Predicting Alzheimer's progression in MCI: a DTI-based white matter network model.

OBJECTIVE: This study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients. METHODS: This study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation. RESULTS: APOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers. CONCLUSIONS: A comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter.

Objective: Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships. Methods: 967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features. Results: The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05). Conclusion: The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.

Disastrous cerebral and ocular vascular complications after cosmetic facial filler injections: a retrospective case series study.

Soft tissue filler injections are among the most popular facial rejuvenation methods. Cerebral infarction and ophthalmic artery occlusion are rare and catastrophic complications, especially when facial cosmetic fillers are injected by inexperienced doctors. Radiologists and plastic surgeons need to increase their awareness of the complications associated with fillers, which allows early diagnosis and intervention to improve patient prognosis. Regarding the mechanism by which vascular occlusion occurs after facial filler injections, a retrograde embolic mechanism is currently the predominant theory. Numerous case reports have been presented regarding complications associated with injections of facial aesthetics. However, the small sample sizes of these studies did not allow for an adequate assessment of the clinical and imaging manifestations based on the location of the occlusion and the type of filler, and detailed elaboration of multiple cerebral infarctions is also lacking. Therefore, this study aimed to investigate the clinical and radiological features of severe cerebral and ocular complications caused by cosmetic facial filler injections. In addition, we discuss the pathogenesis, treatment, and prognosis of these patients. The clinical, computed tomography (CT), magnetic resonance imaging (MRI), and digital subtraction angiography (DSA) findings were described and analysed. Radiological examinations are crucial for demonstrating severe complications, and brain MRI is especially strongly suggested for patients with cosmetic filler-induced vision loss to identify asymptomatic cerebral infarctions. Extreme caution and care should be taken during facial injections by plastic surgeons.

Using mixed reality technique combines multimodal imaging signatures to adjuvant glioma photodynamic therapy.

Background: Photodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT's key operating zones. Methods: We used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival. Results: We constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment. Conclusion: Using the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

Fibroblast-Conditioned Media Enhance the Yield of Microglia Isolated from Mixed Glial Cultures.

Microglia are the main immune cells of the central nervous system (CNS) and comprise various model systems used to investigate inflammatory mechanisms in CNS disorders. Currently, shaking and mild trypsinization are widely used microglial culture methods; however, the problems with culturing microglia include low yield and a time-consuming process. In this study, we replaced normal culture media (NM) with media containing 25% fibroblast-conditioned media (F-CM) to culture mixed glia and compared microglia obtained by these two methods. We found that F-CM significantly improved the yield and purity of microglia and reduced the total culture time of mixed glia. The microglia obtained from the F-CM group showed longer ramified morphology than those from the NM group, but no difference was observed in cell size. Microglia from the two groups had similar phagocytic function and baseline phenotype markers. Both methods yielded microglia were responsive to various stimuli such as lipopolysaccharide (LPS), interferon-γ (IFN-γ), and interleukin-4 (IL-4). The current results suggest that F-CM affect the growth of primary microglia in mixed glia culture. This method can produce a high yield of primary microglia within a short time and may be a convenient method for researchers to investigate inflammatory mechanisms and some CNS disorders.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study.

Objective: To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH). Methods: A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models. Results: CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)]. Conclusion: pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.

Multilayer perceptron-based prediction of stroke mimics in prehospital triage.

The identification of stroke mimics (SMs) in patients with stroke could lead to delayed diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an accurate tool for clinical applications. However, MLP haven't been applied in patients with suspected stroke onset within 24 h. Here, we aimed to develop a MLP model to predict SM in patients. We retrospectively reviewed the data of patients with a prehospital diagnosis of suspected stroke between July 2017 and June 2021. SMs were confirmed during hospitalization. We included demographic information, clinical manifestations, medical history, and systolic and diastolic pressure on admission. First, the cohort was randomly divided into a training set (70%) and an external testing set (30%). Then, the least absolute shrinkage and selection operator (LASSO) method was used in feature selection and an MLP model was trained based on the selected items. Then, we evaluated the performance of the model using the ten-fold cross validation method. Finally, we used the external testing set to compare the MLP model with FABS scoring system (FABS) and TeleStroke Mimic Score (TM-Score) using a receiver operator characteristic (ROC) curve. In total, 402 patients were included. Of these, 82 (20.5%) were classified as SMs. During the ten-fold cross validation, the mean area under the ROC curve (AUC) of 10 training sets and 10 validation sets were 0.92 and 0.87, respectively. In the external testing set, the AUC of the MLP model was significantly higher than that of the FABS (0.855 vs. 0.715, P = 0.038) and TM-Score (0.855 vs. 0.646, P = 0.006). The MLP model had significantly better performance in predicting SMs than FABS and TM-Score.

RhFGF21 Protects Epidermal Cells against UVB-Induced Apoptosis through Activating AMPK-Mediated Autophagy.

Ultraviolet irradiation, especially ultraviolet B (UVB) irradiation, increases the risks of various skin diseases, such as sunburn, photo-aging and cancer. However, few drugs are available to treat skin lesions. Therefore, the discovery of drugs to improve the health of irradiated skin is urgently needed. Fibroblast growth factor 21 (FGF21) is a metabolic factor that plays an important role in the protection and repair of various types of pathological damage. The effects of FGF21 on skin injury caused by UVB-irradiation were the focus of this study. We found that UVB irradiation promoted the expression of FGF21 protein in mouse epidermal cells, and exogenous recombinant human FGF21 (rhFGF21) protected mouse skin tissue against UVB-induced injury. RhFGF21 inhibited the inflammatory responses and epidermal cell apoptosis as well as promotion of autophagy in UVB-irradiated mice. Moreover, we found that rhFGF21 protected HaCaT cells against UVB-induced apoptosis, and the protective effect was enhanced by treatment with an autophagy activator (rapamycin) but was inhibited by treatment with an autophagy inhibitor (3-methyladenine, 3MA). AMP-activated protein kinase (AMPK), as a cellular energy sensor, regulates autophagy. RhFGF21 increased the expression of p-AMPK protein in epidermal cells irradiated with UVB in vivo and in vitro. Moreover, rhFGF21 increased autophagy levels and the viability were diminished by treatment with an AMPK inhibitor (compound C). RhFGF21 protects epidermal cells against UVB-induced apoptosis by inducing AMPK-mediated autophagy.

Overall survival time prediction for glioblastoma using multimodal deep KNN.

Glioblastoma (GBM) is a severe malignant brain tumor with bad prognosis, and overall survival (OS) time prediction is of great clinical value for customized treatment. Recently, many deep learning (DL) based methods have been proposed, and most of them build deep networks to directly map pre-operative images of patients to the OS time. However, such end-to-end prediction is sensitive to data inconsistency and noise. In this paper, inspired by the fact that clinicians usually evaluate patient prognosis according to previously encountered similar cases, we propose a novel multimodal deep KNN based OS time prediction method. Specifically, instead of the end-to-end prediction, for each input patient, our method first search itsKnearest patients with known OS time in a learned metric space, and the final OS time of the input patient is jointly determined by theKnearest patients, which is robust to data inconsistency and noise. Moreover, to take advantage of multiple imaging modalities, a new inter-modality loss is introduced to encourage learning complementary features from different modalities. The in-house single-center dataset containing multimodal MR brain images of 78 GBM patients is used to evaluate our method. In addition, to demonstrate that our method is not limited to GBM, a public multi-center dataset (BRATS2019) containing 211 patients with low and high grade gliomas is also used in our experiment. As benefiting from the deep KNN and the inter-modality loss, our method outperforms all methods under evaluation in both datasets. To the best of our knowledge, this is the first work, which predicts the OS time of GBM patients in the strategy of KNN under the DL framework.

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

BACKGROUND: Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE: This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS: CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS: The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION: The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer.

OBJECTIVES: To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model. METHODS: We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, diffusion-weighted imaging, and enhanced T1-weighted imaging of rectal cancer, followed by the dimension reduction of the features. Logistic regression, support vector machine, Bayes, K-nearest neighbor, and random forests algorithms were trained to obtain the radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each radiomics signature. The best radiomics signature was selected and combined with clinical and radiological characteristics to construct a joint model for predicting EMVI. Finally, the predictive performance of the joint model was assessed. RESULTS: The Bayes-based radiomics signature performed well in both the training set and the test set, with the AUCs of 0.744 and 0.738, sensitivities of 0.754 and 0.728, and specificities of 0.887 and 0.918, respectively. The joint model performed best in both the training set and the test set, with the AUCs of 0.839 and 0.835, sensitivities of 0.633 and 0.714, and specificities of 0.901 and 0.885, respectively. CONCLUSIONS: The joint model demonstrated the best diagnostic performance for the preoperative prediction of EMVI in patients with rectal cancer. Hence, it can be used as a key tool for clinical individualized EMVI prediction. KEY POINTS: • Radiomics features from magnetic resonance imaging can be used to predict extramural venous invasion (EMVI) in rectal cancer. • Machine learning can improve the accuracy of predicting EMVI in rectal cancer. • Radiomics can serve as a noninvasive biomarker to monitor the status of EMVI.

Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities.

Purpose: The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images. Methods: In this study, 232 people were retrospectively analyzed at Medical Center A (training and testing groups) and Medical Center B (external validation group). A visual rating scale was used to divide all patients into WMH progression and non-progression groups. Two regions of interest (ROIs)-ROI whole-brain white matter (WBWM) and ROI WMH penumbra (WMHp)-were segmented from the baseline image. For predicting WMH progression, logistic regression was applied to create radiomic models in the two ROIs. Then, age, sex, clinical course, vascular risk factors, and imaging factors were incorporated into a stepwise regression analysis to construct the combined diagnosis model. Finally, the presence of a correlation between radiomic findings and the speed of progression was analyzed. Results: The area under the curve (AUC) was higher for the WMHp-based radiomic model than the WBWM-based radiomic model in training, testing, and validation groups (0.791, 0.768, and 0.767 vs. 0.725, 0.693, and 0.691, respectively). The WBWM-based combined model was established by combining age, hypertension, and rad-score of the ROI WBWM. Also, the WMHp-based combined model is built by combining the age and rad-score of the ROI WMHp. Compared with the WBWM-based model (AUC = 0.779, 0.716, 0.673 in training, testing, and validation groups, respectively), the WMHp-based combined model has higher diagnostic efficiency and better generalization ability (AUC = 0.793, 0.774, 0.777 in training, testing, and validation groups, respectively). The speed of WMH progression was related to the rad-score from ROI WMHp (r = 0.49) but not from ROI WBWM. Conclusion: The heterogeneity of the penumbra could help identify the individuals at high risk of WMH progression and the rad-score of it was correlated with the speed of progression.

Predictive Value of Pretreatment Lymphocyte-to-Monocyte Ratio and Platelet-to-Lymphocyte Ratio in the Survival of Nasopharyngeal Carcinoma Patients.

OBJECTIVE: The present study aimed to investigate the predictive value of some indexes, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory response index (SIRI), and systemic immune-inflammatory index (SII) in the survival of nasopharyngeal carcinoma (NPC) and provide reference for the treatment. METHODS: A retrospective analysis was performed on 216 patients from 2016 to 2018. The cutoff values of these indexes were determined by the receiver operating characteristic (ROC) curve. The prognostic value of the indexes was evaluated according to the rate of overall survival (OS), regional recurrence-free survival (RRFS), locoregional recurrence-free survival (LRRFS), and distant metastasis-free survival (DMFS). RESULTS: The survival analysis showed that NLR ≤2.695 (P = 0.017) and PLR ≤140.065 (P = 0.041) were associated with poor OS; however, the LMR and SIRI showed no significant statistical significance. NLR ≤2.045 (P = 0.018) and PLR ≤125.605 (P = 0.003) were associated with poor RRFS, LMR ≤2.535 (P = 0.027) and PLR ≤140.065 (P = 0.009) were associated with poor DMFS, NLR ≤2.125 (P = 0.018) and PLR ≤132.645 (P = 0.026) were associated with poor LRRFS, respectively. Logistic regression analysis showed that low LMR (≤2.535) was significantly inferior in OS (HR 23.085, 95% CI 3.425-155.622, P = 0.001) and DMFS (HR 22.839, 95% CI 4.096-127.343, P < 0.001). Moreover, low PLR (≤140.065) remained significantly related to worse OS (HR 11.908, 95% CI 1.295-109.517, P = 0.029) and DMFS (HR 9.556, 95% CI 1.448-63.088, P = 0.019). CONCLUSION: The index LMR and PLR can be used for predicting survival in NPC patients.

A New Classification System for Postinterventional Cerebral Hyperdensity: The Influence on Hemorrhagic Transformation and Clinical Prognosis in Acute Stroke.

Background: Postinterventional cerebral hyperdensity (PCHD) is commonly seen in acute ischemic patients after mechanical thrombectomy. We propose a new classification of PCHD to investigate its correlation with hemorrhagic transformation (HT). The clinical prognosis of PCHD was further studied. Methods: Data from 189 acute stroke patients were analyzed retrospectively. According to the European Cooperative Acute Stroke Study criteria (ECASS), HT was classified as hemorrhagic infarction (HI-1 and HI-2) and parenchymal hematoma (pH-1 and pH-2). Referring to the classification of HT, PCHD was classified as PCHD-1, PCHD-2, PCHD-3, and PCHD-4. The prognosis included early neurological deterioration (END) and the modified Rankin Scale (mRS) score at 3 months. Results: The incidence of HT was 14.8% (12/81) in the no-PCHD group and 77.8% (84/108) in the PCHD group. PCHD was highly correlated with HT (r = 0.751, p < 0.01). After stepwise regression analysis, PCHD and the National Institutes of Health Stroke Scale (NIHSS) score at admission were found to be independent factors for END (p < 0.001, p = 0.015, respectively). The area of curves (AUC) of PCHD, the NIHSS at admission, and the combined model were 0.810, 0.667, and 0.832, respectively. The optimal diagnostic cutoff of PCHD for END was PCHD > 2. PCHD, the NIHSS score at admission, and good vascular recanalization (VR) were independently associated with 3-month mRS (all p < 0.05). The AUC of PCHD, the NIHSS at admission, good VR, and the combined model were 0.779, 0.733, 0.565, and 0.867, respectively. And the best cutoff of PCHD for the mRS was PCHD > 1. Conclusion: The relationship of PCHD and HT suggested PCHD was an early risk indicator for HT. The occurrence of PCHD-3 and PCHD-4 was a strong predictor for END. PCHD-1 is considered to be relatively benign in relation to the 3-month mRS.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model.

OBJECTIVE: This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: 357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T1WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD. RESULTS: Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798. CONCLUSION: The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland.

OBJECTIVE: This study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors via multicentre cohorts. MATERIALS AND METHODS: A cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images. RESULTS: Analysis of variance and multivariable logistic regression analysis showed that location, lymph node metastases, and rad-score were independent predictors of tumor malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679, and 0.632, respectively; specificity was 0.869, 0.878, 0.734, and 0.773; and sensitivity was 0.731, 0.808, 0.723, and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653, and 0.608, respectively; the specificity was 0.741, 0.889, 0.852, and 0.812; and the sensitivity was 0.818, 0.790, 0.731, and 0.716. CONCLUSIONS: The combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumors and can help clinicians guide clinical tumor management.

The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.

BACKGROUND: Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique. A pragmatic and objective method in the clinic to assess OS is strongly in need. PURPOSE: To construct a radiomics nomogram to stratify GBM patients into long- vs. short-term survival. STUDY TYPE: Retrospective. POPULATION: One-hundred and fifty-eight GBM patients from Brain Tumor Segmentation Challenge 2018 (BRATS2018) were for model construction and 32 GBM patients from the local hospital for external validation. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T MRI Scanners, T1 WI, T2 WI, T2 FLAIR, and contrast-enhanced T1 WI sequences ASSESSMENT: All patients were divided into long-term or short-term based on a survival of greater or fewer than 12 months. All BRATS2018 subjects were divided into training and test sets, and images were assessed for ependymal and pia mater involvement (EPI) and multifocality by three experienced neuroradiologists. All tumor tissues from multiparametric MRI were fully automatically segmented into three subregions to calculate the radiomic features. Based on the training set, the most powerful radiomic features were selected to constitute radiomic signature. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, sensitivity, specificity, and the Hosmer-Lemeshow test. RESULTS: The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set. The ROC curve showed the accuracy of the nomogram, radiomic signature, age, and EPI for external validation set were 0.858, 0.826, 0.664, and 0.66 in the validate set, respectively. DATA CONCLUSION: Radiomics nomogram integrated with radiomic signature, EPI, and age was found to be robust for the stratification of GBM patients into long- vs. short-term survival. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter.

PURPOSE: This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD). METHODS: PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T1 -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility. RESULTS: Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600. CONCLUSION: Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

Prediction of Malignant Acute Middle Cerebral Artery Infarction via Computed Tomography Radiomics.

Malignant middle cerebral artery infarction (mMCAi) is a serious complication of cerebral infarction usually associated with poor patient prognosis. In this retrospective study, we analyzed clinical information as well as non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) data from patients with cerebral infarction in the middle cerebral artery (MCA) territory acquired within 24 h from symptoms onset. Then, we aimed to develop a model based on the radiomics signature to predict the development of mMCAi in cerebral infarction patients. Patients were divided randomly into training (n = 87) and validation (n = 39) sets. A total of 396 texture features were extracted from each NCCT image from the 126 patients. The least absolute shrinkage and selection operator regression analysis was used to reduce the feature dimension and construct an accurate radiomics signature based on the remaining texture features. Subsequently, we developed a model based on the radiomics signature and Alberta Stroke Program Early CT Score (ASPECTS) based on NCCT to predict mMCAi. Our prediction model showed a good predictive performance with an AUC of 0.917 [95% confidence interval (CI), 0.863-0.972] and 0.913 [95% CI, 0.795-1] in the training and validation sets, respectively. Additionally, the decision curve analysis (DCA) validated the clinical efficacy of the combined risk factors of radiomics signature and ASPECTS based on NCCT in the prediction of mMCAi development in patients with acute stroke across a wide range of threshold probabilities. Our research indicates that radiomics signature can be an instrumental tool to predict the risk of mMCAi.