Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI.

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

Joint radiomics and spatial distribution model for MRI-based discrimination of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.

OBJECTIVE: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD). METHODS: Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists. RESULTS: For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%). CONCLUSIONS: The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD. CLINICAL RELEVANCE STATEMENT: The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder. KEY POINTS: • Our study introduces an approach by combining radiomics and spatial distribution models. • The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases. • The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.

Real-world effectiveness and safety of evolocumab in very high-risk atherosclerotic cardiovascular disease patients with acute ischemic stroke.

BACKGROUND: We investigated evolocumab's real-world effectiveness and safety on a background of statin therapy in the acute phase of ischemic stroke (IS) patients with a very high-risk of atherosclerotic cardiovascular disease (ASCVD). METHODS: A real-world, single-center, retrospective study was conducted in the neurology department at Tianjin Huanhu Hospital in China. Patients were divided into two groups: evolocumab treatment (140 mg every two weeks) or the standard of care (SOC) group. The primary efficacy outcome of the study was the achievement of a targeted lipid control rate and the incidence of major adverse cardiovascular events (MACE) by the end of the follow-up. MACE was defined as a composite of various cardiovascular events, cerebrovascular events such as stroke or TIA, and event-related deaths. Propensity score matching (PSM) analysis was utilized to account for confounding factors between groups. Survival analyses were performed using the Kaplan-Meier method and COX regression modeling. RESULTS: 1080 AIS patients with very high-risk ASCVD were recruited. After PSM, there were 528 individuals, with 206 in the evolocumab group and 322 in the SOC group. At 12 months of follow-up, the proportion of LDL-C < 1.4mmol/L and ≥ 50% reduction was 44.91% in the evolocumab group, compared with only 3.12% of SOC-treated patients (p < 0.01). The median follow-up time for clinical events was 15 months. The evolocumab group was associated with a lower risk of cerebrovascular events compared to the SOC group (HR, 0.45; 95% CI, 0.23-0.89; p = 0.02). CONCLUSIONS: This real-world study suggested that evolocumab on a background of statin reduced the LDL-C levels significantly and lowered the incidence of recurrent cerebrovascular events in the very high-risk ASCVD patients with AIS in China.

Quantitative differentiation of non-invasive bladder urothelial carcinoma and inverted papilloma based on CT urography.

PURPOSE: To investigate the value of CT urography (CTU) indicators in the quantitative differential diagnosis of bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB). MATERIAL AND METHODS: The clinical and preoperative CTU imaging data of continuous 103 patients with histologically confirmed BUC or IPB were retrospectively analyzed. The imaging data included 6 qualitative indicators and 7 quantitative measures. The recorded clinical information and imaging features were subjected to univariate and multivariate logistic regression analysis to find independent risk factors for BUC, and a combined multi-indicator prediction model was constructed, and the prediction model was visualized using nomogram. ROC curve analysis was used to calculate and compare the predictive efficacy of independent risk factors and nomogram. RESULTS: Junction smoothness, maximum longitudinal diameter, tumor-wall interface and arterial reinforcement rate were independent risk factors for distinguishing BUC from IPB. The AUC of the combined model was 0.934 (sensitivity = 0.808, specificity = 0.920, accuracy = 0.835), and its diagnostic efficiency was higher than that of junction smoothness (AUC=0.667, sensitivity = 0.654, specificity = 0.680, accuracy = 0.660), maximum longitudinal diameter (AUC=0.757, sensitivity = 0.833, specificity = 0.604, accuracy = 0.786), tumor-wall interface (AUC=0.888, sensitivity = 0.755, specificity = 0.808, accuracy = 0.816) and Arterial reinforcement rate (AUC=0.786, sensitivity = 0.936, specificity = 0.640, accuracy = 0.864). CONCLUSION: Above qualitative and quantitative indicators based on CTU and the combination of them may be helpful to the differential diagnosis of BUC and IPB, thus better assisting in clinical decision-making. KEY POINTS: 1. Bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB) exhibit similar clinical symptoms and imaging presentations. 2. The diagnostic value of CT urography (CTU) in distinguishing between BUC and IPB has not been documented. 3. BUC and IPB differ in lesion size, growth pattern and blood supply. 4. The diagnostic efficiency is optimized by integrating multiple independent risk factors into the prediction model.

Decoding the role of long noncoding RNAs in the healthy aging of centenarians.

Aging is the largest risk factor of major human diseases. Long noncoding RNAs (lncRNAs) as the key regulatory elements have shown a strong impact on multiple biological processes as well as human disease mechanisms. However, the roles of lncRNAs in aging/healthy aging processes remain largely unknown. Centenarians are good models for healthy aging studies due to avoiding major chronic diseases and disabilities. To illustrate their ubiquitous nature in the genome and the 'secrets' of healthy aging regulation from the perspective of lncRNAs, peripheral blood samples from two regions consisting 76 centenarians (CENs), 54 centenarian-children (F1) and 41 spouses of centenarian-children (F1SP) were collected for deep RNA-seq. We identified 11 CEN-specific lncRNAs that is particularly expressed in longevous individuals. By kmers clustering, hundreds of human lncRNAs show similarities with CEN-specific lncRNAs, especially with ENST00000521663 and ENST00000444998. Using F1SP as normal elder controls (age: 59.9 ± 6.6 years), eight lncRNAs that are differentially expressed in longevous elders (CEN group, age: 102.2 ± 2.4 years) were identified as candidate aging/health aging-related lncRNAs (car-lncs). We found that the expression of eight car-lncs in human diploid fibroblasts displayed dynamic changes during cell passage and/or H2O2/rapamycin treatment; of which, overexpression either of THBS1-IT1 and THBS1-AS1, two lncRNAs that highly expressed in CENs, can remarkably decrease p16, p21 and the activity of senescent related ß-galactosidase, suggesting that THBS1-IT1 and THBS1-AS1 can inhibit cellular senescence. We provided the first comprehensive analysis of lncRNA expression in longevous populations, and our results hinted that dysregulated lncRNAs in CENs are potential protective factors in healthy aging process.

Online decision tools for personalized survival prediction and treatment optimization in elderly patients with lung squamous cell carcinoma: a retrospective cohort study.

BACKGROUND: Despite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualized survival prediction and treatment optimization in elderly patients with LSCC. METHODS: Clinical data of 4564 patients (stageIB-IIIB) diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for prognostic nomograms development. The proposed models were externally validated using a separate group consisting of 1299 patients (stage IB-IIIB) diagnosed from 2012-2015 in China. The prognostic performance was measured using the concordance index (C-index), calibration curves, the average time-dependent area under the receiver operator characteristic curves (AUC), and decision curve analysis. RESULTS: Eleven candidate prognostic variables were identified by the univariable and multivariable Cox regression analysis. The calibration curves showed satisfactory agreement between the actual and nomogram-estimated Lung Cancer-Specific Survival (LCSS) rates. By calculating the c-indices and average AUC, our nomograms presented a higher prognostic accuracy than the current staging system. Clinical usefulness was revealed by the decision curve analysis. User-friendly online decision tools integrating proposed nomograms were created to estimate survival for patients with different treatment regimens. CONCLUSIONS: The decision tools for individualized survival prediction and treatment optimization might facilitate clinicians with decision-making, medical teaching, and experimental design. Online tools are expected to be integrated into clinical practice by using the freely available website ( https://loyal-brand-611803.framer.app/ ).

Altered Neurovascular Coupling in Patients With Mitochondrial Myopathy, Encephalopathy, Lactic Acidosis, and Stroke-Like Episodes (MELAS): A Combined Resting-State fMRI and Arterial Spin Labeling Study.

BACKGROUND: Coupling between neuronal activity and blood perfusion is termed neurovascular coupling (NVC), and it provides a potentially new mechanistic perspective into understanding numerous brain diseases. Although abnormal brain activity and blood supply have been separately reported in mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), whether anomalous NVC would be present is unclear. PURPOSE: To investigate NVC changes and potential neural basis in MELAS by combining resting-state functional MRI (rs-fMRI) and arterial spin labeling (ASL). STUDY TYPE: Prospective. SUBJECTS: Twenty-four patients with MELAS (age: 29.8 ± 7.3 years) in the acute stage and 24 healthy controls (HCs, age: 26.4 ± 8.1 years). Additionally, 12 patients in the chronic stage were followed up. FIELD STRENGTH/SEQUENCE: 3.0 T, resting-state gradient-recalled echo-planar imaging and pseudo-continuous 3D ASL sequences. ASSESSMENT: Amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity strength (FCS) were calculated from rs-fMRI, and cerebral blood flow (CBF) was computed from ASL. Global NVC was assessed by correlation coefficients of CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-FCS. Regional NVC was also evaluated by voxel-wise and lesion-wise ratios of CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS. STATISTICAL TESTS: Two-sample t-test, paired-sample t-test, Gaussian random fields correction. A P value <0.05 was considered statistically significant. RESULTS: Compared with HC, MELAS patients in acute stage showed significantly reduced global CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-FCS coupling (P < 0.001). Altered CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS ratios were found mainly distributed in the middle cerebral artery territory in MELAS patients. In addition, significantly increased NVC ratios were found in the acute stroke-like lesions in acute stage (P < 0.001), with a recovery trend in chronic stage. DATA CONCLUSIONS: This study showed dynamic alterations in NVC in MELAS patients from acute to chronic stage, which may provide a novel insight for understanding the pathogenesis of MELAS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Iron quantitative analysis of motor combined with bulbar region in M1 cortex may improve diagnosis performance in ALS.

OBJECTIVES: To explore whether the combined analysis of motor and bulbar region of M1 on susceptibility-weighted imaging (SWI) can be a valid biomarker for amyotrophic lateral sclerosis (ALS). METHODS: Thirty-two non-demented ALS patients and 35 age- and gender-matched healthy controls (HC) were retrospectively recruited. SWI and 3D-T1-MPRAGE images were obtained from all individuals using a 3.0-T MRI scan. The bilateral posterior band of M1 was manually delineated by three neuroradiologists on phase images and subdivided into the motor and bulbar regions. We compared the phase values in two groups and performed a stratification analysis (ALSFRS-R score, duration, disease progression rate, and onset). Receiver operating characteristic (ROC) curves were also constructed. RESULTS: ALS group showed significantly increased phase values in M1 and the two subregions than the HC group, on the all and elderly level (p < 0.001, respectively). On all-age level comparison, negative correlations were found between phase values of M1 and clinical score and duration (p < 0.05, respectively). Similar associations were found in the motor region (p < 0.05, respectively). On both the total (p < 0.01) and elderly (p < 0.05) levels, there were positive relationships between disease progression rate and M1 phase values. In comparing ROC curves, the entire M1 showed the best diagnostic performance. CONCLUSIONS: Combining motor and bulbar analyses as an integral M1 region on SWI can improve ALS diagnosis performance, especially in the elderly. The phase value could be a valuable biomarker for ALS evaluation. KEY POINTS: • Integrated analysis of the motor and bulbar as an entire M1 region on SWI can improve the diagnosis performance in ALS. • Quantitative analysis of iron deposition by SWI measurement helps the clinical evaluation, especially for the elderly patients. • Phase value, when combined with the disease progression rate, could be a valuable biomarker for ALS.

Risk prediction of gestational diabetes mellitus in women with polycystic ovary syndrome based on a nomogram model.

Women with polycystic ovary syndrome are prone to develop gestational diabetes mellitus, a disease which may have significant impact on the postpartum health of both mother and infant. We performed a retrospective cohort study to develop and test a model that could predict gestational diabetes mellitus in the first trimester in women with polycystic ovary syndrome. Our study included 434 pregnant women who were referred to the obstetrics department between December 2017 and March 2020 with a diagnosis of polycystic ovary syndrome. Of these women, 104 were diagnosed with gestational diabetes mellitus in the second trimester. Univariate analysis revealed that in the first trimester, Hemoglobin A1c (HbA1C), age, total cholesterol(TC), low-density lipoprotein cholesterol (LDL-C), SBP (systolic blood pressure), family history, body mass index (BMI), and testosterone were predictive factors of gestational diabetes mellitus (P < 0.05). Logistic regression revealed that TC, age, HbA1C, BMI and family history were independent risk factors for gestational diabetes mellitus. The area under the ROC curve of the gestational diabetes mellitus risk prediction model was 0.937 in this retrospective analysis, demonstrating a great discriminatory ability. The sensitivity and specificity of the prediction model were 0.833 and 0.923, respectively. The Hosmer-Lemeshow test also showed that the model was well calibrated.

Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study.

PURPOSE: To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. METHODS: The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer-lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. RESULTS: The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). CONCLUSIONS: The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Brain MRI features of anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis secondary to central nervous system infection in adult patients.

BACKGROUND: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis secondary to central nervous system (CNS) infection is a unique subtype of the autoimmune-mediated disease, of which the imaging features are unclear. PURPOSE: To compare the brain magnetic resonance imaging (MRI) features between the anti-NMDAR encephalitis secondary to CNS infection and that without initial infection. MATERIAL AND METHODS: A total of 70 adult patients with anti-NMDAR encephalitis were retrospectively enrolled (24 in the post-infection group, 46 in the non-infection-related group). Their clinical and imaging features (lesion distribution, lesion shape, enhancement pattern, brain atrophy) were reviewed and summarized. Lesion distributions were compared between the two groups on lesion probability maps. RESULTS: The patients with normal brain MRI scans in the post-infection group were less than those in the non-infection related group (29% vs. 63%; P = 0.0113). Among the 24 patients in the post-infection group, visible lesions were shown at the anti-NMDAR encephalitis onset in 17 patients; lesion distribution was more diffuse than the non-infection-related group, showing higher lesion peak probabilities in the bilateral hippocampus, frontal lobe, temporal lobe, insula, and cingulate. The lesions with contrast enhancement were also more common in the post-infection group than the non-infection-related group (7/13 vs. 2/10). Brain atrophy was observed in eight patients in the post-infection group and three in the non-infection-related group. CONCLUSION: Anti-NMDAR encephalitis secondary to CNS infection has its imaging features-extensive lesion distribution, leptomeningeal enhancement, early atrophy, and necrosis-that could deepen the understanding of the pathophysiology and manifestation of the autoimmune encephalitis besides the classic type.

Anterior cruciate ligament femoral footprint is oblong-ovate, triangular, or two-tears shaped in healthy young adults: three-dimensional MRI analysis.

PURPOSE: This study aimed to evaluate the morphology of the anterior cruciate ligament (ACL) femoral footprint with three-dimensional magnetic resonance imaging (3D MRI) in healthy knees. METHODS: Fifty subjects with healthy knees were recruited, utilising 3D-SPACE sequences for ACL evaluation. The ACL was manually segmented, and the shape, size and location of the ACL femoral footprint were evaluated on a reformatted oblique-sagittal plane, which aligned closely with the ACL attachment. Statistical analysis included one-way ANOVA for continuous variables and Fisher's exact test for categorical variables, with a P value < 0.05 considered significant. RESULTS: Three types of ACL femoral footprint shape were identified, namely, oblong-ovate (OO) in 33 knees (66%), triangular (Tr) in 12 knees (24%) and two-tears (TT) in 5 knees (10%), with the mean areas being 58, 47 and 68 mm2, respectively. Within group TT, regions with similar sizes but different locations were identified: high tear (TT-H) and low tear (TT-L). Notably, group OO demonstrated a larger notch height index, whilst group TT was characterised by a larger α angle and lateral femoral condyle index. A noticeable variation was observed in the location of the femoral footprint centre across groups, with group TT-L and group Tr showing a more distal position relative to the apex of the deep cartilage. According to the Bernard and Hertel (BH) grid, the ACL femoral footprint centres in group TT-L exhibited a shallower and higher position than other groups. Furthermore, compared to group OO and TT-H, group Tr showed a significantly higher position according to the BH grid. CONCLUSION: In this study, the morphology of the ACL femoral footprint in healthy young adults was accurately evaluated using 3D MRI, revealing three distinct shapes: OO, Tr and TT. The different ACL femoral footprint types showed similar areas but markedly different locations. These findings emphasise the necessity of considering both the shape and precise location of the ACL femoral footprint during clinical assessments, which might help surgeons enhance patient-specific surgical plans before ACL reconstruction. LEVEL OF EVIDENCE: IV.

Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus.

OBJECTIVES: To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE). METHODS: A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists. RESULTS: The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023). CONCLUSIONS: The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases. KEY POINTS: • Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.

MET inhibition enhances PARP inhibitor efficacy in castration-resistant prostate cancer by suppressing the ATM/ATR and PI3K/AKT pathways.

Up to 30% of patients with metastatic castration-resistant prostate cancer (CRPC) patients carry altered DNA damage response genes, enabling the use of poly adenosine diphosphate-ribose polymerase (PARP) inhibitors in advanced CRPC. The proto-oncogene mesenchymal-epithelial transition (MET) is crucial in the migration, proliferation, and invasion of tumour cells. Aberrant expression of MET and its ligand hepatocyte growth factor is associated with drug resistance in cancer therapy. Here, we found that MET was highly expressed in human CRPC tissues and overexpressed in DU145 and PC3 cells in a drug concentration-dependent manner and is closely related to sensitivity to PARP inhibitors. Combining the PARP inhibitor olaparib with the MET inhibitor crizotinib synergistically inhibited CRPC cell growth both in vivo and in vitro. Further analysis of the underlying molecular mechanism underlying the MET suppression-induced drug sensitivity revealed that olaparib and crizotinib could together downregulate the ATM/ATR signaling pathway, inducing apoptosis by inhibiting the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway, enhancing the olaparib-induced antitumour effect in DU145 and PC3 cells. In conclusion, we demonstrated that MET inhibition enhances sensitivity of CRPC to PARP inhibitors by suppressing the ATM/ATR and PI3K/AKT pathways and provides a novel, targeted therapy regimen for CRPC.

Transcriptome evidence reveals enhanced autophagy-lysosomal function in centenarians.

Centenarians (CENs) are excellent subjects to study the mechanisms of human longevity and healthy aging. Here, we analyzed the transcriptomes of 76 centenarians, 54 centenarian-children, and 41 spouses of centenarian-children by RNA sequencing and found that, among the significantly differentially expressed genes (SDEGs) exhibited by CENs, the autophagy-lysosomal pathway is significantly up-regulated. Overexpression of several genes from this pathway, CTSB, ATP6V0C, ATG4D, and WIPI1, could promote autophagy and delay senescence in cultured IMR-90 cells, while overexpression of the Drosophila homolog of WIPI1, Atg18a, extended the life span in transgenic flies. Interestingly, the enhanced autophagy-lysosomal activity could be partially passed on to their offspring, as manifested by their higher levels of both autophagy-encoding genes and serum beclin 1 (BECN1). In light of the normal age-related decline of autophagy-lysosomal functions, these findings provide a compelling explanation for achieving longevity in, at least, female CENs, given the gender bias in our collected samples, and suggest that the enhanced waste-cleaning activity via autophagy may serve as a conserved mechanism to prolong the life span from Drosophila to humans.

Brain structural alterations in MOG antibody diseases: a comparative study with AQP4 seropositive NMOSD and MS.

BACKGROUND: Brain structural alterations and their clinical significance of myelin oligodendrocyte glycoprotein antibody disease (MOGAD) have not been determined. METHODS: We recruited 35 MOGAD, 38 aquaporin 4 antibody positive neuromyelitis optica spectrum diseases (AQP4+ NMOSD), 37 multiple sclerosis (MS) and 60 healthy controls (HC) who underwent multimodal brain MRI from two centres. Brain lesions, volumes of the whole brain parenchyma, cortical and subcortical grey matter (GM), brainstem, cerebellum and cerebral white matter (WM) and diffusion measures (fractional anisotropy, FA and mean diffusivity, MD) were compared among the groups. Associations between the MRI measurements and the clinical variables were assessed by partial correlations. Logistic regression was performed to differentiate MOGAD from AQP4+ NMOSD and MS. RESULTS: In MOGAD, 19 (54%) patients had lesions on MRI, with cortical/juxtacortical (68%) as the most common location. MOGAD and MS showed lower cortical and subcortical GM volumes than HC, while AQP4+ NMOSD only demonstrated a decreased cortical GM volume. MS demonstrated a lower cerebellar volume, a lower FA and an increased MD than MOGAD and HC. The subcortical GM volume was negatively correlated with Expanded Disability Status Scale in MOGAD (R=-0.51; p=0.004). A combination of MRI and clinical measures could achieve an accuracy of 85% and 93% for the classification of MOGAD versus AQP4+ NMOSD and MOGAD versus MS, respectively. CONCLUSION: MOGAD demonstrated cortical and subcortical atrophy without severe WM rarefaction. The subcortical GM volume correlated with clinical disability and a combination of MRI and clinical measures could separate MOGAD from AQP4+ NMOSD and MS.

Altered Dynamic Functional Connectivity in Patients With Mitochondrial Encephalomyopathy With Lactic Acidosis and Stroke-Like Episodes (MELAS) at Acute and Chronic Stages: Shared and Specific Brain Connectivity Abnormalities.

BACKGROUND: Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a rare maternally inherited genetic disease; however, little is known about its underlying brain basis. Furthermore, the dynamic functional connectivity (dFC) of brain networks in MELAS has not been explored. PURPOSE: To investigate the abnormalities of dFC in patients with MELAS at the acute and chronic stages, and to determine the possible relations between dynamic connectivity alterations and volumes of stroke-like lesions (SLLs). STUDY TYPE: Prospective. SUBJECTS: Twenty-two MELAS patients at the acute stage, 23 MELAS patients at the chronic stage, and 22 healthy controls. FIELD STRENGTH/SEQUENCE: Single-shot gradient-recalled echo planar imaging (EPI) sequence at 3T. ASSESSMENT: Dynamic FC states were estimated using the sliding window approach and k-means clustering analyses. Combined with graph theory, the topological properties of the dFC network were also accessed. STATISTICAL TESTS: Permutation test, Pearson correlation coefficient, and false discovery rate correction. RESULTS: We identified four dFC states and found that MELAS patients (especially at the acute stage) spent more time in a state with weaker connectivity (state 1) and less time in states with stronger connectivity. In addition, volumes of acute SLLs were positively correlated with mean dwell time in state 1 (r = 0.539, P < 0.05) and negatively correlated with the number of transitions (r = -0.520, P < 0.05). Furthermore, MELAS patients at the acute stage exhibited significantly increased global efficiency (P < 0.01) and decreased local efficiency (P < 0.001) compared to the controls and the patients at the chronic stage. Patients at the chronic stage only showed significantly (P < 0.001) decreased local efficiency compared to the controls. DATA CONCLUSION: Our findings suggest similar and distinct dFC alterations in MELAS patents at the acute and chronic stages, providing novel insights for understanding the neuropathological mechanisms of MELAS. Level of Evidence 2 Technical Efficacy Stage Stage 2 J. MAGN. RESON. IMAGING 2021;53:427-436.

Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.

BACKGROUND: Preoperative differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) is important to guide neurosurgical decision-making. PURPOSE: To validate the generalization ability of radiomics models based on multiparametric-MRI (MP-MRI) for differentiating PCNSL from GBM. STUDY TYPE: Retrospective. POPULATION: In all, 240 patients with GBM (n = 129) or PCNSL (n = 111). FIELD STRENGTH/SEQUENCE: 3.0T scanners (two vendors). Sequences: fluid-attenuation inversion recovery, diffusion-weighted imaging (DWI), and contrast-enhanced T1 -weighted imaging (CE-T1 WI). Apparent diffusion coefficients (ADCs) were derived from DWI. ASSESSMENT: Cross-vendor and mixed-vendor validation were conducted. In cross-vendor validation, the training set was 149 patients' data from vendor 1, and test set was 91 patients' data from vendor 2. In mixed-vendor validation, a training set was 80% of data from both vendors, and the test set remained at 20% of data. Single and multisequence radiomics models were built. The diagnoses by radiologists with 5, 10, and 20 years' experience were obtained. The integrated models were built combining the diagnoses by the best-performing radiomics model and each radiologist. Model performance was validated in the test set using area under the ROC curve (AUC). Histological results were used as the reference standard. STATISTICAL TESTS: DeLong test: differences between AUCs. U-test: differences of numerical variables. Fisher's exact test: differences of categorical variables. RESULTS: In cross-vendor and mixed-vendor validation, the combination of CE-T1 WI and ADC produced the best-performing radiomics model, with AUC of 0.943 vs. 0.935, P = 0.854. The integrated models had higher AUCs than radiologists, with 5 (0.975 vs. 0.891, P = 0.002 and 0.995 vs. 0.885, P = 0.007), 10 (0.975 vs. 0.913, P = 0.029 and 0.995 vs. 0.900, P = 0.030), and 20 (0.975 vs. 0.945, P = 0.179 and 0.995 vs. 0.923, P = 0.046) years' experiences. DATA CONCLUSION: Radiomics for differentiating PCNSL from GBM was generalizable. The model combining MP-MRI and radiologists' diagnoses had superior performance compared to the radiologists alone. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies. PURPOSE: To investigate the use of a convolutional neural network (CNN) model for differentiation of PCNSL and GBM without tumor delineation. STUDY TYPE: Retrospective. POPULATION: A total of 289 patients with PCNSL (136) or GBM (153) were included, the average age of the cohort was 54 years, and there were 173 men and 116 women. FIELD STRENGTH/SEQUENCE: 3.0 T Axial contrast-enhanced T1 -weighted spin-echo inversion recovery sequence (CE-T1 WI), T2 -weighted fluid-attenuation inversion recovery sequence (FLAIR), and diffusion weighted imaging (DWI, b = 0 second/mm2 , 1000 seconds/mm2 ). ASSESSMENT: A single-parametric CNN model was built using CE-T1 WI, FLAIR, and the apparent diffusion coefficient (ADC) map derived from DWI, respectively. A decision-level fusion based multi-parametric CNN model (DF-CNN) was built by combining the predictions of single-parametric CNN models through logistic regression. An image-level fusion based multi-parametric CNN model (IF-CNN) was built using the integrated multi-parametric MR images. The radiomics models were developed. The diagnoses by three radiologists with 6 years (junior radiologist Y.Y.), 11 years (intermediate-level radiologist Y.T.), and 21 years (senior radiologist Y.L.) of experience were obtained. STATISTICAL ANALYSIS: The 5-fold cross validation was used for model evaluation. The Pearson's chi-squared test was used to compare the accuracies. U-test and Fisher's exact test were used to compare clinical characteristics. RESULTS: The CE-T1 WI, FLAIR, and ADC based single-parametric CNN model had accuracy of 0.884, 0.782, and 0.700, respectively. The DF-CNN model had an accuracy of 0.899 which was higher than the IF-CNN model (0.830, P = 0.021), but had no significant difference in accuracy compared to the radiomics model (0.865, P = 0.255), and the senior radiologist (0.906, P = 0.886). DATA CONCLUSION: A CNN model can differentiate PCNSL from GBM without tumor delineation, and comparable to the radiomics models and radiologists. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Bi/BiVO4/NiFe-LDH heterostructures with enhanced photoelectrochemical performance for streptomycin detection.

Streptomycin (STR) plays an essential role in bacterial infection treatments. Selectivity and sensitivity of photoelectrochemical (PEC) sensors are the two most important parameters, which can be measured using the photosensitivity of its active material. We prepared a novel PEC sensor to detect STR using Bi/BiVO4/LDH (layered double hydroxides) heterostructures as an active material, which is photoactive in the visible light wavelength range. The simultaneous presence of LDH and Bi/BiVO4 enhanced the material photocurrent response, which was linear to the STR concentrations in the 0.01-500 nmol/L range. The STR detection limit by this sensor was 0.0042 nmol/L. Our novel PEC-based sensing strategy includes using an ultra-sensitive and highly selective sensor for STR detection. Additionally, the two-pot synthesis of Bi/BiVO4/LDH developed in this work is environmentally friendly.