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Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma.

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Title: Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma.
Authors: Alabi, Rasheed Omobolaji (AUTHOR), Elmusrati, Mohammed (AUTHOR), Leivo, Ilmo (AUTHOR), Almangush, Alhadi (AUTHOR), Mäkitie, Antti A. (AUTHOR)
Source: Acta Oto-Laryngologica. Feb2026, Vol. 146 Issue 2, p133-140. 8p.
Subjects: Squamous cell carcinoma, Public health surveillance, Mouth tumors, Prediction models, Research funding, Logistic regression analysis, Cancer patients, Descriptive statistics, Cancer chemotherapy, Cluster sampling, Machine learning, Tumor classification, Overall survival, Algorithms, Sensitivity & specificity (Statistics)
Abstract (English): Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC). Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined. Methodology: Altogether, 9439 OSCC patients were extracted from the Surveillance, Epidemiology, and End Results database (US). Five ML models – voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression were used to predict OS. Three of these ML algorithms were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm following model training. Results: The performance accuracy of the voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression models was 70.2%, 69.9%, 69.1%, 69.4%, and 69.5% respectively, following model training. When the voting ensemble model was compared with cML using temporal validation, the cML showed a comparable performance accuracy. The most significant prognostic factors were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiotherapy, ethnicity, chemotherapy, and M stage. Conclusions: cML appears to give reliability to the final prediction and thereby may mark a paradigm shift from model individualism to a more cooperative paradigm. This approach may aid in determining an enhanced individualized treatment for OSCC patients. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 缺乏口腔鳞状细胞癌 (OSCC)总生存期 (OS) 的预测指标。 我们研究了协作机器学习 (cML) 在估计 OSCC 患者 OS 的作用。研究了临床病理参数的预后意义。 总共从监测、流行病学和最终结果数据库 (美国) 中提取了 9439 名 OSCC 患者。使用五种 ML 模型(投票集成、堆叠集成、极端梯度提升、轻度提升和逻辑回归)来预测 OS。将这些ML 算法中的三种组合在一起, 形成一个 cML 模型集群。将 cML 的性能与单个 ML 算法进行了比较。 投票集成、堆叠集成、极端梯度提升、轻度提升、逻辑回归和 cML 模型的性能准确度分别为 70.2%、69.2%、69.6%、69.7%、69.5% 和 72.8%。因此, cML 的性能准确度略有提高。预后因素按重要性降序排列为患者年龄、T 分期、肿瘤分级、婚姻状况、性别、原发部位、手术、N 分期、放疗、种族、化疗和 M 分期。 cML 为最终预测提供了可靠性, 从而标志着从模型个体性到更具综合性的范式转变。这种方法可能有助于确定针对 OSCC 患者的强化了的个体性治疗。 [ABSTRACT FROM AUTHOR]
Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Alabi%2C+Rasheed+Omobolaji%22">Alabi, Rasheed Omobolaji</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elmusrati%2C+Mohammed%22">Elmusrati, Mohammed</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leivo%2C+Ilmo%22">Leivo, Ilmo</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Almangush%2C+Alhadi%22">Almangush, Alhadi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mäkitie%2C+Antti+A%2E%22">Mäkitie, Antti A.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Acta+Oto-Laryngologica%22">Acta Oto-Laryngologica</searchLink>. Feb2026, Vol. 146 Issue 2, p133-140. 8p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Squamous+cell+carcinoma%22">Squamous cell carcinoma</searchLink><br /><searchLink fieldCode="DE" term="%22Public+health+surveillance%22">Public health surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Mouth+tumors%22">Mouth tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+patients%22">Cancer patients</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+chemotherapy%22">Cancer chemotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+sampling%22">Cluster sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor+classification%22">Tumor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Overall+survival%22">Overall survival</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC). Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined. Methodology: Altogether, 9439 OSCC patients were extracted from the Surveillance, Epidemiology, and End Results database (US). Five ML models – voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression were used to predict OS. Three of these ML algorithms were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm following model training. Results: The performance accuracy of the voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression models was 70.2%, 69.9%, 69.1%, 69.4%, and 69.5% respectively, following model training. When the voting ensemble model was compared with cML using temporal validation, the cML showed a comparable performance accuracy. The most significant prognostic factors were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiotherapy, ethnicity, chemotherapy, and M stage. Conclusions: cML appears to give reliability to the final prediction and thereby may mark a paradigm shift from model individualism to a more cooperative paradigm. This approach may aid in determining an enhanced individualized treatment for OSCC patients. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 缺乏口腔鳞状细胞癌 (OSCC)总生存期 (OS) 的预测指标。 我们研究了协作机器学习 (cML) 在估计 OSCC 患者 OS 的作用。研究了临床病理参数的预后意义。 总共从监测、流行病学和最终结果数据库 (美国) 中提取了 9439 名 OSCC 患者。使用五种 ML 模型(投票集成、堆叠集成、极端梯度提升、轻度提升和逻辑回归)来预测 OS。将这些ML 算法中的三种组合在一起, 形成一个 cML 模型集群。将 cML 的性能与单个 ML 算法进行了比较。 投票集成、堆叠集成、极端梯度提升、轻度提升、逻辑回归和 cML 模型的性能准确度分别为 70.2%、69.2%、69.6%、69.7%、69.5% 和 72.8%。因此, cML 的性能准确度略有提高。预后因素按重要性降序排列为患者年龄、T 分期、肿瘤分级、婚姻状况、性别、原发部位、手术、N 分期、放疗、种族、化疗和 M 分期。 cML 为最终预测提供了可靠性, 从而标志着从模型个体性到更具综合性的范式转变。这种方法可能有助于确定针对 OSCC 患者的强化了的个体性治疗。 [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/00016489.2024.2437012
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 133
    Subjects:
      – SubjectFull: Squamous cell carcinoma
        Type: general
      – SubjectFull: Public health surveillance
        Type: general
      – SubjectFull: Mouth tumors
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Cancer patients
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Cancer chemotherapy
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      – SubjectFull: Cluster sampling
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Tumor classification
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      – SubjectFull: Overall survival
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Sensitivity & specificity (Statistics)
        Type: general
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      – TitleFull: Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma.
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              Text: Feb2026
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              Y: 2026
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