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.

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.

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.

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.

Prediction Model for Intermediate-Stage Hepatocellular Carcinoma Response to Transarterial Chemoembolization.

BACKGROUND: The outcome of intermediate-stage hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) is greatly heterogeneous. Current means for predicting HCC response to TACE are lacking. PURPOSE: To investigate whether the combination of parameters derived from amide proton transfer (APT) and intravoxel incoherent motion (IVIM) imaging, and morphological characteristics of tumor can establish a better prediction model than the univariant model for HCC response to TACE. STUDY TYPE: Prospective. SUBJECTS: 56 patients with intermediate-stage HCC (50 males and six females). FIELD STRENGTH/SEQUENCES: 3.0T; T2 -weighted-fast spin echo, 3D liver acquisition with volume flex, single-shot fast spin echo-planar imaging (EPI), spin echo-EPI. ASSESSMENT: Pretreatment APT signal intensities (SIs), apparent diffusion coefficient (ADC), true molecular diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) for tumor, peritumoral, and normal tissues were measured. Follow-up MRI scanning was performed, and the patients were classified as responders or nonresponders based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. STATISTICAL TESTS: The imaging parameters were compared among the three tissues and between the two groups using analysis of variance (ANOVA) or two-sample t-test. The prediction model's variables were derived from univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was used to explore the predictive performance. RESULTS: Based on the logistic regression analysis results, we established a prediction model that integrated the APT SI and D values in the tumor tissue and the tumor size. ROC analyses revealed that the model was better able to predict tumor response to TACE (area under the ROC curve = 0.851) than the individual parameters on their own. DATA CONCLUSION: A prediction model incorporating pretreatment APT SI, D in the tumor tissue and tumor size may be useful for predicting the response of intermediate-stage HCC to TACE. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1 J. MAGN. RESON. IMAGING 2020;52:1657-1667.

Comparative Study of Amide Proton Transfer-Weighted Imaging and Intravoxel Incoherent Motion Imaging in Breast Cancer Diagnosis and Evaluation.

BACKGROUND: Amide proton transfer-weighted imaging (APTWI) and intravoxel incoherent motion imaging (IVIM) are valuable MRI techniques applied to cancer. PURPOSE: To compare APTWI and IVIM in the diagnosis of benign and malignant breast lesions and to evaluate the correlations between different parameters (MTRasym [3.5 ppm], D, D*, and f) and prognostic factors for breast cancer. STUDY TYPE: Retrospective. POPULATION: In all, 123 breast lesions were studied before treatment, including 58 benign lesions and 65 malignant lesions. FIELD STRENGTH/SEQUENCE: Conventional MRI (T1 WI, T2 WI, and diffusion-weighted imaging [DWI]), APTWI, and IVIM MRI at 3T. ASSESSMENT: The magnetization transfer ratio asymmetry at 3.5 ppm (MTRasym [3.5 ppm]), diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) values were compared between the benign and malignant groups and between groups with different expression levels of prognostic factors. STATISTICAL TESTS: Individual sample t-test, χ2 test, Spearman correlation, logistic regression, and the Delong test. RESULTS: The D and MTRasym (3.5 ppm) values of the malignant group were lower than those of the benign group; however, D* and f values were higher than those of the benign group (all P < 0.05). The areas under the curve (AUCs) of D, MTRasym (3.5 ppm), D*, and f were 0.809, 0.778, 0.670, and 0.766, respectively; however, only the difference between AUC (D) and AUC (D*) was significant (Z = 2.374, P < 0.05). The D value showed a low correlation with the pathological grade and Ki-67 expression (| r | = 0.294, 0.367); the f value showed a low correlation with estrogen receptor (ER) expression (| r | = 0.382); and the MTRasym (3.5 ppm) value showed a low correlation with pathological grade (| r | = 0.371). DATA CONCLUSION: This analysis revealed that both IVIM and APTWI could be used for the differential diagnosis of benign and malignant breast lesions, and APTWI-derived MTRasym (3.5 ppm), IVIM-derived D, D*, and f values showed correlations with some prognostic factors for breast cancer. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1175-1186.

Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer.

OBJECTIVES: To analyze the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating cervical cancer (CC) pathological type, grade, and stage. METHODS: One hundred and twelve women underwent pelvic APTWI and DKI. The magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm), apparent kurtosis coefficient (Kapp), and non-Gaussian diffusion coefficient (Dapp) were calculated by histological subtype, grade, and stage. The differences, efficacy, and correlation between parameters were determined. RESULTS: The MTRasym(3.5 ppm) and Dapp values of the adenocarcinoma (CA) group were higher than those of the cervical squamous carcinoma (CSC) group, while the Kapp values were lower than those of the CSC group. The MTRasym(3.5 ppm) and Kapp values of the high-grade group were higher than those of the low-grade group, while the Dapp values were lower than those of the low-grade group. The Dapp values of the advanced-stage group were lower than those of the early-stage group, while the Kapp values were greater than those of the early-stage group. The Kapp showed the highest efficacy in differentiating CSC and CA, high- and low-grade CC, and advanced- and early-stage CC. In the CSC and CA groups, both the Kapp and Dapp were highly correlated with pathological grade, and the MTRasym(3.5 ppm) was weakly correlated with pathological grade. The Kapp, Dapp, and MTRasym(3.5 ppm) were all weakly correlated with pathological stage. CONCLUSION: Both DKI and APTWI can be used in preliminary evaluations of CC, but DKI has advantages in the identification of pathological type, grade, and stage. KEY POINTS: • PTWI and DKI provide new information regarding cervical cancer. • MTRasym(3.5 ppm), Dapp, and Kapp are valid parameters to characterize tissue microstructure. • DKI is superior to APTWI in the study of cervical cancer.

Comparative analysis of the diagnostic values of T2 mapping and diffusion-weighted imaging for sacroiliitis in ankylosing spondylitis.

OBJECTIVE: To investigate the diagnostic values of T2 mapping and diffusion-weighted imaging (DWI) for active sacroiliitis in ankylosing spondylitis (AS) and to evaluate the correlations of T2 and ADC values with Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Spondyloarthritis Research Consortium of Canada (SPARCC) scores. METHODS: A total of 77 AS patients with sacroiliitis and 45 healthy controls were enrolled. All patients were scanned by standard magnetic resonance imaging longitudinal relaxation time (T1)-weighted imaging (T1WI), fat-saturated T2-weighted imaging (FS-T2WI)] and DWI, and T2 mapping of the sacroiliac joints. According to whether subchondral bone marrow edema was present in the FS-T2WI sequence, the 77 patients were divided into an active group (41 cases) and an inactive group (36 cases). The T2 and apparent diffusion coefficient (ADC) values of the subchondral bone marrow were measured in the active group, the inactive group, and the healthy control group. The average T2 and ADC values were compared among the three groups. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic efficacy of T2 and ADC values for sacroiliitis. The correlations of T2 and ADC values with the BASDAI score and the SPARCC score were analyzed. RESULTS: The T2 and ADC values in the active group were higher than those in the inactive group, while that in the inactive group were significantly higher than those in the healthy control group (p < 0.0001). The T2 and ADC values of the AS patients were positively correlated with BASDAI scores, and the correlation coefficients (r) were 0.786 (p < 0.0001) and 0.842 (p < 0.0001), respectively. The areas under the ROC curves (AUCs) of T2 and ADC values between the active and inactive groups, the active group and the healthy control group, and the inactive group and the healthy control group were 0.889 (95% CI, 0.80-0.95) and 0.917 (95% CI, 0.83-0.97), 0.982 (95% CI, 0.93-1.00) and 0.984 (95% CI, 0.93-1.00), and 0.628 (95% CI, 0.51-0.73) and 0.871 (95% CI, 0.78-0.94), respectively. The T2 and ADC values of the AS patients in the active group were positively correlated with SPARCC scores, and the correlation coefficients (r) were 0.757 (p < 0.0001) and 0.764 (p < 0.0001), respectively. CONCLUSION: T2 and ADC values can be used to quantitatively assess the activity of AS, and the efficacy of the ADC value in the diagnosis of AS was higher than that of the T2 value.

Transcriptomic and proteomic feature of salt stress-regulated network in Jerusalem artichoke (Helianthus tuberosus L.) root based on de novo assembly sequencing analysis.

MAIN CONCLUSION: Ribosome activation and sugar metabolic process mainly act on the regulation of salt tolerance in the bioenergy crop Helianthus tuberosus L. as dissected by integrated transcriptomic and proteomic analyses. Helianthus tuberosus L. is an important halophyte plant that can survive in saline-alkali soil. It is vitally necessary to build an available genomic resource to investigate the molecular mechanisms underlying salt tolerance in H. tuberosus. De novo assembly and annotation of transcriptomes were built for H. tuberosus using a HiSeq 4000 platform. 293,823 transcripts were identified and annotated into 190,567 unigenes. In addition, iTRAQ-labeled quantitative proteomics was carried out to detect global protein profiling as a response to salt stress. Comparative omics analysis showed that 5432 genes and 43 proteins were differentially expressed in H. tuberosus under salt stress, which were enriched in the following processes: carbohydrate metabolism, ribosome activation and translation, oxidation-reduction and ion binding. The reprogramming of transcript and protein works suggested that the induced activity of ribosome and sugar signaling may endue H. tuberosus with salt tolerance. With high-quality sequencing and annotation, the obtained transcriptomics and proteomics provide a robust genomic resource for dissecting the regulatory molecular mechanism of H. tuberosus in response to salt stress.

Elicitation of Jerusalem artichoke (Helianthus tuberosus L.) cell suspension culture for enhancement of inulin production and altered degree of polymerisation.

BACKGROUND: Plant cell suspension cultures have emerged as a potential source of secondary metabolites for food additives and pharmaceuticals. In this study inulin accumulation and its degree of polymerisation (DP) in the treated cells in the same medium were investigated after treatment with six types of elicitors. RESULTS: An in vitro cell suspension culture of Jerusalem artichoke (Helianthus tuberosus L.) was optimised by adding an extra nitrogen source. According to the growth kinetics, a maximum biomass of 5.48 g L-1 was obtained from the optimal cell suspension medium consisted of Murashige and Skoog basic medium (MS) + 1.0 mg L-1 α-naphthalene acetic acid (NAA) + 1.0 mg L-1 6-benzylaminopurine (6-BA) + 0.5 mg L-1 proline + 1.0 mg L-1 glutamine. Methyl jasmonate (MeJA, 250 µmol L-1 ) treatment for 15 days led to the highest levels of inulin (2955.27 ± 9.81 mg L-1 compared to control of 1217.46 ± 0.26 mg L-1 ). The elicited effect of five elicitors to the suspension cells of Jerusalem artichoke is as follows: AgNO3 (Ag, 10 µmol L-1 ), salicylic acid (SA, 75 µmol L-1 ), chitosan (KJT, 40 mg L-1 ), Trichoderma viride (Tv, 90 mg L-1 ), yeast extract (YE, 0.25 mg L-1 ), and the corresponding content of inulin is increased by 2.05-, 1.93-, 1.76-, 1.44- and 1.18-fold compared to control, respectively. The obvious effect on the percentage of lower DP in inulin was observed in cells treated with 40 mg L-1 KJT, 0.25 mg L-1 YE and 10 µmol L-1 Ag. CONCLUSIONS: Among the six types of elicitors, the descending order of inulin content is MeJA > Ag > SA > KJT > Tv > YE. For the purpose inulin with lower DP and its application to prebiotic food, three elicitors, including KJT, YE and Ag, can be used for the elicitation. © 2016 Society of Chemical Industry.

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.

Targeted next-generation sequencing reveals the genetic mechanism of Chinese Marfan syndrome cohort with ocular manifestation.

BACKGROUND: Marfan syndrome (MFS) is a hereditary connective tissue disorder involving multiple systems, including ophthalmologic abnormalities. Most cases are due to heterozygous mutations in the fibrillin-1 gene (FBN1). Other associated genes include LTBP2, MYH11, MYLK, and SLC2A10. There is significant clinical overlap between MFS and other Marfan-like disorders. PURPOSE: To expand the mutation spectrum of FBN1 gene and validate the pathogenicity of Marfan-related genes in patients with MFS and ocular manifestations. METHODS: We recruited 318 participants (195 cases, 123 controls), including 59 sporadic cases and 88 families. All patients had comprehensive ophthalmic examinations showing ocular features of MFS and met Ghent criteria. Additionally, 754 cases with other eye diseases were recruited. Panel-based next-generation sequencing (NGS) screened mutations in 792 genes related to inherited eye diseases. RESULTS: We detected 181 mutations with an 84.7% detection rate in sporadic cases and 87.5% in familial cases. The overall detection rate was 86.4%, with FBN1 accounting for 74.8%. In cases without FBN1 mutations, 23 mutations from seven Marfan-related genes were identified, including four pathogenic or likely pathogenic mutations in LTBP2. The 181 mutations included 165 missenses, 10 splicings, three frameshifts, and three nonsenses. FBN1 accounted for 53.0% of mutations. The most prevalent pathogenic mutation was FBN1 c.4096G>A. Additionally, 94 novel mutations were detected, with 13 de novo mutations in 14 families. CONCLUSION: We expanded the mutation spectrum of the FBN1 gene and provided evidence for the pathogenicity of other Marfan-related genes. Variants in LTBP2 may contribute to the ocular manifestations in MFS, underscoring its role in phenotypic diversity.

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.

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.

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.

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.

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.

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.