Single-cell transcriptomic sequencing identifies subcutaneous patient-derived xenograft recapitulated medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is one of the most common malignant brain tumors that mainly affect children. Various approaches have been used to model MB to facilitate investigating tumorigenesis. This study aims to compare the recapitulation of MB between subcutaneous patient-derived xenograft (sPDX), intracranial patient-derived xenograft (iPDX), and genetically engineered mouse models (GEMM) at the single-cell level. METHODS: We obtained primary human sonic hedgehog (SHH) and group 3 (G3) MB samples from six patients. For each patient specimen, we developed two sPDX and iPDX models, respectively. Three Patch+/- GEMM models were also included for sequencing. Single-cell RNA sequencing was performed to compare gene expression profiles, cellular composition, and functional pathway enrichment. Bulk RNA-seq deconvolution was performed to compare cellular composition across models and human samples. RESULTS: Our results showed that the sPDX tumor model demonstrated the highest correlation to the overall transcriptomic profiles of primary human tumors at the single-cell level within the SHH and G3 subgroups, followed by the GEMM model and iPDX. The GEMM tumor model was able to recapitulate all subpopulations of tumor microenvironment (TME) cells that can be clustered in human SHH tumors, including a higher proportion of tumor-associated astrocytes and immune cells, and an additional cluster of vascular endothelia when compared to human SHH tumors. CONCLUSIONS: This study was the first to compare experimental models for MB at the single-cell level, providing value insights into model selection for different research purposes. sPDX and iPDX are suitable for drug testing and personalized therapy screenings, whereas GEMM models are valuable for investigating the interaction between tumor and TME cells.

Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and Analysis.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma.

INTRODUCTION: Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of identifying glioma TuR. METHODS: A total of 131 patients were enrolled and randomly divided into the training set (n = 91) and the test set (n = 40). Radiomic features were extracted from the postoperative enhancement (PoE) region and edema (ED) region from four routine MRI sequences. After analyses of Spearman's rank correlation coefficient, and least absolute shrinkage and selection operator, the key radiomic features were selected to construct support vector machine (SVM) and k-nearest neighbor (KNN) models. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to analyze the performance. RESULTS: The PoE model had a significantly higher area under curve (AUC) than the ED model (p < 0.05). Among the models constructed with a single sequence, the model using PoE regional features from CE-T1WI was superior to other models, with an AUC of 0.905 for SVM and 0.899 for KNN. In multimodality models, the PoE model outperformed the ED model with an AUC of 0.931 for SVM and 0.896 for KNN. The multimodality model, which combined routine sequences and the whole regional features, showed a slightly better performance with an AUC of 0.965 for SVM and 0.955 for KNN. Decision curve analysis showed the good clinical utility of multimodal radiomics models. CONCLUSIONS: Multimodality radiomics can identify glioma TuR and TrE, potentially aiding clinical decision-making for individualized treatment. And edematous regions may provide useful information for recognizing recurrence. RETROSPECTIVELY REGISTERED: 2021.04.15, No:2020039.

Quantitative dynamic contrast-enhanced parameters and intravoxel incoherent motion facilitate the prediction of TP53 status and risk stratification of early-stage endometrial carcinoma.

BACKGROUND: The aim of the study was to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel incoherent motion (IVIM) in differentiating TP53-mutant from wild type, low-risk from non-low-risk early-stage endometrial carcinoma (EC). PATIENTS AND METHODS: A total of 74 EC patients underwent pelvic MRI. Parameters volume transfer constant (Ktrans), rate transfer constant (Kep), the volume of extravascular extracellular space per unit volume of tissue (Ve), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were compared. The combination of parameters was investigated by logistic regression and evaluated by bootstrap (1000 samples), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: In the TP53-mutant group, Ktrans and Kep were higher and D was lower than in the TP53-wild group; Ktrans, Ve, f, and D were lower in the non-low-risk group than in the low-risk group (all P < 0.05). In the identification of TP53-mutant and TP53-wild early-stage EC, Ktrans and D were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.867; sensitivity, 92.00%; specificity, 80.95%), which was significantly better than D (Z = 2.169, P = 0.030) and Ktrans (Z = 2.572, P = 0.010). In the identification of low-risk and non-low-risk early-stage EC, Ktrans, Ve, and f were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.947; sensitivity, 83.33%; specificity, 93.18%), which was significantly better than D (Z = 3.113, P = 0.002), f (Z = 4.317, P < 0.001), Ktrans (Z = 2.713, P = 0.007), and Ve (Z = 3.175, P = 0.002). The calibration curves showed that the above two combinations of independent predictors, both have good consistency, and DCA showed that these combinations were reliable clinical prediction tools. CONCLUSIONS: Both DCE-MRI and IVIM facilitate the prediction of TP53 status and risk stratification in early-stage EC. Compare with each single parameter, the combination of independent predictors provided better predictive power and may serve as a superior imaging marker.

Use of biexponential and stretched exponential models of intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging to assess the proliferation of endometrial carcinoma.

Background: It is important to assess the proliferation of endometrial carcinoma (EC) noninvasively using imaging methods. This prospective diagnostic study investigated the value of biexponential and stretched exponential models of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the Ki-67 status of EC. Methods: In all, 70 patients with EC underwent pelvic MRI. The diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index (α), volume transfer constant (Ktrans), rate transfer constant (Kep), and volume of extravascular extracellular space per unit volume of tissue (Ve) were compared. The area under the receiver operating characteristic (ROC) curve (AUC) was used to quantify diagnostic efficacy. Multivariate logistic regression and bootstrap (1,000 samples) analyses were used to establish and evaluate, respectively, the optimal model to predict Ki-67 status. Results: D, Ktrans, and Kep were lower while α was higher in the high-proliferation group as compared with low-proliferation group (all P values<0.05). D and Kep were independent predictors of Ki-67 status in EC, and the combination of these parameters had optimal diagnostic efficacy (AUC =0.920; sensitivity 85.71%; specificity 89.29%), which was significantly better than that of D (AUC =0.753; Z=2.874; P=0.004), α (AUC =0.715; Z=3.505; P=0.001), Ktrans (AUC =0.808; Z=2.741; P=0.006), and Kep (AUC =0.832; Z=2.147; P=0.032) alone. The validation model showed good accuracy (AUC =0.882; 95% confidence interval 0.861-0.897) and consistency (C-statistic =0.902). D, Kep, Ktrans, and α showed a slightly negative (r=-0.271), moderately negative (r=-0.534), slightly negative (r=-0.409), and slightly positive (r=0.488) correlation with the Ki-67 index, respectively (all P values <0.05). Conclusions: IVIM- and DCE-MRI-derived parameters, including D, α, Ktrans, and Kep, were associated with Ki-67 status in EC, and the combination of D and Kep may serve as a superior imaging marker for the identification of low- and high-proliferation EC.

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

De Novo Mutations Contributes Approximately 7% of Pathogenicity in Inherited Eye Diseases.

Purpose: The purpose of this study was to describe genotype-phenotype associations and novel insights into genetic characteristics in a trio-based cohort of inherited eye diseases (IEDs). Methods: To determine the etiological role of de novo mutations (DNMs) and genetic profile in IEDs, we retrospectively reviewed a large cohort of proband-parent trios of Chinese origin. The patients underwent a detailed examination and was clinically diagnosed by an ophthalmologist. Panel-based targeted exome sequencing was performed on DNA extracted from blood samples, containing coding regions of 792 IED-causative genes and their flanking exons. All participants underwent genetic testing. Results: All proband-parent trios were divided into 22 subgroups, the overall diagnostic yield was 48.67% (605/1243), ranging from 4% to 94.44% for each of the subgroups. A total of 108 IED-causative genes were identified, with the top 24 genes explaining 67% of the 605 genetically solved trios. The genetic etiology of 6.76% (84/1243) of the trio was attributed to disease-causative DNMs, and the top 3 subgroups with the highest incidence of DNM were aniridia (n = 40%), Marfan syndrome/ectopia lentis (n = 38.78%), and retinoblastoma (n = 37.04%). The top 10 genes have a diagnostic yield of DNM greater than 3.5% in their subgroups, including PAX6 (40.00%), FBN1 (38.78%), RB1 (37.04%), CRX (10.34%), CHM (9.09%), WFS1 (8.00%), RP1L1 (5.88%), RS1 (5.26%), PCDH15 (4.00%), and ABCA4 (3.51%). Additionally, the incidence of DNM in offspring showed a trend of correlation with paternal age at reproduction, but not statistically significant with paternal (P = 0.154) and maternal (P = 0.959) age at reproduction. Conclusions: Trios-based genetic analysis has high accuracy and validity. Our study helps to quantify the burden of the full spectrum IED caused by each gene, offers novel potential for elucidating etiology, and plays a crucial role in genetic counseling and patient management.

Genetic Characteristics and Variation Spectrum of USH2A-Related Retinitis Pigmentosa and Usher Syndrome.

Purposes: We aimed to characterize the USH2A genotypic spectrum in a Chinese cohort and provide a detailed genetic profile for Chinese patients with USH2A-IRD. Methods: We designed a retrospective study wherein a total of 1,334 patients diagnosed with IRD were included as a study cohort, namely 1,278 RP and 56 USH patients, as well as other types of IEDs patients and healthy family members as a control cohort. The genotype-phenotype correlation of all participants with USH2A variant was evaluated. Results: Etiological mutations in USH2A, the most common cause of RP and USH, were found in 16.34% (n = 218) genetically solved IRD patients, with prevalences of 14.87% (190/1,278) and 50% (28/56). After bioinformatics and QC processing, 768 distinct USH2A variants were detected in all participants, including 136 disease-causing mutations present in 665 alleles, distributed in 5.81% of all participants. Of these 136 mutations, 43 were novel, nine were founder mutations, and two hot spot mutations with allele count ≥10. Furthermore, 38.5% (84/218) of genetically solved USH2A-IRD patients were caused by at least one of both c.2802T>G and c.8559-2 A>G mutations, and 36.9% and 69.6% of the alleles in the RP and USH groups were truncating, respectively. Conclusion: USH2A-related East Asian-specific founder and hot spot mutations were the major causes for Chinese RP and USH patients. Our study systematically delineated the genotype spectrum of USH2A-IRD, enabled accurate genetic diagnosis, and provided East Asian and other ethnicities with baseline data of a Chinese origin, which would better serve genetic counseling and therapeutic targets selection.

Mutation spectrum in a cohort with familial exudative vitreoretinopathy.

PURPOSE: To expand the mutation spectrum of patients with familial exudative vitreoretinopathy (FEVR) disease. PARTICIPANTS: 74 probands (53 families and 21 sporadic probands) with familial exudative vitreoretinopathy (FEVR) disease and their available family members (n = 188) were recruited for sequencing. METHODS: Panel-based targeted screening was performed on all subjects. Before sanger sequencing, variants of LRP5, NDP, FZD4, TSPAN12, ZNF408, KIF11, RCBTB1, JAG1, and CTNNA1 genes were verified by a series of bioinformatics tools and genotype-phenotype co-segregation analysis. RESULTS: 40.54% (30/74) of the probands were sighted to possess at least one etiological mutation of the nine FEVR-causative genes. The etiological mutation detection rate was 37.74% (20/53) in family-attainable probands while 47.62% (10/21) in sporadic cases. The diagnosis rate of patients in the early-onset subgroup (≤5 years old, 45.4%) is higher than that of the children or adolescence-onset subgroup (6-16 years old, 42.1%) and the late-onset subgroup (≥17 years old, 39.4%). A total of 36 etiological mutations were identified in this study, comprising 26 novel mutations and 10 reported mutations. LRP5 was the most prevalent mutant gene among the 36 mutation types with a percentage of 41.67% (15/36). Followed by FZD4 (10/36, 27.78%), TSPAN12 (5/36, 13.89%), NDP (4/36, 11.11%), KIF11 (1/36, 2.78%), and RCBTB1 (1/36, 2.78%). Among these mutations, 63.89% (23/36) were missense mutations, 25.00% (9/36) were frameshift mutations, 5.56% (2/36) were splicing mutations, 5.56% (2/36) were nonsense mutations. Moreover, the clinical pathogenicity of these variants was defined according to American College of Medical Genetics (ACMG) and genomics guidelines: 41.67% (15/36) were likely pathogenic variants, 27.78% (10/36) pathogenic variants, 30.55% (11/36) variants of uncertain significance. No etiological mutations discovered in the ZNF408, JAG1, and CTNNA1 genes in this FEVR cohort. CONCLUSIONS: We systematically screened nine FEVR disease-associated genes in a cohort of 74 Chinese probands with FEVR disease. With a detection rate of 40.54%, 36 etiological mutations of six genes were authenticated in 30 probands, including 26 novel mutations and 10 reported mutations. The most prevalent mutated gene is LRP5, followed by FZD4, TSPAN12, NDP, KIF11, and RCBTB1. In total, a de novo mutation was confirmed. Our study significantly clarified the mutation spectrum of variants bounded up to FEVR disease.

Evaluation of Amide Proton Transfer-Weighted Imaging for Risk Factors in Stage I Endometrial Cancer: A Comparison With Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging.

Background: Endometrial cancer (EC) is one of the most common gynecologic malignancies in clinical practice. This study aimed to compare the value of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and amide proton transfer-weighted imaging (APTWI) in the assessment of risk stratification factors for stage I EC including histological subtype, grade, stage, and lymphovascular space invasion (LVSI). Methods: A total of 72 patients with stage I EC underwent pelvic MRI. The apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), and magnetization transfer ratio asymmetry (MTRasym at 3.5 ppm) were calculated and compared in risk groups with the Mann-Whitney U test or independent samples t-test. Spearman's rank correlation was applied to depict the correlation of each parameter with risk stratification. The diagnostic efficacy was evaluated with receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. A multivariate logistic regression was conducted to explore the optimal model for risk prediction. Results: There were significantly greater MTRasym (3.5 ppm) and MK and significantly lower ADC and MD in the non-adenocarcinoma, stage IB, LVSI-positive, high-grade, and non-low-risk groups (all p < 0.05). The MK and MTRasym (3.5 ppm) were moderately positively correlated with risk stratification as assessed by the European Society for Medical Oncology (EMSO) clinical practice guidelines (r = 0.640 and 0.502, respectively), while ADC and MD were mildly negatively correlated with risk stratification (r = -0.358 and -0.438, respectively). MTRasym (3.5 ppm), MD, and MK were identified as independent risk predictors in stage I EC, and optimal predictive performance was obtained with their combinations (AUC = 0.906, sensitivity = 70.97%, specificity = 92.68%). The results of the validation model were consistent with the above results, and the calibration curve showed good accuracy and consistency. Conclusions: Although similar performance was obtained with each individual parameter of APTWI, DWI, and DKI for the noninvasive assessment of aggressive behavior in stage I EC, the combination of MD, MK, and MTRasym (3.5 ppm) provided improved predictive power for non-low-risk stage I EC and may serve as a superior imaging marker.

Identification of histological features of endometrioid adenocarcinoma based on amide proton transfer-weighted imaging and multimodel diffusion-weighted imaging.

BACKGROUND: Noninvasive identification of the histological features of endometrioid adenocarcinoma is necessary. This study aimed to investigate whether amide proton transfer-weighted imaging (APTWI) and multimodel (monoexponential, biexponential, and stretched exponential) diffusion-weighted imaging (DWI) could predict the histological grade of endometrial adenocarcinoma (EA). In addition, we analyzed the correlation between each parameter and the Ki-67 index. METHODS: A total of 90 EA patients who received pelvic magnetic resonance imaging (MRI) were enrolled. The magnetization transfer ratio asymmetry [MTRasym (3.5 ppm)], apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured and compared. Correlation coefficients between each parameter and histological grade and the Ki-67 index were calculated. Statistical methods included the independent samples t test, Spearman's correlation, and logistic regression. RESULTS: MTRasym (3.5 ppm) [(3.72%±0.31%) vs. (3.27%±0.48%)], f [(3.15%±0.36%) vs. (2.69%±0.83%)], and α [(0.89±0.05) vs. (0.81±0.09)] were higher and ADC [(0.82±0.08) vs. (0.89±0.10) ×10-3 mm2/s], D [(0.67±0.09) vs. (0.81±0.11) ×10-3 mm2/s], and DDC [(1.04±0.09) vs. (1.13±0.13) ×10-3 mm2/s] were lower in high-grade EA than in low-grade EA (P<0.05). MTRasym (3.5 ppm) and D were independent predictors for the histological grade of EA. The combination of MTRasym (3.5 ppm) and D were better able to identify high- and low-grade EA than was each parameter. MTRasym (3.5 ppm) and α were moderately and weakly positively correlated, respectively, with histological grade and the Ki-67 index (r=0.528, r=0.514, r=0.395, and r=0.367; P<0.05). D was moderately negatively correlated with histological grade and the Ki-67 index (r=-0.540 and r=-0.529; P<0.05). DDC was weakly and moderately negatively correlated with histological grade and the Ki-67 index, respectively (r=-0.473 and r=-0.515; P<0.05). ADC was weakly negatively correlated with histological grade and the Ki-67 index (r=-0.417 and r=-0.427; P<0.05). f was weakly positively correlated with histological grade and the Ki-67 index (r=0.294 and r=0.355; P<0.05). CONCLUSIONS: Our study found that both multimodel DWI and APTWI could be used to estimate the histological grade and Ki-67 index of EA, and the combination of high MTRasym (3.5 ppm) and low D may be an effective imaging marker for predicting the grade of EA.

Comparative analysis of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in evaluating the histological grade of cervical squamous carcinoma.

BACKGROUND: Uterine cervical cancer (UCC) was the fourth leading cause of cancer death among women worldwide. The conventional MRI hardly revealing the microstructure information. This study aimed to compare the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in evaluating the histological grade of cervical squamous carcinoma (CSC) in addition to routine diffusion-weighted imaging (DWI). METHODS: Forty-six patients with CSC underwent pelvic DKI and APTWI. The magnetization transfer ratio asymmetry (MTRasym), apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were calculated and compared based on the histological grade. Correlation coefficients between each parameter and histological grade were calculated. RESULTS: The MTRasym and MK values of grade 1 (G1) were significantly lower than those of grade 2 (G2), and those parameters of G2 were significantly lower than those of grade 3 (G3). The MD and ADC values of G1 were significantly higher than those of G2, and those of G2 were significantly higher than those of G3. MTRasym and MK were both positively correlated with histological grade (r = 0.789 and 0.743, P <  0.001), while MD and ADC were both negatively correlated with histological grade (r = - 0.732 and - 0.644, P <  0.001). For the diagnosis of G1 and G2 CSCs, AUC (APTWI+DKI + DWI) > AUC (DKI + DWI) > AUC (APTWI+DKI) > AUC (APTWI+DWI) > AUC (MTRasym) > AUC (MK) > AUC (MD) > AUC (ADC), where the differences between AUC (APTWI+DKI + DWI), AUC (DKI + DWI) and AUC (ADC) were significant. For the diagnosis of G2 and G3 CSCs, AUC (APTWI+DKI + DWI) > AUC (APTWI+DWI) > AUC (APTWI+DKI) > AUC (DKI + DWI) > AUC (MTRasym) > AUC (MK) > AUC (MD > AUC (ADC), where the differences between AUC (APTWI+DKI + DWI), AUC (APTWI+DWI) and AUC (ADC) were significant. CONCLUSION: Compared with DWI and DKI, APTWI is more effective in identifying the histological grades of CSC. APTWI is recommended as a supplementary scan to routine DWI in CSCs.

A Visual Analytics Approach for Structural Differences Among Graphs via Deep Learning.

Representing and analyzing structural differences among graphs help gain insight into the difference related patterns such as dynamic evolutions of graphs. Conventional solutions leverage representation learning techniques to encode structural information, but lack an intuitive way of studying structural semantics of graphs. In this article, we propose a representation-and-analysis scheme for structural differences among graphs. We propose a deep-learning-based embedding technique to encode multiple graphs while preserving semantics of structural differences. We design and implement a web-based visual analytics system to support comparative study of features learned from the embeddings. One distinctive feature of our approach is that it supports semantics-aware construction, quantification, and investigation of latent relations encoded in graphs. We validate the usability and effectiveness of our approach through case studies with three datasets.

Amide Proton Transfer-Weighted Imaging and Multiple Models Diffusion-Weighted Imaging Facilitates Preoperative Risk Stratification of Early-Stage Endometrial Carcinoma.

BACKGROUND: Endometrial carcinoma (EC) risk stratification is generally based on histological assessment. It would be beneficial to perform risk stratification noninvasively by MRI. PURPOSE: To investigate the application of amide proton transfer-weighted imaging (APTWI), monoexponential, biexponential, and stretched exponential intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for the evaluation of risk stratification in early-stage EC. STUDY TYPE: Prospective. POPULATION: Eighty patients with early-stage EC (47 classified as low risk, 20 as medium risk, and 13 as high risk by histological grade and International Federation of Gynecology and Obstetrics stage). FIELD STRENGTH/SEQUENCE: T1-weighted imaging, T2-weighted imaging, IVIM, APTWI, and DKI MRI at 3 T. ASSESSMENT: The magnetization transfer ratio asymmetry (MTRasym [3.5 ppm]), apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index (α), mean kurtosis (MK), and mean diffusivity (MD) were calculated and compared between low-risk and non-low-risk groups. STATISTICAL TESTS: Individual sample t test, analysis of variance, and logistic regression. A P-value <0.05 was considered statistically significant. RESULTS: The α, ADC, D, DDC, and MD were significantly higher and the f, MK, and MTRasym (3.5 ppm) were significantly lower in the low-risk group than in the non-low-risk group. The difference in D* between the two groups was not significant (P = 0.289). MTRasym (3.5 ppm), D, and MK were independent predictors of risk stratification. The combination of these three parameters was better able to identify low- and non-low-risk groups than each individual parameter. DATA CONCLUSION: The IVIM, DKI, and APTWI parameters have potential as imaging markers for risk stratification in early-stage EC. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer.

OBJECTIVES: This study aims to evaluate and compare the diagnostic value of DKI and APT in prostate cancer (PCa), and their correlation with Gleason Score (GS). MATERIALS AND METHODS: DKI and APT imaging of 49 patients with PCa and 51 patients with benign prostatic hyperplasia (BPH) were collected and analyzed, respectively. According to the GS, the patients with PCa were divided into high-risk, intermediate-risk and low-risk groups. The mean kurtosis (MK), mean diffusion (MD) and magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm) values among PCa, BPH, and different GS groups of PCa were compared and analyzed respectively. The diagnostic accuracy of each parameter was evaluated by using the receiver operating characteristic (ROC) curve. The correlation between each parameter and GS was analyzed by using Spearman's rank correlation. RESULTS: The MK and MTRasym (3.5 ppm) values were significantly higher in PCa group than in BPH group, while the MD value was significantly lower than in BPH group. The differences of MK/MD/MTRasym (3.5 ppm) between any two of the low-risk, intermediate-risk, and high-risk groups were all statistically significant (p <0.05). The MK value showed the highest diagnostic accuracy in differentiating PCa and BPH, BPH and low-risk, low-risk and intermediate-risk, intermediate-risk and high-risk (AUC = 0.965, 0.882, 0.839, 0.836). The MK/MD/MTRasym (3.ppm) values showed good and moderate correlation with GS (r = 0.844, -0.811, 0.640, p <0.05), respectively. CONCLUSION: DKI and APT imaging are valuable in the diagnosis of PCa and demonstrate strong correlation with GS, which has great significance in the risk assessment of PCa.

Evaluation of the Genetic Variation Spectrum Related to Corneal Dystrophy in a Large Cohort.

AIMS: To characterize the genetic landscape and mutation spectrum of patients with corneal dystrophies (CDs) in a large Han ethnic Chinese Cohort with inherited eye diseases (IEDs). METHODS: Retrospective study. A large IED cohort was recruited in this study, including 69 clinically diagnosed CD patients, as well as other types of eye diseases patients and healthy family members as controls. The 792 genes on the Target_Eye_792_V2 chip were used to screen all common IEDs in our studies, including 22 CD-related genes. RESULTS: We identified 2334 distinct high-quality variants on 22 CD-related genes in a large IEDs cohort. A total of 21 distinct pathogenic or likely pathogenic mutations were identified, and the remaining 2313 variants in our IED cohort had no evidence of CD-related pathogenicity. Overall, 81.16% (n = 56/69) of CD patients received definite molecular diagnoses, and transforming growth factor-beta-induced protein (TGFBI), CHTS6, and SLC4A11 genes covered 91.07, 7.14, and 1.79% of the diagnosed cases, respectively. Twelve distinct disease-associated mutations in the TGFBI gene were identified, 11 of which were previously reported and one is novel. Four of these TGFBI mutations (p.D123H, p.M502V, p.P501T, and p.P501A) were redefined as likely benign in our Han ethnic Chinese IED cohort after performing clinical variant interpretation. These four TGFBI mutations were detected in asymptomatic individuals but not in CD patients, especially the previously reported disease-causing mutation p.P501T. Among 56 CD patients with positive detected mutations, the recurrent TGFBI mutations were p.R124H, p.R555W, p.R124C, p.R555Q, and p.R124L, and the proportions were 32.14, 19.64, 14.29, 10.71, and 3.57%, respectively. Twelve distinct pathogenic or likely pathogenic mutations of CHTS6 were detected in 28 individuals. The recurrent mutations were p.Y358H, p.R140X, and p.R205W, and the proportions were 25.00, 21.43, and 14.29%, respectively. All individuals associated with TGFBI were missense mutations; 74.19% associated with CHTS6 mutations were missense mutations, and 25.81% were non-sense mutations. Hot regions were located in exons 4 and 12 of TGFBI individuals and located in exon 3 of CHTS6 individuals. No de novo mutations were identified. CONCLUSION: For the first time, our large cohort study systematically described the variation spectrum of 22 CD-related genes and evaluated the frequency and pathogenicity of all 2334 distinct high-quality variants in our IED cohort. Our research will provide East Asia and other populations with baseline data from a Han ethnic population-specific level.

Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging.

OBJECTIVES: To investigate whether amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) can be used to evaluate endometrial carcinoma (EC) in terms of clinical type, histological grade, subtype, and Ki-67 index. METHODS: Eighty-eight patients with EC underwent pelvic DKI and APTWI. The non-Gaussian diffusion coefficient (Dapp), apparent kurtosis coefficient (Kapp), and magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)) were calculated and compared based on the clinical type (type I, II), histological grade (high- and low-grade), and subtype (endometrioid adenocarcinoma (EA) and non-EA). Correlation coefficients were calculated for each parameter with histological grades and the Ki-67 index. RESULTS: The MTRasym (3.5 ppm) and Kapp values were higher in the type II group and high-grade group than in the type I and low-grade groups, respectively, while the Dapp values were lower in the type I and low-grade groups, respectively (all p < 0.05). The Kapp value was higher in the EA group than in the non-EA group (p = 0.022). The Kapp value was the only independent predictor for the histological grade of EA and the clinical type of EC. The AUC (DKI) was higher than the AUC (APTWI) in the identification of type I and II EC and high- and low-grade EA (Z = 2.042, 2.013, p = 0.041, 0.044), while in the identification of EA and non-EA, only the difference in Kapp was statistically significant. Moreover, the Kapp and MTRasym (3.5 ppm) values and Dapp values correlated positively and negatively, respectively, with histological grade (r = 0.759, 0.555, 0.624, and 0.462, all p < 0.05) and Ki-67 index (r = -0.704, -0.507, all p < 0.05). CONCLUSION: Both DKI- and APTWI-related parameters have potential as imaging markers in estimating the histological features of EC, while DKI shows better performance than APTWI in this study. KEY POINTS: • DKI and APTWI can be used to preliminarily evaluate the histological characteristics of endometrial carcinoma (EC). • The Kapp was the only independent predictor for the histological grade of EA and the clinical type of EC. • The Kapp, MTRasym (3.5 ppm), and Dapp correlated positively and negatively, respectively, with histological grade and Ki-67 index.

A comparative study of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in the diagnosis and evaluation of breast cancer.

OBJECTIVES: To compare the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign and malignant breast lesions and analyze the correlations between the derived parameters and prognostic factors of breast cancer. METHODS: One hundred thirty-five women underwent breast APTWI and DKI. The magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), apparent kurtosis coefficient (Kapp), and non-Gaussian diffusion coefficient (Dapp) were calculated according to the histological subtype, grade, and prognostic factors (Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), lymph node metastasis, and maximum lesion diameter). The differences, efficacy, and correlation between the parameters were determined. RESULTS: The Kapp value was higher and the Dapp and MTRasym (3.5 ppm) values were lower in the malignant group than in the benign group (all p < 0.001; AUC (Kapp) = 0.913, AUC (Dapp) = 0.910, and AUC (MTRasym (3.5 ppm)) = 0.796). The differences in the AUC between Kapp and MTRasym (3.5 ppm) and between Dapp and MTRasym (3.5 ppm) were significant (p = 0.023, 0.046). Kapp was moderately correlated with the pathological grade (|r| = 0.724) and mildly correlated with Ki-67 and HER-2 expression (|r| = 0.454, 0.333). Dapp was moderately correlated with the pathological grade (|r| = 0.648) and mildly correlated with Ki-67 expression (|r| = 0.400). MTRasym (3.5 ppm) was only mildly correlated with the pathological grade (|r| = 0.468). CONCLUSION: DKI is superior to APTWI in differentiating between benign and malignant breast lesions. Each parameter is correlated with some prognostic factors to a certain extent. KEY POINTS: • DKI and APTWI provide valuable information regarding lesion characterization. • Kapp, Dapp, and MTRasym (3.5 ppm) are valid parameters for the characterization of tissue microstructure. • DKI is superior to APTWI in the study of breast cancer.

Comparative Study of Amide Proton Transfer Imaging and Intravoxel Incoherent Motion Imaging for Predicting Histologic Grade of Hepatocellular Carcinoma.

Background: Preoperative grading of hepatocellular carcinoma (HCC) is an important factor associated with prognosis after liver resection. The promising prediction of the differentiation of HCC remains a challenge. The purpose of our study was to investigate the value of amide proton transfer (APT) imaging in predicting the histological grade of HCC, compared with the intravoxel incoherent motion (IVIM) imaging. Methods: From September 2018 to February 2020, 88 patients with HCC were enrolled and divided into four groups (G1, G2, G3, and G4) based on the histologic grades. Preoperative APT signal intensity (SI), apparent diffusion coefficient (ADC), true molecular diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f ) of HCC were independently measured by two radiologists. The averaged values of those parameters were compared using an analysis of variance. The Spearman rank analysis was used to compare the correlation between those imaging parameters and the histological grades. Receiver operating characteristic (ROC) curve analysis was used to explore the predictive performance. Results: There were significant differences in APT SI, ADC, D, and f among the four grades of HCC (all P < 0.001). A moderate to good relationship was found between APT SI and the histologic grade of HCC (r = 0.679, P < 0.001). APT SI had an area under the ROC curve (AUC) of 0.890 (95% CI: 0.805-0.947) for differentiating low- from high-grade HCC, and the corresponding sensitivity and specificity were 85.71% and 82.05%, respectively. Comparison of ROC curves demonstrated that the AUC of APT SI was significantly higher than those of IVIM-derived parameter (Z = 2.603, P = 0.0092; Z = 2.099, P = 0.0358; Z = 4.023, P = 0.0001; Z = 2.435, P = 0.0149, compared with ADC, D, D*, and f , respectively). Moreover, the combination of both techniques further improved the diagnostic performance, with an AUC of 0.929 (95% CI: 0.854-0.973). Conclusion: APT imaging may be a potential noninvasive biomarker for the prediction of histologic grading of HCC and complements IVIM imaging for the more accurate and comprehensive characterization of HCC.

Amide Proton Transfer Imaging vs Diffusion Kurtosis Imaging for Predicting Histological Grade of Hepatocellular Carcinoma.

BACKGROUND: To investigate the value of amide proton transfer (APT) imaging in predicting the histological grade of hepatocellular carcinoma (HCC), compared with diffusion kurtosis imaging (DKI). METHODS: A total of 88 patients with HCC were enrolled and divided into four groups (G1, G2, G3, and G4) based on histologic grades. Preoperative APT signal intensity (SI), mean diffusivity (MD), mean kurtosis (MK) of HCC were measured and compared. Those quantitative magnetic resonance imaging (qMRI) parameters were compared using an analysis of variance. The correlations between the qMRI parameters and the histological grades were determined using Spearman's rank analysis. In addition, the predictive performance for differentiating low- (G1 and G2) from high-grade (G3 and G4) HCC was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Significant differences were found in APT SIs, MD, and MK among the four groups (P<0.05). Moderate to good relationships were found between the histologic grade of HCC and APT SI and MK (r=0.679, P<0.001 and r=0.539, P<0.001, respectively). The area under the ROC curves (AUCs) of APT SI, MK, and MD for differentiating low- from high-grade HCC were 0.890 (95%CI: 0.805-0.947), 0.765 (95%CI: 0.662-0.849) and 0.717 (95%CI: 0.611-0.808), respectively. Comparison of ROC curves showed a significantly higher AUC of APT SI compared with those of the DKI-derived parameters (P <0.05). CONCLUSION: The APT imaging may be more accurate than DKI for predicting the histological grade of HCC.