Development and Validation of a Nomogram Prognostic Model for Small-Cell Lung Cancer Patients
Menée à partir de données portant sur 24 680 patients atteints d'un cancer du poumon à petites cellules diagnostiqué entre 2004 et 2011, puis validée sur 9 700 patients complémentaires, cette étude évalue la performance d'un nomogramme, basé sur 8 critères clinico-pathologiques (âge, sexe, stade TNM, score Charlson/Deyo, type de traitement, latéralité de la maladie, critères ethniques), pour établir avec précision un pronostic
Background : Small-cell lung cancer (SCLC) accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB).
Methods : Clinical data of 24,680 SCLC patients diagnosed from 2004 to 2011 were used to develop the nomogram prognostic model. The model was then validated using an independent cohort of 9,700 SCLC patients diagnosed from 2012 to 2013. The prognostic performance was evaluated using p value, concordance index and integrated Area Under the (time-dependent Receiver Operating Characteristic) Curve.
Results : The following variables were contained in the final prognostic model: age, gender, race, ethnicity, Charlson/Deyo Score, TNM Stage (assigned according to the AJCC 8th edition), treatment type (combination of surgery, radiation therapy and chemotherapy), and laterality. The model was validated in an independent testing group with a concordance index of 0.722 ± 0.004 and an integrated AUC of 0.79. The nomogram model has a significantly higher prognostic accuracy than previously developed models, including the AJCC 8th edition TNM-staging system. We implemented the proposed nomogram and four previously published nomograms in an online webserver.
Conclusions : We developed a nomogram prognostic model for SCLC patients, and validated the model using an independent patient cohort. The nomogram performs better than earlier models, including models using AJCC staging.
Journal of Thoracic Oncology , résumé, 2017