Clinical characteristics and prognostic model for extensive‐stage small cell lung cancer: A retrospective study over an 8‐year period. Issue 4 (30th December 2021)

• Record Type: Journal Article
• Authors: Ni, Jun; Zhang, Xiaotong; Wang, Hanping; Si, Xiaoyan; Xu, Yan; Zhao, Jing; Chen, Minjiang; Zhang, Li; Wang, Mengzhao
• Is Part Of: Thoracic cancer. Volume 13:Issue 4(2022)
• Page Start: 539
• Page End: 548
• Publication Date: 2021-12-30
• Subjects: clinical characteristics -- Cox regression analysis -- extensive‐stage SCLC -- nomogram -- risk factors; Chest -- Cancer -- Periodicals; Chest -- Cancer -- Treatment -- Periodicals; Chest -- Surgery -- Periodicals; 616.99494005
• DOI: 10.1111/1759-7714.14289
• Languages: English
• ISSNs: 1759-7706

Abstract

Abstract: Background: Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with a short replication time and a rapid growth rate. Prognostic factors for SCLC in clinical practice are scarce. Retrospective analysis of 8‐year extensive‐stage SCLC data from the Department Respiratory and Intensive Care Unit, Peking Union Medical College Hospital (Beijing, China) was performed to develop a risk prediction model that can facilitate the identification of extensive‐stage SCLC with differing prognosis in clinical practice. Methods: A retrospective analysis of data from patients with extensive‐stage SCLC at a single‐center from January 2013 to January 2021, including age, sex, ECOG physical score, immunohistochemistry (CgA, Syn, CD56, TTF1, and Ki67), staging, treatment regimen, laboratory examinations, and survival period, was performed. Clinical variables with potential prognostic significance were screened by univariate Cox analysis. Next, multifactor Cox risk prediction regression analysis was performed to establish an extensive‐stage SCLC risk prognostic model. Survival curves and ROC curves for high and low risk groups were plotted according to risk scores. Nomogram and calibration curves were developed to assess the accuracy of the risk prediction model. Results: This study included 300 patients who were diagnosed with extensive‐stage SCLC at our center from January 2013 to January 2021. The most common first presentation was respiratory symptoms, especially cough (162, 54%). The most common extra‐thoracic metastatic organs were bone (36.3%), liver (24.7%), brain (15.7%), and adrenal glands (15.7%). A total of 99% of patients received first‐line systemic therapy, with 86.3% of patients treated with platinum‐etoposide and 10.7% of patients treated with immune checkpoint inhibitor combined with platinum‐etoposide backbone. First‐line progression‐free survival was up to 198 days, and the median OS was 439 days. After Cox regression screening and backward stepwise selection, "time from initial therapy to relapse or progression (PFS1), liver metastases, adrenal metastases, M stage and first‐line treatment pattern" were retained to establish a prognostic model with an AUC value of 0.763. The prognostic model was shown as a nomogram with good agreement between predicted and observed outcomes. Conclusions: The first‐line treatment of SCLC patients admitted to our hospital in the past 8 years was relatively standardized, and the progression‐free survival and OS were slightly longer than those reported in the literature. We developed a prognostic risk score model for extensive‐stage SCLC to calculate individual survival in clinical practice. Abstract : We developed a nomogram model for extensive‐stage small cell lung cancer to calculate individual survival. The results confirmed that a first‐line treatment paradigm of immune checkpoint inhibitors combined with chemotherapy prolongs overall survival.