Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Nomogram predicting overall and cancer specific prognosis for poorly differentiated lung adenocarcinoma after resection based on SEER cohort analysis

The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

Prognostic Value of Myosteatosis and Creatinine-to-Cystatin C Ratio in Patients with Pancreatic Cancer Who Underwent Radical Surgery

BACKGROUND

Myosteatosis is correlated with poor prognosis in some malignancies. The creatinine-to-cystatin ratio (CCR) is revealed to predict gastric cancer prognosis. However, the prognostic abilities of CCR and the combination of CCR and myosteatosis in patients with pancreatic cancer (PC) who underwent radical surgery remains unclear.

METHODS

The retrospective cohort study included 215 patients with PC who underwent radical surgery (January 2016-October 2021). Clinicopathological and serological data were collected on admission. Myosteatosis and other body composition indices were assessed by using computed tomography. The cutoff value of CCR was determined by using the Youden index. Risk factors responsible for poor overall survival (OS) and disease-free survival (DFS) were determined by the Cox proportional hazards model.

RESULTS

The myosteatosis group included 104 patients (average age, 61.3 ± 9.1 years). The best cutoff value for CCR was 1.09. CCR ≤ 1.09 was an independent predictive biomarker inversely corelated with OS (P = 0.036). Myosteatosis was an independent risk factor associated with OS and DFS (P = 0.032 and P = 0.004, respectively). Patients with concomitant myosteatosis and CCR ≤ 1.09 had the worst OS (P = 0.016).

CONCLUSIONS

Myosteatosis and CCR are prognostic biomarkers for survival in PC patients who underwent radical surgery. Patients with the coexistence of myosteatosis and CCR ≤ 1.09 deserve more attention.

The coexistence of myosteatosis and the creatinine/cystatin C ratio are determinants of outcomes in cholangiocarcinoma patients undergoing curative surgery

Background

Myosteatosis is a well-established predictor of poor prognosis in many types of cancer, and a decreased Creatinine/Cystatin C ratio (CCR) is a known indicator of unfavorable outcomes in patients with metabolic disorders and cancer. Despite this knowledge, the significance of concurrent CCR and myosteatosis in predicting the prognosis of patients with cholangiocarcinoma (CCA) who undergo radical surgery remains uncertain.

Method

Data from 757 patients with cholangiocarcinoma who underwent the first radical resection in the Affiliated Hospital of Qingdao University from January 2017 to March 2022 were collected. According to the inclusion and exclusion criteria, 149 patients were finally included in the retrospective study cohort. Various clinicopathological, serological, and radiological data were collected at admission. Myosteatosis was evaluated using sliceOmatic software on computed tomography (CT) images. The study used receiver operating characteristic (ROC) curve analysis to determine the critical value of CCR, which predicts overall survival (OS) based on the Kaplan-Meier method. Univariate and multivariate Cox regression analyses were employed to identify the risk factors associated with OS and RFS confidently.

Results

The group identified as the myosteatosis cohort consisted of 79 patients with an average age of 64.3 ± 7.8 years. The ROC curve analysis revealed an optimal critical CCR value of 10.834. A low CCR ≤ 10.834 and myosteatosis were found to be associated with poor OS and RFS outcomes (P = 0.022; P = 0.017; P = 0.038; P = 0.030 respectively). Moreover, patients with myosteatosis and a CCR ≤ 10.834 had the worst OS and RFS outcomes (P = 0.035; P = 0.027).

Conclusion

After radical excision in CCA patients, the presence of myosteatosis and CCR had a negative correlation with prognosis. A more accurate prediction of OS and RFS was possible by combining CCR and myosteatosis, compared to CCR alone.

An Accelerated Failure Time Model to Predict Cause-Specific Survival and Prognostic Factors of Lung and Bronchus Cancer Patients with at Least Bone or Brain Metastases: Development and Internal Validation Using a SEER-Based Study

BACKGROUND

This study addresses the significant challenge of low survival rates in patients with cause-specific lung cancer accompanied by bone or brain metastases. Recognizing the critical need for an effective predictive model, the research aims to establish survival prediction models using both parametric and non-parametric approaches.

METHODS

Clinical data from lung cancer patients with at least one bone or brain metastasis between 2000 and 2020 from the SEER database were utilized. Four models were constructed: Cox proportional hazard, Weibull accelerated failure time (AFT), log-normal AFT, and Zografos-Balakrishnan log-normal (ZBLN). Independent prognostic factors for cause-specific survival were identified, and model fit was evaluated using Akaike's and Bayesian information criteria. Internal validation assessed predictive accuracy and discriminability through the Harriel Concordance Index (C-index) and calibration plots.

RESULTS

A total of 20,412 patients were included, with 14,290 (70%) as the training cohort and 6122 (30%) validation. Independent prognostic factors selected for the study were age, race, sex, primary tumor site, disease grade, total malignant tumor in situ, metastases, treatment modality, and histology. Among the accelerated failure time (AFT) models considered, the ZBLN distribution exhibited the most robust model fit for the 3- and 5-year survival, as evidenced by the lowest values of Akaike's information criterion of 6322 and 79,396, and the Bayesian information criterion of 63,495 and 79,396, respectively. This outperformed other AFT and Cox models (AIC = [156,891, 211,125]; BIC = [158,848, 211,287]). Regarding predictive accuracy, the ZBLN AFT model achieved the highest concordance C-index (0.682, 0.667), a better performance than the Cox model (0.669, 0.643). The calibration curves of the ZBLN AFT model demonstrated a high degree of concordance between actual and predicted values. All variables considered in this study demonstrated significance at the 0.05 level for the ZBLN AFT model. However, differences emerged in the significant variations in survival times between subgroups. The study revealed that patients with only bone metastases have a higher chance of survival compared to only brain and those with bone and brain metastases.

CONCLUSIONS

The study highlights the underutilized but accurate nature of the accelerated failure time model in predicting lung cancer survival and identifying prognostic factors. These findings have implications for individualized clinical decisions, indicating the potential for screening and professional care of lung cancer patients with at least one bone or brain metastasis in the future.

The mitotic cell cycle-associated nomogram predicts overall survival in lung adenocarcinoma

BACKGROUND

This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) associated with mitotic cell cycle. The model will predict the probability of survival at different time points and serve as a reference tool to evaluate the effectiveness of LUAD treatment.

METHODS

A cohort of 442 patients with LUAD from the gene expression omnibus (GEO) database was randomly divided into a training group (n = 299) and a validation group (n = 99). The least absolute shrinkage and selection operator (LASSO)-COX algorithm was used to reduce the number of predictors based on the clinicopathological and RNA sequencing data to establish mutant characteristics that could predict patient survival. Additionally, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) analyses were conducted on the mutant characteristics. The performance of the developed nomogram was evaluated using calibration curves and the C-index.

RESULTS

The mutant characteristics had prognostic value for LUAD and acted as an independent prognostic factor. The mutant characteristics profile derived from the LASSO-COX algorithm demonstrated a significant association with overall survival in patients with LUAD. Functional annotation based on the mutant score, its involvement in the phase transition of the mitotic cell cycle, and its regulatory processes. The nomogram, which combined the mutant score with clinical factors associated with prognosis, showed robust accuracy in both the training and validation groups.

CONCLUSION

This study presents the first individualized model that establishes a mutant score for predicting survival in LUAD. This model can be used as a predictive tool for determining 1-, 2-, 3-, and 5-year survival probabilities in patients with LUAD.

Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

BACKGROUND

Prognostic factors and survival outcomes of non-small cell lung cancer (NSCLC) with Ipsilateral pulmonary metastasis (IPM) are not well-defined. Thus, this study intended to identify the prognostic factors for these patients and construct a predictive nomogram model.

METHODS

One thousand, seven hundred thirty-two patients with IPM identified between 2000 to 2019 were from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified using multivariate Cox regression analyses. Nomograms were constructed to predict the overall survival (OS), C-index, the area under the curve (AUC), and the calibration curve to determine the predictive accuracy and discrimination; the decision curve analysis was used to confirm the clinical utility.

RESULTS

Patients were randomly divided into training (n = 1213) and validation (n = 519) cohorts. In the training cohort, the multivariable analysis demonstrated that age, sex, primary tumor size, N status, number of regional lymph nodes removed, tumor grade, and chemotherapy were independent prognostic factors for IPM. We constructed a 1-year, 3-year, and 5-year OS prediction nomogram model using independent prognostic factors. The C-index of this model for OS prediction was 0.714 (95% confidence interval [CI], 0.692 to 0.773) in the training cohort and 0.695 (95% CI, 0.660 to 0.730) in the validation cohort. Based on the AUC of the receiver operating characteristic analysis, calibration plots, and decision curve analysis, we concluded that the prognosis model of IPM exhibited excellent performance. Patients with total nomogram points greater than 96 were considered high-risk.

CONCLUSION

We constructed and internally validated a nomogram to predict 1-year, 3-year, and 5-year OS for NSCLC patients with IPM according to independent prognostic factors. This nomogram demonstrated good calibration, discrimination, clinical utility, and practical decision-making effects for the prognosis of NSCLC patients with IPM.