基于标准血液检测结果和吸烟状态并采用可解释机器学习方法的专家级肺癌检测
Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach
AI总结：
本研究提出基于动态集成选择的机器学习模型，利用标准血液检测和吸烟史数据检测肺癌，其表现超越五位呼吸科医生，并识别出关键预测因素。
摘要：
肺癌（LC）仍是癌症相关死亡的主要原因，这在很大程度上归因于晚期诊断。因此，有效的早期检测策略至关重要。近年来，机器学习（ML）通过促进多种疾病的检测，在医疗保健领域展现了巨大潜力。在这项回顾性开发与验证研究中，我们开发了一种基于动态集成选择（DES）的ML模型用于LC检测。该模型利用了丹麦高风险人群的标准血液样本分析和吸烟史数据。该研究纳入了2009年至2018年间在南丹麦大区因疑似LC而接受检查的所有患者。我们验证了DES模型的预测结果，并与五位呼吸科医生的诊断进行了比较。在38,944名患者中，有9,940名具有完整数据，其中2,505名（25%）患有LC。DES模型实现了0.77±0.01的ROC曲线下面积，76.2%±2.4%的灵敏度，63.8%±2.3%的特异度，41.6%±1.2%的阳性预测值，以及53.8%±1.1%的F1分数。DES模型的表现优于所有五位呼吸科医生，其灵敏度比他们的平均水平高出9%。该模型确定吸烟状态、年龄、总钙水平、中性粒细胞计数和乳酸脱氢酶是检测LC的最重要因素。结果突出了ML方法在检测LC方面的成功应用，超越了呼吸科医生的表现。在未来的风险评估模型中纳入临床和实验室数据可以改善决策并促进及时转诊。
英文摘要：
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.
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