Actual Survival Research Articles

Evaluation of different scoring systems for spinal metastases based on a Chinese cohort.

The spine is one of the most common sites of metastasis for malignancies. This study aimed to compare the predictive performance of seven commonly used prognostic scoring systems for surgically treated spine metastases. It is expected to assist surgeons in selecting appropriate scoring systems to support clinical decision-making and better inform patients. We performed a retrospective study involving 268 surgically treated patients with spine metastases between 2017 and 2020 at a single regional oncology center in China. The revised Tokuhashi, Tomita, modified Bauer, revised Katagiri, van der Linden, Skeletal Oncology Research Group (SORG) nomogram, and SORG machine-learning (ML) scoring systems were externally validated. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate sensitivity and specificity at different postoperative time points. The actual survival time was compared with the reference survival time provided in the original publication. In the present study, the median survival was 16.6months. The SORG ML scoring system demonstrated the highest accuracy in predicting 90-day (AUC: 0.743) and 1-year survival (AUC: 0.787). The revised Katagiri demonstrated the highest accuracy (AUC: 0.761) in predicting 180-day survival. The revised Katagiri demonstrated the highest accuracy (AUC: 0.779) in predicting 2-year survival. Based on this series, the actual life expectancy was underestimated compared with the original reference survival time. None of the scoring systems can perform optimally at all time points and for all pathology types, and the reference survival times provided in the original study need to be updated. A cautious awareness of the underestimation by these models is of paramount importance in relation to current patients.

Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST.

Background. More than 50% of operable GEA relapse after curative-intent resection. We aimed at externally validating a nomogram to enable a more accurate estimate of individualized risk in resected GEA. Methods. Medical records of a training cohort (TC) and a validation cohort (VC) of patients undergoing radical surgery for c/uT2-T4 and/or node-positive GEA were retrieved, and potentially interesting variables were collected. Cox proportional hazards in univariate and multivariate regressions were used to assess the effects of the prognostic factors on OS. A graphical nomogram was constructed using R software’s package Regression Modeling Strategies (ver. 5.0-1). The performance of the prognostic model was evaluated and validated. Results. The TC and VC consisted of 185 and 151 patients. ECOG:PS > 0 (p < 0.001), angioinvasion (p < 0.001), log (Neutrophil/Lymphocyte ratio) (p < 0.001), and nodal status (p = 0.016) were independent prognostic values in the TC. They were used for the construction of a nomogram estimating 3- and 5-year OS. The discriminatory ability of the model was evaluated with the c-Harrell index. A 3-tier scoring system was developed through a linear predictor grouped by 25 and 75 percentiles, strengthening the model’s good discrimination (p < 0.001). A calibration plot demonstrated a concordance between the predicted and actual survival in the TC and VC. A decision curve analysis was plotted that depicted the nomogram’s clinical utility. Conclusions. We externally validated a prognostic nomogram to predict OS in a joint independent cohort of resectable GEA; the NOMOGAST could represent a valuable tool in assisting decision-making. This tool incorporates readily available and inexpensive patient and disease characteristics as well as immune-inflammatory determinants. It is accurate, generalizable, and clinically effectivex.

Epithelioid Hemangioendothelioma: Incidence, Mortality, Prognostic Factors, and Survival Analysis Using the Surveillance, Epidemiology, and End Results Database.

Background Although epithelioid hemangioendothelioma (EHE) is a rare and aggressive vascular tumor, its demographic characteristics remain unclear. We used the surveillance, epidemiology, and end results (SEER) database to determine the clinical features, incidence, and prognostic factors associated with overall survival in patients with EHE. Methods The demographic and clinical data of patients with EHE were extracted from the SEER database (1975-2019) to calculate the incidence of EHE and survival rate in these patients. The Cox proportional hazards model and Kaplan-Meier method were used to analyze the prognostic factors of overall survival in these patients. A nomogram and time-dependent receiver operating characteristic (ROC) curve were employed to predict the 3- and 5-year survival rate. Results The overall incidence rate (IR) of EHE was 0.230 (95%confidence interval [CI] = 0.201–0.263) per 1,000,000 person-years. According to the age-stratified IR, the highest age-adjusted IR was observed in patients aged 60–79 years (0.524 per 1,000,000 person-years, 95%CI = 0.406–0.665). The majority (30.8%) of the tumors were located in the soft tissue and skin, followed by lesions in the abdomen (28%), respiratory system (19%), bone and joint (8.6%), and others. The 5-year overall survival rate was 55.6% (95%CI = 32.8–73.5%). Multiple Cox regression analysis revealed that age >80 years (hazard ratio [HR] = 8.57, 95%CI = 2.32–31.63, P < 0.001), African-American race (HR = 2.52, 95%CI = 1.31–4.85, P < 0.01), “American Indian/Alaska Native” or “Asian or Pacific Islander” (HR = 2.99, 95%CI = 1.5–5.96, P < 0.01) race, and respiratory tumors (HR = 2.55, 95%CI = 1.37–4.75, P < 0.01) were distinctly related to worse overall survival. The calibration plots demonstrated good consistency between nomogram-predicted and actual survival. The area under the time-dependent ROC curve was 0.721 (95%CI = 0.63–0.81) and 0.719 (95%CI = 0.63–0.81) for the 3- and 5-year survival, respectively. For the convenience of researchers and clinicians, we designed an online dynamics nomogram to predict the survival rate. Conclusion EHE is a relatively rare vascular tumor, which principally occurs in the soft tissue and skin. It most commonly occurs in patients aged 60–79 years and its incidence has increased in recent years. Age at diagnosis, race, and tumor location may affect the overall survival outcomes.

Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power Approaching World Health Organization Histologic Grading in a Cohort of 1181 Patients.

Recent advances in machine learning have enabled image-based prediction of local tissue pathology in gliomas, but the clinical usefulness of these predictions is unknown. We aimed to evaluate the prognostic ability of imaging-based estimates of cellular density for patients with gliomas, with comparison to the gold standard reference of World Health Organization grading. Data from 1181 (207 grade II, 246 grade III, 728 grade IV) previously untreated patients with gliomas from a single institution were analyzed. A pretrained random forest model estimated voxelwise tumor cellularity using MR imaging data. Maximum cellular density was correlated with the World Health Organization grade and actual survival, correcting for covariates of age and performance status. A maximum estimated cellular density of >7681 nuclei/mm2 was associated with a worse prognosis and a univariate hazard ratio of 4.21 (P < .001); the multivariate hazard ratio after adjusting for covariates of age and performance status was 2.91 (P < .001). The concordance index between maximum cellular density (adjusted for covariates) and survival was 0.734. The hazard ratio for a high World Health Organization grade (IV) was 7.57 univariate (P < .001) and 5.25 multivariate (P < .001). The concordance index for World Health Organization grading (adjusted for covariates) was 0.761. The maximum cellular density was an independent predictor of overall survival, and a Cox model using World Health Organization grade, maximum cellular density, age, and Karnofsky performance status had a higher concordance (C = 0.764; range 0.748-0.781) than the component predictors. Image-based estimation of glioma cellularity is a promising biomarker for predicting survival, approaching the prognostic power of World Health Organization grading, with added values of early availability, low risk, and low cost.

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.

Prognostic Factors and Nomograms for Overall and Cancer-Specific Survival of Patients with Uveal Melanoma without Metastases: A SEER Analysis of 4119 Cases.

Purpose To determine prognostic factors for patients with uveal melanoma without metastases and to construct nomograms to predict their 3- and 5-year overall survival (OS) and cancer-specific survival (CSS). Methods We included 4119 patients who were registered from 2004 to 2015 in the Surveillance, Epidemiology, and End Results database. The median follow-up time was 5.8 years. Independent risk factors affecting OS and CSS were identified with univariate and multivariate Cox regression analyses and used to construct nomograms. Internal and external validation were carried out by using the bootstrap method to calculate the concordance indices (C-indices) and plot the calibration curves. Results Age, primary site, histological type, T-stage, and treatment were independent risk factors for OS and CSS; marital status and sequence number were factors only for OS. The C-indices for internal validation of OS and CSS were 0.713 (95% CI, 0.697–0.729) and 0.708 (95% CI, 0.688–0.728), respectively, and for external validation they were 0.729 (95% CI, 0.705–0.753) and 0.731 (95% CI, 0.700–0.762), respectively. The calibration curves also revealed good agreement between the predicted and actual survival rates. Conclusions We constructed nomograms to predict the 3- and 5-year OS and CSS of patients with uveal melanoma without metastases. Our nomograms may improve prognostication and assist with the development of individualized treatment strategies.

Development and validation of a prognostic nomogram for adult patients with renal sarcoma: A retrospective study based on the SEER database.

BackgroundRenal sarcoma (RS) is rarely seen in clinical practice. The purpose of this study was to develop a prognostic nomogram model, which could predict the probability of overall survival (OS) and cancer-specific survival (CSS) in adult patients with RS.MethodsPatients diagnosed with RS were recruited from the SEER database between 2004 and 2015, and randomized to two cohorts: the training cohort and the validation cohort. Uni- and multivariate Cox regression analyses in the training cohort were used to screen independent prognostic factors for OS and CSS. Prognostic nomograms for OS and CSS were created separately for adult RS patients based on independent risk factors. The area under the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to validate the nomograms.ResultsA total of 232 eligible patients were recruited, including 162 in the training cohort and 70 in the validation cohort. Sex, histological type, SEER stage, and surgery were independent prognostic factors for OS, while histological type, SEER stage, surgery, chemotherapy were independent prognostic factors for CSS. Based on the above independent prognostic factors, prognostic nomograms for OS and CSS were created respectively. In the training cohort, the AUCs of the nomograms for OS and CSS were 0.742 and 0.733, respectively. In the validation cohort, the AUCs of the nomograms for OS and CSS were 0.837 and 0.758, respectively. The calibration curves of the nomograms showed high consistencies between the predicted and actual survival rates. Finally, the DCA demonstrated that the nomograms in the wide high-risk threshold had a higher net benefit than the SEER stage.ConclusionA prognostic nomogram for renal sarcoma was created and validated for reliability and usefulness in our study, which assisted urologists in accurately assessing the prognosis of adult RS patients.

Can a Bayesian belief network for survival prediction in patients with extremity metastases (PATHFx) be externally validated in an Asian cohort of 356 surgically treated patients?

Background and purposePredicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis.Patients and methodsWe included 356 patients who underwent surgery for skeletal extremity metastasis in a tertiary center in Taiwan between 2014 and 2019 to validate PATHFx’s survival predictions at 6 different time points. Model performance was assessed by concordance index (c-index), calibration analysis, decision curve analysis (DCA), Brier score, and model consistency (MC).ResultsThe c-indexes for the 1-, 3-, 6-, 12-, 18-, and 24-month survival estimations were 0.71, 0.66, 0.65, 0.69, 0.68, and 0.67, respectively. The calibration analysis demonstrated positive calibration intercepts for survival predictions at all 6 timepoints, indicating PATHFx tended to underestimate the actual survival. The Brier scores for the 6 models were all less than their respective null model’s. DCA demonstrated that only the 6-, 12-, 18-, and 24-month predictions appeared useful for clinical decision-making across a wide range of threshold probabilities. The MC was < 0.9 when the 6- and 12-month models were compared with the 12-month and 18-month models, respectively.InterpretationIn this Asian cohort, PATHFx’s performance was not as encouraging as those of prior validation studies. Clinicians should be cognizant of the potential decline in validity of any tools designed using data outside their particular patient population. Developers of survival prediction tools such as PATHFx might refine their algorithms using data from diverse, contemporary patients that is more reflective of the world’s population.

411P Laparoscopic versus open liver resection for colorectal cancer liver metastases: Five-year actual survival of the previously reported randomized controlled trial – The OSLO-COMET Trial

Research on laparoscopic liver resection has shown shorter recovery time, a better quality of life, and fewer postoperative complications. Our previous report of the OSLO-COMET trial showed that laparoscopic surgery in patients with colorectal cancer liver metastases was equivalent to open resection regarding oncologic outcomes. The current report aimed to investigate the survival outcomes of this trial after a minimum follow-up of 6 years. Patients with resectable colorectal liver metastases from February 2012 to January 2016 were randomized between laparoscopic and open parenchyma-sparing liver resection with curative intent (ClinicalTrials.gov: NCT01516710). The trial's primary outcome was postoperative morbidity within 30 days, while overall survival was a predefined secondary endpoint. Patients received perioperative chemotherapy following Norwegian guidelines, at the discretion of the multidisciplinary team. Two hundred eighty patients were randomized to laparoscopic (n = 133) or open (n = 147) surgery. Current survival analysis was performed on January 20, 2022, with a minimum of 72 months follow-up and with a median follow-up of 94 months (95%CI, 91 to 97). Median overall survival was 71 months (95% CI 47 to 95) in the laparoscopic surgery group and 74 months (95%CI, 52 to 96) in the open surgery group (p=0.970), and the 5-year actual survival compiled 55% and 54%, respectively (HR 1.006 [0.738 to 1.372]). Median recurrence-free survival (RFS) was 17 months (95%CI, 10 to 23) after laparoscopy and 16 months (95%CI, 8 to 24) after open surgery, with a 5-year RFS of 33% and 31% (p = 0.705). These long-term outcomes after a minimum follow-up of six years, together with short-term results, further support the use of laparoscopic surgery to treat colorectal cancer liver metastases.

Establishment and Validation of a Predictive Nomogram for Postoperative Survival of Stage I Non-Small Cell Lung Cancer.

BackgroundSurgical procedure is the preferred option for people with early-stage non-small cell lung cancer (NSCLC), while nearly 30% of patients experienced metastatic or recurrent tumor after operation. The primary intention of this context is to summarize high-risk prognostic factors and set up a novel nomogram to predict the overall survival of individuals with stage I NSCLC after resection.MethodsResearch objects, 10,218 patients with stage I NSCLC after operation from 2010 to 2015, were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors, confirmed by Cox regression analyses, were integrated into a nomogram, to predict the 3-and 5-year overall survival of these individuals. The model experienced internal validation of testing cohorts above and external validation crewed by 160 patients from China. Finally, the nomogram was evaluated through several verification methods such as concordance index (C-index), calibration plots and receiver operating characteristic curve (ROC).ResultsMultivariate analysis identified that age, gender, histologic type, differentiation class, type of operation, T stage and treatment were significant predictive factors for the survival of stage I NSCLC. Based on these factors, a nomogram was constructed to predict the 3- and 5-year overall survival of these individuals. Meanwhile, in the training set, this nomogram displayed excellent superiority over the TNM staging system with abroad application, especially in C-index (0.669 vs 0.580) and the AUC (the Area Under ROC Curve) for the 3- and 5-year survival (0.678 vs 0.582; 0.650 vs 0.576). In the calibration curve, the curve representing predicted survival tended to align with the line representing actual survival as well.ConclusionA nomogram was successfully created and verified to achieve the goal that made a rounded accurate prediction on the survival of postoperative I NSCLC patients in terms of the SEER database.

Screening and verification of prognostic lncRNA markers related to immune infiltration in the metastasis of osteosarcoma.

BackgroundWe sought to screen and verify the long non-coding ribonucleic acids (lncRNAs) related to immune infiltration in metastatic osteosarcoma (OS).MethodsWe downloaded the RNA-sequencing expression data from The Cancer Genome Atlas (TCGA) database as the training data set. We downloaded the GSE39055 data set from the National Center for Biotechnology Information, Gene Expression Omnibus as the validation data set. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to screen the optimized lncRNA combinations. Kaplan-Meier curves were used to evaluate the associations between the lncRNAs and actual prognosis. The independent survival prognosis clinical factors were obtained by univariate and multivariate Cox analyses. A nomogram was established to explore the correlation between survival rate and risk information. The Tumor IMmune Estimation Resource was applied to estimate the composition of 6 subtypes of immune infiltration cells.ResultsIn total, 1,398 lncRNAs and 14,631 messenger RNAs were screened from TCGA data set, and divided into the low and high immunity groups. The Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) scores differed significantly between the samples in the two groups. Additionally, 5 optimized lncRNA combinations were obtained using the LASSO algorithm. Risk factors including age, metastatic tumor, and risk-score (RS) were related to the prognosis of OS patients. The survival rates predicted by the nomogram model were consistent with the actual survival rates of OS patients. Finally, we found that RS was negatively correlated with the proportion of immune cells.ConclusionsIn total, 5 feature lncRNAs were identified as novel biomarkers for OS. Next, a RS nomogram model was constructed based on the 5 identified lncRNAs. This model predicted the survival rates and prognoses of OS patients well.

Clinical Prediction Nomograms to Assess Overall Survival and Disease-Specific Survival of Patients with Salivary Gland Adenoid Cystic Carcinoma.

Aim Salivary gland adenoid cystic carcinoma (SACC) is the second highest incidence of malignant salivary gland tumor. The purpose of this study was to establish nomograms combined with SACC patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods Patients with SACC were included in the SEER∗Stat Database from 2004 to 2016. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to filter potential prognostic clinical variables. Multivariate analysis from the Cox proportional hazards model was performed to determine the independent prognostic factors on overall survival (OS) and disease-specific survival (DSS), applied to develop nomograms. The Schönfeld residual test verified the proportional hazard assumption. The discrimination and consistency of nomograms was assessed and validated according to concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration curves using an internal 1,000 times bootstrap resampling. The nomogram's net clinical benefit was assessed through decision curve analysis (DCA). Results A total of 658 patients with SACC were included. Age, T stage, N stage, M stage, histologic grade, and surgery were independent prognostic factors for OS and DSS. Based on these independent prognostic factors, nomograms were developed to predict 3-, 5-, and 10-year OS and DSS. In the validation of 1,000 times bootstrap resampling, the C-index and ROC curves had good discriminatory ability. The calibration curves indicated excellent consistency between the predicted and actual survival results in the nomograms. The DCA curves demonstrated that the nomograms had good clinical benefit and were superior to the TNM stage and other variables. Conclusions Two nomograms developed in this study precisely predicted the 3-, 5-, and 10-year OS and DSS rates of patients with SACC in accordance with independent prognostic factors, and their clinical value is better than TNM staging, providing a prognostic reference for other SACC patients.

Establishment of prognostic model for postoperative patients with metaplastic breast cancer: Based on a retrospective large data analysis and Chinese multicenter study.

Purpose: Models for predicting postoperative overall survival of patients with metaplastic breast cancer have not yet been discovered. The purpose of this study is to establish a model for predicting postoperative overall survival of metaplastic breast cancer patients. Methods: Patients in the Surveillance, Epidemiology, and End Results database diagnosed with MBC from 2010 to 2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell’s consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. We verify the performance of the prediction model with a Chinese multi-center data set. Results: Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the eighth edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. It has also been verified in the SEER internal verification set and the Chinese multi-center data set. Conclusion: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.

Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma.

BACKGROUNDHepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.AIMTo develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.METHODSA total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups.RESULTSIn total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001).CONCLUSIONThe nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.

Prognosis after Palliative Surgery for Patients with Spinal Metastasis: Comparison of Predicted and Actual Survival.

Simple SummaryIncreased options for cancer treatment have made the prediction of prognosis an important factor in therapeutic decision making. The aim of this study was to assess the clinical significance of prognosis–scoring systems and to identify predictors for 6–month mortality after palliative surgery. The median actual survival period was longer than the predicted life expectancy based on the revised Tokuhashi score and new Katagiri score. However, 21.3% of patients died of cancers within 6 months after palliative surgery. A statistical analysis showed that a higher CRP/albumin ratio (odds ratio: 0.39; cut–off 0.409) and absence of postoperative adjuvant therapy (odds ratio: 7.15) were independent risk factors for poor survival. Our findings suggest the need for careful consideration to determine if palliative surgery is the best option for a patient with these negative prognostic factors, regardless of life expectancy predicted based on a prognosis score.Prediction of prognosis is a key factor in therapeutic decision making due to recent the development of therapeutic options for spinal metastases. The aim of the study was to examine predictive scoring systems and identify prognostic factors for 6–month mortality after palliative surgery. The participants were 75 patients with spinal metastases who underwent palliative surgery and had a minimum follow–up period of 1 year. Associations of actual survival with categories based on the revised Tokuhashi score and new Katagiri score were evaluated. Univariate and multivariate analyses were performed to identify prognostic factors for 6–month mortality after palliative surgery. The median actual survival period was longer than those predicted using the scoring systems. However, 21.3% of patients died of cancers within 6 months after surgery. A higher CRP/albumin ratio (odds ratio: 0.39; cut–off 0.409) and absence of postoperative adjuvant therapy (odds ratio: 7.15) were independent risk factors for 6–month mortality. There was no association of mortality with primary site, severity of sarcopenia, or other biomarkers. These results suggest that careful consideration is needed to determine whether palliative surgery is the best option for patients with a high preoperative CRP/albumin ratio and/or absence of postoperative adjuvant therapy, regardless of predictions made from scoring systems.

Development and validation of the Massachusetts General Hospital/Memorial Sloan Kettering nomogram to predict overall survival of resected patients with pancreatic ductal adenocarcinoma treated with neoadjuvant therapy

BackgroundPrognostication in patients undergoing resection for pancreatic ductal adenocarcinoma following neoadjuvant therapy remains challenging. In this study, we aimed to develop and validate a nomogram for the prediction of overall survival of these patients. MethodsPatients who underwent neoadjuvant therapy followed by surgical resection at the Massachusetts General Hospital were analyzed (training cohort). Patients from Memorial Sloan Kettering were included as a validation cohort. A nomogram to predict overall survival was designed, trained, and subjected to internal (bootstrap) validation. ResultsA total of 325 patients were identified from Massachusetts General Hospital. Multivariable Cox regression analysis demonstrated that age (hazard ratio 1.828, 95% confidence interval 1.251–2.246; P = .007), serum carbohydrate antigen 19-9 ≥ 37 U/mL (HR 1.602, 95% confidence interval 1.187–3.258; P = .015), tumor size (hazard ratio 2.278, 95% confidence interval 1.405–4.368; P = .003), nodal status (hazard ratio 1.309, 95% confidence interval 1.108–2.439; P = .032), and R1 tumor resection (hazard ratio 1.481, 95% confidence interval 1.049–2.091; P = .026) were independent factors associated with overall survival. A nomogram that incorporated these significant prognostic factors was established. The calibration plots demonstrated high concordance between predictive nomogram values and actual overall survival for 1-year, 3-year, and 5-year overall survival. The model demonstrated excellent discriminatory power in both the Massachusetts General Hospital and Memorial Sloan Kettering cohorts, with adjusted Harrel’s concordance index values of 0.729 and 0.712, respectively. ConclusionIn this report, we established and validated a novel nomogram for predicting the survival of patients who underwent neoadjuvant therapy followed by pancreatectomy. This model allows clinicians to better estimate the survival of these specific patients.

A Prognostic Nomogram Based on Response to Bortezomib and BTK Expression for Treatment-Experienced Multiple Myeloma Patients

To establish a prognostic nomogram based on response to bortezomib and BTK expression for treatment-experienced multiple myeloma patients. The Oncomine database was utilized to determine BTK expression, sex, age, albumin, Mayo index, response to bortezomib treatment, follow-up time and survival status in multiple myeloma(MM) patients. Cut-off point for BTK expression was calculated using R software. Univariate and multivariate analyses by Cox proportional hazards regression were then performed. Significant prognostic factors were combined to build a nomogram. The discrimination ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves. Multivariate analysis showed that response to bortezomib, BTK expression and sex were independent risk factors for prognosis. The C-index value of the nomogram made according to the independent risk factors was 0.729 (95%CI, 0.642-0.8164). The calibration curves showed good consistency between predicted and actual survivals for 1-year and 2-year overall survival. The proposed nomogram is accurate in predicting the prognosis of patients with MM.

Establishment and verification of a nomogram for predicting survival in patients with triple-positive breast cancer.

BackgroundTriple-positive breast cancer (TPBC) is a specific type of breast cancer characterized by the positive expression of estrogen receptor (ER)/progesterone receptor (PR)/human epidermal growth factor receptor 2 (HER-2). In recent years, the research on breast cancer has been increasing year by year, but there are few studies on TPBC, especially the lack of analysis with large sample size. In this study, sufficient samples were provided through the SEER database, explore the factors affecting the prognosis of TPBC, and construct a prediction model, in order to assess the individual survival of patients, and help clinicians accurately identify high-risk patients and develop personalized treatment plans.MethodsPatients pathologically diagnosed with TPBC were recruited from Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups (7:3 ratio). Univariate analysis was used to analyze the related factors affecting the prognosis of TPBC patients in the modeling group, and then multivariate Cox proportional hazards model was used to analyze the significant factors to screen out the independent risk factors affecting the 3- and 5-year overall survival (OS) rate and construct the prediction model. Using the concordance index (C-index) and calibration curve were performed to evaluate the predictive ability of the model.ResultsThe results of the Cox risk-scale model showed that race, age, marital status, tumor grade, tumor, node, metastasis stage, surgical treatment, chemotherapy, and radiotherapy affected the prognosis of TPBC patients (P<0.05) in the training group, and the factors were used to construct a nomogram. The internal and external validation of the nomogram chart indicated that the C-index of the training group was 0.85 [95% confidence interval (CI): 0.836, 0.863] and that of the verification group was 0.833 (95% CI: 0.807, 0.858). The calibration curves of the 2 groups showed that the OS predicted by the model was consistent with the actual survival of the patients.ConclusionsThe prediction model accurately predicted the prognosis of and identified high-risk TPBC patients.

Development and External Validation of a Clinical Nomogram for Individually Predicting Survival of Metaplastic Breast Cancer

BackgroundFew studies have concerned the prognosis of metaplastic breast cancer (MpBC), a rare and diverse malignancy. A prognostic index estimating the MpBC survival would be attractive in clinical practice. Patients and MethodsWe retrospectively analyzed MpBC patients from the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified and the final nomogram was developed to predict the 1-, 3-, or 5-year overall survival (OS). Calibration curves were provided to internally validate the performance of the nomogram and discriminative ability was appraised by concordance index (C-index). ResultsA total of 1017 MpBC patients diagnosed between 2010 and 2015 were assigned into 3:1 as training set (n = 763) and SEER validation set (n = 254). An external validation was performed by an individual set of 94 MpBC patients from National Cancer Center in China from 2010 to 2018. The nomogram finally consisted of 7 independent prognostic factors and presented a good accuracy for predicting the OS with the C-index of 0.77 (95% CI: 0.751-0.786). Interestingly, the nomogram based on the western (including 92.5% non-Asian) SEER validation population (C-index of nomogram: 0.76, 95% CI: 0.737-0.796) also has an optimal discrimination in Asian population (C-index of nomogram: 0.70). The calibration plots of the nomogram predictions were also accurate and corresponded closely with the actual survival rates. ConclusionThis novel nomogram was accurate enough to predict the OS by using readily available clinicopathologic factors in MpBC general population, which could provide individualized recommendations for patients and clinical decisions for physicians.

Within trial comparison of survival time projections from short-term follow-up with long-term follow-up findings.

Data on long-term treatment effects are scarce, despite the intent to use new therapies for many years and the need of patients, physicians and payers to have a better understanding of the lifetime benefits of treatments. The restricted mean or median survival time (RMST) calculated using age instead of time, hypothetically enables estimation of long-term gain in event-free or overall survival from the short-term (within-trial) effects of an intervention, compared with its control. Tha aim of the study is to use trials with long-term follow-up available through extension studies to compare the long-term projections estimated using RMST from within-trial follow-up data with the actual long-term outcomes in the extension studies. We estimated the median long-term survival time using age instead of follow-up time and compared these model-based projections with the actual long-term estimates in the (i) SCD-HeFT trial vs. SCD-HeFT long-term outcomes; (ii) SOLVD trial vs. SOLVD 12year follow-up; (iii) STICH trial vs. STICHES; and (iv) ACCORD study vs. ACCORDION. In the long-term follow-up of SCD-HeFT, gain in survival with ICD vs. placebo over a median of 11.0years was +1.4years of life. The RMST model-derived survival projection from the within-trial data (median follow-up of 3.4years) gave an estimated survival gain of +1.2years. In STICHES, over a median follow-up of 9.8years, coronary artery bypass grafting (CABG) vs. medical care led to a survival extension of +1.4years in favour of CABG. RMST projections using within-trial data from STICH (median follow-up of 4.9years), gave an extended survival of +2.4years in favour of CABG in younger patients. In the long-term follow-up of SOLVD, enalapril vs. placebo led to a survival gain of +0.8years over a median follow-up of 12.1years. The RMST projections from the within-trial data (median follow-up of 2.8years) gave a survival extension of +0.3years in favour of enalapril. In the long-term follow-up ACCORDION study, with a median follow-up of 8.8years, intensive vs. a standard anti-hyperglycaemic treatment did not influence long-term survival, which was concordant with the RMST projections from the short-term ACCORD study with median follow-up of 4.9years. Age-based survival projections using within-trial data generally provided concordant results with the actual survival measured in long-term follow-up extension studies. Our findings suggest that age-based lifetime projections may be used as means to assess the long-term treatment effects.