论文标题
通过机器学习和临床数据改善甲状腺癌的诊断
Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data
论文作者
论文摘要
甲状腺癌是甲状腺中发生的常见内分泌癌。已经为改善诊断而投入了很多努力，甲状腺切除术仍然是主要治疗方法。无需不必要的侧伤的成功手术取决于术前诊断。当前人类对甲状腺结节恶性肿瘤的评估容易出错，并且可能无法保证准确的术前诊断。这项研究提出了一个机器框架，以根据我们收集的新型临床数据集预测甲状腺结节恶性肿瘤。将10倍的交叉验证，引导分析和置换预测指标的重要性用于估计和解释不确定性下的模型性能。模型预测与专家评估之间的比较表明，在预测甲状腺结节恶性肿瘤时，我们框架比人类判断的优势。我们的方法是准确，可解释的，因此可作为甲状腺癌术前诊断中的其他证据。
Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine framework to predict thyroid nodule malignancy based on a novel clinical dataset we collected. The 10-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis for thyroid cancer.
