| 唐雷,王仕霞,李武超,曾宪春,安云昭,宋彬.术前CT影像组学模型预测体内泌尿系结石成分[J].中国医学影像技术,2024,40(8):1216~1220 |
| 术前CT影像组学模型预测体内泌尿系结石成分 |
| Preoperative CT radiomics models for predicting composition of in vivo urinary calculi |
| 投稿时间：2024-03-19 修订日期：2024-05-27 |
| DOI：10.13929/j.issn.1003-3289.2024.08.023 |
| 中文关键词: 尿路结石症 体层摄影术,X线计算机 影像组学 |
| 英文关键词:urolithiasis tomography, X-ray computed radiomics |
| 基金项目:贵州省卫生健康委科学技术基金项目(gzwkj2022-182)。 |
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| 中文摘要: |
| 目的 观察术前CT影像组学模型预测体内泌尿系结石成分的价值。方法 回顾性分析543例尿石症患者,根据结石成分将其分为一水草酸钙结石组(A组,n=373)、无水尿酸结石组(B组,n=86)、碳酸磷灰石组(C组,n=30)、尿酸铵结石组(D组,n=28)及六水磷酸铵镁结石组(E组,n=26);同时按7 ∶ 3比例划分训练集与测试集。基于泌尿系平扫CT提取并筛选影像组学特征,采用最小绝对收缩和选择算子算法分别构建用于预测体内泌尿系结石成分的二分类(与A~E组结石成分对应为模型A~E)及五分类模型;绘制受试者工作特征曲线,计算曲线下面积(AUC),评估二分类模型的效能;以准确率、精确率、召回率及F1分数分析五分类模型的效能。结果 二分类模型预测相应体内泌尿系结石成分效能均良好,其在训练集的AUC为0.860~0.948,在测试集为0.856~0.933。五分类模型预测训练集泌尿系结石成分的准确率、精确率、召回率及F1分数分别为82.25%、83.79%、46.23%及0.596,在测试集分别为80.63%、75.26%、43.48%及0.551。结论 术前CT影像组学二分类模型预测体内泌尿系结石成分的效能良好;五分类模型的准确率较高但稳定性欠佳。 |
| 英文摘要: |
| Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi. Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group (group A, n=373), anhydrous uric acid stone group (group B, n=86), carbonate apatite group (group C, n=30), ammonium urate stone group (group D, n=28) and ammonium magnesium phosphate hexahydrate stone group (group E, n=26) according to the composition of calculi, also divided into training set and test set at the ratio of 7 ∶ 3. Radiomics features were extracted and screened based on plain CT images of urinary system. Five binary task models (model A—E corresponding to group A—E) and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo. Then receiver operating characteristic curves were drawn, and the area under the curves (AUC) were calculated to evaluate the predictive efficacy of binary task models, while the accuracy, precision, recall and F1 score were used to evaluate the predictive efficacy of the quinary task model. Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo, with AUC of 0.860—0.948 in training set and of 0.856—0.933 in test set. The accuracy, precision, recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25%, 83.79%, 46.23% and 0.596 in training set, respectively, while was 80.63%, 75.26%, 43.48% and 0.551 in test set, respectively. Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi, while the quinary task radiomics model had high accuracy but relatively poor stability. |
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