{"title":"结合免疫炎症生物标志物和临床病理参数的早期舌鳞癌患者复发风险模型的建立和验证","authors":"Xiangxiang Liao, Hongwen Wu, Hui Yang, Xiaoting Pan, Wei Wu, Ke Xu, Yanghong Xu, Yaling Chen, Xiangcheng Liu, Mengcheng Liu, Hui Li, Hui Huang","doi":"10.62347/CMXU1610","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a machine learning-based predictive model incorporating immuno-inflammatory biomarkers and clinicopathological parameters to predict recurrence risk in early-stage tongue squamous cell carcinoma (TSCC) patients.</p><p><strong>Methods: </strong>This retrospective study included 515 early-stage TSCC patients treatment at Xinyu People's Hospital between May 2014 and May 2019. Medical records and laboratory data were reviewed. Patients were randomly divided into a training cohort (n=339) and a validation cohort (n=176). Feature selection was performed using LASSO, Xgboost, and Support Vector Machine (SVM) algorithms to identify key features associated with recurrence. A predictive nomogram was then built based on multivariate Cox regression analysis. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).</p><p><strong>Results: </strong>Recurrence was observed in 160 cases (31.07%), with 111 (32.74%) in training cohort (n=339) and 49 (27.84%) in the validation cohort (n=176). Machine learning algorithms identified several key risk factors for recurrence, including immuno-inflammatory markers (e.g., white blood cell count [WBC], platelet count [PLT], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], systemic inflammation response index [SIRI], C-reactive protein-to-albumin ratio [CAR]) and clinicopathological characteristics (e.g., pathological classification, chemotherapy status, tumor location). The nomogram achieved areas under the ROC curve (AUCs) of 0.902 (95% CI: 0.866-0.937) in the training set and 0.819 (95% CI: 0.759-0.876) in the validation set. Calibration curves demonstrated good predictive consistency (P=0.621). DCA showed a clear net clinical benefit across a wide range of thresholds probabilities (P<0.001).</p><p><strong>Conclusion: </strong>This predictive model, integrating immuno-inflammatory markers and clinicopathological features, exhibits excellent predictive performance for recurrence risk in early-stage STCC and offers substantial clinical utility.</p>","PeriodicalId":7437,"journal":{"name":"American journal of cancer research","volume":"15 7","pages":"2970-2987"},"PeriodicalIF":2.9000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344176/pdf/","citationCount":"0","resultStr":"{\"title\":\"Establishment and validation of a recurrence risk model in early-stage tongue squamous cell carcinoma patients incorporating immune-inflammatory biomarkers and clinicopathological parameters.\",\"authors\":\"Xiangxiang Liao, Hongwen Wu, Hui Yang, Xiaoting Pan, Wei Wu, Ke Xu, Yanghong Xu, Yaling Chen, Xiangcheng Liu, Mengcheng Liu, Hui Li, Hui Huang\",\"doi\":\"10.62347/CMXU1610\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To develop and validate a machine learning-based predictive model incorporating immuno-inflammatory biomarkers and clinicopathological parameters to predict recurrence risk in early-stage tongue squamous cell carcinoma (TSCC) patients.</p><p><strong>Methods: </strong>This retrospective study included 515 early-stage TSCC patients treatment at Xinyu People's Hospital between May 2014 and May 2019. Medical records and laboratory data were reviewed. Patients were randomly divided into a training cohort (n=339) and a validation cohort (n=176). Feature selection was performed using LASSO, Xgboost, and Support Vector Machine (SVM) algorithms to identify key features associated with recurrence. A predictive nomogram was then built based on multivariate Cox regression analysis. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).</p><p><strong>Results: </strong>Recurrence was observed in 160 cases (31.07%), with 111 (32.74%) in training cohort (n=339) and 49 (27.84%) in the validation cohort (n=176). Machine learning algorithms identified several key risk factors for recurrence, including immuno-inflammatory markers (e.g., white blood cell count [WBC], platelet count [PLT], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], systemic inflammation response index [SIRI], C-reactive protein-to-albumin ratio [CAR]) and clinicopathological characteristics (e.g., pathological classification, chemotherapy status, tumor location). The nomogram achieved areas under the ROC curve (AUCs) of 0.902 (95% CI: 0.866-0.937) in the training set and 0.819 (95% CI: 0.759-0.876) in the validation set. Calibration curves demonstrated good predictive consistency (P=0.621). DCA showed a clear net clinical benefit across a wide range of thresholds probabilities (P<0.001).</p><p><strong>Conclusion: </strong>This predictive model, integrating immuno-inflammatory markers and clinicopathological features, exhibits excellent predictive performance for recurrence risk in early-stage STCC and offers substantial clinical utility.</p>\",\"PeriodicalId\":7437,\"journal\":{\"name\":\"American journal of cancer research\",\"volume\":\"15 7\",\"pages\":\"2970-2987\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344176/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American journal of cancer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.62347/CMXU1610\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 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/CMXU1610","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Establishment and validation of a recurrence risk model in early-stage tongue squamous cell carcinoma patients incorporating immune-inflammatory biomarkers and clinicopathological parameters.
Objective: To develop and validate a machine learning-based predictive model incorporating immuno-inflammatory biomarkers and clinicopathological parameters to predict recurrence risk in early-stage tongue squamous cell carcinoma (TSCC) patients.
Methods: This retrospective study included 515 early-stage TSCC patients treatment at Xinyu People's Hospital between May 2014 and May 2019. Medical records and laboratory data were reviewed. Patients were randomly divided into a training cohort (n=339) and a validation cohort (n=176). Feature selection was performed using LASSO, Xgboost, and Support Vector Machine (SVM) algorithms to identify key features associated with recurrence. A predictive nomogram was then built based on multivariate Cox regression analysis. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).
Results: Recurrence was observed in 160 cases (31.07%), with 111 (32.74%) in training cohort (n=339) and 49 (27.84%) in the validation cohort (n=176). Machine learning algorithms identified several key risk factors for recurrence, including immuno-inflammatory markers (e.g., white blood cell count [WBC], platelet count [PLT], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], systemic inflammation response index [SIRI], C-reactive protein-to-albumin ratio [CAR]) and clinicopathological characteristics (e.g., pathological classification, chemotherapy status, tumor location). The nomogram achieved areas under the ROC curve (AUCs) of 0.902 (95% CI: 0.866-0.937) in the training set and 0.819 (95% CI: 0.759-0.876) in the validation set. Calibration curves demonstrated good predictive consistency (P=0.621). DCA showed a clear net clinical benefit across a wide range of thresholds probabilities (P<0.001).
Conclusion: This predictive model, integrating immuno-inflammatory markers and clinicopathological features, exhibits excellent predictive performance for recurrence risk in early-stage STCC and offers substantial clinical utility.
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.
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