MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculous meningitis: a preliminary multicenter study

Abstract

Objective To develop and evaluate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for identification of invisible basal cisterns changes in tuberculous meningitis (TBM) patients. Methods Our retrospective study enrolled 184 TBM patients and 187 non-TBM controls from 3 Chinese hospitals (training dataset, 158 TBM patients and 159 non-TBM controls; testing dataset, 26 TBM patients and 28 non-TBM controls). nnU-Net was used to segment basal cisterns in fluid-attenuated inversion recovery (FLAIR) images. Subsequently, radiomics features were extracted from segmented basal cisterns in FLAIR and T2-weighted (T2W) images. Feature selection was carried out in three steps. Support vector machine (SVM) and logistic regression (LR) classifiers were applied to construct the radiomics signature to directly identify basal cisterns changes in TBM patients. Finally, the diagnostic performance was evaluated by the receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). ResultsThe segmentation model achieved the mean Dice coefficients of 0.920 and 0.727 in the training and testing datasets, respectively. The SVM model with 7 T2WI-based radiomics features achieved best discrimination capability for basal cisterns changes with an AUC of 0.796 (95% CI, 0.744-0.847) in the training dataset, and an AUC of 0.751 (95% CI, 0.617-0.886) with good calibration in the testing dataset. DCA confirmed its clinical usefulness. Conclusion The T2WI-based radiomics signature combined with deep learning segmentation could provide a fully automatic, non-invasive tool to identify invisible changes of basal cisterns, which has the potential to assist in the diagnosis of TBM. Key Points• The T2WI-based radiomics signature was useful for identifying invisible basal cistern changes in TBM.• The nnU-Net model achieved acceptable results for the auto-segmentation of basal cisterns.• Combining radiomics and deep learning segmentation provided an automatic, non-invasive approach to assist in the diagnosis of TBM.

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

“…The evidence from this review shows that in all studies, ML techniques have been an effective and positive approach to facilitating disease diagnosis and prediction. According to the findings of our study, in eight cases of incoming studies, ML algorithms were used to diagnose the disease, and all studies indicated the high ability of ML algorithms to diagnose meningitis diseases 48,49,52,54–56,60,61 . So even in Jeong and colleagues study, it was stated that the ANN model can have better diagnostic performance than a nonspecialist doctor.…”

“…He concluded that the T2‐weighted radiomic signature (T2WI) developed using the SVM classifier shows special diagnostic significance for TBM that may not be detected by an unaided eye in conventional MR images. Therefore, the automated segmentation of base reservoirs and the developed radiomic signature may provide complementary data to help diagnose TBM in a fully automated manner before the appearance of lesions with visible features 48 . Also, Babenk and his colleagues showed the ability to determine procalcitonin and C‐reactive protein (CRP) with cut‐off values for distinguishing between BM and EVM in children using the fast and cost‐effective decision tree (FFTree) approach.…”

“…According to the findings of our study, in eight cases of incoming studies, ML algorithms were used to diagnose the disease, and all studies indicated the high ability of ML algorithms to diagnose meningitis diseases. 48,49,52,[54][55][56]60,61 So even in Jeong and colleagues study, it was stated that the ANN model can have better diagnostic performance than a nonspecialist doctor. The foundation of this approach was the validation of variables such demographics and standard and clinical diagnostic tests, which had the best power and sensitivity (100%) for separating BM from EVM.…”

“…67 The majority of the included studies (56%) were carried out in low-and middle-income countries (LMICs), according to the results of our systematic review. [46][47][48]50,56,57,60,61 This can become a strong point for improving primary care services and thus improving their economies, especially due to the unequal distribution of resources and health care provision in LMICs. It is also critical to identify the agents of disease spread to implement control and prevention measures, as the identification of these factors can yield forecasts that help policymakers make informed decisions about vaccination purchases, public awareness campaigns, and health professional training programs.…”

“…According to Table 3, half of the included studies examined the impact of using ML algorithms on the diagnosis of meningitis diseases. 48,49,52,[54][55][56]60,61 In their study, Eho and colleagues chose a feedforward ANN to develop an expert system. Two ML algorithms were used to develop this ANN.…”