Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.

PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. METHODS: 202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. RESULTS: The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. CONCLUSIONS: The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. METHODS: The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. RESULTS: The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. CONCLUSIONS: The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

Impaired cardiorespiratory coupling in young normotensives with a family history of hypertension.

OBJECTIVES: Although recent animal studies have highlighted the importance of cardiorespiratory coupling in the pathogenesis of hypertension, little research has assessed the cardiorespiratory coupling in humans at high risk of developing hypertension. The aim of this study was to investigate the cardiorespiratory coupling in healthy young individuals genetically predisposed to hypertension at both rest and mental stress conditions. METHODS: We studied 39 normotensive male participants [21 with (FH+) and 18 without (FH-) a family history of hypertension]. Electrocardiography, impedance cardiography, beat-to-beat blood pressure and respiratory signal were simultaneously recorded during 5 min of rest and 5 min of mental arithmetic task (MAT). Stroke volume, cardiac output, systemic vascular resistance, baroreflex sensitivity and aortic pulse wave velocity were calculated. Autonomic activity was approximated noninvasively by the spectral analysis of cardiovascular variability. Respiratory sinus arrhythmia (RSA) and cardiorespiratory phase synchronization (CRPS) were used to define the amplitude and phase relationships of cardiorespiratory coupling. RESULTS: All resting parameters were similar between FH- and FH+ groups except resting CRPS, which was lower in FH+ group. Furthermore, the changes in hemodynamic parameters and cardiovascular variability at MAT were comparable in FH- and FH+ groups. Moreover, MAT elicited a decrease in CRPS of FH- group, whereas CRPS of participants in FH+ group remained unchanged during MAT. CONCLUSION: Healthy offspring of hypertensive parents have lower CRPS at rest, indicating an early impairment of cardiorespiratory coupling. Furthermore, CRPS decreased under mental stress in participants without a family history of hypertension, whereas this reactivity of CRPS was absent in participants with a family history of hypertension.