Long Non-Coding RNA H19 Leads to Upregulation of γ-Globin Gene Expression during Erythroid Differentiation.

Long noncoding RNAs (lncRNAs) are important because they are involved in a variety of life activities and have many downstream targets. Moreover, there is also increasing evidence that some lncRNAs play important roles in the expression and regulation of γ-globin genes. In our previous study, we analyzed genetic material from nucleated red blood cells (NRBCs) extracted from premature and full-term umbilical cord blood samples. Through RNA sequencing (RNA-Seq) analysis, lncRNA H19 emerged as a differentially expressed transcript between the two blood types. While this discovery provided insight into H19, previous studies had not investigated its effect on the γ-globin gene. Therefore, the focus of our study was to explore the impact of H19 on the γ-globin gene. In this study, we discovered that overexpressing H19 led to a decrease in HBG mRNA levels during erythroid differentiation in K562 cells. Conversely, in CD34+ hematopoietic stem cells and human umbilical cord blood-derived erythroid progenitor (HUDEP-2) cells, HBG expression increased. Additionally, we observed that H19 was primarily located in the nucleus of K562 cells, while in HUDEP-2 cells, H19 was present predominantly in the cytoplasm. These findings suggest a significant upregulation of HBG due to H19 overexpression. Notably, cytoplasmic localization in HUDEP-2 cells hints at its potential role as a competing endogenous RNA (ceRNA), regulating γ-globin expression by targeting microRNA/mRNA interactions.

Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.

OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT. METHODS: This retrospective study included 1049 patients with 1082 lesions from two independent hospitals that were pathologically confirmed as HCC or non-HCC. All patients underwent a four-phase CT imaging protocol. All lesions were graded (LR 4/5/M) by radiologists and divided into an internal (n = 886) and external cohort (n = 196) based on the examination date. In the internal cohort, Swin-Transformer based on different CT protocols were trained and tested for their ability to LI-RADS grading and distinguish HCC from non-HCC, and then validated in the external cohort. We further developed a combined model with the optimal protocol and clinical information for distinguishing HCC from non-HCC. RESULTS: In the test and external validation cohorts, the three-phase protocol without pre-contrast showed κ values of 0.6094 and 0.4845 for LI-RADS grading, and its accuracy was 0.8371 and 0.8061, while the accuracy of the radiologist was 0.8596 and 0.8622, respectively. The AUCs in distinguishing HCC from non-HCC were 0.865 and 0.715 in the test and external validation cohorts, while those of the combined model were 0.887 and 0.808. CONCLUSION: The Swin-Transformer based on three-phase CT protocol without pre-contrast could feasibly simplify LI-RADS grading and distinguish HCC from non-HCC. Furthermore, the DL model have the potential in accurately distinguishing HCC from non-HCC using imaging and highly characteristic clinical data as inputs. CLINICAL RELEVANCE STATEMENT: The application of deep learning model for multiphase CT has proven to improve the clinical applicability of the Liver Imaging Reporting and Data System and provide support to optimize the management of patients with liver diseases. KEY POINTS: • Deep learning (DL) simplifies LI-RADS grading and helps distinguish hepatocellular carcinoma (HCC) from non-HCC. • The Swin-Transformer based on the three-phase CT protocol without pre-contrast outperformed other CT protocols. • The Swin-Transformer provide help in distinguishing HCC from non-HCC by using CT and characteristic clinical information as inputs.

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.

BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images. METHODS: We retrospectively collected data from 828 AIS patients undergoing recombinant tissue plasminogen activator (rt-PA) treatment within a 4.5-h time window (n = 665) or of undergoing urokinase treatment within a 6-h time window (n = 163) and divided them into the HT group (n = 69) and non-HT group (n = 759). HT was defined based on the criteria of the European Cooperative Acute Stroke Study-II trial. To address the problems of indiscernible features and imbalanced data, a weakly supervised deep learning (WSDL) model for HT prediction was constructed based on multiple instance learning and active learning using admission NCCT images and clinical information in addition to conventional deep learning models. Threefold cross-validation and transfer learning were performed to confirm the robustness of the network. Of note, the predictive value of the commonly used scales in clinics associated with NCCT images (i.e., the HAT and SEDAN score) was also analysed and compared to measure the feasibility of our proposed DL algorithms. RESULTS: Compared to the conventional DL and ML models, the WSDL model had the highest AUC of 0.799 (95% CI 0.712-0.883). Significant differences were observed between the WSDL model and five ML models (P < 0.05). The prediction performance of the WSDL model outperforms the HAT and SEDAN scores at the optimal operating point (threshold = 1.5). Further subgroup analysis showed that the WSDL model performed better for symptomatic intracranial haemorrhage (AUC = 0.833, F1 score = 0.909). CONCLUSIONS: Our WSDL model based on NCCT images had relatively good performance for predicting HT in AIS and may be suitable for assisting in clinical treatment decision-making.

Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility study.

BACKGROUND: Chest radiography is the standard investigation for identifying rib fractures. The application of artificial intelligence (AI) for detecting rib fractures on chest radiographs is limited by image quality control and multilesion screening. To our knowledge, few studies have developed and verified the performance of an AI model for detecting rib fractures by using multi-center radiographs. And existing studies using chest radiographs for multiple rib fracture detection have used more complex and slower detection algorithms, so we aimed to create a multiple rib fracture detection model by using a convolutional neural network (CNN), based on multi-center and quality-normalised chest radiographs. METHODS: A total of 1080 radiographs with rib fractures were obtained and randomly divided into the training set (918 radiographs, 85%) and the testing set (162 radiographs, 15%). An object detection CNN, You Only Look Once v3 (YOLOv3), was adopted to build the detection model. Receiver operating characteristic (ROC) and free-response ROC (FROC) were used to evaluate the model's performance. A joint testing group of 162 radiographs with rib fractures and 233 radiographs without rib fractures was used as the internal testing set. Furthermore, an additional 201 radiographs, 121 with rib fractures and 80 without rib fractures, were independently validated to compare the CNN model performance with the diagnostic efficiency of radiologists. RESULTS: The sensitivity of the model in the training and testing sets was 92.0% and 91.1%, respectively, and the precision was 68.0% and 81.6%, respectively. FROC in the testing set showed that the sensitivity for whole-lesion detection reached 91.3% when the false-positive of each case was 0.56. In the joint testing group, the case-level accuracy, sensitivity, specificity, and area under the curve were 85.1%, 93.2%, 79.4%, and 0.92, respectively. At the fracture level and the case level in the independent validation set, the accuracy and sensitivity of the CNN model were always higher or close to radiologists' readings. CONCLUSIONS: The CNN model, based on YOLOv3, was sensitive for detecting rib fractures on chest radiographs and showed great potential in the preliminary screening of rib fractures, which indicated that CNN can help reduce missed diagnoses and relieve radiologists' workload. In this study, we developed and verified the performance of a novel CNN model for rib fracture detection by using radiography.

A Rare Case of Abnormal Hemoglobin Variant Hb Mizuho: [HBB: c.206T > C ß 68(E12) Leu-Pro]: A First Report in the Chinese Population.

A 6-month-old female infant presented with unexplained hemolytic anemia, showing no abnormalities by capillary electrophoresis and genetic testing for α- and ß-thalassemia mutations that are commonly seen in the Chinese population. A rare Hb Mizuho: [HBB: c.206T > C ß 68(E12) Leu- Pro] variant was identified by next-generation sequencing (NGS) and verified by Sanger sequencing. Hb Mizuho: [HBB: c.206T > C ß 68(E12) Leu- Pro] is not easily detectable because it is extremely unstable, and the correct diagnosis is usually made via DNA sequencing. This is the first report of this variant in the Chinese population.

Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI.

BACKGROUND: Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources. PURPOSE: To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation. FIELD STRENGTH/SEQUENCE: 3.0 T; fast spin-echo (T1 WI, T2 WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging. ASSESSMENT: Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists. STATISTICAL TESTS: The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant). RESULTS: In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%). DATA CONCLUSION: The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard. METHODS: Patients with lumbar VCFs were retrospectively enrolled from January 2011 to May 2020. All patients underwent DR and MRI scanning. VCFs were categorized as fresh or old according to MRI results, and the VCF grade and type were assessed. The raw DR data were sent to InferScholar Center for annotation. A DL-based prediction model was built, and its diagnostic performance was evaluated. The DeLong test was applied to assess differences in ROC curves between different models. RESULTS: A total of 1877 VCFs in 1099 patients were included in our study and randomly divided into development (n = 824 patients) and test (n = 275 patients) datasets. The ensemble model identified fresh and old VCFs, reaching an AUC of 0.80 (95% confidence interval [CI], 0.77-0.83), an accuracy of 74% (95% CI, 72-77%), a sensitivity of 80% (95% CI, 77-83%), and a specificity of 68% (95% CI, 63-72%). Lateral (AUC, 0.83) views exhibited better performance than anteroposterior views (AUC, 0.77), and the best performance among respective subgroupings was obtained for grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups. CONCLUSION: The proposed DL model achieved adequate performance in identifying fresh VCFs from DR. KEY POINTS: • The ensemble deep-learning model identified fresh VCFs from DR, reaching an AUC of 0.80, an accuracy of 74%, a sensitivity of 80%, and a specificity of 68% with the reference standard of MRI. • The lateral views (AUC, 0.83) exhibited better performance than anteroposterior views (AUC, 0.77). • The grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups showed the best performance among their respective subgroupings.

Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms.

OBJECTIVE: To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images. METHODS: A total of 151 ASD children and 151 age-matched typically developing (TD) controls were included in this study. The data from these subjects were assigned to training and validation datasets. An additional 20 ASD children and 25 TD controls were acquired, whose data were utilized in an independent test set. All subjects underwent cMRI and diffusion-weighted imaging examination of the brain. We developed a series of DL models to separate ASD from TD based on the cMRI and ADC data. The seven models used include five single-sequence models (SSMs), one dominant-sequence model (DSM), and one all-sequence model (ASM). To enhance the feature detection of the models, we embed an attention mechanism module. RESULTS: The highest AUC (0.824 ~ 0.850) was achieved when applying the SSM based on either FLAIR or ADC to the validation and independent test sets. A DSM using the combination of FLAIR and ADC showed an improved AUC in the validation (0.873) and independent test sets (0.876). The ASM also showed better diagnostic value in the validation (AUC = 0.838) and independent test sets (AUC = 0.836) compared to the SSMs. Among the models with attention mechanism, the DSM achieved the highest diagnostic performance with an AUC, accuracy, sensitivity, and specificity of 0.898, 84.4%, 85.0%, and 84.0% respectively. CONCLUSIONS: This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. KEY POINTS: • Deep learning models based on conventional MRI and ADC can be used to diagnose ASD. • The model (DSM) based on the FLAIR and ADC sequence achieved the best diagnostic performance with an AUC of 0.836 in the independent test sets. • The attention mechanism further improved the diagnostic performance of the models.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD. METHODS: A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients. RESULTS: The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale. CONCLUSIONS: The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. KEY POINTS: • Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.

Whole-exome sequencing identified five novel de novo variants in patients with unexplained intellectual disability.

BACKGROUND: Intellectual disability (ID) represents a neurodevelopmental disorder, which is characterized by marked defects in the intellectual function and adaptive behavior, with an onset during the developmental period. ID is mainly caused by genetic factors, and it is extremely genetically heterogeneous. This study aims to identify the genetic cause of ID using trio-WES analysis. METHODS: We recruited four pediatric patients with unexplained ID from non-consanguineous families, who presented at the Department of Pediatrics, Guizhou Provincial People's Hospital. Whole-exome sequencing (WES) and Sanger sequencing validation were performed in the patients and their unaffected parents. Furthermore, conservative analysis and protein structural and functional prediction were performed on the identified pathogenic variants. RESULTS: We identified five novel de novo mutations from four known ID-causing genes in the four included patients, namely COL4A1 (c.2786T>A, p.V929D and c.2797G>A, p.G933S), TBR1 (c.1639_1640insCCCGCAGTCC, p.Y553Sfs*124), CHD7 (c.7013A>T, p.Q2338L), and TUBA1A (c.1350del, p.E450Dfs*34). These mutations were all predicted to be deleterious and were located at highly conserved domains that might affect the structure and function of these proteins. CONCLUSION: Our findings contribute to expanding the mutational spectrum of ID-related genes and help to deepen the understanding of the genetic causes and heterogeneity of ID.

Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images.

BACKGROUND AND AIMS: Recent advances in deep convolutional neural networks (CNNs) have led to remarkable results in digestive endoscopy. In this study, we aimed to develop CNN-based models for the differential diagnosis of benign esophageal protruded lesions using endoscopic images acquired during real clinical settings. METHODS: We retrospectively reviewed the images from 1217 patients who underwent white-light endoscopy (WLE) and EUS between January 2015 and April 2020. Three deep CNN models were developed to accomplish the following tasks: (1) identification of esophageal benign lesions from healthy controls using WLE images; (2) differentiation of 3 subtypes of esophageal protruded lesions (including esophageal leiomyoma [EL], esophageal cyst (EC], and esophageal papilloma [EP]) using WLE images; and (3) discrimination between EL and EC using EUS images. Six endoscopists blinded to the patients' clinical status were enrolled to interpret all images independently. Their diagnostic performances were evaluated and compared with the CNN models using the area under the receiver operating characteristic curve (AUC). RESULTS: For task 1, the CNN model achieved an AUC of 0.751 (95% confidence interval [CI], 0.652-0.850) in identifying benign esophageal lesions. For task 2, the proposed model using WLE images for differentiation of esophageal protruded lesions achieved an AUC of 0.907 (95% CI, 0.835-0.979), 0.897 (95% CI, 0.841-0.953), and 0.868 (95% CI, 0.769-0.968) for EP, EL, and EC, respectively. The CNN model achieved equivalent or higher identification accuracy for EL and EC compared with skilled endoscopists. In the task of discriminating EL from EC (task 3), the proposed CNN model had AUC values of 0.739 (EL, 95% CI, 0.600-0.878) and 0.724 (EC, 95% CI, 0.567-0.881), which outperformed seniors and novices. Attempts to combine the CNN and endoscopist predictions led to significantly improved diagnostic accuracy compared with endoscopists interpretations alone. CONCLUSIONS: Our team established CNN-based methodologies to recognize benign esophageal protruded lesions using routinely obtained WLE and EUS images. Preliminary results combining the results from the models and the endoscopists underscored the potential of ensemble models for improved differentiation of lesions in real endoscopic settings.

A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI.

BACKGROUND: Preoperative differentiation between primary sacral chordoma (SC), sacral giant cell tumor (SGCT), and sacral metastatic tumor (SMT) is important for treatment decisions. PURPOSE: To develop and validate a triple-classification radiomics model for the preoperative differentiation of SC, SGCT, and SMT based on T2-weighted fat saturation (T2w FS) and contrast-enhanced T1-weighted (CE T1w) MRI. STUDY TYPE: Retrospective. POPULATION: A total of 120 pathologically confirmed sacral patients (54 SCs, 30 SGCTs, and 36 SMTs) were retrospectively analyzed and divided into a training set (n = 83) and a validation set (n = 37). FIELD STRENGTH/SEQUENCE: The 3.0T axial T2w FS and CE T1w MRI. ASSESSMENT: Morphology, intensity, and texture features were assessed based on Formfactor, Haralick, Gray-level co-occurrence matrix (GLCM), Gray-level run-length matrix (GLRLM), histogram. STATISTICAL TESTS: Analysis of variance, least absolute shrinkage and selection operator (LASSO), Pearson correlation, Random Forest (RF), area under the receiver operating characteristic curve (AUC) and accuracy analysis. RESULTS: The median age of SGCT (33.5, 25.3-45.5) was significantly lower than those of SC (58.0, 48.8-64.3) and SMT (59.0, 46.3-65.5) groups (χ2 = 37.6; P < 0.05). No significant difference was found when compared in terms of genders, tumor locations, and tumor sizes of SC, SGCT, and SMT ( χgender2=3.75,χlocation2=2.51,χsize2=5.77 ; P1 = 0.15, P2 = 0.29, P3 = 0.06). For the differential value, features extracted from joint T2w FS and CE T1w images outperformed those from T2w FS or CE T1w images alone. Compared with CE T1w images, features derived from T2w FS images yielded higher AUC in both training and validating set. The best performance of radiomics model based on joint T2w FS and CE T1w images reached an AUC of 0.773, an accuracy of 0.711. DATA CONCLUSION: Our 3.0T MRI-based triple-classification radiomics model is feasible to differentiate SC, SGCT, and SMT, which may be applied to improve the precision of preoperative diagnosis in clinical practice. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:752-759.

Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI.

BACKGROUND: Tumor heterogeneity can be assessed by texture analysis (TA). TA has been applied using diffusion-weighted imaging and apparent diffusion coefficient maps to predict pathological responses to preoperative chemoradiation therapy (CRT) in patients with locally advanced rectal cancer (LARC). PURPOSE: To evaluate the texture parameters obtained from K trans maps derived from dynamic contrast-enhanced (DCE)-MRI for predicting pathological responses to preoperative CRT for LARCs. STUDY TYPE: Retrospective. POPULATION: Altogether, 83 patients (26 women, 57 men) with rectal cancer met the inclusion criteria. FIELD STRENGTH/SEQUENCE: 3.0T/T1 -weighted DCE-MRI sequence. ASSESSMENT: After CRT, each tumor was assessed by a pathologist who assigned a tumor regression grade (TRG), thereby identifying pathologically complete responders (pCR; TRG 1) and good responders (GR; TRG1 + TRG2). TA was then applied to the DCE-MRI K trans maps. The K trans value, several TA parameters, and tumor volumes were calculated. STATISTICAL TESTS: The Shapiro-Wilk test was used to verify that the data had normal distribution. Results of parameters measured before and after CRT were compared using paired-sample t-tests. Value changes of each parameter in the combined pCR/GR group were compared using independent sample t-tests. Receiver operating characteristic curves and areas under the curve (AUC) were calculated to assess the diagnostic performance of each parameter related to CRT effectiveness. RESULTS: There were 15 pCR (16.9%) and 21 GR (25.3%) patients. Tumor volume, mean K trans , entropy, and correlation decreased and energy values increased significantly in these groups compared with those of the non-PCR and non-GR groups. ΔCorrelation (Δcorrelation = postcorrelation - precorrelation) was found to be a valuable parameter for identifying pCR/GR patients (AUC 0.895, sensitivity 86.7%, specificity 81.8%). DATA CONCLUSION: TA parameters from the DCE-MRI K trans map can predict the efficacy of CRT for treating LARCs. Also, Δcorrelation may be useful for identifying patients who will be responsive to CRT. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:885-893.

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

BACKGROUND: Multiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores. PURPOSE: To develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores. STUDY TYPE: Retrospective. POPULATION: In all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study. FIELD STRENGTH/SEQUENCE: Conventional and diffusion-weighted MR images (b values = 0, 1000 sec/mm2 ) were acquired on a 3.0T MR scanner. ASSESSMENT: A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T2 WI, respectively. A predictive model was constructed for differentiating PCa from non-PCa and high-grade from low-grade PCa. The diagnostic performance of each radiomics-based model was compared with that of the PI-RADS v2 scores. STATISTICAL TESTS: A radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups. RESULTS: For PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T2 WI, ADC, and T2 WI&ADC features, respectively. For low-grade versus high-grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T2 WI, ADC, and T2 WI&ADC features, respectively. PI-RADS v2 had an AUC of 0.867 in differentiating PCa from non-PCa and an AUC of 0.763 in differentiating high-grade from low-grade PCa. DATA CONCLUSION: Both the T2 WI- and ADC-based radiomics models showed high diagnostic efficacy and outperformed the PI-RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high-grade vs. low-grade PCa. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.

Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.

OBJECTIVE: We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features. METHODS: A total of 95 patients were divided into a training set and a validation set. Three best feature selection methods (Relief, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF)) and three classification methods, including generalised linear models (GLM), support vector machines (SVM) and RF, were compared for their performance in distinguishing SC and SGCT. The performance of the radiomics model was investigated via area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) analysis. RESULTS: The selection method LASSO + classifier GLM had the highest AUC of 0.984 and ACC of 0.897 in the validating set, followed by Relief + GLM (AUC = 0.909, ACC = 0.862) and LASSO + SVM (AUC = 0.900, ACC = 0.862) based on CTE features. For CT features, RF + GLM had the highest AUC of 0.889, while LASSO + GLM achieved a high ACC of 0.793 in the validating set. Regardless of the methods, CTE features significantly outperformed those from CT for the differentiation of SC and SGCT (ZAUC = -3.029, ZACC = -4.553; p < 0.05). CONCLUSIONS: Our study demonstrated CTE features performed better than CT features. The selection method LASSO + classifier GLM had the best performance in differentiation of SC and SGCT, which could enhance the application of radiomics methods in sacral tumours. KEY POINTS: • Sacral chordoma and sacral giant cell tumour are the two most common primary tumours of the sacrum with many common clinical and imaging characteristics. • A radiomics model helps clinicians to identify the histology of a sacral tumour. • CTE features should be preferred.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging.

OBJECTIVE: To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH). METHODS: 57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve. RESULTS: Median, minimum, the 10th, 25th percentile ADC in both ADC1000 and ADC2000 and skewness in ADC2000 had significant differences between TZ cancer and BPH (for all, P < 0.05).The 10th percentile ADC showed highest area under the ROC curve (AUC) in both ADC1000 and ADC2000.The 10th percentile ADC of ADC2000 showed significantly higher AUC than did ADC1000 (P = 0.0385). CONCLUSIONS: The 10th percentile ADC obtained from ultrahigh b-value DWI performed better for differentiating TZ cancer from BPH.

Alterations of default mode functional connectivity in individuals with end-stage renal disease and mild cognitive impairment.

BACKGROUND: Mild cognitive impairment (MCI) occurs frequently in many end stage renal disease (ESRD) patients, may significantly worsen survival odds and prognosis. However, the exact neuropathological mechanisms of MCI combined with ESRD are not fully clear. This study examined functional connectivity (FC) alterations of the default-mode network (DMN) in individuals with ESRD and MCI. METHODS: Twenty-four individuals with ESRD identified as MCI patients were included in this study; of these, 19 and 5 underwent hemodialysis (HD) and peritoneal dialysis (PD), respectively. Another group of 25 age-, sex- and education level-matched subjects were recruited as the control group. All participants underwent resting-state functional MRI and neuropsychological tests; the ESRD group underwent additional laboratory testing. Independent component analysis (ICA) was used for DMN characterization. With functional connectivity maps of the DMN derived individually, group comparison was performed with voxel-wise independent samples t-test, and connectivity changes were correlated with neuropsychological and clinical variables. RESULTS: Compared with the control group, significantly decreased functional connectivity of the DMN was observed in the posterior cingulate cortex (PCC) and precuneus (Pcu), as well as in the medial prefrontal cortex (MPFC) in the ESRD group. Functional connectivity reductions in the MPFC and PCC/Pcu were positively correlated with hemoglobin levels. In addition, functional connectivity reduction in the MPFC showed positive correlation with Montreal Cognitive Assessment (MoCA) score. CONCLUSION: Decreased functional connectivity in the DMN may be associated with neuropathological mechanisms involved in ESRD and MCI.

Dynamic contrast-enhanced MRI in orbital lymphoproliferative disorders: Effects of region of interest selection methods on time efficiency, measurement reproducibility, and diagnostic ability.

BACKGROUND: Previous studies indicated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could serve as a useful biomarker for differentiating malignant from benign orbital lymphoproliferative disorders (OLPDs). PURPOSE: To investigate the influence of different region of interest (ROI) selection methods on the measurements of DCE-MRI parameters, and their diagnostic ability in discriminating malignant from benign OLPDs. STUDY TYPE: Retrospective study. POPULATION: In all, 46 patients with OLPDs (22 benign and 24 malignant). FIELD STRENGTH/SEQUENCE: 3.T DCE-MRI using a 2D turbo fast low angle shot sequence postcontrast. ASSESSMENT: DCE-MRI data were analyzed using three different ROI selection methods, including whole-tumor ROI (ROIWT ), single-slice ROI (ROISS ) and hot-spot ROI (ROIHS ). Quantitative parameters (Ktrans , Kep , Ve ) were calculated based on a modified Tofts model. STATISTICAL TESTING: Analysis of variance test, intraclass correlation coefficient (ICC), Bland-Altman plots, independent t-test, and receiver operating characteristic curve analyses were used for statistical analyses. RESULTS: The time required for outlining ROIWT was significantly longer than ROISS and ROIHS (P < 0.001). The measurements of DCE-MRI-derived parameters based on ROIHS demonstrated lowest ICC, followed by ROISS and ROIWT . Malignant OLPDs showed significantly higher Kep than benign mimics (P < 0.001), while no significant differences were found on Ktrans (ROIWT , P = 0.535; ROISS , P = 0.557; ROIHS , P = 0.400) and Ve (ROIWT , P = 0.071; ROISS , P = 0.079; ROIHS , P = 0.057). Kep -ROIWT showed the highest area under curve for differentiating malignant from benign OLPDs, followed by Kep -ROISS , and Kep-ROIHS ; however, the differences were not significant (ROIWT vs. ROISS , P = 0.407; ROIWT vs. ROIHS , P = 0.363; ROISS vs. ROIHS , P = 0.887). DATA CONCLUSION: ROI selection methods could have an influence on the measurements of DCE-MRI parameters. Taking measurement time, reproducibility, and diagnostic ability into account, we suggest single-slice ROI to be used for differentiating malignant from benign OLPDs in clinical practice. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1298-1305.

Benign and malignant orbital lymphoproliferative disorders: Differentiating using multiparametric MRI at 3.0T.

PURPOSE: To determine the optimal combination of parameters derived from 3T multiparametric (conventional magnetic resonance imaging [MRI], diffusion-weighted [DW] and dynamic contrast-enhanced [DCE]) MRI for differentiating malignant from benign orbital lymphoproliferative disorders (OLPDs). MATERIALS AND METHODS: Forty patients with OLPDs (18 benign and 22 malignant) underwent conventional 3.0T MR, DW, and DCE-MRI examination for presurgery evaluation. Conventional MRI features (including tumor laterality, shape, number of involved quadrants, signal intensity on T1 -weighted imaging (WI) and T2 WI, flow void sign on T2 WI, and findings suggestive of sinusitis) were reviewed, and multivariate logistic regression analysis was used to identify the most significant conventional MRI features. Apparent diffusion coefficient (ADC) and DCE-MRI derived parameters (area under curve [AUC], time to peak [TTP], maximum rise slope [Slopemax ]) were measured and compared between two groups. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic ability of each combination that was established based on identified qualitative and quantitative parameters. RESULTS: Multivariate logistic regression analysis showed that the presence of flow void sign on T2 WI significantly associated with benign OLPDs (P = 0.034). Malignant OLPDs demonstrated significantly lower ADC (P = 0.001) and AUC (P = 0.002) than benign mimics. ROC analyses indicted that, ADC alone showed the optimal sensitivity (threshold value, 0.886 × 10-3 mm2 /s; sensitivity, 90.9%), while a combination of no presence of flow void sign on T2 WI + ADC ≤ 0.886 × 10-3 mm2 /s + AUC ≤ 7.366 showed optimal specificity (88.9%) in differentiating benign from malignant OLPDs. CONCLUSION: Multiparametric MRI can help to differentiate malignant from benign OLPDs. DWI offers optimal sensitivity, while the combination of conventional MRI, DWI, and DCE-MRI offers optimal specificity. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:167-176.

Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image.

PURPOSE: To evaluate the diagnostic performance of extended models of diffusion-weighted (DW) imaging to help differentiate the epidermal growth factor receptor (EGFR) mutation status in stage IIIA-IV lung adenocarcinoma. MATERIALS AND METHODS: This retrospective study had institutional research board approval and was HIPAA compliant. Preoperative extended DW imaging including intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) 3 Tesla MRI were retrospectively evaluated in 53 patients with pathologically confirmed non-early stage (IIIA-IV) lung adenocarcinoma. EGFR mutationsat exons 18-21 were determined by using polymerase chain reaction-based ARMS. Quantitative parameters (mean, kurtosis, skewness, 10th and 90th percentiles) of IVIM (true-diffusion coefficient D, pseudo-diffusion coefficient D*, and perfusion fraction f) and DKI (kurtosis value Kapp, kurtosis corrected diffusion coefficient Dapp) were calculated by outlining entire-volume histogram analysis. Receiver operating characteristic analysis was constructed to determine the diagnostic performance of each parameter. Multivariate logistic regression was used to differentiate the probability of EGFR mutation status. RESULTS: Twenty-four of 53 patients with lung adenocarcinoma were EGFR mutations, which occurred most often in acinar (10 of 13 [76.9%]) and papillary predominant tumors (9 of 13 [69.2%]). Patients with EGFR mutation showed significant higher 10th percentile of D, lower D* value in terms of kurtosis, and lower Kapp value in terms of mean, skewness, 10th and 90th percentiles (all P values < 0.05). The 90th Kapp showed significantly higher sensitivity (97%; P < 0.05) and Az (0.817; P < 0.05) value. Multivariate logistic regression showed 90th Kapp was a independent factor for determining EGFR mutation with odds ratio -1.657. CONCLUSION: Multiple IVIM and DKI parameters, especially the histogram 90th Kapp value, helped differentiate EGFR mutation status in stage IIIA-IV lung adenocarcinoma. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:281-289.