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基于MRI的影像组学特征识别结核性脑膜炎中看不见的基底蓄水池变化：一项初步的多中心研究-PubMed
Source: pubmed.ncbi.nlm.nih.gov
Article summary:
1. This study developed and evaluated MRI-based radiomics features to identify invisible basal cisterns changes in tuberculous meningitis (TBM) patients.
2. A deep learning model, nnU-Net, was used to segment the basal cisterns from FLAIR images. Radiomics features were then extracted from the segmented basal cisterns in FLAIR and T2 weighted (T2W) images.
3. The SVM model with 7 T2WI-based radiomics features achieved an AUC of 0.796 in the training dataset and 0.751 in the testing dataset, demonstrating good diagnostic performance for identifying basal cistern changes in TBM patients.
Article analysis:
This article is a multicenter study that aimed to develop and evaluate MRI-based radiomics features for identification of invisible basal cisterns changes in tuberculous meningitis (TBM) patients. The study recruited 187 TBM patients from 184 Chinese hospitals and 3 non-TBM control groups (158 TBM patients and 159 non-TBM controls for the training dataset; 26 TBM patients and 28 non-TBM controls for the testing dataset). The results showed that the SVM model with 7 T2WI-based radiomics features achieved an AUC of 0.796 in the training dataset and 0.751 in the testing dataset, demonstrating good diagnostic performance for identifying basal cistern changes in TBM patients.
The article is generally reliable as it provides detailed information on its methodology, results, discussion, conclusion, etc., which are supported by evidence from experiments conducted on a large sample size of 187 TMB patients from 184 Chinese hospitals across multiple centers. Furthermore, it also provides figures to illustrate its findings clearly and concisely.
However, there are some potential biases that should be noted when interpreting this article’s findings: firstly, since all participants were recruited from China only, this may limit generalizability of its findings to other populations; secondly, since only two imaging modalities (FLAIR and T2W) were used for analysis, other imaging modalities such as diffusion weighted imaging or dynamic contrast enhanced MRI may have been useful but were not explored; thirdly, although a deep learning model was used to segment the basal cisterns from FLAIR images accurately with acceptable results (average Dice coefficient of 0.920 for training dataset and 0.727 for testing dataset), manual labeling was still required to validate these results which may introduce bias due to interobserver variability; finally, although ROC curves analysis was performed to assess diagnostic performance of the SVM model with 7 T2WI-based radiomics features on both training and testing datasets respectively, further studies are needed to confirm its clinical utility before it can be applied clinically as a tool for diagnosing TBMs accurately