The nomogram for the prediction of overall survival in patients with metastatic lung adenocarcinoma undergoing primary site surgery: A retrospective population-based study

Standardized uptake valuemax of the primary lesion combined with tumor markers for clinically predicting distant metastasis in de novo lung adenocarcinoma

BACKGROUND

To examine standardized uptake valuemax of the primary lesion (pSUVmax) and tumor markers (TMs) for clinically predicting distant metastasis in novo lung adenocarcinoma.

METHODS

The current retrospective observational study examined individuals diagnosed with de novo lung adenocarcinoma at Shanxi Cancer Hospital between February 2015 and December 2019.

RESULTS

Totally, 532 de novo lung adenocarcinoma cases were included. They were aged 60.8 ± 9.7 years and comprised 224 women and 268 patients with distant metastasis. The areas under the curves (AUCs) of pSUVmax, lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA21-1), carbohydrate antigen 125 (CA125), and Grade of TMs for predicting distant metastasis were 0.742, 0.601, 0.671, 0.700, 0.736, and 0.745, respectively. The combination of pSUVmax, LDH, CEA, CYFRA21-1, CA125, and grade of TMs in predicting distant metastasis had an AUC value of 0.816 (95%CI: 0.781-0.851), with sensitivity of 89.2%, specificity of 58.7%, positive predictive value of 73.7%, and negative predictive value of 79.7%, respectively.

CONCLUSIONS

pSUVmax combined with serum levels of LDH, CEA, CYFRA21-1, CA125, and the grade of TMs may have good performance in predicting distant metastasis of de novo lung adenocarcinoma.

The nomogram for the prediction of overall survival after surgery in patients in early-stage NSCLC based on SEER database and external validation cohort

BACKGROUND & AIMS

Currently, there is a lack of effective tools for predicting the prognostic outcome of early-stage lung cancer after surgery. We aim to create a nomogram model to help clinicians assess the risk of postoperative recurrence or metastasis.

MATERIALS AND METHODS

This work obtained 16,459 NSCLC patients based on SEER database from 2010 to 2015. In addition, we also enrolled 385 NSCLC patients (2017/01-2019/06) into external validation cohort at Tianjin Medical University General Hospital. Univariable as well as multivariable Cox regression was carried out for identifying factors independently predicting OS. In addition, we built a nomogram by incorporating the above prognostic factors for the prediction of OS.

RESULTS

Tumor size was positively correlated with the risk of poor differentiation. Advanced age, male and adenocarcinoma patients were factors independently predicting poor prognosis. The risk of white race is higher, followed by Black race, Asians and Indians, which is consistent with previous study. Chemotherapy is negatively related to prognostic outcome in patients of Stage IA NSCLC and positively related to that in those of Stage IB NSCLC. Lymph node dissection can reduce the postoperative mortality of patients. AUCs of the nomograms for 1, 2, and 3-year OS was 0.705, 0.712, and 0.714 for training cohort, while those were 0.684, 0.688, and 0.688 for validation cohort.

CONCLUSIONS

The nomogram could be used as a tool to predict the postoperative prognosis of patients with Stage I non-small cell lung cancer.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.