Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children.

Henoch-Schönlein purpura nephritis (HSPN) is one of the most common kidney diseases in children. The current diagnosis and classification of HSPN depend on pathological biopsy, which is seriously limited by its invasive and high-risk nature. The aim of the study was to explore the potential of radiomics model for evaluating the histopathological classification of HSPN based on the ultrasound (US) images. A total of 440 patients with Henoch-Schönlein purpura nephritis proved by biopsy were analyzed retrospectively. They were grouped according to two histopathological categories: those without glomerular crescent formation (ISKDC grades I-II) and those with glomerular crescent formation (ISKDC grades III-V). The patients were randomly assigned to either a training cohort (n = 308) or a validation cohort (n = 132) with a ratio of 7:3. The sonologist manually drew the regions of interest (ROI) on the ultrasound images of the right kidney including the cortex and medulla. Then, the ultrasound radiomics features were extracted using the Pyradiomics package. The dimensions of radiomics features were reduced by Spearman correlation coefficients and least absolute shrinkage and selection operator (LASSO) method. Finally, three radiomics models using k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established, respectively. The predictive performance of such classifiers was assessed with receiver operating characteristic (ROC) curve. 105 radiomics features were extracted from derived US images of each patient and 14 features were ultimately selected for the machine learning analysis. Three machine learning models including k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established for HSPN classification. Of the three classifiers, the SVM classifier performed the best in the validation cohort [area under the curve (AUC) =0.870 (95% CI, 0.795-0.944), sensitivity = 0.706, specificity = 0.950]. The US-based radiomics had good predictive value for HSPN classification, which can be served as a noninvasive tool to evaluate the severity of renal pathology and crescentic formation in children with HSPN.

Identification of discriminative neuroimaging markers for patients on hemodialysis with insomnia: a fractional amplitude of low frequency fluctuation-based machine learning analysis.

BACKGROUND AND OBJECTIVE: Insomnia is one of the common problems encountered in the hemodialysis (HD) population, but the mechanisms remain unclear. we aimed to (1) detect the spontaneous brain activity pattern in HD patients with insomnia (HDWI) by using fractional fractional amplitude of low frequency fluctuation (fALFF) method and (2) further identify brain regions showing altered fALFF as neural markers to discriminate HDWI patients from those on hemodialysis but without insomnia (HDWoI) and healthy controls (HCs). METHOD: We compared fALFF differences among HDWI subjects (28), HDWoI subjects (28) and HCs (28), and extracted altered fALFF features for the subsequent discriminative analysis. Then, we constructed a support vector machine (SVM) classifier to identify distinct neuroimaging markers for HDWI. RESULTS: Compared with HCs, both HDWI and HDWoI patients exhibited significantly decreased fALFF in the bilateral calcarine (CAL), right middle occipital gyrus (MOG), left precentral gyrus (PreCG), bilateral postcentral gyrus (PoCG) and bilateral temporal middle gyrus (TMG), whereas increased fALFF in the bilateral cerebellum and right insula. Conversely, increased fALFF in the bilateral CAL/right MOG and decreased fALFF in the right cerebellum was observed in HDWI patients when compared with HDWoI patients. Moreover, the SVM classification achieved a good performance [accuracy = 82.14%, area under the curve (AUC) = 0.8202], and the consensus brain regions with the highest contributions to classification were located in the right MOG and right cerebellum. CONCLUSION: Our result highlights that HDWI patients had abnormal neural activities in the right MOG and right cerebellum, which might be potential neural markers for distinguishing HDWI patients from non-insomniacs, providing further support for the pathological mechanism of HDWI.

Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis.

Background: Migraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment. Methods: A total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group. Results: A biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p < 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = -0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = -0.684, p < 0.001), respectively. Conclusions: By machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus.

Early Fractional Amplitude of Low Frequency Fluctuation Can Predict the Efficacy of Transcutaneous Auricular Vagus Nerve Stimulation Treatment for Migraine Without Aura.

Migraine is a common primary headache disorder. Transcutaneous auricular vagus nerve stimulation (taVNS) has been verified to be effective in patients with migraine without aura (MWoA). However, there are large interindividual differences in patients' responses to taVNS. This study aimed to explore whether pretreatment fractional amplitude of low frequency fluctuation (fALFF) features could predict clinical outcomes in MWoA patients after 4-week taVNS. Sixty MWoA patients and sixty well-matched healthy controls (HCs) were recruited, and migraineurs received 4-week taVNS treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected, and the significant differences of fALFF were detected between MWoA patients and HCs using two-sample t-test. A mask of these significant regions was generated and used for subsequent analysis. The abnormal fALFF in the mask was used to predict taVNS efficacy for MWoA using a support vector regression (SVR) model combining with feature select of weight based on the LIBSVM toolbox. We found that (1) compared with HCs, MWoA patients exhibited increased fALFF in the left thalamus, left inferior parietal gyrus (IPG), bilateral precentral gyrus (PreCG), right postcentral gyrus (PoCG), and bilateral supplementary motor areas (SMAs), but decreased in the bilateral precuneus and left superior frontal gyrus (SFG)/medial prefrontal cortex (mPFC); (2) after 4-week taVNS treatment, the fALFF values significantly decreased in these brain regions based on the pretreatment comparison. Importantly, the decreased fALFF in the bilateral precuneus was positively associated with the reduction in the attack times (r = 0.357, p = 0.005, Bonferroni correction, 0.05/5), whereas the reduced fALFF in the right PoCG was negatively associated with reduced visual analog scale (VAS) scores (r = -0.267, p = 0.039, uncorrected); (3) the SVR model exhibited a good performance for prediction (r = 0.411, p < 0.001),which suggests that these extracted fALFF features could be used as reliable biomarkers to predict the treatment response of taVNS for MWoA patients. This study demonstrated that the baseline fALFF features have good potential for predicting individualized treatment response of taVNS in MWoA patients, and those weight brain areas are mainly involved in the thalamocortical (TC) circuits, default mode network (DMN), and descending pain modulation system (DPMS). This will contribute to well understanding the mechanism of taVNS in treating MWoA patients and may help to screen ideal patients who respond well to taVNS treatment.

Coronavirus Disease 2019: Initial Detection on Chest CT in a Retrospective Multicenter Study of 103 Chinese Patients.

PURPOSE: To evaluate the performance of chest CT regarding the initial presentation of patients suspected of having coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: Data from 103 patients who were under investigation for COVID-19 based on inclusion criteria according to the World Health Organization Interim Guidance were retrospectively collected from January 21, 2020, to February 14, 2020. All patients underwent chest CT scanning and reverse-transcription polymerase chain reaction (RT-PCR) testing for COVID-19 at hospital presentation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) (with 95% confidence intervals) were calculated to evaluate the performance of CT. Subgroup analyses were also performed based on the geographical distribution of these cases in the province of Henan, China. RESULTS: There were 88/103 (85%) patients with COVID-19 confirmed by RT-PCR testing. The overall sensitivity, specificity, PPV, and NPV were 93% (85%, 97%), 53% (27%, 77%), 92% (83%, 96%), and 42% (18%, 70%), respectively. Similar results were shown in both geographic regions. The respective sensitivity, specificity, PPV, and NPV for chest CT in the districts of Xinyang and Zhumadian (n = 56) were 92% (80%, 97%), 63% (26%, 90%), 93% (81%, 98%), and 56% (23%, 85%), while these indicators in the district of Anyang (n = 47) were 95% (81%, 99%), 43% (12%, 80%), 90% (76%, 97%), and 60% (17%-93%). There were no significant differences in the prevalence of positive examinations in the two geographic subgroups for CT (P = .423) or RT-PCR (P = .931). CONCLUSION: Although initial chest CT obtained at hospital presentation showed high sensitivity in patients under investigation for COVID-19 in the two geographic regions in Henan Province, the NPV was only modest, suggesting a low value of CT as a screening tool.© RSNA, 2020.