Associations of CSF BACE1 with amyloid pathology, neurodegeneration, and cognition in Alzheimer's disease.

Β-site amyloid precursor protein (APP) cleaving enzyme (BACE1) is a crucial protease in the production of amyloid-ß (Aß) in Alzheimer's disease (AD) patients. However, the side effects observed in clinical trials of BACE1 inhibitors, including reduction in brain volume and cognitive worsening, suggest that the exact role of BACE1 in AD pathology is not fully understood. To further investigate this, we examined cerebrospinal fluid (CSF) levels of BACE1 and its cleaved product sAPPß that reflects BACE1 activity in the China Aging and Neurodegenerative Disorder Initiative cohort. We found significant correlations between CSF BACE1 or sAPPß levels and CSF Aß40, Aß42, and Aß42/Aß40 ratio, but not with amyloid deposition detected by 18F-Florbetapir PET. Additionally, CSF BACE1 and sAPPß levels were positively associated with cortical thickness in multiple brain regions, and higher levels of sAPPß were linked to increased cortical glucose metabolism in frontal and supramarginal areas. Interestingly, individuals with higher baseline levels of CSF BACE1 exhibited slower rates of brain volume reduction and cognitive worsening over time. This suggests that increased levels and activity of BACE1 may not be the determining factor for amyloid deposition, but instead, may be associated with increased neuronal activity and potentially providing protection against neurodegeneration in AD.

A Case of Giant Left Circumflex Coronary Fistula Aneurysm with Unidentified Fistula.

A coronary aneurysm is a rare type of cardiovascular disease. We report a case of a 53-year-old male patient who presented to our hospital with a giant left circumflex coronary fistula aneurysm (LCCA) (75 mm × 70 mm). Since coronary angiography and coronary computed tomography angiography failed to detect the fistula of the coronary aneurysm, interventional occlusion surgery could not be performed. We discovered the fistula in the right atrium by anterograde perfusion with blood-containing myocardial protective fluid after switching to intraoperative exploration during cardiac surgery. The coronary aneurysm's fistula and inlet were then sutured, and the aneurysm was resected. The patient recovered successfully after the operation. This case was instructive in managing LCCA, especially with an unidentified fistula.

A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.

INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort. METHODS: A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non-AD dementia. Fluid biomarkers were measured with single molecule array. Amyloid beta (Aß) deposition was determined by 18 F-Flobetapir positron emission tomography (PET), and brain atrophy was quantified using magnetic resonance imaging (MRI). RESULTS: Aß42/Aß40 was decreased, whereas levels of phosphorylated tau (p-tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD. CSF Aß42/Aß40, CSF p-tau, and plasma p-tau showed a high concordance in discriminating between AD and non-AD dementia or elderly controls. A combination of plasma p-tau, apolipoprotein E (APOE) genotype, and MRI measures accurately predicted amyloid PET status. DISCUSSION: These results revealed a universal applicability of the "A/T/N" framework in a Chinese population and established an optimal diagnostic model consisting of cost-effective and non-invasive approaches for diagnosing AD.

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework. METHODS: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia. RESULTS: 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model. CONCLUSIONS: We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.

PURPOSE: In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time. METHODS: From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years). Patient CT images from one hospital were divided among training, validation and test datasets with an 80%:10%:10% ratio. An end-to-end representation learning method using a large-scale bi-directional generative adversarial network (BigBiGAN) architecture was designed to extract semantic features from the CT images. The semantic feature matrix was input for linear classifier construction. Patients from the other hospital were used for external validation. Differentiation accuracy was evaluated using a receiver operating characteristic curve. RESULTS: Based on the 120-dimensional semantic features extracted by BigBiGAN from each image, the linear classifier results indicated that the area under the curve (AUC) in the training, validation and test datasets were 0.979, 0.968 and 0.972, respectively, with an average sensitivity of 92% and specificity of 91%. The AUC for external validation was 0.850, with a sensitivity of 80% and specificity of 75%. Publicly available architecture and computing resources were used throughout the study to ensure reproducibility. CONCLUSION: This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.

Predictive value of dual-energy spectral computed tomographic imaging on the histological origin of carcinomas in the ampullary region.

PURPOSE: To investigate the value of dual-energy spectral computed tomographic imaging (DESCT) to predict the origin of carcinomas in the ampullary region. MATERIALS AND METHODS: Fifty-seven patients with suspected ampullary region carcinomas underwent DESCT prior to biopsy or surgery. Among those patients, 30 were pancreatic adenocarcinomas, 11 were biliary adenocarcinomas, 16 were adenocarcinomas of the ampulla diagnosed by biopsy and/or pathological examination before or after surgical operation. We compared the CT spectral imaging features among the adenocarcinomas with the above-mentioned three different origins. RESULTS: Iodine concentration thresholds of 16.36, 21.86, and 21.86 mg/mL yielded a sensitivity and specificity of 100% for distinguishing between common bile duct adenocarcinomas and pancreatic adenocarcinomas in the arterial phase (AP), portal venous phase (PP), and delayed phase (DP), respectively. Thresholds of 16.70, 24.33, and 26.43 mg/mL yielded a sensitivity and specificity of 100% for distinguishing between common bile duct adenocarcinomas and ampullary adenocarcinomas in the AP, PP, and DP, respectively. Iodine concentration thresholds of 16.66 and 17.78 mg/mL yielded a sensitivity and specificity of 100% for distinguishing between ampullary adenocarcinomas and pancreatic adenocarcinomas in the PP and DP, respectively. CONCLUSION: DESCT with multiple parameters can provide useful diagnostic information and may be used to predict the histological origin of carcinomas in the ampullary region.

[Use of computed tomography gemstone spectral curve in evaluation on histodifferentiation of gastric cancer].

OBJECTIVE: To evaluate the application value of spectral curve in gemstone spectral imaging (GSI) in evaluating the differentiation state of gastric cancer. METHODS: Triple-phase enhanced GSI CT scanning was performed on 65 gastric cancer patients whose differentiation state was later confirmed through gastroscopy. The 101 monochromatic image sets obtained with spectral CT imaging were analyzed with the GSI Viewer software. CT values at 40, 70 and 140 keV images were measured to calculate the slopes for spectral curve in the 40-140, 40-70 and 70-140 keV energy range. They were divided into 3 groups based on the pathological results and the corresponding slope measurements were compared with independent t test. Receiver operating characteristic curves were generated to determine the parameters for optimizing sensitivity and specificity in differentiation state of gastric cancer. RESULTS: As verified pathologically, there were 21 well-differentiated, 22 moderately-differentiated and 22 poorly-differentiated adenocarcinoma cases. The slope for spectral curve was larger at the foci of gastric cancer than that in normal gastric wall: the lower differentiation state, the larger the slope. During the arterial phase, the slope of the normal gastric wall , well-differentiated adenocarcinoma , moderately-differentiated adenocarcinoma and poorly-differentiated adenocarcinoma in the 40-70 keV energy range was 1.78 ± 0.12, 1.86 ± 0.30, 1.87 ± 0.28, 2.59 ± 0.31, the slopes of well and moderately-differentiated adenocarcinoma and normal gastric wall were significantly different from poorly-differentiated adenocarcinoma in the 40-70 keV energy range (P < 0.05), but were not different in the other 2 energy ranges (P > 0.05); during the portal vein phase, the slope of the normal gastric wall, well-differentiated adenocarcinoma, moderately-differentiated adenocarcinoma and poorly-differentiated adenocarcinoma in the 40-70 keV energy range was 3.30 ± 1.13, 3.96 ± 0.29, 4.10 ± 1.28, 4.98 ± 2.15, the slopes in all 3 energy ranges were significantly different in each group (P < 0.05). Using the slopes in the 40-70 keV energy range, the sensitivities and specificities for determining the differentiation states were >81% and >86% respectively during the arterial phase and >72% and >72% during the portal venous phase. CONCLUSION: The spectral curve at the foci of gastric cancer was correlated with the histological differentiation state. And the slope of spectral curve of GSI may offer a new index for evaluating the differentiation state of gastric cancer before operation.

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC.

We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset. EME semantic features were extracted to construct an EME-prognostic model to stratify the prognosis of EGFR-mutant NSCLC patients receiving EGFR-TKI. A comparison of EME and radiomics was conducted. Additionally, we included patients from The Cancer Genome Atlas lung adenocarcinoma dataset with both CT images and RNA sequencing data to explore the biological associations between EME score and EGFR-related biological processes. EME obtained an area under the curve (AUC) of 0.907 (95% CI 0.840-0.926) on the test dataset, superior to the radiomics model (P = 0.007). The EME and radiomics fusion model showed better (AUC, 0.941) but not significantly increased performance (P = 0.895) compared with EME. In prognostic stratification, the EME-prognostic model achieved the best performance (C-index, 0.711). Moreover, the EME-prognostic score showed strong associations with biological pathways related to EGFR expression and EGFR-TKI efficacy. EME demonstrated a non-invasive and biologically interpretable approach to predict EGFR status, stratify survival prognosis, and correlate biological pathways in patients with NSCLC.

Based on Gadolinium Ethoxybenzyl DTPA-Enhanced MRI: Diagnostic Performance of the Category-Modified LR-5 Criteria in Patients At Risk for Hepatocellular Carcinoma.

Introduction: We aimed to modify the LR-5 strategy to improve the diagnostic sensitivity for hepatocellular carcinoma (HCC) in high-risk patients while maintaining specificity. Methods: This study retrospectively analyzed 412 patients with 445 liver observations who underwent preoperative gadolinium ethoxybenzyl DTPA (GD-EOB-DTPA)-enhanced MRI followed by surgical procedures or biopsies. All observations were classified according to LI-RADS v2018, and the classifications were adjusted by modifying major features (MF)(substituting threshold growth with a more HCC-specific ancillary features (AF): presence of blood products within the mass, arterial phase hyperenhancement (APHE) was interpreted with hypointensity on precontrast imaging- isointensity in arterial phase (AP) and extending washout to transitional phase (TP)(2 min)). The specificity, sensitivity, and positive predictive value (PPV) were assessed to compare LR-5 (definitely HCC) diagnostic efficacy between LI-RADS version 2018 and modified LI-RADS. Results: Apart from nonenhancing "capsule", the interreader agreement of MFs and HCC-specific AFs between the two readers reached substantial or excellent ranges (κ values ranging from 0.631 to 0.911). According to LI-5 v2018, the specificity, sensitivity and PPV of HCC were 90.74%, 82.35%, and 98.17%, respectively. Based on a more HCC-specific AF, signal intensity in AP and TP (2 min), the sensitivity of the three modified strategies were 86.19%, 93.09%, 96.67% (P < .05)), while maintaining high specificity and PPV rates at 88.89% and 98.25% (P > .05) Conclusion: Further investigation into the efficacy of threshold growth as a MF is warranted. By utilizing GD-EOB-DTPA-enhanced MRI, enhancing the sensitivity of the modified LR-5 category may be achieved without compromising specificity and PPV in diagnosing HCC among high-risk patients.

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

PURPOSE: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC). METHODS: Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs. RESULTS: RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts. CONCLUSIONS: Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.

Higher betweenness and degree centrality in the frontal and cerebellum cortex of Meige's syndrome patients than hemifacial spasm patients.

Meige's syndrome and hemifacial spasm (HFS) are two different forms of dystonic movement disorder, but their difference in terms of resting state functional connectivity (rsFC) remains unclear. The present study applied resting state fMRI on the patients and quantified their functional connectivity with graph theoretical measures, including the degree centrality and the betweenness centrality. Fifteen Meige's syndrome patients and 19 HFS patients matched in age and gender were recruited and their MRI data were collected. To analyze the rsFC, we adopted the Anatomical Automatic Labeling (AAL) template, a brain atlas system including 90 regions of interest (ROIs) covering all the brain regions of cerebral cortex. For each participant, the time-course of each ROI was extracted, and the corresponding degree centrality and betweenness centrality of each ROI were computed. These measures were then compared between the Meige's syndrome patients and the HFS patients. Meige's syndrome patients showed higher betweenness centrality and degree centrality of bilateral superior medial frontal cortex, the left cerebellum cortex, etc. than the HFS patients. Our results suggest that the rsFC pattern in Meige's syndrome patients might become more centralized toward the prefrontal and vestibular cerebellar systems, indicating less flexibility in their functional connections. These results preliminarily revealed the characteristic abnormality in the functional connection of Meige's patients and may help to explore better treatment.

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors.

PURPOSE: Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors. METHODS: One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model. RESULTS: The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model. CONCLUSIONS: The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

Investigation of brain iron levels in Chinese patients with Alzheimer's disease.

Introduction: We aimed (i) to explore the diagnostic value of deep gray matter magnetic susceptibility in Alzheimer's disease (AD) in China and (ii) to analyze its correlation with neuropsychiatric scales. Moreover, we conducted subgroup analysis based on the presence of the APOE-ε4 gene to improve the diagnosis of AD. Methods: From the prospective studies of the China Aging and Neurodegenerative Initiative (CANDI), a total of 93 subjects who could undergo complete quantitative magnetic susceptibility imaging and APOE-ε4 gene detection were selected. Differences in quantitative susceptibility mapping (QSM) values between and within groups, including AD patients, individuals with mild cognitive impairment (MCI), and healthy controls (HCs), both APOE-ε4 carriers and non-carriers, were analyzed. Results: In primary analysis, the magnetic susceptibility values of the bilateral caudate nucleus and right putamen in the AD group and of the right caudate nucleus in the MCI group were significantly higher than those in the HCs group (P < 0.05). In APOE-ε4 non-carriers, there were significant differences in more regions between the AD, MCI, and HCs groups, such as the left putamen and the right globus pallidus (P < 0.05). In subgroup analysis, the correlation between QSM values in some brain regions and neuropsychiatric scales was even stronger. Discussion: Exploration of the correlation between deep gray matter iron levels and AD may provide insight into the pathogenesis of AD and facilitate early diagnosis in elderly Chinese. Further subgroup analysis based on the presence of the APOE-ε4 gene may further improve the diagnostic efficiency and sensitivity.

A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.

Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self-supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images.

Blood-based biomarkers for Alzheimer's disease: a multicenter-based cross-sectional and longitudinal study in China.

Discrepancies in diagnostic biomarkers for Alzheimer's Disease (AD) may arise from racial disparities, risk factors, or lifestyle differences. Moreover, there has been a lack of systematic and multicenter studies to evaluate baselines of the AD biomarkers in Chinese populations. Thus, there is an urgent need for research to investigate the effectiveness of blood biomarkers for AD, specifically in the Chinese Han population, using a multicenter approach. In the present multicenter-based cross-sectional and longitudinal study, we evaluated 817 blood samples from 6 different clinical centers. We measured plasma amyloid beta (Aß)-40, Aß42, phosphorylated tau 181 (pTau), total tau (tTau), serum neurofilament light (NFL), and glial fibrillary acidic protein (GFAP). Additionally, 18F-florbetapir positron electron tomography and magnetic resonance imaging were also performed. A combination of the APOE genotype with plasma pTau and serum GFAP demonstrated exceptional performance in distinguishing Aß status. Furthermore, baseline GFAP levels exhibited a strong association with cognitive decline over time and brain atrophy, with higher GFAP levels predicting a faster rate of neurodegeneration. In summary, these results validate the practicality of blood biomarkers in the Chinese Han population, encompassing various regions within China. Additionally, they emphasize the potential of pTau and GFAP as non-invasive methods for detecting and screening AD at an early stage.

Imaging presentation of biliary adenofibroma: A case report.

BACKGROUND: Biliary adenofibroma (BF) is a rare benign epithelial tumor with the possibility of malignant transformation. Its main pathological feature is a well-defined cystic or honeycomb mass. BF has no specific clinical manifestations or laboratory and imaging findings; thus, it is easily misdiagnosed before surgery. This report describes a case in which biliary cystadenoma was misdiagnosed preoperatively and BF was diagnosed postoperatively. The imaging features, particularly the magnetic resonance imaging (MRI) features, were analyzed and summarized. CASE SUMMARY: A 68-year-old Chinese man was admitted to our hospital with a 2-mo history of abdominal discomfort. Following admission to our hospital, laboratory examinations showed normal tumor marker concentrations and liver function. Hepatocellular carcinoma was considered after contrast-enhanced ultrasound examination. MRI suggested the possibility of cystadenoma of the bile duct. However, postoperative pathological examination confirmed the diagnosis of BF. No local recurrence was found 1 mo after surgery. CONCLUSION: Our objective is to highlight the imaging diagnostic value of BF, especially on an MRI enhanced scan with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid.

Application of diffusion kurtosis imaging to the study of edema in solid and peritumoral areas of glioma.

OBJECTIVE: When gliomas grow in an infiltrative form, high-grade malignant glioma tissue extends beyond the contrast-enhancing tumor boundary, and this diffuse non-enhancing tumor infiltration is not visible on conventional MRI. The purpose of this study was to evaluate the of diffusion kurtosis imaging (DKI)-derived parameters in a group of patients with pre-operative gliomas, evaluating changes in the solid tumor and peritumoral edema area, and investigating their use for evaluating the recurrence and prognosis of gliomas. METHODS: In this retrospective study, 51 patients with gliomas who underwent biopsy or surgery underwent DKI scans before surgery. DKI scans were performed to generate DKI parameter maps of the solid tumor and peritumoral edema areas. In the solid tumor area, the kurtosis parameters showed the highest area under the curve (AUC), sensitivity, and specificity for distinguishing high- and low-grade gliomas (all P < 0.01). RESULTS: In the peritumoral edema area, significant differences were found between groups with grade III and IV gliomas (P < 0.05). DKI parameters were found to correlate with clinical Ki-67 scores within the solid tumor area (MK: R2 = 0.288, P < 0.001; Kr: R2 = 0.270, P < 0.001; Ka: R2 = 0.274, P < 0.001; MD: R2 = 0.223, P < 0.001; FA: R2 = 0.098, P < 0.01). No significant correlations were found between Ki-67 and kurtosis parameters of peritumoral edema. CONCLUSIONS: In this study, DKI showed potential utility for studying solid tumor and peritumoral edema of high grade gliomas.

Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma.

PURPOSE: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models. METHODS: EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis. Three radiomics and three clinical models were then constructed for comparison. RESULTS: Patients with EfficientNetV2-based model score ≤ 0.5 had better TTP than those with higher scores (hazard ratio [HR]: 0.32, 95%CI: 0.22-0.46, P < 0.0001; HR: 0.28, 95%CI: 0.20-0.41, P < 0.0001; and HR: 0.55, 95%CI: 0.36-0.88, P = 0.005 in the training, validation, and test datasets, respectively). Patients with model score ≤ 0.5 had better OS (38.8 months vs 20.9 months, HR: 0.58, 95%CI: 0.37-0.90, P = 0.008). Compared with the radiomics (intra-tumoral and peri-tumoral) and three clinical models, the EfficientNetV2-based model showed better survival prognosis for TACE (P < 0.05) in the test dataset. CONCLUSIONS: The EfficientNetV2-based model enables assessment of both TTP and OS prognosis of TACE in treatment-naïve, intermediate-stage HCC. Patients with lower scores will benefit from TACE. The model can potentially be used by clinicians to improve decision making regarding TACE treatment choices.