Development and validation of a nomogram to predict long-term cancer-specific survival for patients with osteosarcoma

www.nature.com/scientificreports OPEN Development and validation of a nomogram to predict long‑term cancer‑specific survival for patients with osteosarcoma Yali Yu 1, Shaohua Wang 2, Jia Liu 3, Jiejie Ge 4 & Hongya Guan 3* The present work aimed to establish a new model to accurately estimate overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma. Osteosarcoma cases were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017 and randomized as training or validation sets. Then, the OS- and CSS-related variables were discovered through multivariate Cox regression analysis to develop new nomograms to predict the 1-, 3- and 5-year OS and CSS. Besides, consistency index (C-index), decision curve analysis (DCA), along with calibration curve were adopted for assessing the predicting ability of our constructed nomograms after calibrating for 1-, 3- and 5-year OS and CSS. Altogether, 1727 osteosarcoma cases were enrolled in the present study and randomly divided as training (n = 1149, 70%) or validation (n = 576, 30%) set. As shown by univariate as well as multivariate Cox regression analyses, age, grade, T stage, M stage, surgery, chemotherapy, and histological type were identified to be the adverse factors to independently predict OS and CSS among the osteosarcoma cases. Besides, based on results of multivariate Cox regression analysis, we constructed the OS and CSS prediction nomograms. The C-index in training set was 0.806 (95% CI 0.769–0.836) for OS nomogram and 0.807 (95% CI 0.769–0.836) for CSS nomogram. In the meantime, C-index value in validation set was 0.818 (95% CI 0.789–0.847) for OS nomogram, while 0.804 (95% CI 0.773–0.835) for CSS nomogram. Besides, those calibration curves regarding the 3- and 5-year CSS of our constructed nomogram were highly consistent between the predicted values and the measurements in the training set as well as the external validation set. Our constructed nomogram outperformed the TNM stage in prediction. Our constructed nomogram is facile, creditable, and feasible; it efficiently predicts OS and CSS for osteosarcoma cases and can assist clinicians in assessing the prognosis for individuals and making decisions. Osteosarcoma is among the frequently occurring primary bone cancers, which particularly affects adolescents aged below 24 years and has an estimated annual prevalence of 0.34/100,000. Osteosarcoma is marked by its aggressiveness1,2, which manifests in the early lung metastasis and fast local invasion, resulting in high relapse and mortality rates3. After the emergence of adjuvant chemotherapy as well as limb salvage surgery, patients’ survival rate increased by 50% to about 70% in the most recent two d ecades4,5. Nonetheless, most osteosarcomas lead to disease- and treatment-associated morbidities or mortalities. Identification of the high-risk patients early is very important so as to offer a suitable clinical option or adjuvant therapy. Osteosarcoma can have a unique challenge in a clinical setting; as a result, it is urgently needed to establish the prognostic approaches to precisely estimate survival rates from osteosarcoma. Currently, the TNM classification system is commonly employed to predict osteosarcoma p rognosis6. Typically, the TNM classification system, released by the American Joint Commission on Cancer (AJCC), has been extensively employed to classify cancer patient survivals according to tumor invasion (T), regional lymph node (N) as well as distant metastasis (M)7,8. However, it is limited to evaluate cancer prognosis using the TNM 1 Department of Clinical Laboratory, Zhengzhou Orthopaedics Hospital, Zhengzhou 450000, Henan, China. 2Department of Joint Surgery, Zhengzhou Orthopaedics Hospital, Zhengzhou 450000, Henan, China. 3Department of Translational Medicine Center, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, Henan, People’s Republic of China. 4Department of Clinical Laboratory, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, Henan, China. *email: Scientific Reports | (2023) 13:10230 | https://doi.org/10.1038/s41598-023-37391-8 1 Vol.:(0123456789) www.nature.com/scientificreports/ classification system alone, which may not comprehensively assess the clinicopathological variables, including sex, age, race, or additional factors and is commonly employed to predict prognosis of osteosarcoma in extremities. It might not suitable to be used in axial location. Nomogram represents the statistical approach to determine the clinical event probabilities through taking into account those pre-weight values of all factors9,10. Recently, nomogram is extensively utilized for predicting diverse cancer survival11–15. In recent years, the massive data of cancer patients based on open-accessed data and bioinformatics methods make it possible for us to explore the independent risk factors for cancer p rognosis16–18. The publically accessible SEER database includes cancer patient data across 18 registered sites that cover about almost 28% USA population19,20. This work was conducted to construct a creditable nomogram for predicting overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma cases21; thus, assisting clinicians in providing better-customized treatment options to reduce the rate of metastasis and improve the survival rate of patients. Materials and methods Patients selection. The osteosarcoma cases in this work were selected from the SEER database, and their corresponding anonymous clinical data were extracted accordingly. The SEER*Stat software designed by the National Cancer Institute (version 8.3.6, https://seer.cancer.gov/seerstat/) was utilized, which covered the SEER 18 Regs custom data containing more therapeutic fields and the Nov 2018 Sub database (covering 2004–2016 data). In this work, osteosarcoma cases conforming to the following inclusion criteria were selected: (1) Those with the diagnosis of osteosarcoma as the primary malignant tumor from 1983 to 2014 based on the International Classification of Diseases for Oncology [ICD-O] 9180–9187, 9192–919422; (2) Those whose osteosarcomas were confirmed histologically; (3) Those with osteosarcoma in extremities (long/short bones in the four extremities) or at the axial location (skull, ribs, spine, and pelvis); (4) Those whose histological type was determined; (5) Those with estimated survival time or identified cause of mortality after they were diagnosed. Exclusion criteria. Patients whose survival time was unavailable or unclear were excluded from this study. The patient clinicopathological characteristics, such as age, gender, race, grade, histological type, tumor site, size, surgery, stage of surgery, chemotherapy, radiotherapy, and survival time, were harvested. As for age, the cases were divided as 0–24, 25–59 and > 59 years groups. The races were classified as black, white, or ot (...truncated)