Salehi Amir M, Wang Lixiao, Gu Xiaolian, Coates Philip J, Norberg Spaak Lena, Sgaramella Nicola, Nylander Karin
Department of Medical Biosciences/Pathology, Umeå University, SE 901 85 Umeå, Sweden.
Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno 656 53, Czech Republic.
Oncol Lett. 2024 Feb 6;27(4):142. doi: 10.3892/ol.2024.14275. eCollection 2024 Apr.
Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recurrence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2-year survival (P=0.025) compared with non-diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recurrence and improved survival compared with non-diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treat diabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigate the impact of diabetes in other SCCHN subtypes.
局部区域复发和远处转移是头颈部鳞状细胞癌（SCCHN）患者面临的主要问题。由于SCCHN是一组具有不同特征的异质性肿瘤，本研究聚焦于舌鳞状细胞癌（SCCOT）亚组，以探讨使用机器学习方法从诊断时可用的常规临床数据预测复发风险。该方法还确定了识别和分类复发风险的最重要参数。共纳入66例SCCOT患者。使用统计分析和机器学习方法对诊断时可用的临床数据进行分析。肿瘤复发与T分期（P=0.001）、颈部影像学转移（P=0.010）和糖尿病（P=0.003）相关。使用了基于随机森林算法且具有伴随可解释性的机器学习模型。虽然SCCOT队列中糖尿病患者比例过高，但与非糖尿病患者相比，糖尿病患者的复发率较低（在调整年龄和其他临床特征后P=0.015），且2年生存率有所提高（P=0.025）。利用诊断时可用的临床、影像学和组织学数据为SCCOT患者建立了一个预后模型。使用机器学习预测复发产生了一个准确率为71.2%的分类模型。值得注意的是，该模型特征重要性排名的一项发现是，即使在考虑了肿瘤大小和诊断时患者年龄等独立预后变量后，糖尿病患者与非糖尿病患者相比仍表现出较少的复发和更好的生存率。这些数据表明，用于治疗糖尿病的血糖水平治疗性调控可能对SCCOT患者有用，无论其糖尿病状态如何。有必要进一步研究糖尿病在其他SCCHN亚型中的影响。
