ABSTRACT
Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with symptoms appearing from early childhood. Behavioral modifications, special education, and medicines are used to treat ASD; however, the effectiveness of the treatments depends on early diagnosis of the disorder. The primary approach in diagnosing ASD is based on clinical interviews and valid scales. Still, methods based on brain imaging could also be possible diagnostic biomarkers for ASD. Methods: To identify the amount of information the functional magnetic resonance imaging (fMRI) reveals on ASD, we reviewed 292 task-based fMRI studies on ASD individuals. This study is part of a systematic review with the registration number CRD42017070975. Results: We observed that face perception, language, attention, and social processing tasks were mainly studied in ASD. In addition, 73 brain regions, nearly 83% of brain grey matter, showed an altered activation between the ASD and normal individuals during these four tasks, either in a lower or a higher activation. Conclusion: Using imaging methods, such as fMRI, to diagnose and predict ASD is a great objective; research similar to the present study could be the initial step.
ABSTRACT
PURPOSE: To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imaging (PWI). MATERIALS AND METHODS: Forty-two women confirmed to have a total of 60 masses (10 uterine sarcomas and 50 benign leiomyomas) were included. The reference diagnosis was based on postoperative histopathological examination. All women underwent the standard MRI protocol with 3-Tesla MR imager (Magnetom Trio, Siemens, Erlangen, Germany) for assessment of myometrial masses, followed by PWI. For each mass, two regions of interest (ROI) were outlined manually by an experienced radiologist; one (ROIL) represented the entire tumor while the other (ROIs) was placed on the area of the lesion with the most marked contrast enhancement. Two additional ROIs with diameters similar to ROIs (3.0 to 3.1 mm) were placed on psoas muscle (ROIP) and myometrium (ROIM) in order to provide baselines for comparisons. The obtained ROIs of PWI images were then analyzed using the DCE Tool plug-in (version 2.0SP1) within ClearCanvas (Toronto, Ontario, Canada) framework. The DCE Tool provides seven parameters (Ktrans, kep, Vb, IAUC, initial slope, peak, the mean squared error) for modelling contrast uptake within an ROI using the modified Tofts model. Parameters extracted from the ROIs were fed into a decision tree ensemble, which classified the corresponding lesions either as malignant or benign. The leave-one-out cross validation (LOOCV) was utilized to evaluate the performance of the classifier. RESULTS: None of the parameters extracted from ROIL or ROIs differed significantly between uterine sarcoma and benign leiomyomas (all p > 0.05). The overall accuracy of 66.7% was obtained by feeding seven parameters extracted from ROIL to the classifier. When 21 features extracted from ROIL, ROIM, and ROIP were fed into the classifier an accuracy of 91.7%, sensitivity of 100%, and specificity of 90% were achieved in the optimal operating point of classifier. CONCLUSION: Although none of the PWI parameters differed significantly between benign and malignant lesions, when the information provided by the extracted features was aggregated using a machine learning method, a promising discriminative power was obtained. This suggests that the proposed model for combining the PWI parameters is potentially useful for differentiating uterine sarcoma from leiomyomas.
