Construction of a Nomogram to Predict the Progression Free Survival in Patients With Lung Cancer

Abstract PURPOSE For a personalized cancer prognosis, a nomogram is a practical and helpful tool. In addition to creating a clinical nomogram to forecast progression free survival (PFS) for patients with various histological types, clinical stages, and treatment regimens, our goals included assessing the prognostic variables of lung cancer (LC) patients. METHODS Clinical characteristics, peripheral blood parameters, and treatment records were collected from 1200 newly diagnosed LC patients in the Medical Oncology Department at Mohammed VI University Hospital in Marrakech between 2013 and 2021. Cox Proportional Hazards Regression Analysis was used to identify the independent prognostic factors. The nomogram we created and tested was used to predict the PFS of patients with LC. The Kaplan-Meier survival curves were drawn, stratified, and compared using the log rank test. RESULTS A total of 342 individuals met the inclusion criteria and were then included in the study. Prognostic factors for LC included gender, tabacco status, number of cures of the first-line chemotherapy, radiotherapy, and thrombocytopenia; these factors were combined to create the nomogram. The clinical prediction model performed satisfactorily in prognosis prediction, as evidenced by the calibration and receiver operating characteristics curves. In comparison to the clinical TNM staging method for a one-year prediction, the nomogram's area under the ROC curve (AUC) value for 6- and 12-month PFS rates was 0.8 and 0.83, respectively. CONCLUSION We developed and verified a unique nomogram that can offer personalised PFS predictions for Moroccan and African LC patients. The development of this tool is extremely important for clinical study design and decision-making.