Long-distance growth and connectivity of neural stem cells after severe spinal cord injury.

Neural stem cells (NSCs) expressing GFP were embedded into fibrin matrices containing growth factor cocktails and grafted to sites of severe spinal cord injury. Grafted cells differentiated into multiple cellular phenotypes, including neurons, which extended large numbers of axons over remarkable distances. Extending axons formed abundant synapses with host cells. Axonal growth was partially dependent on mammalian target of rapamycin (mTOR), but not Nogo signaling. Grafted neurons supported formation of electrophysiological relays across sites of complete spinal transection, resulting in functional recovery. Two human stem cell lines (566RSC and HUES7) embedded in growth-factor-containing fibrin exhibited similar growth, and 566RSC cells supported functional recovery. Thus, properties intrinsic to early-stage neurons can overcome the inhibitory milieu of the injured adult spinal cord to mount remarkable axonal growth, resulting in formation of new relay circuits that significantly improve function. These therapeutic properties extend across stem cell sources and species.

Radiological characteristics predicting early poor drug response in patients with hemifacial spasm.

OBJECTIVES: Patients with hemifacial spasm (HFS) often resort to botulinum toxin injections or microvascular decompression surgery when medication exhibits limited effectiveness. This study aimed to identify MRI and demographic factors associated with poor drug response at an early stage in patients with HFS. METHODS: We retrospectively included patients with HFS who underwent pre-therapeutic MRI examination. The presence, location, severity, and the offending vessels of neurovascular compression were blindly evaluated using MRI. Drug responses and clinical data were obtained from the medical notes or phone follow-ups. Logistic regression analysis was performed to identify potential factors. RESULTS: A total of 116 patients were included, with an average age at the time of first examination of 50.4 years and a median duration of onset of 18 months. Forty-nine (42.2%) patients reported no symptom relief. Thirty-seven (31.9%) patients reported poor symptom relief. Twenty-two (19.0%) patients reported partial symptom relief. Eight (6.9%) patients achieved complete symptom relief. The factors that were statistically significant associated with poor drug responses were contact in the attach segment of the facial nerve and aged 70 and above, with an odds ratio of 7.772 (p = 0.002) and 0.160 (p = 0.028), respectively. CONCLUSIONS: This study revealed that mild compression in the attach segment of the facial nerve in pre-therapeutic MRI increases the risk of poor drug responses in patients with HFS, while patients aged 70 and above showed a decreased risk. These findings may assist clinician to choose optimal treatment at an early stage.

Exploration of the molecular characteristics of the tumor-immune interaction and the development of an individualized immune prognostic signature for neuroblastoma.

Neuroblastoma (NBL) exists in a complex tumor-immune microenvironment. Immune cell infiltration and tumor-immune molecules play a critical role in tumor development and significantly impact the prognosis of patients. However, the molecular characteristics describing the NBL-immune interaction and their prognostic potential have yet to be investigated systematically. We first employed multiple machine learning algorithms, such as Gene Sets Enrichment Analysis and cell type identification by estimating relative subsets of RNA transcripts, to identify immunophenotypes and immunological characteristics in NBL patient data from public databases and then investigated the prognostic potential and regulatory networks of identified immune-related genes involved in the NBL-immune interaction. The immunity signature combining nine immunity genes was confirmed as more effective for individual risk stratification and survival outcome prediction in NBL patients than common clinical characteristics (area under the curve [AUC] = 0.819, C-index = 0.718, p < .001). A mechanistic exploration revealed the regulatory network of molecules involved in the NBL-immune interaction. These immune molecules were also discovered to possess a significant correlation with plasma cell infiltration, MYCN status, and the level of chemokines and macrophage-related molecules (p < .001). A nomogram was constructed based on the immune signature and clinical characteristics, which showed high potential for prognosis prediction (AUC = 0.856, C-index = 0.755, p < .001). We systematically elucidated the complex regulatory mechanisms and characteristics of the molecules involved in the NBL-immune interaction and their prognostic potential, which may have important implications for further understanding the molecular mechanism of the NBL-immune interaction and identifying high-risk NBL patients to guide clinical treatment.

Correction to: A multiparametric analysis based on DCE-MRI to improve the accuracy of parotid tumor discrimination.

Figure captions of Figures 2, 3, and 4 were incorrect in the original version of this article.

A multiparametric analysis based on DCE-MRI to improve the accuracy of parotid tumor discrimination.

BACKGROUND: Recently, semiquantitative time-intensity curve (TIC) analysis based on DCE-MRI and apparent diffusion coefficient (ADC) value-based diffusion-weighted imaging (DWI) were used to improve the diagnostic efficiency when diagnosing parotid tumors (PTs). However, quantitative DCE-MRI biomarkers have not been emphasized previously. PURPOSE: To explore the diagnostic efficiency of perfusion parameters alone or in combination based on quantitative DCE-MRI and DWI in the differential diagnosis of PTs. METHODS: In total, 112 patients with parotid masses were prospectively recruited in our hospital from August 2013 to March 2017. All patients were evaluated with DCE-MRI and DWI before surgery. TIC and quantitative parameters based on DCE MRI and ADCs were analyzed. Receiver operating characteristic analysis and linear discriminant analysis (LDA) was used to determine their diagnostic performance. RESULTS: In total, 87% (27/31) of pleomorphic adenoma (PA) showed type A TIC, 74% (65/88) of Warthin's tumors showed type B TIC, and 95% (19/20) of malignant tumors showed TIC type C. Pearson X2 test showed a significant difference between TIC patterns in benign and malignant tumors (X2 = 38.78, p < 0.001). ROC analysis revealed that ADC achieved the best diagnostic performance for distinguishing PA and Warthin's tumor from others, with area under the curve (AUC) values of 0.945 and 0.925 (p < 0.01), respectively. Furthermore, the TIC type was the only useful biomarker for distinguishing malignant from benign PTs, with an AUC of 0.846 (p < 0.01). Concerning the accuracy of the combined application of multiple parameters of DCE-MRI and ADC values, a combination of TIC pattern and extracellular volume ratio (Ve) provided the best results among five protocols, producing the highest accuracy of 0.75, followed by the combined use of the TIC pattern and ADC (accuracy was 0.70). CONCLUSION: TIC pattern in combination with the Ve biomarker based on DCE-MRI could achieve optimal diagnostic accuracy in the differential diagnosis of PTs.

Gene expression profiling of osteoblasts subjected to dexamethasone-induced apoptosis with/without GSK3ß-shRNA.

OBJECTIVE: Glucocorticoids (GCs)-induced osteoblast apoptosis has been identified as an important cause of GCs related osteonecrosis of the femoral head (ONFH). Glycogen synthase kinase 3ß (GSK3ß) has been proved to mediate dexamethasone (Dex)-induced osteoblast apoptosis. This study aimed to investigate the underlying mechanism of GSK3ß in Dex-induced osteoblast apoptosis. METHODS: Osteoblast cells were transfected with lentivirus expressing GSK3ß-shRNA, and a DNA microarray was performed to analyze gene expression after Dex treatment with or without GSK3ß-shRNA. Some differentially expressed genes were further validated by quantitative real-time-PCR (qRT-PCR). RESULTS: 460 genes were up-regulated (at least 2-fold) with Dex treatment but down-regulated (at least 2-fold) with GSK3ß-shRNA treatment. In addition, 315 genes were down-regulated (at least 2-fold) with Dex treatment but up-regulated (at least 2-fold) with GSK3ß-shRNA treatment. Among these genes, the apoptosis-related genes Hoxb8, Kif18a, Dock8, Dlk1, Tnfsf14, Casq2, Bcl2l14 and mechanosensation-related gene Piezo2 were selected for further qRT-PCR analysis. 7 of 8 genes (Piezo2, Hoxb8, Kif18a, Dlk1, Tnfsf14, Casq2, Bcl2l14) showed the same tendency between gene chip results and qRT-PCR results. The microarray data also showed that apoptotic pathway, MAPK pathway, TGFß pathway and Wnt pathway might be related to the mechanism of GSK3ß in Dex-induced osteoblast apoptosis. CONCLUSION: Our findings indicate that GSK3ß-shRNA treatment can alter various genes expression levels and change diverse signaling pathways involved in Dex-induced osteoblast apoptosis. Furthermore, Piezo2, Hoxb8, Kif18a, Dlk1, Tnfsf14, Casq2 and Bcl2l14 genes may play an important role in the GSK3ß-mediated osteoblast apoptosis process.

Developing targeted therapies for neuroblastoma by dissecting the effects of metabolic reprogramming on tumor microenvironments and progression.

Rationale: Synergic reprogramming of metabolic dominates neuroblastoma (NB) progression. It is of great clinical implications to develop an individualized risk prognostication approach with stratification-guided therapeutic options for NB based on elucidating molecular mechanisms of metabolic reprogramming. Methods: With a machine learning-based multi-step program, the synergic mechanisms of metabolic reprogramming-driven malignant progression of NB were elucidated at single-cell and metabolite flux dimensions. Subsequently, a promising metabolic reprogramming-associated prognostic signature (MPS) and individualized therapeutic approaches based on MPS-stratification were developed and further validated independently using pre-clinical models. Results: MPS-identified MPS-I NB showed significantly higher activity of metabolic reprogramming than MPS-II counterparts. MPS demonstrated improved accuracy compared to current clinical characteristics [AUC: 0.915 vs. 0.657 (MYCN), 0.713 (INSS-stage), and 0.808 (INRG-stratification)] in predicting prognosis. AZD7762 and etoposide were identified as potent therapeutics against MPS-I and II NB, respectively. Subsequent biological tests revealed AZD7762 substantially inhibited growth, migration, and invasion of MPS-I NB cells, more effectively than that of MPS-II cells. Conversely, etoposide had better therapeutic effects on MPS-II NB cells. More encouragingly, AZD7762 and etoposide significantly inhibited in-vivo subcutaneous tumorigenesis, proliferation, and pulmonary metastasis in MPS-I and MPS-II samples, respectively; thereby prolonging survival of tumor-bearing mice. Mechanistically, AZD7762 and etoposide-induced apoptosis of the MPS-I and MPS-II cells, respectively, through mitochondria-dependent pathways; and MPS-I NB resisted etoposide-induced apoptosis by addiction of glutamate metabolism and acetyl coenzyme A. MPS-I NB progression was fueled by multiple metabolic reprogramming-driven factors including multidrug resistance, immunosuppressive and tumor-promoting inflammatory microenvironments. Immunologically, MPS-I NB suppressed immune cells via MIF and THBS signaling pathways. Metabolically, the malignant proliferation of MPS-I NB cells was remarkably supported by reprogrammed glutamate metabolism, tricarboxylic acid cycle, urea cycle, etc. Furthermore, MPS-I NB cells manifested a distinct tumor-promoting developmental lineage and self-communication patterns, as evidenced by enhanced oncogenic signaling pathways activated with development and self-communications. Conclusions: This study provides deep insights into the molecular mechanisms underlying metabolic reprogramming-mediated malignant progression of NB. It also sheds light on developing targeted medications guided by the novel precise risk prognostication approaches, which could contribute to a significantly improved therapeutic strategy for NB.

Multi­parameter quantitative magnetic resonance imaging in the early assessment of radiation­induced parotid damage in patients with nasopharyngeal carcinoma following intensity­modulated radiotherapy.

The present study aimed to investigate the value of intravoxel incoherent motion imaging (IVIM) and three-dimensional pulsed continuous arterial spin labeling (ASL) in assessing dynamic changes of the parotid gland in patients with nasopharyngeal carcinoma (NPC) following radiotherapy (RT). A total of 18 patients with NPC who underwent intensity-modulated RT were enrolled in the present study. All patients underwent conventional magnetic resonance imaging, plus IVIM and ASL imaging of the bilateral parotid glands within 2 weeks prior to RT, and 1 week (1W) and 3 months (3M) following RT. Pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (F) and blood flow (BF) were analyzed. D and BF values were significantly increased from pre-RT to 1W post-RT [change rate: Median (IQR), ΔD1W%: 39.28% (38.23%) and ΔBF1W%: 60.84% (54.88%)] and continued to increase from 1W post-RT to 3M post-RT [55.44% (40.56%) and ΔBF%: 120.39% (128.74%)]. In addition, the F value was significantly increased from pre-RT to 1W post-RT, [change rate: Median (IQR), ΔF1W%: 28.13% (44.66%)], and this decreased significantly from 1W post-RT to 3M post-RT. However, no significant differences were observed between pre-RT and 3M post-RT. Results of the present study also demonstrated that the D* value was significantly decreased from pre-RT to 1W post-RT and 3M post-RT [change rate: Median (IQR), ΔD*1w%: -41.86% (51.71%) and ΔD*3M: -29.11% (42.67%)]. No significant difference was observed between the different time intervals post-RT. There was a significant positive correlation between percentage change in ΔBF1W and radiation dose (ρ=0.548, P=0.001). Thus, IVIM-diffusion-weighted imaging and ASL may aid in the detection and prediction of radiation-induced parotid damage in the early stages following RT. They may contribute to further understanding the potential association between damage to the parotid glands and patient-/treatment-related variables, through the assessment of individual microcapillary perfusion and tissue diffusivity.

Development and validation of a nomogram based on CT texture analysis for discriminating minimally invasive adenocarcinoma from glandular precursor lesions in sub­centimeter pulmonary ground glass nodules.

In a recent reclassification, adenocarcinoma in situ has been redefined as a glandular precursor lesion (GPL), alongside adenomatous hyperplasia. This updated classification necessitates corresponding adaptations in clinical diagnostic and therapeutic protocols. Consequently, the present study aimed to construct and validate a nomogram utilizing computed tomography (CT) texture features to effectively discriminate between minimally invasive adenocarcinoma (MIA) and GPL within sub-centimeter pulmonary ground glass nodules (GGNs). To achieve this objective, the present study employed rigorous statistical methodologies, including the Mann-Whitney U test and binary logistic regression analysis, to identify distinguishing features and establish predictive models. Subsequently, the diagnostic performance of these models underwent evaluation through receiver operating characteristic (ROC) curves. The area under the curve (AUC) in ROC curves was compared using DeLong's test. Additionally, the nomogram was constructed using R software and its diagnostic performance was validated through calibration curves. Within both the training and validation datasets, the AUCs were observed to be 0.992 [95% confidence interval (CI): 0.980-1.000] and 0.975 (95% CI: 0.935-1.000), respectively. DeLong's test revealed significant disparities in the AUCs between the nomogram and single-parameter models (P<0.001). Furthermore, calibration curves demonstrated concordance between the training and validation datasets. In conclusion, the application of a CT texture-based nomogram model has demonstrated aptitude in differentiating between MIA and GPL within sub-centimeter GGNs. This model streamlines the identification of optimal surgical interventions and enhances the sphere of clinical decision-making and management.

Evaluating magnetic resonance imaging characteristics and risk factors for hemifacial spasm.

PURPOSE: The specific neurovascular compression (NVC) event responsible for the symptomatic manifestation of hemifacial spasm (HFS) remains difficult to assess accurately using magnetic resonance imaging (MRI). We aim to evaluate the MRI characteristics of HFS. METHOD: We retrospectively included patients with HFS and divided them into a test group (n = 186) and a validation group (n = 28). The presence, severity, and offending vessel type of NVC in each portion, and the orientation of the offending vessel around the facial nerve, were recorded. Conditional logistic regression analyses were performed to evaluate correlations using test group. The validation group was used to verify whether our findings improved diagnostic performance. RESULTS: Deformity in the proximal cisternal segment was significantly correlated with HFS occurrence (odds ratio [OR]: 256.58, p = .002), whereas contact was not (p = .233). Both contact and deformity in the root detachment point (OR: 19.98 and 37.22, p < .001 and p = .013, respectively) or attached segment (OR: 4.99 and 252.52, p = .001 and p < .001, respectively) were significantly correlated with HFS occurrence. Our findings improved specificity, positive predictive value, and accuracy of diagnosis than conventional diagnostic methods. The vertebral artery predominantly compress the facial nerve in the inferior-anterior position, the anterior inferior cerebellar artery predominantly in the inferior position, the posterior inferior cerebellar artery predominantly in the inferior position, vein predominantly in the posterior-superior position. CONCLUSIONS: This study further demonstrates that within the susceptible portion of facial nerve, different portions of the nerve respond differently to NVC. Each offending vessel has its own preferred conflict orientation. Our study offers reference for neurosurgeons in diagnosis and treatment.

Nanofiber scaffolds facilitate functional regeneration of peripheral nerve injury.

Peripheral nerve injury still remains a refractory challenge for both clinical and basic researchers. A novel nanofiber conduit made of blood vessel and filled with amphiphilic hydrogel of self-assembling nanofiber scaffold (SAPNS) was implanted to repair a 10 mm nerve gap after sciatic nerve transection. Empty blood vessel conduit was implanted serving as control. Results showed that this novel nanofiber conduit enabled the peripheral axons to regenerate across and beyond the 10 mm gap. Motoneuron protection, axonal regeneration and remyelination were significantly enhanced with SAPNS scaffold treatments. The target reinnervation and functional recovery induced by the regenerative nerve conduit suggest that SAPNS-based conduit is highly promising application in the treatment of peripheral nerve defect. FROM THE CLINICAL EDITOR: In this paper by Zhan et al, a novel self-assembling nanofiber scaffold is reported to promote regeneration of peripheral nerves in a sciatic nerve injury model. The promising results and the obvious medical need raises hope for a clinical translation of this approach hopefully in the near future.

Integrative Scoring System for Survival Prediction in Patients With Locally Advanced Nasopharyngeal Carcinoma: A Retrospective Multicenter Study.

PURPOSE: Tumor stage is crucial for prognostic evaluation and therapeutic decisions in locally advanced nasopharyngeal carcinoma (NPC) but is imprecise. We aimed to propose a new prognostic system by integrating quantitative imaging features and clinical factors. MATERIALS AND METHODS: This retrospective study included 1,319 patients with stage III-IVa NPC between April 1, 2010, and July 31, 2019, who underwent pretherapy magnetic resonance imaging (MRI) and received concurrent chemoradiotherapy with or without induction chemotherapy. The hand-crafted and deep-learned features were extracted from MRI for each patient. After feature selection, the clinical score, radiomic score, deep score, and integrative scores were constructed via Cox regression analysis. The scores were validated in two external cohorts. The predictive accuracy and discrimination were measured by the area under the curve (AUC) and risk group stratification. The end points were progression-free survival (PFS), overall survival (OS), and distant metastasis-free survival (DMFS). RESULTS: Both radiomics and deep learning were complementary to clinical variables (age, T stage, and N stage; all P < .05). The clinical-deep score was superior or equivalent to clinical-radiomic score, whereas it was noninferior to clinical-radiomic-deep score (all P > .05). These findings were also verified in the evaluation of OS and DMFS. The clinical-deep score yielded an AUC of 0.713 (95% CI, 0.697 to 0.729) and 0.712 (95% CI, 0.693 to 0.731) in the two external validation cohorts for predicting PFS with good calibration. This scoring system could stratify patients into high- and low-risk groups with distinct survivals (all P < .05). CONCLUSION: We established and validated a prognostic system integrating clinical data and deep learning to provide an individual prediction of survival for patients with locally advanced NPC, which might inform clinicians in treatment decision making.

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC. Methods: A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model's effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort. Results: Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts. Conclusions: A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study.

Background: MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT. Methods: This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test. Findings: The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P < 0.0001) at the same individual-specificities. The AI model achieved moderate precision with a median dice similarity coefficient of 0.67. AI-aided MRI improved specificity (92.5% vs. 85.0%, P = 0.034), equaled PET/CT in the internal validation subcohort, and increased sensitivity (81.9% vs. 70.8%, P = 0.021) in the external validation subcohort. In the prospective cohort of 1248 patients, the AI group had higher sensitivity than the non-AI group (78.6% vs. 67.3%, P = 0.23), albeit nonsignificant. In future randomized controlled trials, a sample size of 3943 patients in each arm would be required to demonstrate the statistically significant difference. Interpretation: The AI model equaled MRI by expert radiologists, and AI-aided MRI by expert radiologists equaled PET/CT. A larger randomized controlled trial is warranted to demonstrate the AI's benefit sufficiently. Funding: The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), and Guangzhou Science and Technology Program (2023A04J1788).

MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method.

Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.

A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information.

Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model (M1NCM) to remove the noncentral χ -distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed M1NCM-NSS-LSS-PR method with various versions of M1NCM-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed M1NCM-NSS-LSS-PR method was robust to the number of diffusion directions.

Renal epithelioid angiomyolipoma: computed tomography manifestation and radiologic-pathologic correlation depending on different epithelioid component percentages.

BACKGROUND: Renal epithelioid angiomyolipoma (EAML) is a rare and potentially malignant mesenchymal lesion mainly composed of epithelioid cells. Although some case reports or small case series have been published, the computed tomography (CT) manifestations and radiologic-pathologic correlation depending on different epithelioid component percentages have not been studied before. OBJECTIVE: To investigate the CT manifestation and radiologic-pathologic correlation between renal EAML and angiomyolipoma (AML) with epithelioid component. METHODS: The clinicopathologic and imaging data of 53 patients with an original diagnosis of EAML or AML with epithelioid component were retrospectively collected from three hospitals. All tissue specimens were re-sectioned and re-observed under the microscope. Samples were divided into an EAML group (≥ 80% epithelioid component, n = 25) and AML with epithelioid component group (5% ≤ epithelioid component < 80%, n = 28). Two radiologists reviewed the images in consensus, describing and comparing the CT manifestation, including the long diameter of the tumor, morphology, presence of necrosis or cystic change, hemorrhage, fat, calcification, enlarged blood vessels, and dynamic enhancement pattern according to the Hounsfield unit value of each CT phase between the two groups. The radiologic-pathologic correlation depending on the different percentages of epithelioid component were studied. RESULTS: The long diameter of the tumor, presence of necrosis or cystic change, fat, enhancement pattern, and tumor-to-cortex enhancement ratio of the cortical phase between the two groups were significantly different (z = - 2.932, P = 0.003; χ2 = 18.020, P < 0.001; χ2 = 16.377, P < 0.001; P = 0.020; and T = - 3.944, P < 0.001, respectively). In multivariate logistic regression analysis, the significant predictive factors of EAML included the presence of necrosis or cystic change [odds ratio (OR) 11.864, P = 0.001] and absence of fat (OR 0.095, P = 0.003). Correlation analysis found that the presence of necrosis or cystic change (r = 0.679, P < 0.001) and fat (r = - 0.603, P < 0.001) were both moderately related to the epithelioid component percentage. The combined model based on the presence of necrosis or cystic change and absence of fat yielded the best diagnostic performance in discriminating EAML and AML with epithelioid component with the highest area under the curve (0.887). CONCLUSION: EAML has characteristic CT signs; these characteristic CT signs are closely related to the epithelioid component percentage. The presence of necrosis or cystic change and the absence of fat were independent predictors of EAML.

Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study.

In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy on survivals of patients with nasopharyngeal carcinoma. Two thousand ninety-seven patients from three independent hospitals were identified and randomly assigned. When the deep-learning signatures of the primary tumor and clinically involved gross cervical lymph nodes extracted from MR images were added to the clinical data and TNM staging for the progression-free survival prediction model, the combined model achieved better prediction performance. Its application is among patients deciding on treatment regimens. Under the same conditions, with the increasing MRI signatures, the survival benefits achieved by induction chemotherapy are increased. In nasopharyngeal carcinoma, these prediction models are the first to provide an individualized estimation of survivals and model the benefit of induction chemotherapy on survivals.

Combination of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Provides Potential Biomarkers for Early-Stage Parkinson's Disease.

Purpose: This study aimed to detect changes in iron deposition and neural microstructure in the substantia nigra (SN), red nucleus (RN), and basal ganglia of Parkinson's disease (PD) patients at different stages using quantitative susceptibility mapping and diffusion kurtosis imaging to identify potential indicators of early-stage PD. Methods: We enrolled 20 early-stage and 15 late-stage PD patients, as well as 20 age- and sex-matched controls. All participants underwent quantitative susceptibility mapping and diffusion kurtosis imaging to determine magnetic susceptibility (MS), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) in several brain regions. Results: Compared with the control group, MS and MK values in the SN were significantly increased in the early- and late-stage PD group, whereas MS values in the red nucleus (RN), globus pallidus (GP), and caudate nucleus (CN), FA value in the CN and GP, and MK value in the CN and putamen (PU) were significantly increased in the late-stage PD group. There were positive correlations between MS and MK values in the CN and MS and FA values in the GP. Furthermore, the combination of MS and MK values in the SN provided high accuracy for distinguishing early-stage PD patients from controls. Conclusions: This study identified MS and MK in the SN as potential indicators of early-stage PD.