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Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone tumor in children and adolescents, producing osteoid and immature bone. Numerous high quality studies have been published in the OSA field, however, no bibliometric study related to this area has been reported thus far. Therefore, the present study retrieved the published data from 2000 to 2022 to reveal the dynamics, development trends, hotspots and future directions of the OSA. Methods: Publications regard to osteogenic sarcoma and prognosis were searched in the core collection on Web of Science database. The retrieved publications were analyzed by publication years, journals, categories, countries, citations, institutions, authors, keywords and clusters using the two widely available bibliometric visualization tools, VOS viewer (Version 1.6.16), Citespace (Version 6.2. R1). Results: A total of 6260 publications related to the current topic were retrieved and analyzed, revealing exponential increase in the number of publications with an improvement in the citations on the OSA over time, in which China and the USA are the most productive nations. Shanghai Jiao Tong University, University of Texas System and Harvard University are prolific institutions, having highest collaboration network. Oncology Letters and Journal of Clinical Oncology are the most productive and the most cited journals respectively. The Wang Y is a prominent author and articles published by Bacci G had the highest number of citations indicating their significant impact in the field. According to keywords analysis, osteosarcoma, expression and metastasis were the most apparent keywords whereas the current research hotspots are biomarker, tumor microenvironment, immunotherapy and DNA methylation. Conclusion: Our findings offer valuable information for researchers to understand the current research status and the necessity of future research to mitigate the mortality of the OS patients.
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Background: The aim of this study was to establish and verify a predictive nomogram for patients with cutaneous verrucous carcinoma (CVC) who will eventually survive and to determine the accuracy of the nomogram relative to the conventional American Joint Committee on Cancer (AJCC) staging system. Methods: Assessments were performed on 1125 patients with CVC between 2004 and 2015, and the results of those examinations were recorded in the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided at a ratio of 7:3 into the training (n = 787) and validation (n = 338) cohorts. Predictors were identified using stepwise regression analysis in the COX regression model for create a nomogram to predict overall survival of CVC patients at 3-, 5-, and 8-years post-diagnosis. We compared the performance of our model with that of the AJCC prognosis model using several evaluation metrics, including C-index, NRI, IDI, AUC, calibration plots, and DCAs. Results: Multivariate risk factors including sex, age at diagnosis, marital status, AJCC stage, radiation status, and surgery status were employed to determine the overall survival (OS) rate (P<0.05). The C-index nomogram performed better than the AJCC staging system variable for both the training (0.737 versus 0.582) and validation cohorts (0.735 versus 0.573), which AUC (> 0.7) revealed that the nomogram exhibited significant discriminative ability. The statistically significant NRI and IDI values at 3-, 5-, and 8-year predictions for overall survival (OS) in the validation cohort (55.72%, 63.71%, and 78.23%, respectively and 13.65%, 20.52%, and 23.73%, respectively) demonstrate that the established nomogram outperforms the AJCC staging system (P < 0.01) in predicting OS for patients with cutaneous verrucous carcinoma (CVC). The calibration plots indicate good performance of the nomogram, while decision curve analyses (DCAs) show that the predictive model could have a favorable clinical impact. Conclusion: This study constructed and validated a nomogram for predicting the prognosis of patients with CVC in the SEER database and assessed it using several variables. This nomogram model can assist clinical staff in making more-accurate predictions than the AJCC staging method about the 3-, 5-, and 8-year OS probabilities of patients with CVC.