Prognostic factors and survival outcome of primary pulmonary acinar cell carcinoma

Abstract

The objective of our study was to investigate the epidemiologic characteristics and prognostic factors in patients with pulmonary acinar cell carcinoma (PACC). Methods: PACC patients diagnosed between 1975 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The trend in PACC incidence was assessed using joinpoint regression software. Overall survival (OS) and disease-specific survival (DSS) were evaluated using the Kaplan-Meier method and logrank test. Univariate and multivariate Cox regression analysis was performed to identify the independent prognostic factors for OS and DSS. Nomograms to predict survival possibilities were constructed based on the identified independent prognostic factors. Results: A total of 2918 patients were identified with PACC. The mean age was 65.2 AE 8.95 years with a female to male of 1.6:1. The incidence of PACC steadily increased by an annual percentage change (APC) of 3.2% (95% CI 2.1-4.4, p < 0.05). Multivariate Cox regression analysis revealed that age, gender, race, stage, grade, tumor size, number of positive lymph nodes, surgery, and chemotherapy were independent prognostic factors for survival. Nomograms specifically for PACC were constructed to predict 1-and 5-year OS and DSS possibility, respectively. The concordance index (C-index) and calibration plots showed the established nomograms had robust and accurate performance. Conclusion: PACC was rare but the incidence has been steadily increasing over the past four decades. Survival has improved in recent years. Surgery or chemotherapy could provide better OS and DSS. The established nomograms specifically for PACC were robust and accurate in predicting 1-and 5-year OS and DSS.

Development and validation of a deep learning survival model for cervical adenocarcinoma patients

“…Clinical data and follow-up information for all tumor patients have been collected since 1973. The huge number of medical records has enabled it to provide information for the survival analysis of a variety of cancers [15][16][17] and to satisfy demands for these machine learning models [18,19]. Besides, another challenge for developing DL systems in survival prediction is clinical validation.…”