A novel nomogram to predict the overall survival in esthesinoeroblastoma
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ABSTRACT:
SUBMITTER: Jiang L
PROVIDER: S-EPMC7556920 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:This multicenter retrospectively observational cohort study was conducted on participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery in 11 medical centers in China from August 1, 2015, to June 31, 2018. Baseline clinicopathologic data and nutritional status assessments including Nutrition Risk Screening 2002 (NRS 2002) score and Patient-generated Subjective Global Assessment (PG-SGA) rating were collected. Variables will be screened using the least absolute shrinkage and selection operator (LASSO) regression model and Cox regression analysis. Internal and external validations will be performed via the receiver operating curve (ROC), the area under the curve (AUC), the concordance index (C-index), calibration plots, decision curve analysis (DCA), and Five folds cross-validation by 200 times.
| 211 | ecrin-mdr-crc
Project description:BACKGROUND:This study aims to develop and validate an effective nomogram to estimate the individual outcome of patients with Gastric neuroendocrine neoplasms (G-NENs). METHODS:A total of 260 patients diagnosed with G-NENs at two medical centers were included, with 156 patients allocated as training set and 104 patients as validation. Predictive nomogram was constructed based on multivariate analyses using RMS package in R version. The predictive accuracy and discriminative ability were analyzed by C-index, risk group stratification and calibration curve, which was compared with other predictive systems for G-NENs. RESULTS:In multivariate analysis, age, Ki-67, mitoses, neutrophil to lymphocyte ratio, serum tumor marker and distant metastasis were significantly associated with overall survival. The constructed prognostic nomogram demonstrated a good calibration and discrimination value with 0.884 and 0.852 C-indices in training and validation dataset. Compare to World Health Organization (WHO) grading system (C-indices=0.760 and 0.732) and American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (C-indices=0.747 and 0.811), the nomogram displayed a better predictive accuracy. CONCLUSIONS:The novel prognostic nomogram showed superior predictive value in overall survival of G-NENs. It might be a useful tool for clinicians in estimating individual survival in G-NENs patients.
| S-EPMC6856574 | biostudies-literature
Project description:BackgroundGene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer.MethodsSomatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores.ResultsTP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) - (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823.ConclusionsThe TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.
| S-EPMC8011162 | biostudies-literature
Project description:Retroperitoneal leiomyosarcomas (RLS) are the second most common type of retroperitoneal sarcoma and one of the most aggressive tumours. The lack of early warning signs and delay in regular checkups lead to a poor prognosis. This study aims to create a nomogram to predict RLS patients' overall survival (OS). Patients diagnosed with RLS in the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were enrolled in this study. First, univariable and multivariable Cox regression analyses were used to identify independent prognostic factors, followed by constructing a nomogram to predict patients' OS at 1, 3, and 5 years. Secondly, the nomogram's distinguishability and prediction accuracy were assessed using receiver operating characteristic (ROC) and calibration curves. Finally, the decision curve analysis (DCA) investigated the nomogram's clinical utility. The study included 305 RLS patients, and they were divided into two groups at random: a training set (216) and a validation set (89). The training set's multivariable Cox regression analysis revealed that surgery, tumour size, tumour grade, and tumour stage were independent prognostic factors. ROC curves demonstrated that the nomogram had a high degree of distinguishability. In the training set, area under the curve (AUC) values for 1, 3, and 5 years were 0.800, 0.806, and 0.788, respectively, while in the validation set, AUC values for 1, 3, and 5 years were 0.738, 0.780, and 0.832, respectively. As evidenced by the calibration curve, the nomogram had high prediction accuracy. Moreover, DCA revealed that the nomogram had high clinical utility. Furthermore, the risk stratification system based on the nomogram could effectively categorise patients into three mortality risk subgroups. Therefore, the developed nomogram and risk stratification system may aid in optimising the treatment decisions of RLS patients to improve treatment prognosis and maximise their healthcare outcomes.
| S-EPMC9279432 | biostudies-literature
Project description:BackgroundPretreatment clinical staging is essential to select therapy. However, there have been no published pretreatment gastric cancer nomograms constructed using pretreatment clinical prognostic factors, including in nonresection patients. We aimed to develop a new pretreatment gastric cancer nomogram for individualized prediction of overall survival (OS).MethodsThe nomogram was developed using data of 5231 Japanese gastric cancer patients, and it was created with a Cox regression model. Fifteen clinical variables, which were obtained at pretreatment, were collected and registered. Data of two independent cohorts of patients from Seoul St. Mary's Hospital (1001 patients), and the University of Verona (389 patients) formed the external validation cohorts. The model was validated internally and externally using measures of discrimination (Harrell's C-index), calibration, and decision curve analysis.ResultsThe developed nomogram showed good discrimination, with a C-index of 0.855; that of the American Joint Committee on Cancer (AJCC) clinical stage was 0.819. In the external validation procedure, the C-indexes were 0.856 (AJCC, 0.795) in the Seoul St. Mary's cohort and 0.714 (AJCC, 0.648) in the University of Verona cohort. The nomogram performed well in the calibration and decision curve analyses when applied to both the internal and external validation cohorts. A stage-specific subset survival analysis of the three risk groups calculated using the nomogram also showed the superiority of nomogram-prediction when compared to AJCC.ConclusionThis new pretreatment model accurately predicts OS in gastric cancer and can be used for patient counseling in clinical practice and stratification in clinical trials.
| S-EPMC7433838 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients. A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran's three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve. The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650-0.734), as well as 0.627 (0.670, 0.686) in the test data. We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.
| S-EPMC8345484 | biostudies-literature
Project description:BackgroundAdjuvant imatinib therapy has been shown to improve overall survival (OS) of gastrointestinal stromal tumor (GIST) significantly. Few nomograms combining the use of adjuvant imatinib and clinicopathological characteristics estimate the outcome of patients. We aimed to establish a more comprehensive nomogram for predicting OS in patients with GIST.MethodsIn total, 1310 GIST patients undergoing curative resection at four high-volume medical centers between 2001 and 2015 were enrolled. Independent prognostic factors were identified by multivariate Cox analysis. Eligible patients were randomly assigned in a ratio of 7:3 into a training set (916 cases) and a validation set (394 cases). A nomogram was established by R software and its predictive power compared with that of the modified National Institutes of Health (NIH) classification using time-dependent receiver operating characteristic (ROC) curves and calibration plot.ResultsAge, tumor site, tumor size, mitotic index, postoperative imatinib and diagnostic delay were identified as independent prognostic parameters and used to construct a nomogram. Of note, diagnostic delay was for the first time included in a prognostic model for GIST. The calibrated nomogram resulted in predicted survival rates consistent with observed ones. And the decision curve analysis suggested that the nomogram prognostic model was clinically useful. Furthermore, time-dependent ROC curves showed the nomogram exhibited greater discrimination power than the modified NIH classification in 3- and 5-year survival predictions for both training and validation sets (all P < 0.05).ConclusionsPostoperative adjuvant imatinib therapy improved the survival of GIST patients. We developed and validated a more comprehensive prognostic nomogram for GIST patients, and it could have important clinical utility in improving individualized predictions of survival risks and treatment decision-making.
| S-EPMC8960199 | biostudies-literature
Project description:BACKGROUND:Although palliative care has been accepted throughout the cancer trajectory, accurate survival prediction for advanced cancer patients is still a challenge. The aim of this study is to identify pre-palliative care predictors and develop a prognostic nomogram for overall survival (OS) in mixed advanced cancer patients. METHODS:A total of 378 consecutive advanced cancer patients were retrospectively recruited from July 2013 to October 2015 in one palliative care unit in China. Twenty-three clinical and laboratory characters were collected for analysis. Prognostic factors were identified to construct a nomogram in a training cohort (n = 247) and validated in a testing cohort (n = 131) from the setting. RESULTS:The median survival time was 48.0 (95% CI: 38.1-57.9) days for the training cohort and 52.0 (95% CI: 34.6-69.3) days for the validation cohort. Among pre-palliative care factors, sex, age, tumor stage, Karnofsky performance status, neutrophil count, hemoglobin, lactate dehydrogenase, albumin, uric acid, and cystatin-C were identified as independent prognostic factors for OS. Based on the 10 factors, an easily obtained nomogram predicting 90-day probability of mortality was developed. The predictive nomogram had good discrimination and calibration, with a high C-index of 0.76 (95% CI: 0.73-0.80) in the development set. The strong discriminative ability was externally conformed in the validation cohort with a C-index of 0.75. CONCLUSIONS:A validated prognostic nomogram has been developed to quantify the risk of mortality for advanced cancer patients undergoing palliative care. This tool may be useful in optimizing therapeutic approaches and preparing for clinical courses individually.
| S-EPMC6551870 | biostudies-other
Project description:To construct and validate a nomogram to predict the overall survival (OS) of colorectal signet ring cell carcinoma (SRCC). The potentially eligible cases were obtained against the SEER database from 2004 to 2015. Log-rank test and Cox analysis were conducted to identify the independent prognostic factors for predicting OS. The identified prognostic factors were later integrated for the construction of an OS prediction nomogram. Altogether 2904 eligible cases were identified, and the median survival time was 18 (range: 0-155) months. As suggested by multivariate analysis, age, primary site, grade, tumor size, T stage, N stage, M stage, surgery, lymph node dissection and chemotherapy were identified as the independent factors for predicting OS. Afterwards, the above variables were incorporated into the nomogram. The C-index indicated better discriminatory ability of the nomogram than AJCC 8th TNM staging and SEER summary stage systems (both P < 0.001). Calibration plots further showed good consistency between the nomogram prediction and actual observation. The time independent area under the curves (tAUCs) for 3-year and 5-year OS in nomogram were larger than AJCC and SEER summary stage system. The constructed nomogram could potentially predict the survival of colorectal SRCC individuals.
| S-EPMC7873243 | biostudies-literature
Project description:BackgroundThe management of locally advanced pancreatic cancer (LAPC) patients remains controversial. Better discrimination for overall survival (OS) at diagnosis is needed. We address this issue by developing and validating a prognostic nomogram and a score for OS in LAPC (PROLAP).MethodsAnalyses were derived from 442 LAPC patients enrolled in the LAP07 trial. The prognostic ability of 30 baseline parameters was evaluated using univariate and multivariate Cox regression analyses. Performance assessment and internal validation of the final model were done with Harrell's C-index, calibration plot and bootstrap sample procedures. On the basis of the final model, a prognostic nomogram and a score were developed, and externally validated in 106 consecutive LAPC patients treated in Besançon Hospital, France.ResultsAge, pain, tumour size, albumin and CA 19-9 were independent prognostic factors for OS. The final model had good calibration, acceptable discrimination (C-index=0.60) and robust internal validity. The PROLAP score has the potential to delineate three different prognosis groups with median OS of 15.4, 11.7 and 8.5 months (log-rank P<0.0001). The score ability to discriminate OS was externally confirmed in 63 (59%) patients with complete clinical data derived from a data set of 106 consecutive LAPC patients; median OS of 18.3, 14.1 and 7.6 months for the three groups (log-rank P<0.0001).ConclusionsThe PROLAP nomogram and score can accurately predict OS before initiation of induction chemotherapy in LAPC-untreated patients. They may help to optimise clinical trials design and might offer the opportunity to define risk-adapted strategies for LAPC management in the future.
| S-EPMC4973163 | biostudies-literature