The development and validation of a nomogram for predicting brain metastases in lung squamous cell carcinoma patients: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database

A clinical model to predict brain metastases in resected early-stage non-small cell lung cancer

BACKGROUND

Despite the rising diagnosis of early-stage non-small cell lung cancer (NSCLC), there remains a limited understanding of the risk factors associated with postoperative brain metastases in early-stage NSCLC. Our goal was to identify the risk factors and construct a predictive model for postoperative brain metastases in this population.

METHODS

This study retrospectively enrolled patients with resected stage I-II NSCLC at the Department of Thoracic Surgery, West China Hospital from January 2015 to January 2021. Risk factors were identified through univariable and multivariable Cox regression analyses, followed by the construction of a nomogram. Evaluation of the model involved metrics such as the area under the curve (AUC), C-index, and calibration curves. To ensure reliability, internal validation was performed through bootstrap resampling.

RESULTS

This study included 2106 patients, among whom 67 (3.18%) patients were diagnosed with postoperative brain metastases. Multivariable Cox regression analysis revealed that higher pT and pN stages, along with specific histological subtypes, particularly solid/micropapillary predominant adenocarcinoma, were identified as independent risk factors for brain metastases. The performance of the nomogram in the training set exhibited AUC values of 0.759, 0.788, and 0.782 for predicting 1-year, 2-year, and 3-year occurrences, respectively. Bootstrap resampling validated its reliability, with C-index values of 0.758, 0.799, and 0.792 for the respective timeframes. Calibration curves affirmed consistency of the model.

CONCLUSIONS

A nomogram was developed to predict the likelihood of postoperative brain metastases in individuals with early-stage NSCLC. The tool aids in identifying high-risk patients and facilitating timely interventions.

Estimating the risk of brain metastasis for patients newly diagnosed with cancer

BACKGROUND

Brain metastases (BM) affect clinical management and prognosis but limited resources exist to estimate BM risk in newly diagnosed cancer patients. Additionally, guidelines for brain MRI screening are limited. We aimed to develop and validate models to predict risk of BM at diagnosis for the most common cancer types that spread to the brain.

METHODS

Breast cancer, melanoma, kidney cancer, colorectal cancer (CRC), small cell lung cancer (SCLC), and non-small cell lung cancer (NSCLC) data were extracted from the National Cancer Database to evaluate for the variables associated with the presence of BM at diagnosis. Multivariable logistic regression (LR) models were developed and performance was evaluated with Area Under the Receiver Operating Characteristic Curve (AUC) and random-split training and testing datasets. Nomograms and a Webtool were created for each cancer type.

RESULTS

We identify 4,828,305 patients from 2010-2018 (2,095,339 breast cancer, 472,611 melanoma, 407,627 kidney cancer, 627,090 CRC, 164,864 SCLC, and 1,060,774 NSCLC). The proportion of patients with BM at diagnosis is 0.3%, 1.5%, 1.3%, 0.3%, 16.0%, and 10.3% for breast cancer, melanoma, kidney cancer, CRC, SCLC, and NSCLC, respectively. The average AUC over 100 random splitting for the LR models is 0.9534 for breast cancer, 0.9420 for melanoma, 0.8785 for CRC, 0.9054 for kidney cancer, 0.7759 for NSCLC, and 0.6180 for SCLC.

CONCLUSIONS

We develop accurate models that predict the BM risk at diagnosis for multiple cancer types. The nomograms and Webtool may aid clinicians in considering brain MRI at the time of initial cancer diagnosis.

Risk factors for distant metastasis of Chondrosarcoma in the middle-aged and elderly people

Chondrosarcoma is the second most common primary bone malignancy with the highest incidence in middle-aged and elderly people, where distant metastasis (DM) still leads to poor prognosis. The purpose of this study was to construct a nomogram for studying the diagnosis of DM in middle-aged and elderly patients with chondrosarcoma. Data on chondrosarcoma patients aged ≥ 40 years diagnosed from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The data were divided into a training set and an internal validation set according to a 7:3 ratio, and the training set data were screened for independent risk factors for DM in chondrosarcoma patients using univariate and multivariate logistic regression analysis. The screened independent risk factors were then used to build a nomogram. In addition, data from 144 patients with chondrosarcoma aged ≥ 40 years diagnosed in a tertiary hospital in China from 2012 to 2021 were collected as the external validation set. The results were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis in the training set, internal validation set, and external validation set. A total of 1462 middle-aged and elderly patients with chondrosarcoma were included, and 92 (6.29%) had DM at the time of diagnosis. Independent risk factors for DM in middle-aged and elderly patients with chondrosarcoma included being married (OR: 2.119, 95% CI: 1.094-4.105), histological type of dedifferentiated chondrosarcoma (OR: 1.290, 95% CI: 1.110-1.499), high-grade tumor (OR: 1.511, 95% CI: 1.079-2.115), T3 stage (OR: 4.184, 95% CI: 1.977- 8.858), and N1 staging (OR: 5.666, 95% CI: 1.964-16.342). The area under the receiver operating characteristic curve (AUC) was 0.857, 0.820, and 0.859 in the training set, internal validation set, and external validation set, respectively. The results of the calibration curve and decision curve analysis also confirmed that the established nomogram could accurately predict DM in middle-aged and elderly patients with chondrosarcoma. Married, histological type of dedifferentiated chondrosarcoma, high-grade tumor, T3 stage, and N1 stage are independent risk factors for DM in middle-aged and elderly chondrosarcoma patients, and clinicians should see more attention.

Risk factors for distant metastasis of chondrosarcoma: A population-based study

Chondrosarcoma is the second largest bone malignancy after osteosarcoma and mainly affects middle-aged adults, where patients with distant metastasis (DM) often have a poor prognosis. Although nomograms have been widely used to predict distant tumor metastases, there is a lack of large-scale data studies for the diagnostic evaluation of DM in chondrosarcoma. Data on patients diagnosed with chondrosarcoma from 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Independent risk factors for having DM from chondrosarcoma were screened using univariate and multivariate logistics regression analysis. A nomogram was created to predict the probability of DM from the screened independent risk factors. The nomogram was then validated using receiver operating characteristic curves and calibration curves. A total of 1870 chondrosarcoma patients were included in the study after data screening, of which 157 patients (8.40%) had DM at the time of diagnosis. Univariate and multivariate logistic regression analysis screened four independent risk factors, including grade, tumor number, T stage, and N stage. receiver operating characteristic curves and calibration curves showed good accuracy of the nomogram in both training and validation sets. The current study screened for independent risk factors for DM from chondrosarcoma, which will help clinicians evaluate patients.

Characteristics and prognostic factors of adult patients with osteosarcoma from the SEER database

Osteosarcoma is the most common bone malignancy. There are many studies on the prognostic factors of children and adolescents, but the characteristics and prognostic factors of adult osteosarcoma are rarely studied. The aim of this study was to construct a nomogram for predicting the prognosis of adult osteosarcoma. Information on all osteosarcoma patients aged ≥ 18 years from 2004 to 2015 was downloaded from the surveillance, epidemiology and end results database. A total of 70% of the patients were included in the training set and 30% of the patients were included in the validation set. Univariate log-rank analysis and multivariate cox regression analysis were used to screen independent risk factors affecting the prognosis of adult osteosarcoma. These risk factors were used to construct a nomogram to predict 3-year and 5-year prognosis in adult osteosarcoma. Multivariate cox regression analysis yielded 6 clinicopathological features (age, primary site, tumor size, grade, American Joint Committee on Cancer stage, and surgery) for the prognosis of adult osteosarcoma patients in the training cohort. A nomogram was constructed based on these predictors to assess the prognosis of adult patients with osteosarcoma. Concordance index, receiver operating characteristic and calibration curves analyses also showed satisfactory performance of the nomogram in predicting prognosis. The constructed nomogram is a helpful tool for exactly predicting the prognosis of adult patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

The critical role of serum thymidine kinase 1(STK1) in predicting prognosis for immunotherapy in T4 stage lung squamous cell carcinoma

Purpose

The role of serum thymidine kinase 1 (STK1) in predicting the prognosis of T4-stage lung squamous cell carcinoma (LUSC) with immunotherapy is the focus of our work.

Methods

A total of 180 LUSC patients were enrolled. In this study, according to the T stage, the patients were divided into two groups: the T1-T2 stage and the T3-T4 stage. Receiver operating characteristic (ROC) curves were used to determine the best cutoff value for predicting overall survival (OS) outcomes. The next step is to use this cutoff value to introduce univariate and multivariate Cox regression models to screen the prognostic factors in different T stages of LUSC. The association of STK1 with other clinicopathological factors was also determined. Finally, to further explore the link between STK1 and the staging of LUSC patients, we have further divided the staging into T1-3 and T4 stages. We identified factors influencing the prognosis of patients who received immunotherapy in T4 stage LUSC.

Results

First, we determined that the optimal cutoff for STK1 for predicting OS outcome was 1.165 pmol/L. Correlation analysis revealed that STK1 was over-expressed in LUSC patients at the T3-4 stage. Univariate and multivariate analysis showed that immunotherapy was an independent prognostic factor in patients with T4 stage LUSC. In the group of patients who received immunotherapy or not, the STK1 expression level was found to be an independent prognostic factor in T4 LUSC patients receiving PD-1/PD-L1 inhibitor treatment; patients with high levels of STK1 had an increased risk of death (95%CI = 1.028-2.04).

Conclusion

STK1 is associated with a higher T stage and may be an effective prognostic marker for advanced LUSC immunotherapy patients.

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.

A nomogram for predicting postoperative overall survival of patients with lung squamous cell carcinoma: A SEER-based study

Background

Lung squamous cell carcinoma (LSCC) is a common subtype of non-small cell lung cancer. Our study aimed to construct and validate a nomogram for predicting overall survival (OS) for postoperative LSCC patients.

Methods

A total of 8,078 patients eligible for recruitment between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. Study outcomes were 1-, 2- and 3-year OS. Analyses performed included univariate and multivariate Cox regression, receiver operating characteristic (ROC) curve construction, calibration plotting, decision curve analysis (DCA) and Kaplan-Meier survival plotting.

Results

Seven variables were selected to establish our predictive nomogram. Areas under the ROC curves were 0.658, 0.651 and 0.647 for the training cohort and 0.673, 0.667 and 0.658 for the validation cohort at 1-, 2- and 3-year time-points, respectively. Calibration curves confirmed satisfactory consistencies between nomogram-predicted and observed survival probabilities, while DCA confirmed significant clinical usefulness of our model. For risk stratification, patients were divided into three risk groups with significant differences in OS on Kaplan-Meier analysis (P < 0.001).

Conclusion

Here, we designed and validated a prognostic nomogram for OS in postoperative LSCC patients. Application of our model in the clinical setting may assist clinicians in evaluating patient prognosis and providing highly individualized therapy.

Development and Validation of Prognostic Nomograms for Elderly Patients with Osteosarcoma

Background

The aim of the current study was to construct prognostic nomograms for individual risk prediction in elderly patients with osteosarcoma.

Methods

Data for 816 elderly patients (≥40 years old) with osteosarcoma between 2004 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were randomly assigned to training (N=573) and internal validation (N=243) sets. The essential clinical predictors were identified based on least absolute shrinkage and selection operator (Lasso) Cox regression. Nomograms were constructed to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS).

Results

Our LASSO regression analyses of the training set yielded five clinicopathological features (age, chemotherapy, surgery, AJCC stage, and summary stage) in the training cohort for the prognosis of elderly patients with osteosarcoma, while grade was only associated with OS and M stage was only associated with CSS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of elderly patients with osteosarcoma. The C-index, calibration and decision curve analysis also showed the satisfactory performance of these nomograms for prognosis prediction.

Conclusion

The constructed nomograms are helpful tools for exactly predicting the prognosis of elderly patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.