{"title":"鼻咽癌患者同时接受放化疗后发生骨坏死的风险因素分析和预测模型的开发。","authors":"Ming-Jie Gong, Zhi-Gang Lai, Yun-Xia Zhang, Na Hu","doi":"10.62347/RIWX7204","DOIUrl":null,"url":null,"abstract":"<p><p>Nasopharyngeal carcinoma (NPC) is a malignant tumor that targets the nasopharyngeal mucosal epithelium. Concurrent chemoradiotherapy (CCRT) is a pivotal treatment modality for NPC, yet it poses a risk for osteoradionecrosis (ORN), a complication that can impede further treatment. This study sought to explore the risk factors for ORN in NPC patients post-CCRT and to construct predictive models. We performed a retrospective analysis of clinical data from 417 NPC patients treated with CCRT at the Affiliated Hospital of Jiangnan University, with 204 patients from Longyan First Hospital as a validation cohort for the models. Our findings indicated that a high radiation dose, tooth extraction after radiotherapy, inadequate oral hygiene, smoking, anemia, and advanced T staging were associated with an elevated risk of ORN in NPC patients following CCRT. We formulated risk prediction models for ORN utilizing a nomogram, gradient boosting machine (GBM), and random forest (RF) algorithms. The area under the curve (AUC) was 0.813 (95% CI: 0.724-0.902) for the nomogram model in the validation cohort, 0.821 (95% CI: 0.732-0.910) for the GBM, and 0.735 (95% CI: 0.614-0.855) for the RF. Delong's test indicated no statistically significant differences in the AUC values among the three models. The nomogram has strong performance across both the training and validation cohorts, featuring a straightforward structure that is both intuitive and comprehensible. Taking into account the model's discriminative power, generalizability, and clinical practicability, the nomogram was proven to be highly applicable in the current study.</p>","PeriodicalId":7437,"journal":{"name":"American journal of cancer research","volume":"14 10","pages":"4760-4771"},"PeriodicalIF":3.6000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560816/pdf/","citationCount":"0","resultStr":"{\"title\":\"Risk factor analysis and development of predictive models for osteoradionecrosis in patients with nasopharyngeal carcinoma after concurrent chemoradiotherapy.\",\"authors\":\"Ming-Jie Gong, Zhi-Gang Lai, Yun-Xia Zhang, Na Hu\",\"doi\":\"10.62347/RIWX7204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Nasopharyngeal carcinoma (NPC) is a malignant tumor that targets the nasopharyngeal mucosal epithelium. Concurrent chemoradiotherapy (CCRT) is a pivotal treatment modality for NPC, yet it poses a risk for osteoradionecrosis (ORN), a complication that can impede further treatment. This study sought to explore the risk factors for ORN in NPC patients post-CCRT and to construct predictive models. We performed a retrospective analysis of clinical data from 417 NPC patients treated with CCRT at the Affiliated Hospital of Jiangnan University, with 204 patients from Longyan First Hospital as a validation cohort for the models. Our findings indicated that a high radiation dose, tooth extraction after radiotherapy, inadequate oral hygiene, smoking, anemia, and advanced T staging were associated with an elevated risk of ORN in NPC patients following CCRT. We formulated risk prediction models for ORN utilizing a nomogram, gradient boosting machine (GBM), and random forest (RF) algorithms. The area under the curve (AUC) was 0.813 (95% CI: 0.724-0.902) for the nomogram model in the validation cohort, 0.821 (95% CI: 0.732-0.910) for the GBM, and 0.735 (95% CI: 0.614-0.855) for the RF. Delong's test indicated no statistically significant differences in the AUC values among the three models. The nomogram has strong performance across both the training and validation cohorts, featuring a straightforward structure that is both intuitive and comprehensible. Taking into account the model's discriminative power, generalizability, and clinical practicability, the nomogram was proven to be highly applicable in the current study.</p>\",\"PeriodicalId\":7437,\"journal\":{\"name\":\"American journal of cancer research\",\"volume\":\"14 10\",\"pages\":\"4760-4771\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560816/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American journal of cancer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.62347/RIWX7204\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/RIWX7204","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Risk factor analysis and development of predictive models for osteoradionecrosis in patients with nasopharyngeal carcinoma after concurrent chemoradiotherapy.
Nasopharyngeal carcinoma (NPC) is a malignant tumor that targets the nasopharyngeal mucosal epithelium. Concurrent chemoradiotherapy (CCRT) is a pivotal treatment modality for NPC, yet it poses a risk for osteoradionecrosis (ORN), a complication that can impede further treatment. This study sought to explore the risk factors for ORN in NPC patients post-CCRT and to construct predictive models. We performed a retrospective analysis of clinical data from 417 NPC patients treated with CCRT at the Affiliated Hospital of Jiangnan University, with 204 patients from Longyan First Hospital as a validation cohort for the models. Our findings indicated that a high radiation dose, tooth extraction after radiotherapy, inadequate oral hygiene, smoking, anemia, and advanced T staging were associated with an elevated risk of ORN in NPC patients following CCRT. We formulated risk prediction models for ORN utilizing a nomogram, gradient boosting machine (GBM), and random forest (RF) algorithms. The area under the curve (AUC) was 0.813 (95% CI: 0.724-0.902) for the nomogram model in the validation cohort, 0.821 (95% CI: 0.732-0.910) for the GBM, and 0.735 (95% CI: 0.614-0.855) for the RF. Delong's test indicated no statistically significant differences in the AUC values among the three models. The nomogram has strong performance across both the training and validation cohorts, featuring a straightforward structure that is both intuitive and comprehensible. Taking into account the model's discriminative power, generalizability, and clinical practicability, the nomogram was proven to be highly applicable in the current study.
期刊介绍:
The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.