A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT

Objectives Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI on non-contrast computed tomography (NCCT) is currently very low. Thus, we aimed to develop and validate the radiomics feature-based machine learning models to detect EBI (RMEBIs) on NCCT. Methods In this retrospective observational study, 355 participants from a multicentre multimodal database established by Huashan Hospital were randomly divided into two data sets: a training cohort (70%) and an internal validation cohort (30%). Fifty-seven participants from the Second Affiliated Hospital of Xuzhou Medical University were included as the external validation cohort. Brainstems were segmented by a radiologist committee on NCCT and 1781 radiomics features were automatically computed. After selecting the relevant features, 7 machine learning models were assessed in the training cohort to predict early brainstem infarction. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the prediction models. Results The multilayer perceptron (MLP) RMEBI showed the best performance (AUC: 0.99 [95% CI: 0.96-1.00]) in the internal validation cohort. The AUC value in external validation cohort was 0.91 (95% CI: 0.82-0.98). Conclusions RMEBIs have the potential in routine clinical practice to enable accurate computer-assisted diagnoses of early brainstem infarction in patients with NCCT, which may have important clinical value in reducing therapeutic decision-making time.