Survival Prediction Model For Patients Research Articles

Development and validation of survival prediction models for patients with hepatocellular carcinoma treated with transcatheter arterial chemoembolization plus tyrosine kinase inhibitors.

Due to heterogeneity of molecular biology and microenvironment, therapeutic efficacy varies among hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) and tyrosine kinase inhibitors (TKIs). We examined combined models using clinicoradiological characteristics, mutational burden of signaling pathways, and radiomics features to predict survival prognosis. Two cohorts comprising 111 patients with HCC were used to build prognostic models. The training and test cohorts included 78 and 33 individuals, respectively. Mutational burden was calculated based on 17 cancer-associated signaling pathways. Radiomic features were extracted and selected from computed tomography images using a pyradiomics system. Models based on clinicoradiological indicators, mutational burden, and radiomics score (rad-score) were built to predict overall survival (OS) and progression-free survival (PFS). Eastern Cooperative Oncology Group performance status, Child-Pugh class, peritumoral enhancement, PI3K_AKT and hypoxia mutational burden, and rad-score were used to create a combined model predicting OS. C-indices were 0.805 (training cohort) and 0.768 (test cohort). The areas under the curve (AUCs) were 0.889, 0.900, and 0.917 for 1-year, 2-year, and 3-year OS, respectively. To predict PFS, alpha-fetoprotein level, tumor enhancement pattern, hypoxia and receptor tyrosine kinase mutational burden, and rad-score were used. C-indices were 0.782 (training cohort) and 0.766 (test cohort). AUCs were 0.885 and 0.925 for 6-month and 12-month PFS, respectively. Calibration and decision curve analyses supported the model's accuracy and clinical potential. The nomogram models are hopeful to predict OS and PFS in patients with intermediate-advanced HCC treated with TACE plus TKIs, offering a promising tool for treatment decisions and monitoring patient progress.

Survival prediction of colorectal liver metastases underwent surgical resection after neoadjuvant chemotherapy: Tumor response combined with the genetic and morphological evaluation score

IntroductionNeoadjuvant chemotherapy is becoming routine for colorectal liver metastasis (CRLM) in patients with high risks of recurrence or in whom resection is difficult. This retrospective study aimed to establish a modified survival prediction model for patients with CRLM who underwent hepatectomy after neoadjuvant chemotherapy. Materials and methodsA total of 619 patients who received neoadjuvant chemotherapy followed by hepatectomy between 2006 and 2021 were included and divided into training and validation groups at a ratio of 2:1. The model was established in training group and validated in validation group. Chemotherapy response was integrated into the genetic and morphological evaluation (GAME) score as a new NeoGAME model, with assigned points based on the hazard ratio in the multivariate Cox regression. The NeoGAME score grouping cutoff was divided using X-tile, and the predictive power was compared with that of traditional models. ResultsThe 5-year overall survival were significantly different in the NeoGAME low-risk (0–2 points), medium-risk (3–4 points) and high-risk (≥5 points) groups (training group, P < 0.001; validation group, P = 0.0012). The area under the curve in predicting 5-year survival was 0.67 and 0.66 for the training and validation groups, respectively. Time-dependent receiver operating characteristic curve showed better discrimination ability of NeoGAME than the GAME score in predicting 5-year survival. ConclusionsThe newly established NeoGAME score can predict survival more precisely for patients with CRLM receiving neoadjuvant chemotherapy. Moreover, the model offers a useful tool for assessing tumor behavior and selecting a benefiting population for liver resection.

Comparison of Classically and Machine Learning Generated Survival Prediction Models for Patients With Spinal Metastasis - A meta-Analysis of Two Recently Developed Algorithms.

A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study. (1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input. After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC). The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (P < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA. These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.

Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension.

Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation. To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS. The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT. Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model's accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72. This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

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.

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 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 for heart failure complicated by sepsis: based on machine learning methods.

Heart failure is a cardiovascular disorder, while sepsis is a common non-cardiac cause of mortality. Patients with combined heart failure and sepsis have a significantly higher mortality rate and poor prognosis, making early identification of high-risk patients and appropriate allocation of medical resources critically important. We constructed a survival prediction model for patients with heart failure and sepsis using the eICU-CRD database and externally validated it using the MIMIC-IV database. Our primary outcome is the 28-day all-cause mortality rate. The Boruta method is used for initial feature selection, followed by feature ranking using the XGBoost algorithm. Four machine learning models were compared, including Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was assessed using metrics such as area under the curve (AUC), accuracy, sensitivity, and specificity, and the SHAP method was utilized to visualize feature importance and interpret model results. Additionally, we conducted external validation using the MIMIC-IV database. We developed a survival prediction model for heart failure complicated by sepsis using data from 3891 patients in the eICU-CRD and validated it externally with 2928 patients from the MIMIC-IV database. The LR model outperformed all other machine learning algorithms with a validation set AUC of 0.746 (XGBoost: 0.726, AdaBoost: 0.744, GNB: 0.722), alongside accuracy (0.685), sensitivity (0.666), and specificity (0.712). The final model incorporates 10 features: age, ventilation, norepinephrine, white blood cell count, total bilirubin, temperature, phenylephrine, respiratory rate, neutrophil count, and systolic blood pressure. We employed the SHAP method to enhance the interpretability of the model based on the LR algorithm. Additionally, external validation was conducted using the MIMIC-IV database, with an external validation AUC of 0.699. Based on the LR algorithm, a model was constructed to effectively predict the 28-day all-cause mortality rate in patients with heart failure complicated by sepsis. Utilizing our model predictions, clinicians can promptly identify high-risk patients and receive guidance for clinical practice.

Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression.

A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds. The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.

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.