Related Experiment Video
Updated: Apr 14, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Clinical characteristics, prognosis, and nomograms for lung invasive mucinous adenocarcinoma with distant metastasis:
Xiaomei Luo1,2, Qian Chen1,2
1Department of Pediatrics, Children Hematological Oncology and Birth Defects Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background:
Lung invasive mucinous adenocarcinoma (LIMA) is a rare and distinct subtype of lung cancer, characterized by a relatively poor prognosis. Among LIMA patients, distant metastasis (DM) is a commonly observed and fatal feature, however, the prognostic patterns in these patients remain poorly understood. Identifying the factors influencing DM and prognosis in LIMA patients is crucial for improving individualized management and treatment strategies. This study aimed to identify key factors associated with LIMA-related DM and prognosis, and construct predictive nomograms to guide clinical practice.
Methods:
This retrospective cohort study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database, including LIMA patients diagnosed between 2004 and 2015. Logistic regression analysis was conducted to identify independent risk factors for DM in LIMA patients, while Cox regression analysis was used to determine independent prognostic factors for LIMA patients with DM. Based on these analyses, two nomograms were developed to predict the incidence and prognosis of DM in LIMA patients. The performance of these monograms was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, and Kaplan-Meier (K-M) analyses.
Results:
A total of 4,439 LIMA patients were included in the study, of which 830 patients presented with DM at the time of diagnosis. Independent risk factors for DM in LIMA patients included primary tumor site, histological grade, T-stage, and N-stage. Prognostic factors for LIMA patients with DM were age, sex, grade, tumor size, T-stage, N-stage, receipt of surgery, and chemotherapy. The validation of the nomograms in both the training and validation cohorts demonstrated robust predictive accuracy for the incidence and prognosis of DM in LIMA patients, as evidenced by the ROC curves, calibration curves, DCA, and K-M analysis.
Conclusions:
Two nomograms were developed to predict the risk of DM and the prognosis of LIMA patients with DM. Both nomograms demonstrated high predictive accuracy and may serve as useful tools for personalized clinical decision-making, facilitating the management of LIMA patients with DM.
