Construction of a Nomogram After Using Propensity Score Matching to Reveal the Prognostic Benefit of Tumor Resection of Stage IV M1a Nonsmall Cell Lung Cancer Patients

Development of a nomogram for predicting survival in clinical T1N0M1 lung adenocarcinoma: a population-based study

OBJECTIVE

This study aimed to establish a prognostic model for clinical T1N0M1 (cT1N0M1) lung adenocarcinoma patients to evaluate the prognosis of patients in terms of overall survival (OS) rate and cancer-specific survival (CSS) rate.

METHODS

Data of patients with metastatic lung adenocarcinoma from 2010 to 2016 were collected from the Surveillance, Epidemiology and End Results database. Multivariate Cox regression analysis was conducted to identify relevant prognostic factors and used to develop nomograms. The receiver operating characteristic (ROC) curve and calibration curve are used to evaluate the predictive ability of the nomograms.

RESULTS

A total of 45610 patients were finally included in this study. The OS and CSS nomograms were constructed by same clinical indicators such as age (<60 years or ≥60 years), sex (female or male), race (white, black, or others), surgery, radiation, chemotherapy, and the number of metastatic sites, based on the results of statistical Cox analysis. From the perspective of OS and CSS, surgery contributed the most to the prognosis. The ROC curve analysis showed that the survival nomograms could accurately predict OS and CSS. According to the points obtained from the nomograms, survival was estimated by the Kaplan-Meier method, then cT1N0M1 patients were divided into three groups: low-risk group, intermediate-risk group, and high-risk group, and the OS ( P  < 0.001) and CSS ( P  < 0.001) were significantly different among the three groups.

CONCLUSION

The nomograms and risk stratification model provide a convenient and reliable tool for individualized evaluation and clinical decision-making of patients with cT1N0M1 lung adenocarcinoma.

A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study

Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors.

Nomogram for prognosis of elderly patients with cervical cancer who receive combined radiotherapy

This retrospective study identified prognostic factors to help guide the clinical treatment of elderly patients (≥ 65 years) with cervical cancer who had undergone radiotherapy. A personalized model to predict 3- and 5-years survival was developed. A review was conducted of 367 elderly women with cervical cancer (staged II-III) who had undergone radiotherapy in our hospital between January 2012 and December 2016. The Cox proportional hazards regression model was used for survival analysis that considered age, hemoglobin, squamous cell carcinoma antigen, pathologic type, stage, pelvic lymph node metastasis status, and others. A nomogram was constructed to predict the survival rates. The median follow-up time was 71 months (4-118 months). The 3- (5-) years overall, progression-free, local recurrence-free, and distant metastasis-free survival rates were, respectively, 91.0% (84.4%), 92.3% (85.9%), 99.18% (99.01%), and 99.18% (97.82%). The following were significant independent prognostic factors for overall survival: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The C-index of the line chart was 0.699 (95% CI 0.652-0.746). The areas under the receiver operating characteristic curves for 3- and 5-years survival were 0.751 and 0.724. The nomogram was in good concordance with the actual survival rates. The independent prognostic factors for overall survival in elderly patients with cervical cancer after radiotherapy were: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The novel prognostic nomogram based on these factors showed good concordance with the actual survival rates and can be used to guide personalized clinical treatment.

Myeloid cell leukemia-1 as a candidate prognostic biomarker in cancers: a systematic review and meta-analysis

INTRODUCTION

Studies have shown that myeloma cell leukemia-1 (MCL-1) is associated with the prognosis of patients with cancer. To further validate the prognostic value of MCL-1 in cancer, a meta-analysis was conducted.

METHODS

Six databases were searched using Boolean logic search formulas. Data were extracted from the included literature, and pooled odds ratio, hazard ratio, and 95% confidence interval were calculated to determine the relationship between MCL-1 levels and clinicopathological characteristics and prognosis of patients with cancer. When heterogeneity was found to be significant, a random effects model was used, otherwise, a fixed effects model was used.

RESULTS

Twelve articles were included in this meta-analysis, totaling 2208 patients with cancer across 14 studies. A high MCL-1 expression level was associated with patients with high T stage, M stage, and TNM stage in some cancers. Additionally, high MCL-1 expression was likely to be observed in patients with poorly differentiated digestive system tumors and patients with lung adenocarcinoma. Notably, a higher expression of MCL-1 was found to be associated with shorter overall survival in patients with hematological tumors, digestive system tumors, and lung cancer.

CONCLUSION

MCL-1 may be a prognostic biomarker in patients with some types of cancer.

A clinical nomogram based on absolute count of lymphocyte subsets for predicting overall survival in patients with non-small cell lung cancer

BACKGROUND

The absolute count of lymphocyte subsets (ACLS) is correlated to the prognosis of multiple malignancies. This study aimed to combine the ACLS with the clinicopathological parameters to develop a nomogram to accurately predict the prognosis of non-small cell lung cancer (NSCLC) patients.

METHODS

This retrospective study included a training cohort (n = 1685) and validation cohort (n = 337) with NSCLC patients treated in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine between January 2018 and January 2021. Cox regression were conducted to identify factors associated with overall survival. The nomogram was built based on 10 significant factors, and evaluated by the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve.

RESULTS

In the training cohort, the multivariate cox proportional hazard regression analysis showed that the independent factors for overall survival (OS) included age, brain metastases, hepatic metastases, respiratory system diseases, clinical stages, surgery, absolute count (AC) of CD3⁺, CD4⁺, CD8⁺, and NK cells, which were all applied in the nomogram. The C-index of the nomogram to predict OS was 0.777 (95% CI, 0.751-0.802) in training cohort and 0.822 (95% CI, 0.798-0.846) in validation cohort. The area under the ROC showed a good discriminative ability in both cohorts. Calibration curves presented an excellent consistence between the nomogram predicted probability and actual observation.

CONCLUSIONS

We established a prognostic nomogram to predict OS of the NSCLC patient. This nomogram provided a more quantitative, scientific and objective basis for accurate diagnosis and individual management of NSCLC patients.

Nomogram for prognosis of elderly patients with cervical cancer who receive combined radiotherapy.. [DOI: 10.21203/rs.3.rs-2367005/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective: This retrospective study identified prognostic factors to help guide the clinical treatment of elderly patients (≥65 y) with cervical cancer who had undergone radiotherapy. A personalized model to predict 3- and 5-year survival was developed. Methods: A review was conducted of 367 elderly women with cervical cancer (staged II-III) who had undergone radiotherapy in our hospital between January 2012 and December 2016. The Cox proportional hazards regression model was used for survival analysis that considered age, hemoglobin, squamous cell carcinoma antigen, pathologic type, stage, pelvic lymph node metastasis status, and others. A nomogram was constructed to predict the survival rates. Results: The median follow-up time was 71 months (4-118 mo). The 3- (5-) year overall, progression-free, local recurrence-free, and distant metastasis-free survival rates were, respectively, 91.0% (84.4%), 92.3% (85.9%), 99.18% (99.01%), and 99.18% (97.82%). The following were significant independent prognostic factors for overall survival: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The C-index of the line chart was 0.699 (95% CI: 0.652-0.746). The areas under the receiver operating characteristic curves for 3- and 5-year survival were 0.751 and 0.724. The nomogram was in good concordance with the actual survival rates. Conclusions: The independent prognostic factors for overall survival in elderly patients with cervical cancer after radiotherapy were: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The novel prognostic nomogram based on these factors can be an asset for personalized clinical management.

Analysis of prognostic factors of undifferentiated pleomorphic sarcoma and construction and validation of a prediction nomogram based on SEER database

Background

Undifferentiated pleomorphic sarcoma (UPS) is considered one of the most common types of soft tissue sarcoma (STS). Current studies have shown that the prognosis of UPS is related to some of its clinical characteristics, but no survival prediction model for the overall survival (OS) of UPS patients has been reported. The purpose of this study is to construct and validate a nomogram for predicting OS in UPS patients at 3, 5 years after the diagnosis.

Methods

According to the inclusion and exclusion criteria, 1079 patients with UPS were screened from the SEER database and randomly divided into the training cohort (n = 755) and the validation cohort (n = 324). Patient demographic and clinicopathological characteristics were first described, and the correlation between the two groups was compared, using the Kaplan–Meier method and Cox regression analysis to determine independent prognostic factors. Based on the identified independent prognostic factors, a nomogram for OS in UPS patients was established using R language. The nomogram’s performance was then validated using multiple indicators, including the area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA).

Results

Both the C-index of the OS nomogram in the training cohort and the validation cohort were greater than 0 .75, and both the values of AUC were greater than 0.78. These four values were higher than their corresponding values in the TNM staging system, respectively. The calibration curves of the Nomogram prediction model and the TNM staging system were well fitted with the 45° line. Decision curve analysis showed that both the nomogram model and the TNM staging system had clinical net benefits over a wide range of threshold probabilities, and the nomogram had higher clinical net benefits than the TNM staging system as a whole.

Conclusion

With good discrimination, accuracy, and clinical practicability, the nomogram can individualize the prediction of 3-year and 5-year OS in patients with UPS, which can provide a reference for clinicians and patients to make better clinical decisions.

Surgery and Surgery Approach Affect Survival of Patients With Stage I-IIA Small-Cell Lung Cancer: A Study Based SEER Database by Propensity Score Matching Analysis

Purpose

The purpose of this study was to observe the significance of surgery and its approach in stage I-IIA (according to 8th American Joint Committee on Cancer Staging Manual) small-cell lung cancer (SCLC) using the Surveillance, Epidemiology, and End Results (SEER) database.

Patients and Methods

A total of 1,421 patients from ages 31 to 93 years who were diagnosed with stage I-IIA SCLC in the SEER database from 2010 to 2015 were analyzed. The 1:1 propensity score matching analysis was used to minimize the effect of selection bias, and 355 pairs of patients' data was performed subsequent statistical analysis. K–M analysis and a Cox proportional hazards model were used to observe the role of surgery and other clinical features in the patients' prognoses on cancer-specific survival (CSS).

Results

Overall, within the whole cohort, the 3- and 5-year CSS rates were 41.0 and 34.0%, respectively. In a Cox regression that adjusted for other clinical features, patients were more likely to benefit from the surgery [hazard ratio (HR) 0.292, 95% confidence interval (CI) 0.237–0.361, P < 0.001]. Unadjusted 5-year cancer-specific survival among those with surgery was 55.0%, compared with 23.0% among those without surgery. In the propensity scored-matched dataset, however, 5-year CSS among those with surgery was 54.0%, compared with 17.0% among those without surgery (HR 0.380, 95%CI 0.315–0.457, P < 0.001). In patients who received surgery, cases with lobectomy had a better 5-year CSS than those without lobectomy (65.0 vs. 39.0%). The lobectomy might be a protective factor for patients who underwent resection in CSS (HR 0.433, 95%CI 0.310–0.604, P < 0.001).

Conclusions

We suggested that the surgery and lobectomy were the independent prognostic as well as the protective factors in stage I-IIA SCLC patients. We recommended that patients with no surgical contraindications receive surgery, preferably, lobectomy.

A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases

Introduction

The current American Joint Committee on Cancer (AJCC) M1a staging of non‐small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis.

Methods

Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan–Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1‐year and 0.727 for 2‐year OS prediction; validation cohort: 0.737 for 1‐year and 0.734 for 2‐year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1‐year: 0.327; 2‐year: 0.302) and IDI (1‐year: 0.138; 2‐year: 0.130).

Conclusion

Our study established a nomogram for the prediction of 1‐ and 2‐year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare workers to accurately evaluate the individual survival of M1a NSCLC patients. The accuracy and clinical applicability of this nomogram were validated.

Development and Validation of a Nomogram for Predicting Albumin Transfusion After Spinal Tuberculosis Surgery: Based on Propensity Score Matching Analysis

BACKGROUND

There have been few literature reports on the use of perioperative parameters to predict the risk of albumin transfusion after spinal tuberculosis surgery based on the application of nomogram and propensity score matching (PSM) analysis.

OBJECTIVE

The purpose was to predict the risk of albumin transfusion after spinal tuberculosis surgery based on a combination of PSM and nomogram.

METHODS

The clinical data of the patients were collected in our hospital, including preoperative clinical data, preoperative laboratory tests, and postoperative clinical data. All data were divided into 2 groups, including the albumin transfusion group and the non-albumin transfusion group. The PSM analysis was used to adjust the baseline data of the 2 groups. The nomogram was further constructed. The practicability and predictive ability of the model were evaluated.

RESULTS

A total of 494 cases were collected in this article; 102 pairs by PSM analysis were used to construct the nomogram. There were statistical differences in surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis by logistic analysis, and these parameters were included in the construction of the nomogram. The C-index of the prediction model was 0.734. The area under the curve was 0.73 and the net benefit was between 0.13 and 0.99. The calculated C-index was 0.71 by the internal verification method.

CONCLUSIONS

The PSM analysis had a good matching effect and the nomogram had a good predictive ability. Surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis might be predictors of albumin transfusion after spinal tuberculosis surgery.

Construction and Validation of a Novel Nomogram to Predict the Overall Survival of Patients With Combined Small Cell Lung Cancer: A Surveillance, Epidemiology, and End Results Population-Based Study

INTRODUCTION

Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the overall survival (OS) of patients with C-SCLC.

METHODS

In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and predictive ability compared with the classic TNM staging system.

RESULTS

Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage, brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group, suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the validation cohort.

CONCLUSION

We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC and predict their OS.

The Positive Lymph Node Ratio Predicts Survival in T1-4N1-3M0 Non-Small Cell Lung Cancer: A Nomogram Using the SEER Database

Background: An increasing number of studies have shown that the positive lymph node ratio (pLNR) can be used to evaluate the prognosis of non-small cell lung cancer (NSCLC) patients. To determine the predictive value of the pLNR, we collected data from the Surveillance, Epidemiology, and End Results (SEER) database and performed a retrospective analysis. Methods: We collected survival and clinical information on patients with T_1-4N_1-3M₀ NSCLC diagnosed between 2010 and 2016 from the SEER database and screened them according to inclusion and exclusion criteria. X-tile software was used to obtain the best cut-off value for the pLNR. Then, we randomly divided patients into a training set and a validation set at a ratio of 7:3. Pearson's correlation coefficient, tolerance and the variance inflation factor (VIF) were used to detect collinearity between variables. Univariate and multivariate Cox regression analyses were used to identify significant prognostic factors, and nomograms was constructed to visualize the results. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomogram. We divided the patient scores into four groups according to the interquartile interval and constructed a survival curve using Kaplan-Meier analysis. Results: A total of 6,245 patients were initially enrolled. The best cut-off value for the pLNR was determined to be 0.55. The nomogram contained 13 prognostic factors, including the pLNR. The pLNR was identified as an independent prognostic factor for both overall survival (OS) and cancer-specific survival (CSS). The C-index was 0.703 (95% CI, 0.695-0.711) in the training set and 0.711 (95% CI, 0.699-0.723) in the validation set. The calibration curves and DCA also indicated the good predictability of the nomogram. Risk stratification revealed a statistically significant difference among the four groups of patients divided according to quartiles of risk score. Conclusion: The nomogram containing the pLNR can accurately predict survival in patients with T_1-4N_1-3M₀ NSCLC.