A hepatic sparganosis misdiagnosed as intrahepatic mass-forming cholangiocarcinoma: a case report and literature review.

Hepatic sparganosis (HS) is extremely rare and has not been previously reported in Eastern China. We report the diagnosis and treatment of a patient with HS from Xuzhou City, Jiangsu Province, China. The patient was admitted due to an acute biliary tract infection, and the symptoms improved after treatment at the Gastroenterology Department. During an ultrasound examination on admission, an abnormal echo was incidentally discovered at the junction of the left and right lobes of the liver. Thereafter, upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) non-contrast and contrast-enhanced examinations, and serum tumor biomarker examination were completed. After a multidisciplinary treatment (MDT) discussion at the Department of Hepatobiliary Surgery, the patient was diagnosed with intrahepatic mass-type cholangiocarcinoma (IMCC) and surgery was recommended. The patient underwent surgical treatment, and postoperative pathology revealed HS. No signs of intrahepatic recurrence were observed during the 1-year follow-up period.

Preoperative evaluating early recurrence in resectable pancreatic ductal adenocarcinoma by using CT radiomics.

OBJECTIVE: To investigate the feasibility of a radiomics model based on contrast-enhanced CT for preoperatively predicting early recurrence after curative resection in patients with resectable pancreatic ductal adenocarcinoma (PDAC). METHODS: One hundred and eighty-six patients with resectable PDAC who underwent curative resection were included and allocated to training set (131 patients) and validation set (55 patients). Radiomics features were extracted from arterial phase and portal venous phase images. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature construction. The radiomics model based on radiomics signature and clinical features was developed by the multivariate logistic regression analysis. Performance of the radiomics model was investigated by the area under the receiver operating characteristic (ROC) curve. RESULTS: The radiomics signature, consisting of three arterial phase and three venous phase features, showed optimal prediction performance for early recurrence in both training (AUC = 0.73) and validation sets (AUC = 0.66). Multivariate logistic analysis identified the radiomics signature (OR, 2.58; 95% CI 2.36-3.17; p = 0.002) and clinical stage (OR, 1.60; 95% CI 1.15-2.30; p = 0.007) as independent predictors. The AUC values for risk evaluation of early recurrence using the radiomics model incorporating clinical stage were 0.80 (training set) and 0.75 (validation set). CONCLUSION: The radiomics-based model integrating with clinical stage can predict early recurrence after upfront surgery in patients with resectable PDAC.

Establishment of a Prediction Model Based on Preoperative MRI Radiomics for Diffuse Astrocytic Glioma, IDH-Wildtype, with Molecular Features of Glioblastoma.

PURPOSE: In 2021, the WHO central nervous system (CNS) tumor classification criteria added the diagnosis of diffuse astrocytic glioma, IDH wild-type, with molecular features of glioblastoma, WHO grade 4 (DAG-G). DAG-G may exhibit the aggressiveness and malignancy of glioblastoma (GBM) despite the lower histological grade, and thus a precise preoperative diagnosis can help neurosurgeons develop more refined individualized treatment plans. This study aimed to establish a predictive model for the non-invasive identification of DAG-G based on preoperative MRI radiomics. PATIENTS AND METHODS: Patients with pathologically confirmed glioma in Huashan Hospital, Fudan University, between September 2019 and July 2021 were retrospectively analyzed. Furthermore, two external validation datasets from Wuhan Union Hospital and Xuzhou Cancer Hospital were also utilized to verify the reliability and accuracy of the prediction model. Two regions of interest (ROI) were delineated on the preoperative MRI images of the patients using the semi-automatic tool ITK-SNAP (version 4.0.0), which were named the maximum anomaly region (ROI1) and the tumor region (ROI2), and Pyradiomics 3.0 was applied for feature extraction. Feature selection was performed using a least absolute shrinkage and selection operator (LASSO) filter and a Spearman correlation coefficient. Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. Moreover, the performance of prediction models was evaluated using area under the curve (AUC), precision (PRE), and other metrics. RESULTS: According to the inclusion and exclusion criteria, 172 patients with grade 2-3 astrocytoma were finally included in the study, and a total of 44 patients met the diagnosis of DAG-G. In the prediction task of DAG-G, the average AUC of GNB classifier was 0.74 ± 0.07, that of KNN classifier was 0.89 ± 0.04, that of RF classifier was 0.96 ± 0.03, that of AB classifier was 0.97 ± 0.02, that of SVM classifier was 0.88 ± 0.05, and that of MLP classifier was 0.91 ± 0.03, among which, AB classifier achieved the best prediction performance. In addition, the AB classifier achieved AUCs of 0.91 and 0.89 in two external validation datasets obtained from Wuhan Union Hospital and Xuzhou Cancer Hospital, respectively. CONCLUSIONS: The prediction model constructed based on preoperative MRI radiomics established in this study can basically realize the prospective, non-invasive, and accurate diagnosis of DAG-G, which is of great significance to help further optimize treatment plans for such patients, including expanding the extent of surgery and actively administering radiotherapy, targeted therapy, or other treatments after surgery, to fundamentally maximize the prognosis of patients.

The application of decision tree model based on clinicopathological risk factors and pre-operative MRI radiomics for predicting short-term recurrence of glioblastoma after total resection: a retrospective cohort study.

To develop a decision tree model based on clinical information, molecular genetics information and pre-operative magnetic resonance imaging (MRI) radiomics-score (Rad-score) to investigate its predictive value for the risk of recurrence of glioblastoma (GBM) within one year after total resection. Patients with pathologically confirmed GBM at Huashan Hospital, Fudan University between November 2017 and June 2020 were retrospectively analyzed, and the enrolled patients were randomly divided into training and test sets according to the ratio of 3:1. The relevant clinical and MRI data of patients before, after surgery and follow-up were collected, and after feature extraction on preoperative MRI, the LASSO filter was used to filter the features and establish the Rad-score. Using the training set, a decision tree model for predicting recurrence of GBM within one year after total resection was established by the C5.0 algorithm, and scatter plots were generated to evaluate the prediction accuracy of the decision tree during model testing. The prediction performance of the model was also evaluated by calculating area under the receiver operating characteristic (ROC) curve (AUC), ACC, Sensitivity (SEN), Specificity (SPE) and other indicators. Besides, two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University were used to verify the reliability and accuracy of the prediction model. According to the inclusion and exclusion criteria, 134 patients with GBM were finally identified for inclusion in the study, and 53 patients recurred within one year after total resection, with a mean recurrence time of 5.6 months. According to the importance of the predictor variables, a decision tree model for predicting recurrence based on five important factors, including patient age, Rad-score, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, pre-operative Karnofsky Performance Status (KPS) and Telomerase reverse transcriptase (TERT) promoter mutation, was developed. The AUCs of the model in the training and test sets were 0.850 and 0.719, respectively, and the scatter plot showed excellent consistency. In addition, the prediction model achieved AUCs of 0.810 and 0.702 in two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University, respectively. The decision tree model based on clinicopathological risk factors and preoperative MRI Rad-score can accurately predict the risk of recurrence of GBM within one year after total resection, which can further guide the clinical optimization of patient treatment decisions, as well as refine the clinical management of patients and improve their prognoses to a certain extent.

Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases.

OBJECTIVES: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. METHODS: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. RESULTS: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). CONCLUSIONS: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. CLINICAL RELEVANCE STATEMENT: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. KEY POINTS: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.

Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model.

PURPOSE: The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2-4 based on preoperative conventional multimodal MRI radiomics. PATIENTS AND METHODS: Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients' preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA). RESULTS: According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA. CONCLUSIONS: The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2-4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management.

Virtual monoenergetic imaging predicting Ki-67 expression in lung cancer.

This study aimed to optimize slope and energy levels for evaluating Ki-67 expression in lung cancer using virtual monoenergetic imaging and compare the predictive efficiency of different energy spectrum slopes (λHU) for Ki-67. Forty-three patients with primary lung cancer confirmed via pathological examination were enrolled in this study. They underwent baseline arterial-phase (AP) and venous-phase (VP) energy spectrum computed tomography (CT) scanning before surgery. The CT values were 40-190 keV, with 40-140 keV indicating pulmonary lesions at AP and VP, and P < 0.05 indicating a statistically significant difference. An immunohistochemical examination was conducted, and receiver operating characteristic curves were used to analyze the prediction performance of λHU for Ki-67 expression. SPSS Statistics 22.0 (IBM Corp., NY, USA) was used for statistical analysis, and χ2, t, and Mann-Whitney U tests were used for quantitative and qualitative analyses of data. Significant differences were observed at the corresponding CT values of 40 keV (as 40-keV is considered the best for single-energy image for evaluating Ki-67 expression) and 50 keV in AP and at 40, 60, and 70 keV in VP between high- and low-Ki-67 expression groups (P < 0.05). In addition, the λHU values of three-segment energy spectrum curve in both AP and VP were quite different between two groups (P < 0.05). However, the VP data had greater predictive values for Ki-67. The areas under the curve were 0.859, 0.856, and 0.859, respectively. The 40-keV single-energy sequence was the best single-energy sequence to evaluate the expression of Ki-67 in lung cancer and to obtain λHU values using the energy spectrum curve in the VP. The CT values had better diagnostic efficiency.

Prediction of treatment response in patients with brain metastasis receiving stereotactic radiosurgery based on pre-treatment multimodal MRI radiomics and clinical risk factors: A machine learning model.

Objectives: Stereotactic radiosurgery (SRS), a therapy that uses radiation to treat brain tumors, has become a significant treatment procedure for patients with brain metastasis (BM). However, a proportion of patients have been found to be at risk of local failure (LF) after treatment. Hence, accurately identifying patients with LF risk after SRS treatment is critical to the development of successful treatment plans and the prognoses of patients. To accurately predict BM patients with the occurrence of LF after SRS therapy, we develop and validate a machine learning (ML) model based on pre-treatment multimodal magnetic resonance imaging (MRI) radiomics and clinical risk factors. Patients and methods: In this study, 337 BM patients were included (247, 60, and 30 in the training set, internal validation set, and external validation set, respectively). Four clinical features and 223 radiomics features were selected using least absolute shrinkage and selection operator (LASSO) and Max-Relevance and Min-Redundancy (mRMR) filters. We establish the ML model using the selected features and the support vector machine (SVM) classifier to predict the treatment response of BM patients to SRS therapy. Results: In the training set, the SVM classifier that uses a combination of clinical and radiomics features demonstrates outstanding discriminative performance (AUC=0.95, 95% CI: 0.93-0.97). Moreover, this model also achieves satisfactory results in the validation sets (AUC=0.95 in the internal validation set and AUC=0.93 in the external validation set), demonstrating excellent generalizability. Conclusions: This ML model enables a non-invasive prediction of the treatment response of BM patients receiving SRS therapy, which can in turn assist neurologist and radiation oncologists in the development of more precise and individualized treatment plans for BM patients.

A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT.

OBJECTIVES: Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI on non-contrast computed tomography (NCCT) is currently very low. Thus, we aimed to develop and validate the radiomics feature-based machine learning models to detect EBI (RMEBIs) on NCCT. METHODS: In this retrospective observational study, 355 participants from a multicentre multimodal database established by Huashan Hospital were randomly divided into two data sets: a training cohort (70%) and an internal validation cohort (30%). Fifty-seven participants from the Second Affiliated Hospital of Xuzhou Medical University were included as the external validation cohort. Brainstems were segmented by a radiologist committee on NCCT and 1781 radiomics features were automatically computed. After selecting the relevant features, 7 machine learning models were assessed in the training cohort to predict early brainstem infarction. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the prediction models. RESULTS: The multilayer perceptron (MLP) RMEBI showed the best performance (AUC: 0.99 [95% CI: 0.96-1.00]) in the internal validation cohort. The AUC value in external validation cohort was 0.91 (95% CI: 0.82-0.98). CONCLUSIONS: RMEBIs have the potential in routine clinical practice to enable accurate computer-assisted diagnoses of early brainstem infarction in patients with NCCT, which may have important clinical value in reducing therapeutic decision-making time. KEY POINTS: • RMEBIs have the potential to enable accurate diagnoses of early brainstem infarction in patients with NCCT. • RMEBIs are suitable for various multidetector CT scanners. • The patient treatment decision-making time is shortened.

Analysis of Key Factors Associated with Response to Salvage High-Dose Methotrexate Rechallenge in Primary Central Nervous System Lymphoma with First Relapse.

BACKGROUND: Primary central nervous system lymphoma (PCNSL) is a rare extranodal non-Hodgkin's lymphoma that occurs in the central nervous system. Although sensitive to chemotherapy, 35-60% of PCNSL patients still relapse within 2 years after the initial treatment. High-dose methotrexate (HD-MTX) rechallenge is generally used in recurrent PCNSL, especially for patients who have achieved a response after initial methotrexate (MTX) treatment. However, the overall remission rate (ORR) of HD-MTX rechallenge is about 70-80%. Additionally, the side effects of HD-MTX treatment endanger the health of patients and affect their quality of life. METHODS: This is a retrospective study of patients with first relapse PCNSL at Huashan Hospital, Fudan University between January 2000 and November 2020. By comparing the clinical characteristics and radiological manifestations of first relapsed PCNSL patients with remission and non-remission after receiving HD-MTX rechallenge, we screened out the key factors associated with HD-MTX rechallenge treatment response, to provide some help for the selection of salvage treatment strategies for patients with recurrent PCNSL. Additionally, patients with remission after HD-MTX rechallenge were followed up to identify the factors related to progression-free survival of the second time (PFS2) (time from the first relapse to second relapse/last follow-up). The Kruskal-Wallis and Pearson chi-square tests were performed to examine the univariate association. Further, multivariable logistic regression analysis was used to study the simultaneous effect of different variables. RESULTS: A total of 207 patients were enrolled in the study based on the inclusion criteria, including 114 patients in the remission group (RG) and 81 patients in the non-remission group (nRG), and 12 patients were judged as having a stable disease. In Kruskal-Wallis and Pearson chi-square tests, progression-free survival rates for first time (PFS1) and whether the initial treatment was combined with consolidated whole brain radiotherapy (WBRT) were related to the response to HD-MTX rechallenge treatment, which was further validated in regression analysis. Further, after univariate analysis and regression analysis, KPS was related to PFS2. CONCLUSIONS: For PCNSL patients in their first relapse, HD-MTX rechallenge may be an effective salvage treatment. PFS1 and whether initial treatment was combined with consolidation WBRT were associated with HD-MTX rechallenge treatment response. In addition, patients with higher KPS at the time of the first relapse had a longer PFS2 after HD-MTX rechallenge treatment.

Development and validation of comprehensive clinical outcome prediction models for acute ischaemic stroke in anterior circulation based on machine learning.

The current prediction models for the clinical outcome of acute ischaemic stroke (AIS) remain insufficient for individualized patient management strategies. We aimed to investigate machine learning (ML) performance in the clinical outcome prediction of AIS in anterior circulation and evaluate the clinical outcome by combining the quantitative evaluation indicators of perfusion features and basic clinical information. Four ML classifiers, support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and random forest (RF) were trained on a cohort of 389 adult patients (training cohort [70 %]; external validation cohort [30 %]) from the Acute Stroke Center Registry of Huashan Hospital. Model performance was compared by a range of learning metrics. Most imaging parameters were strongly correlated with the outcome (range, 0.57 to 0.81), and the correlation between relative cerebral blood flow (rCBF) < 30 % and clinical outcome was the strongest (ρ = 0.81). As the reference parameters increased, the performance of the four models was greatly improved [SVM (AUC: from 0.79 to 0.99, F1-score: from 0.61 to 0.90), RF (AUC: from 0.88 to 0.98, F1-score: from 0.71 to 0.96), LR (AUC: from 0.80 to 0.97, F1-score: from 0.64 to 0.95), and NB (AUC: from 0.74 to 0.97, F1-score: from 0.66 to 0.92)]. The ensemble classifier model with all parameters had the highest F1-score (0.97). All the ML models, jointly considering the basic clinical information and quantitative evaluation indicators of computed tomography perfusion (CTP), showed good performance in the prediction of clinical outcome of AIS in anterior circulation.

Nomogram Model for Predicting the Prognosis of High-Grade Glioma in Adults Receiving Standard Treatment: A Retrospective Cohort Study.

OBJECTIVES: To identify the critical factors associated with the progression-free survival (PFS) and overall survival (OS) of high-grade glioma (HGG) in adults who have received standard treatment and establish a novel graphical nomogram and an online dynamic nomogram. PATIENTS AND METHODS: This is a retrospective study of adult HGG patients receiving standard treatment (surgery, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy) at Huashan Hospital, Fudan University between January 2017 and December 2019. We used uni- and multi-variable COX models to identify the significant prognostic factors for PFS and OS. Based on the significant predictors, graphical and online nomograms were established. RESULTS: A total of 246 patients were enrolled in the study based on the inclusion criteria. The average PFS and OS were 22.99 ± 11.43 and 30.51 ± 13.73 months, respectively. According to the multi-variable COX model, age, extent of resection (EOR), and IDH mutation were associated with PFS and OS, while edema index (EI) was relevant to PFS. In addition, patients with IDH and TERT promoter co-mutations had longer PFSs and OSs, and no apparent survival benefit was found in the long-cycle TMZ adjuvant chemotherapy compared with the standard Stupp protocol. Based on these critical factors, a graphical nomogram and online nomogram were developed for predicting PFS and OS, respectively. The calibration curve showed favorable consistency between the predicted and actual survival rates. C-index and time-dependent AUC showed good discrimination abilities. CONCLUSIONS: We identified the significant predictors for the PFS and OS of HGG adults receiving standard treatment and established user-friendly nomogram models to assist neurosurgeons in optimizing clinical management and treatment strategies.

WITHDRAWN: Diagnosis of Intracranial Primary Central Nervous System Lymphoma Based on Magnetic Resonance Imaging.

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Two giant peritoneal loose bodies were simultaneously found in one patient: A case report and review of the literature.

INTRODUCTION: Peritoneal loose body(PLB) is usually small, therefore giant Peritoneal loose body(gPLB) with a diameter >5cm has rarely been described in the literatures. We report a case of two gPLB simultaneously found in one patient. PRESENTATION OF CASE: A healthy 79-year-old man palpated himself a solid mass with alternating localizations in his peritoneal cavity 6 months ago. It was not the complaint of frequency of urinatior until he saw the doctor a week ago. Surprisingly, two oval-shaped masses were simultaneously discovered by computed tomography (CT). One was in the peritoneal cavity, measuring 10.4*8.3cm, weight 182.5g, another was in the pelvic cavity, measuring 7.6*6.0cm, weight 98.4g. The case was confirmed by surgical operation. DISCUSSION: The gPLB is considered as uncommon. Two gPLB which were simultaneously discovered in one patient have never been reported in the literatures. The small PLB is usually asmptomatic, occasionally, the gPLB can cause symptoms with acute retention of urine or intestinal obstruction. It is crucial to diagnosis the peritoneal loose body. CONCLUSION: Two gPLB that situated in one patient are rare findings. Clinically, if a solid mass alternating localizations cound be palpated in the Peritoneal cavity, CT or other imaging shows an oval-shaped mass with calcifications in the central region, PLB should be considered. Surgical removal is recommended for the patient with acute retention of urine or intestinal obstruction or unclear diagnosis.

Beneficial clinical effects of grape seed proanthocyanidin extract on the progression of carotid atherosclerotic plaques.

BACKGROUND: Atherosclerotic plaques indicate the occurrence of ischemia events and it is a difficult task for clinical physicians. Grape seed proanthocyanidin extract (GSPE) has been reported to exert an antiatherogenic effect by inducing regression of atherosclerotic plaques in animal experimental studies. In this study, the antiatherogenic effect of GSPE has been investigated in clinical use. METHODS: Consecutive 287 patients diagnosed with asymptomatic carotid plaques or abnormal plaque free carotid intima-media thickness (CIMT) were randomly assigned to the GSPE group (n = 146) or control group (n = 141). The patients in the GSPE group received GSPE 200 mg per day orally, while patients in the control group were only enrolled in a lifestyle intervention program. Carotid ultrasound examination was performed at baseline and 6, 12, 24 months during follow-up. Mean maximum CIMT (MMCIMT), plaque score, echogenicity of plaques and ischemic vascular events were recorded. RESULTS: As anticipated, after treatment, GSPE resulted in significant reduction in MMCIMT progression (4.2% decrease after six months, 4.9% decrease after 12 months and 5.8% decrease after 24 months) and plaque score (10.9% decrease after six months, 24.1% decrease after 12 months and 33.1% decrease after 24 months) for the primary outcome, while MMCIMT and plaque score were stable and even increased with the time going on in control group. The number of plaques and unstable plaques also decreased after treatment of GSPE. Furthermore, the carotid plaque can disappear after treatment with GSPE. The incidence rate for transitory ischemic attack (TIA), arterial revascularization procedure, and hospital readmission for unstable angina in GSPE group were statistically significant lower (P = 0.02, 0.08, 0.002, respectively) compared with the control group. CONCLUSIONS: GSPE inhibited the progression of MMCIMT and reduced carotid plaque size in GSPE treated patients, and with extended treatment, the superior efficacy on MMCIMT and carotid plaque occurred. Furthermore, the GSPE group showed lower rates of clinical vascular events.

Seeding endothelial progenitor cells on a self-expanding metal stent: an in vitro study.

PURPOSE: To demonstrate the feasibility of seeding a self-expanding metal stent with endothelial progenitor cells to enhance rapid reendothelialization, which is postulated to prevent local thrombus formation and restenosis after vascular intervention. MATERIALS AND METHODS: Endothelial progenitor cells and fibrinogen were isolated from the peripheral blood of a domestic swine and then cultured and identified. Ten self-expanding nitinol stents were incubated in the culture medium with a cell concentration of 1 x 10(6)/mL with (n = 5, study group) or without (n = 5, control group) fibrin gel (5 mg/mL fibrinogen and 0.10 NIHU/mL thrombin) for 24 hours. The cell coverage of the stents was documented with en face photography and scanning electron microscopy. After simulated use in vitro, the cells were removed from each stent, counted with a cytometer, sequentially cultured for three passages, and identified again to compare their properties with those of the original seeding line. RESULTS: After seeding the stent with the combination of endothelial progenitor cells and the fibrin gel coating, the stents took on a tube-like appearance with a confluent monolayer membrane. After digestion with trypsin, a mean of 2.5 x 10(5) +/- 1.3 cells were obtained from the fibrin gel stent (study group); fewer cells (4 x 10(4) +/- 1.5) were recovered from the bare stents (control group) (P < .01). The recovered cells, after amplification with culture, demonstrated the properties of the original endothelial progenitor cells. CONCLUSIONS: An endothelial progenitor cell-coated stent can be successfully fabricated by using fibrin gel as the bonding agent in vitro. Further in vivo research is warranted.

Transjugular intrahepatic portosystemic shunt with an autologous endothelial progenitor cell seeded stent: a porcine model.

RATIONALE AND OBJECTIVES: To evaluate the efficacy of a self-expanding metal stent seeded with autologous endothelial progenitor cells (EPCs) for preventing in-stent stenoses in transjugular intrahepatic portosystemic shunt (TIPS) in a swine model. MATERIALS AND METHODS: TIPS was performed in 18 young adult pigs, using a self-expanding nitinol stent (control, n = 8) and an autologous EPC-seeded stent (treatment, n = 10). All pigs were sacrificed at 2 weeks post-TIPS procedure. Portography was performed immediately before the euthanasia. Gross, microscopic, and immunohistochemistry of the TIPS tract specimens were examined. The proliferative response of the shunt was quantified histologically. RESULTS: TIPS was performed successfully in 16 swine, 2 animals died during the procedure. Another pig died of unknown causes 2 days post-procedure. At day 14 follow-up, portography and necropsy of the 15 remaining swine demonstrated that five shunts occluded and one shunt was stenotic (80%) in the control group (n = 6). Five shunts remained patent, two shunts were stenosed (50%, 70%), and the remaining two shunts were occluded in the treatment group (n = 9). The patency rate was significantly lower in the control group than in the treatment group, 0% versus 55.6% (P = .03). Histological analyses showed a significantly greater pseudointimal hyperplasia in the TIPS track of the control group than that of the treatment group (P < .05). Intact endothelium was documented in the lumina of all the EPC-implanted stent group. CONCLUSIONS: The EPC-seeded metal stent is feasibly fabricated in vitro and improves the patency in TIPS in a porcine model.

[In vitro MR imaging of Fe2O3-arginine labeled heNOS gene modified endothelial progenitor cells].

OBJECTIVE: To explore the feasibility of in vitro magnetic resonance imaging on Fe2O3-arginine labeled heNOS gene modified endothelial progenitor cells (EPCs). METHODS: Fe2O3 was incubated with arginine to form Fe2O3-arginine complex. Rabbit peripheral blood mononuclear cells (MNCs) were isolated and EPCs were isolated by adherence method, expanded and modified with heNOS gene using Lipofectamine 2000. After 48 hours, genetically modified EPCs were incubated with Fe2O3-arginine for 24 hours. Intracellular iron was detected by Prussian blue stain. The expression of heNOS gene was detected by Western blot. MTT assay was used to evaluate cell survival and proliferation of Fe2O3-arginine labeled heNOS-EPCs. Flow cytometry was used to measure cell apoptosis. The cells underwent in vitro MR imaging with various sequences. RESULTS: Iron-containing intracytoplasmatic vesicles could be clearly observed with Prussian blue staining, and the labeling rate of labeled heNOS-EPCs were similar to that of labeled EPCs (around 100%). Survival and apoptosis rates obtained by MTT and flow cytometry analysis were similar among labeled heNOS-EPCs, labeled EPCs and unlabeled EPCs with Fe2O3-arginine. The signal intensity on MRI was equally decreased in labeled heNOS-EPCs and labeled EPCs compared with that in unlabeled cells. The percentage change in signal intensity (DeltaSI) was most significant on T2*WI and DeltaSI was significantly lower in cells labeled for 7 days than that labeled for 1 days. CONCLUSIONS: The heNOS gene can be successfully transfected into rabbit peripheral blood EPCs using Lipofectamine2000. The heNOS-EPCs can be labeled with Fe2O3-arginine without significant change in viability and proliferation capacity. The labeled heNOS-EPCs can be imaged with standard 1.5 T MR equipment. The degree of MR signal intensity may indirectly reflect the cell count, growth and division status.

A rabbit model of atherosclerosis at carotid artery: MRI visualization and histopathological characterization.

To induce a rabbit model of atherosclerosis at carotid artery, to visualize the lesion evolution with magnetic resonance imaging (MRI), and to characterize the lesion types by histopathology. Atherosclerosis at the right common carotid artery (RCCA) was induced in 23 rabbits by high-lipid diet following balloon catheter injury to the endothelium. The rabbits were examined in vivo with a 1.5-T MRI and randomly divided into three groups of 6 weeks (n=6), 12 weeks (n=8) and 15 weeks (n=9) for postmortem histopathology. The lesions on both MRI and histology were categorized according to the American Heart Association (AHA) classifications of atherosclerosis. Type I and type II of atherosclerotic changes were detected at week 6, i.e., nearly normal signal intensity (SI) of the injured RCCA wall without stenosis on MRI, but with subendothelial inflammatory infiltration and proliferation of smooth muscle cells on histopathology. At week 12, 75.0% and 62.5% of type III changes were encountered on MRI and histopathology respectively with thicker injured RCCA wall of increased SI on T(1)-weighted and proton density (PD)-weighted MRI and microscopically a higher degree of plaque formation. At week 15, carotid atherosclerosis became more advanced, i.e., type IV and type V in 55.6% and 22.2% of the lesions with MRI and 55.6% and 33.3% of the lesions with histopathology, respectively. Statistical analysis revealed a significant agreement (p<0.05) between the MRI and histological findings for lesion classification (r=0.96). A rabbit model of carotid artery atherosclerosis has been successfully induced and noninvasively visualized. The atherosclerotic plaque formation evolved from type I to type V with time, which could be monitored with 1.5-T MRI and confirmed with histomorphology. This experimental setting can be applied in preclinical research on atherosclerosis.