Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
Eur Radiol. 2023 Sep;33(9):6308-6317. doi: 10.1007/s00330-023-09589-x. Epub 2023 Apr 1.
Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.
We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.
Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05).
The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.
Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.
• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The combined model holds potential to be used as a diagnostic tool in routine clinical practice.
耐多药结核病（MDR-TB）是全球严重的负担和公共卫生威胁。本研究旨在基于树芽征（TIB）和结节建立一个放射组学模型，并验证其对 MDR-TB 的预测性能。
我们回顾性地招募了来自两家医院的 454 名确诊活动性结核病患者，并将其分为三个训练和测试队列：TIB（n=295，102）、结节（n=302，97）和它们的组合（n=261，81）。分别提取与 TIB 和结节相关的放射组学特征。使用最大信息系数和递归特征消除方法，针对这两个征象选择有意义的特征。使用随机森林分类器构建两个放射组学模型来预测 MDR-TB。然后，基于这两个征象构建了一个包含放射组学特征的组合模型。使用 ROC 曲线验证了模型在联合训练和测试队列中的能力。
从 TIB 中提取了 16 个特征，从结节中提取了 15 个特征。在联合训练队列中，组合模型的 AUC 略高于 TIB 模型（0.911 比 0.877，p>0.05）和测试队列（0.820 比 0.786，p<0.05），与结节模型在联合训练队列（0.911 比 0.933，p>0.05）和测试队列（0.820 比 0.855，p>0.05）中的性能相似。
基于 CT 的放射组学模型有望成为预测 MDR-TB 的一种非侵入性工具。
本研究表明，基于 TIB 征象和结节的放射组学可以提供有关 MDR-TB 的补充信息。所提出的放射组学模型可能是预测活动性结核病患者中 MDR 的新标志物。
这是第一项基于树芽征和结节建立、验证和应用放射组学模型预测 MDR-TB 的研究。
放射组学模型对 MDR-TB 的识别具有良好的性能。
联合模型有望成为常规临床实践中的一种诊断工具。
