Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.

IF 2.9 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Quantitative Imaging in Medicine and Surgery Pub Date : 2024-09-01 Epub Date: 2024-08-20 DOI:10.21037/qims-24-601
Yuxin Zheng, Yajiao Zhang, Kefeng Lu, Jiafeng Wang, Linlin Li, Dong Xu, Junping Liu, Jiangyan Lou
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Abstract

Background: Follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) present diagnostic challenges due to overlapping clinical and ultrasound features. Improving the diagnosis of FTC can enhance patient prognosis and effectiveness in clinical management. This study seeks to develop a predictive model for FTC based on ultrasound features using machine learning (ML) algorithms and assess its diagnostic effectiveness.

Methods: Patients diagnosed with FTA or FTC based on surgical pathology between January 2009 and February 2023 at Zhejiang Provincial Cancer Hospital and Zhejiang Provincial People's Hospital were retrospectively included. A total of 562 patients from Zhejiang Provincial Cancer Hospital comprised the training set, and 218 patients from Zhejiang Provincial People's Hospital constituted the validation set. Subsequently, clinical parameters and ultrasound characteristics of the patients were collected. The diagnostic parameters were analyzed using the least absolute shrinkage and selection operator and multivariate logistic regression screening methods. Next, a comparative analysis was performed using seven ML models. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), precision, recall, and comprehensive evaluation index (F-score) were calculated to compare the diagnostic efficacy among the seven models and determine the optimal model. Further, the optimal model was validated, and the SHapley Additive ExPlanations (SHAP) approach was applied to explain the significance of the model variables. Finally, an individualized risk assessment was conducted.

Results: Age, echogenicity, thyroglobulin antibody (TGAb), echotexture, composition, triiodothyronine (T3), thyroglobulin (TG), margin, thyroid-stimulating hormone (TSH), calcification, and halo thickness >2 mm were influential factors for diagnosing FTC. The XGBoost model was identified as the optimal model after a comprehensive evaluation. The AUC of this model in the validation set was 0.969 [95% confidence interval (CI), 0.946-0.992], while its precision sensitivity, specificity, and accuracy were 0.791, 0.930, 0.913 and 0.917, respectively.

Conclusions: XGBoost model based on ultrasound features was constructed and interpreted using the SHAP method, providing evidence for the diagnosis of FTC and guidance for the personalized treatment of patients.

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基于临床超声特征的可解释机器学习模型对甲状腺滤泡癌的诊断价值。
背景:滤泡性甲状腺癌(FTC)和滤泡性甲状腺腺瘤(FTA)的临床和超声特征相互重叠,给诊断带来了挑战。改进 FTC 的诊断可提高患者的预后和临床治疗的有效性。本研究旨在利用机器学习(ML)算法,根据超声波特征建立FTC预测模型,并评估其诊断效果:回顾性纳入2009年1月至2023年2月期间在浙江省肿瘤医院和浙江省人民医院根据手术病理诊断为FTA或FTC的患者。浙江省肿瘤医院的 562 例患者构成训练集,浙江省人民医院的 218 例患者构成验证集。随后,收集了患者的临床参数和超声特征。使用最小绝对收缩和选择算子以及多元逻辑回归筛选方法对诊断参数进行分析。接着,使用七个 ML 模型进行了比较分析。通过计算接收者操作特征曲线(ROC)下面积(AUC)、准确性、灵敏度、特异性、阳性预测值(PPV)、阴性预测值(NPV)、精确度、召回率和综合评价指数(F-score)来比较七个模型的诊断效果,并确定最佳模型。此外,还对最佳模型进行了验证,并采用 SHapley Additive ExPlanations(SHAP)方法来解释模型变量的意义。最后,进行了个体化风险评估:结果:年龄、回声、甲状腺球蛋白抗体(TGAb)、回声纹理、成分、三碘甲状腺原氨酸(T3)、甲状腺球蛋白(TG)、边缘、促甲状腺激素(TSH)、钙化和光环厚度大于2毫米是诊断FTC的影响因素。经过综合评估,XGBoost 模型被确定为最佳模型。该模型在验证集中的AUC为0.969[95%置信区间(CI),0.946-0.992],其精确灵敏度、特异度和准确度分别为0.791、0.930、0.913和0.917:利用SHAP方法构建并解释了基于超声特征的XGBoost模型,为FTC的诊断提供了证据,并为患者的个性化治疗提供了指导。
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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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