database

Development a survival prediction model for patients with Paget disease of the breast based on the SEER database

SOURCE beyond first-line: A survival prediction model for patients with metastatic esophagogastric adenocarcinoma after failure of first-line palliative systemic therapy.

Prior models have been developed to predict survival for patients with esophagogastric cancer undergoing curative treatment or first-line chemotherapy (SOURCE models). Comprehensive clinical prediction models for patients with esophagogastric cancer who will receive second-line chemotherapy or best supportive care are currently lacking. The aim of our study was to develop and internally validate a new clinical prediction model, called SOURCE beyond first-line, for survival of patients with metastatic esophagogastric adenocarcinoma after failure of first-line palliative systemic therapy. Patients with unresectable or metastatic esophageal or gastric adenocarcinoma (2015-2017) who received first-line systemic therapy (N=1067) were selected from the Netherlands Cancer Registry. Patient, tumor and treatment characteristics at primary diagnosis and at progression of disease were used to develop the model. A Cox proportional hazards regression model was developed through forward and backward selection using Akaike's Information Criterion. The model was internally validated through 10-fold cross-validations to assess performance. Model discrimination (C-index) and calibration (slope and intercept) were used to evaluate performance of the complete and cross-validated models. The final model consisted of 11 patient tumor and treatment characteristics. The C-index was 0.75 (0.73-0.78), calibration slope 1.01 (1.00-1.01) and calibration intercept 0.01 (0.01-0.02). Internal cross-validation of the model showed that the model performed adequately on unseen data: C-index was 0.79 (0.77-0.82), calibration slope 0.93 (0.85-1.01) and calibration intercept 0.02 (-0.01 to 0.06). The SOURCE beyond first-line model predicted survival with fair discriminatory ability and good calibration.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer.

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making. We recruited 2794 patients diagnosed with LAGC (2010-2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan-Meier method. Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708-0.736] and CSS was 0.728 (95% CI: 0.713-0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672-0.784) and CSS was 0.727 (95% CI:0.668-0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms. The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

Deep Learning for Automated Outcome Prediction in Oropharyngeal Cancer from Tumor and Lymph Node Imaging Data

<h3>Purpose/Objective(s)</h3> Oropharyngeal carcinoma (OPC) is a malignancy with highly variable prognosis. Improved biomarkers and risk-stratification tools are needed to guide personalized treatment paradigms. Deep learning (DL) has shown significant promise in its ability to synthesize computed tomography (CT) imaging data for risk-stratification. However, thus far, CT models for OPC prognosis have not leveraged combined primary tumor and lymph node data for outcome prediction. In this study, we hypothesized that a CT-based, combined tumor and lymph node DL model could be developed and validated to predict survival in patients with OPC treated with radiotherapy (RT) based treatment. <h3>Materials/Methods</h3> Tumor and lymph node gross tumor volume contours from pretreatment CT scans for 1,033 OPC patients treated with definitive RT from two institutions from 2003-2013 were curated with linked survival data via The Cancer Imaging Archive. The combined primary and nodal contours with underlying CT data served as input for model training (70%, 743), tuning (10%, 83), and blinded test sets (20%, 207). We trained a convolutional neural network, adopted from ResNet101, customized with cause-specific Logistic Hazard loss functions to predict individual overall survival (OS). Multivariable Cox regression was conducted using clinical variables alone (AJCC 7^th Edition Stage, HPV-status, smoking [never, former, current], sex, and age) and combined with DL survival probability (DL-score). Primary endpoint was Concordance Index (C-index) for OS on the test set. Kaplan–Meier curves with log-rank test comparisons were generated for OS. <h3>Results</h3> Among 1,033 patients (median age: 59 years), 1.4%, 4.8%, 13.7% and 80.1% were Stage I, II, III and IV, respectively, 52.7%, 12.9% and 34.4% were HPV+, HPV-, and unknown, respectively, and 66.3% and 30.1% were treated with chemoradiotherapy and radiotherapy alone, respectively. Median follow-up time was 72.4 months. DL-score alone yielded comparable OS prediction compared to the clinical Cox model overall (C-index: 0.70 vs 0.68), and between HPV+ (0.66 vs 0.61) and HPV- (0.77 vs 0.76) patient subgroups. Combining DL-score and clinical variables into the Cox model yielded the highest predictive performance for OS (C-index: 0.74). DL-score alone stratified test set patients overall into three risk groups (low: n=77, intermediate: n=100, and high: n=30) with significant survival curve separation (<i>P</i><0.001), and 5-year OS of 90.9%, 71.0% and 50.0%, respectively. For the HPV+ subgroup (n=114), DL-score also yielded low, intermediate, and high-risk grouping with 5-year OS of 90.7%, 71.9%, and 64.3% (<i>P</i>=.32). <h3>Conclusion</h3> We developed and validated an imaging-based DL survival prediction model for patients with OPC treated with RT that prognosticates well, irrespective of HPV status. Performance is improved further by incorporating HPV and clinicopathologic factors. By improving prognostication, DL could be used to inform trial eligibility and personalized decision-making.

Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy.

Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM). From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs. A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8. Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

ObjectiveThis study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC).MethodsFor the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.ResultsA Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718–0.735) and 0.721 (95% CI, 0.709–0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691–0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system.ConclusionWe developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

A LASSO-based survival prediction model for patients with synchronous colorectal carcinomas based on SEER.

BackgroundThe nomogram for postoperative prediction of overall survival (OS) in patients’ synchronous colorectal carcinomas (SCC) was developed and validated by least absolute shrinkage and selection operator (LASSO)-based Cox regression.MethodsThe data was obtained from the SEER database of patients diagnosed with colorectal cancer (CRC) more than one time between 2004 and 2013. Patients who had CRC more than 3 times or multiple metachronous primary carcinomas were excluded. The cut-off points for the continuous variable were identified by the K-adaptive partitioning algorithm and x-tile software. Using LASSO-based Cox regression, a model for predicting the OS of SCC was built, internally and externally validated, and measured through a calibration curve, C-index, Akaike information criterion (AIC), Bayesian information criterion (BIC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), time-dependent receiver operating characteristic (timeROC), time-dependent area under curve (timeAUC), and decision curve analysis (DCA), and results compared to the model developed by the Cox regression.ResultsPatients with SCC were found to be older, more often men, and likely to have a depth of invasion by T3. In addition, there were no significant differences between the model developed by LASSO-based Cox regression and the Cox regression in the C-index (0.712 and 0.710), AIC (33,420 and 33,431), BIC (4.49), IDI (0.002), NRI (–0.009), timeROC, and DCA. Besides, the model developed by LASSO-based Cox regression was found to perform better than the Cox regression in the timeAUC. Moreover, the model developed by LASSO-based Cox regression showed good C-index (0.712, 0.637, and 0.651), AIC (33,420, 34,043, and 33,994), BIC (1,178.76 and 1,098.57), IDI (–0.072 and –0.064), NRI (0.525 and 0.466), timeROC, timeAUC and had a larger net benefit compared to both the first time TNM staging and the combination of two times TNM staging.ConclusionsThis present study indicates that a close follow-up of older patients, male, and T3 should be made. Compared with the traditional Cox regression model, LASSO-based Cox regression decreases the variables of the model, avoids overfitting and collinearity and has clinical significance.

Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm.

Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC.

Survival Risk Prediction of Esophageal Squamous Cell Carcinoma Based on BES-LSSVM.

Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. In this study, ten indicators related to the survival of patients with ESCC are founded using genetic algorithm feature selection. The prognostic index (PI) for ESCC is established using the binary logistic regression. PI is divided into four stages, and each stage can reasonably reflect the survival status of different patients. By plotting the ROC curve, the critical threshold of patients' age could be found, and patients are divided into the high-age groups and the low-age groups. PI and ten survival-related indicators are used as independent variables, based on the bald eagle search (BES) and least-squares support vector machine (LSSVM), and a survival prediction model for patients with ESCC is established. The results show that five-year survival rates of patients are well predicted by the bald eagle search-least-squares support vector machine (BES-LSSVM). BES-LSSVM has higher prediction accuracy than the existing particle swarm optimization-least-squares support vector machine (PSO-LSSVM), grasshopper optimization algorithm-least-squares support vector machine (GOA-LSSVM), differential evolution-least-squares support vector machine (DE-LSSVM), sparrow search algorithm-least-squares support vector machine (SSA-LSSVM), bald eagle search-back propagation neural network (BES-BPNN), and bald eagle search-extreme learning machine (BES-ELM).

Development and validation of a survival prediction model for patients received mechanical ventilation in the intensive care unit: a large sample size cohort from the MIMIC database.

Mechanical ventilation remains one of the primary management measures for critically ill patients in intensive care units (ICUs). However, previous studies on the prognosis prediction of ICU patients received mechanical ventilation were limited. This study was to develop and validate a nomogram for predicting short- and long-term survival among patients who received mechanical ventilation in the ICU. This was a retrospective cohort study with a 3-year follow-up. Demographic, laboratory, clinical data of 16,775 participants aged ≥18 years who received mechanical ventilation in the ICU were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The outcomes of this study were 1-month, 3-month, 1-year, and 3-year survival. All eligible patients were randomly classified into the training and testing groups with a ratio of 7:3. A multivariate Cox regression in the training group was used to explore the predictors and develop the predictive nomogram. Internal and subgroup validations were performed, and the C-index was calculated to estimate the predictive performance of the nomogram. The time-dependent receiver operating characteristic curves were drawn, and corresponding areas under the curve (AUC) were calculated. Totally 6,291 patients died during the follow-up duration. Age, gender, ethnicity, ICU type, comorbidity, days of mechanical ventilation, white blood cell count, blood urea nitrogen, the fraction of inspiration O2, Sequential Organ Failure Assessment scores, and the Glasgow coma score were predictors of the survival of ICU patients who received mechanical ventilation (P<0.05). The C-index of the nomogram was 0.819 and was validated in the testing group at 0.816. The AUCs for the prognostic nomogram for 1-month, 3-month, 1-year, and 3-year survival were 0.889, 0.892, 0.882, and 0.866, respectively. This nomogram showed good predictive performance for short- and long-term survival in ICU patients treated with mechanical ventilation, which may be a useful tool for clinicians to assess the prognosis of patients and to adjust treatment strategies in time.