Construction of a nomogram to predict the prognosis of non-small-cell lung cancer with brain metastases

Development and validation of a nomogram for predicting the overall survival in non-small cell lung cancer patients with liver metastasis

Background

Among all metastatic lesions in non-small cell lung cancer (NSCLC), liver metastasis (LM) is the most lethal site with a median survival of less than 5 months. Few studies exclusively report on prognostic factors for these unique patients. We aimed to construct and validate a practical model to predict the prognosis of NSCLC patients with LM.

Methods

Cases of NSCLC with LM diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and were randomly split into training and validation cohort (7:3). The overall survival (OS) was measured from diagnosis until date of death or last follow-up. Cox regression analyses were performed to identify potential predictors of the model. A nomogram incorporating those independent factors was constructed and validated by the concordance index (C-index) and calibration plots. The decision curve analysis (DCA) and a risk stratification system were used to evaluate its clinical value.

Results

A total of 2,367 cases were selected for analysis and randomized to the training cohort (n=1,677) and the validation cohort (n=690). The patients were mainly male (59.3%), married (83.1%) and White (77.3%). Apart from LM, 54.2%, 26.7%, and 36.7% of patients also present with bone, brain, and lung metastases, respectively. The median follow-up was 4.0 months for all patients and 23 months for alive cases. The median OS was 5 months [interquartile range (IQR), 2-11 months]. Sex, age, race, grade, T stage, bone metastasis, brain metastasis, surgery, and chemotherapy were identified as the independent risk factors of the OS and used to develop the nomogram. The calibration curves exhibited excellent agreement between the predicted and actual survival in both the training and validation set, with a C-index of 0.700 [95% confidence interval (CI): 0.684-0.716] and 0.677 (95% CI: 0.653-0.701), respectively. The DCA and the risk classification system further supported that the prediction model was clinically effective.

Conclusions

This is the first study to build a prediction model for NSCLC patients with LM. It aids in treatment decisions, focused care, and physician-patient communication. The global prospective data is needed to further improve this model.

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND

Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC).

METHODS

We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients.

RESULTS

We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions.

CONCLUSION

We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

Investigations of the distant metastatic non-small cell lung cancer without local lymph node involvement: Real world data from a large database

INTRODUCTION

This study aimed to investigate the presentations and survival outcomes of the distant metastatic non-small cell lung cancer (NSCLC) without lymph node involvement to obtain a clearer picture of this special subgroup of metastatic NSCLC.

METHOD

A least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was used to select the prognostic variables. A nomogram and corresponding risk-classifying systems were constructed. The C-index and calibration curves were used to evaluate the performance of the model. Overall survival (OS) curves were plotted using the Kaplan-Meier method, and the log-rank test was used to compare OS differences between groups. Propensity score matching (PSM) was performed to reduce bias.

RESULT

A total of 12 610 NSCLC patients with M1 category (N0 group: 3045 cases; N1-3 group: 9565 cases) were included. Regarding the N0 group, multivariate analysis demonstrated that age, sex, race, surgery, grade, tumor size, and M category were independent prognostic factors. A nomogram and corresponding risk-classifying systems were formulated. Favorable validation results were obtained from the C-index, calibration curves, and survival comparisons. Survival curves demonstrated that N0 NSCLC patients had better survival than N1-3 NSCLC patients both before and after PSM. Furthermore, the survival of resected N0M1 patients was superior to that of those without surgery.

CONCLUSION

In this study, a prognostic nomogram and risk-classifying systems designed for the T1-4N0M1 NSCLC patients showed acceptable performance. Primary lung tumor resection might be a feasible treatment for this population subset. Additionally, we proposed that lymph node stage might have a place in the forthcoming tumor-node-metastasis (TNM) staging proposal for NSCLC patients with M1 category.

Establishment of a prognostic risk prediction modelfor non-small cell lung cancer patients with brainmetastases: a retrospective study

Background

Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM.

Methods

A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging.

Results

The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678-0.814), 0.819 (0.761-0.877), and 0.865 (0.774-0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638-0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients' OS.

Conclusion

The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.

Lung cancer in middle and southern Morocco

Purpose

Determining risk factors associated with a fatal disease such as lung cancer (LC) remains an important key to understanding the factors related to its development and therefore using the correct emergent or accessible treatments. For that, we sought to highlight by describing, and analysing, the risk factors related to LC survival, reflecting the actual situation in Morocco.

Patients and methods

We included 987 LC patients diagnosed from 2015 to 2021 at the Medical Oncology Department at the Mohammed VI University Hospital of Marrakech. An overview of the LC situation was described, and analysed, to determine the risk factors related to survival. The independent prognostic factors were determined using Cox Proportional Hazards Regression Analysis. To create a distinction between different risks group in the survival curve, stratification was done, respectively, within sex, age, histology type, treatments and radiation therapy.

Results

We finally included 862 patients with 15 parameters among the 27 extracted, all meeting the inclusion criteria. 89.1% of the patients were male (n = 768) and 10.9% were female (n = 94), of whom 83.5% had a history of tobacco smoking (n = 720). The median survival of both sexes was 716 (5-2,167) days. The average age at diagnosis was 60 years. Five hundred and thirty-four patients presented with advanced stage. Patients above 66 years were the more diagnosed category with adenocarcinoma at T4N2M1c pathological category, and endocrinal comorbidity, in addition to pleurisy syndrome. Moreover, family history was found to be a bad prognostic factor. Interestingly, smoking status was not a bad contributor to survival. Age at diagnosis, histology subtype, performance status, haemoglobin, numbers of cures of the first-line chemotherapy, radiotherapy, anaemia and treatments were identified as risk factors related to survival.

Conclusion

We established a descriptive and analytical overview of the current LC epidemiology situation in the oncology division of Mohammed VI University Hospital in a non-industrialised state taking into account smoking status.

Development and Validation of Prognostic Nomograms for Lung Squamous Cell Carcinoma With Brain Metastasis in Patients Aged 45 Years or Older: A Population-Based Study

PURPOSE

We aimed to establish nomograms to predict the survival in patients aged ≥45  years with lung squamous cell carcinoma and brain metastasis.

METHODS

We collected patients diagnosed as lung squamous cell carcinoma with brain metastasis aged ≥45 years between 2010 and 2019 from the Surveillance, Epidemiology, and End Results database. Prognostic factors were determined by the univariate and multivariate Cox regression analysis, and then the nomogram was constructed to predict cancer-specific survival and overall survival. Nomograms were evaluated by decision curve analysis, the area under the receiver operating characteristic curve, calibration plot, concordance index, and risk group stratification.

RESULTS

In total, 2437 patients were included, with 1706 and 731 in the cohorts of training and validation, respectively. The age, N stage, T stage, liver metastasis, chemotherapy, bone metastasis, along with radiotherapy were significant in predicting the survival, and adopted for the establishment of nomograms. In the training and validation sets, the concordance index were .713(95%CI:0.699-.728) & .700(95%CI:0.677-.722) in predicting cancer-specific survival and .715(95%CI:0.701-.729) & .712(95%CI:0.690-.735) in predicting overall survival, respectively. Besides, the area under the receiver operating characteristic curve for predicting cancer-specific survival and overall survival in the training set were all >.7 at 1-, 2-, and 3- years. Calibration plots proved the survival predicted by nomograms were consistent with the actual values. decision curve analysis revealed better clinical validity of the nomogram in predicting cancer-specific survival and overall survival at 1-year than TNM staging. Patients were stratified into the high-/low-risk groups according to the optimal cutoff value of 100.21 for cancer-specific survival and 91.98 for overall survival. A web-based probability calculator was constructed finally.

CONCLUSION

Two nomograms were developed for the prognostic prediction of lung squamous cell carcinoma patients with brain metastasis aged ≥45 years, providing guidance for decision-making in clinical practice.

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Background

To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).

Methods

One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra- and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.

Results

A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI): 0.903–0.919] and an AP of 0.885 (95% CI: 0.876–0.894) in the training cohort, and an AUC of 0.873 (95% CI: 0.866–0.80) and an AP of 0.827 (95% CI: 0.820–0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427).

Conclusions

The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.

Predictors for survival in patients with bone metastasis of small cell lung cancer: A population-based study

The objective of the current study is to analyze the clinical and demographic characteristics of patients with bone metastasis of small cell lung cancer (SCLC) and explore their survival predictors.We retrospectively extracted patients with bone metastasis of SCLC from the Surveillance, Epidemiology, and End Results database. We applied Cox regression analyses to identify independent survival predictor of overall survival (OS) and cancer-specific survival (CSS). Only significant predictors from univariable analysis were included for multivariable Cox analysis. Kaplan-Meier method was used to evaluate survival differences between groups by the log-rank test.A total of 5120 patients with bone metastasis of SCLC were identified and included for survival analysis. The 1-year OS and CSS rates of bone metastasis of SCLC were 19.8% and 21.4%, respectively. On multivariable analysis, gender, age, radiotherapy, chemotherapy, liver metastasis, brain metastasis, insurance status, and marital status independently predicted OS and CSS. There was no significant difference of OS and CSS in terms of race and tumor size.Independent predictors of survival were identified among patients with bone metastasis of SCLC, which could be valuable to clinicians in treatment decision. Patients with bone metastasis of SCLC may benefit from radiotherapy and chemotherapy.

Serum neurofilament light chain or glial fibrillary acidic protein in the diagnosis and prognosis of brain metastases

INTRODUCTION

Brain metastases (BM) remains the most cumbersome disease burden in patients with lung cancer. This study aimed to investigate whether serum brain injury biomarkers can indicate BM, to further establish related diagnostic models, or to predict prognosis of BM.

MATERIALS AND METHODS

This was a prospective study of patients diagnosed with lung cancer with BM (BM group), with lung cancer without BM (NBM group), and healthy participants (control group). Serum neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were detected at baseline. We identified and integrated the risk factors of BM to establish diagnostic models.

RESULTS

A total of 158 patients were included (n = 37, 57, and 64 in the BM, NBM, and control groups, respectively). Serum biomarker levels were significantly higher in the NBM group than in the control group. Higher serum NfL and GFAP concentrations were associated with BM (odds ratios, 3.06 and 1.79, respectively). NfL (area under curve [AUC] = 0.77, p < 0.001) and GFAP (AUC = 0.64, p = 0.02) had diagnostic value for BM. The final diagnostic model included NfL level, age, Karnofsky Performance Status. The model had an AUC value of 0.83 (95% confidence interval [CI] 0.75-0.92). High NfL concentration was correlated with poor overall survival of patients with BM (hazard ratio, 3.31; 95% CI 1.22-9.04; p = 0.019).

CONCLUSION

Serum NfL and GFAP could be potential diagnostic biomarkers for BM in patients with lung cancer. We established a model that can provide individual diagnoses of BM. Higher NfL level may be associated with poor prognosis of patients with BM.

Brain Metastases Status and Immunotherapy Efficacy in Advanced Lung Cancer: A Systematic Review and Meta-Analysis

Background

Brain metastases (BMs) indicate poor outcomes and are commonly excluded in immunotherapy clinical trials in advanced lung cancer; moreover, the effect of BM status on immunotherapy efficacy is inconsistent and inconclusive. Therefore, we conducted a meta-analysis to assess the influence of BM status on immunotherapy efficacy in advanced lung cancer.

Methods

Electronic databases and all major conference proceedings were searched without language restrictions according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We extracted randomized clinical trials on lung cancer immunotherapy that had available overall survival (OS) and/or progression-free survival (PFS) data based on the BM status. All analyses were performed using random effects models.

Results

Fourteen randomized clinical trials with 9,089 patients were identified. Immunotherapy conferred a survival advantage to BM patients [OS-hazard ratio (HR), 0.72; 95% confidence interval (CI), 0.58-0.90; P = 0.004; and PFS-HR, 0.68; 95% CI, 0.52-0.87, P = 0.003]. Non-BM patients could also derive a survival benefit from immunotherapy (OS-HR, 0.76; 95% CI, 0.71-0.80; P <0.001; and PFS-HR, 0.68; 95% CI, 0.56-0.82, P <0.001). The pooled ratios of OS-HRs and PFS-HRs reported in BM patients versus non-BM patients were 0.96 (95% CI, 0.78-1.18; P = 0.72) and 0.97 (95% CI, 0.79-1.20; P = 0.78), respectively, indicating no statistically significant difference between them. Subsequent sensitivity analyses did not alter the results. Subgroup analyses according to tumor type, line of therapy, immunotherapy type, study design, and representation of BM patients reconfirmed these findings.

Conclusion

We demonstrated that BM status did not significantly influence the immunotherapy efficacy in lung cancer, suggesting that both BM and non-BM patients could obtain comparable benefits.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42020207446).

Prognostic factors and survival after whole-brain radiotherapy for initial brain metastases arising from non-small cell lung cancer

Aim:

To identify prognostic factors and investigate patient survival after whole-brain radiotherapy (WBRT) for initial brain metastases arising from non-small cell lung cancer (NSCLC).

Methods:

Patients diagnosed with NSCLC between 1 January 2010 and 30 September 2019, and who received WBRT upon first developing a brain metastasis, were investigated. Overall survival was determined as related to age, sex, duration between initial examination and brain metastasis detection, stage at the first examination, presence of metastases outside the brain, blood analysis findings, brain metastasis symptoms, radiotherapy dose and completion, imaging findings, therapeutic course of chemotherapy and/or radiation therapy, histological type, and gene mutation status.

Results:

Thirty-one consecutive patients (20 men and 11 women) with a mean age of 63·8 years and median survival of 129 days were included. Multivariate analysis with stepwise testing was performed to investigate differences in survival according to gene mutation status, lactate dehydrogenase (LDH) level, irradiation dose, WBRT completion and Stage status. Of these, a statistically significant difference in survival was observed in patients with gene mutation status (hazard ratio: 0·31, 95% CI: 0·11–0·86, p = 0·025), LDH levels <230 vs. ≥230 IU/L (hazard ratio: 4·08, 95% CI: 1·45–11·5, p < 0·01) received 30 Gy, 30 Gy/10 fractions to 35 Gy/14 fractions, and 37·5 Gy/15 fractions (hazard ratio: 0·26, 95% CI: 0·09–0·71, p < 0·01), and stage IV versus non-stage IV (hazard ratio: 0·13, 95 CI:0·02–0·64, p < 0·01)

Findings:

Gene mutation, LDH, radiation dose and Stage are prognostic factors for patients with initial brain metastases who are treated with WBRT.