BMI-Adapted Double Low-Dose Dual-Source Aortic CT for Endoleak Detection after Endovascular Repair: A Prospective Intra-Individual Diagnostic Accuracy Study.

PURPOSE: To assess the diagnostic accuracy of BMI-adapted, low-radiation and low-iodine dose, dual-source aortic CT for endoleak detection in non-obese and obese patients following endovascular aortic repair. METHODS: In this prospective single-center study, patients referred for follow-up CT after endovascular repair with a history of at least one standard triphasic (native, arterial and delayed phase) routine CT protocol were enrolled. Patients were divided into two groups and allocated to a BMI-adapted (group A, BMI < 30 kg/m2; group B, BMI ≥ 30 kg/m2) double low-dose CT (DLCT) protocol comprising single-energy arterial and dual-energy delayed phase series with virtual non-contrast (VNC) reconstructions. An in-patient comparison of the DLCT and routine CT protocol as reference standard was performed regarding differences in diagnostic accuracy, radiation dose, and image quality. RESULTS: Seventy-five patients were included in the study (mean age 73 ± 8 years, 63 (84%) male). Endoleaks were diagnosed in 20 (26.7%) patients, 11 of 53 (20.8%) in group A and 9 of 22 (40.9%) in group B. Two radiologists achieved an overall diagnostic accuracy of 98.7% and 97.3% for endoleak detection, with 100% in group A and 95.5% and 90.9% in group B. All examinations were diagnostic. The DLCT protocol reduced the effective dose from 10.0 ± 3.6 mSv to 6.1 ± 1.5 mSv (p < 0.001) and the total iodine dose from 31.5 g to 14.5 g in group A and to 17.4 g in group B. CONCLUSION: Optimized double low-dose dual-source aortic CT with VNC, arterial and delayed phase images demonstrated high diagnostic accuracy for endoleak detection and significant radiation and iodine dose reductions in both obese and non-obese patients compared to the reference standard of triple phase, standard radiation and iodine dose aortic CT.

Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung Cancer.

Background The latest large language models (LLMs) solve unseen problems via user-defined text prompts without the need for retraining, offering potentially more efficient information extraction from free-text medical records than manual annotation. Purpose To compare the performance of the LLMs ChatGPT and GPT-4 in data mining and labeling oncologic phenotypes from free-text CT reports on lung cancer by using user-defined prompts. Materials and Methods This retrospective study included patients who underwent lung cancer follow-up CT between September 2021 and March 2023. A subset of 25 reports was reserved for prompt engineering to instruct the LLMs in extracting lesion diameters, labeling metastatic disease, and assessing oncologic progression. This output was fed into a rule-based natural language processing pipeline to match ground truth annotations from four radiologists and derive performance metrics. The oncologic reasoning of LLMs was rated on a five-point Likert scale for factual correctness and accuracy. The occurrence of confabulations was recorded. Statistical analyses included Wilcoxon signed rank and McNemar tests. Results On 424 CT reports from 424 patients (mean age, 65 years ± 11 [SD]; 265 male), GPT-4 outperformed ChatGPT in extracting lesion parameters (98.6% vs 84.0%, P < .001), resulting in 96% correctly mined reports (vs 67% for ChatGPT, P < .001). GPT-4 achieved higher accuracy in identification of metastatic disease (98.1% [95% CI: 97.7, 98.5] vs 90.3% [95% CI: 89.4, 91.0]) and higher performance in generating correct labels for oncologic progression (F1 score, 0.96 [95% CI: 0.94, 0.98] vs 0.91 [95% CI: 0.89, 0.94]) (both P < .001). In oncologic reasoning, GPT-4 had higher Likert scale scores for factual correctness (4.3 vs 3.9) and accuracy (4.4 vs 3.3), with a lower rate of confabulation (1.7% vs 13.7%) than ChatGPT (all P < .001). Conclusion When using user-defined prompts, GPT-4 outperformed ChatGPT in extracting oncologic phenotypes from free-text CT reports on lung cancer and demonstrated better oncologic reasoning with fewer confabulations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hafezi-Nejad and Trivedi in this issue.

[Large language models such as ChatGPT and GPT-4 for patient-centered care in radiology]. / Goße Sprachmodelle wie ChatGPT und GPT-4 für eine patientenzentrierte Radiologie.

BACKGROUND: With the introduction of ChatGPT in late November 2022, large language models based on artificial intelligence have gained worldwide recognition. These language models are trained on vast amounts of data, enabling them to process complex tasks in seconds and provide detailed, high-level text-based responses. OBJECTIVE: To provide an overview of the most widely discussed large language models, ChatGPT and GPT­4, with a focus on potential applications for patient-centered radiology. MATERIALS AND METHODS: A PubMed search of both large language models was performed using the terms "ChatGPT" and "GPT-4", with subjective selection and completion in the form of a narrative review. RESULTS: The generic nature of language models holds great promise for radiology, enabling both patients and referrers to facilitate understanding of radiological findings, overcome language barriers, and improve the quality of informed consent discussions. This could represent a significant step towards patient-centered or person-centered radiology. CONCLUSION: Large language models represent a promising tool for improving the communication of findings, interdisciplinary collaboration, and workflow in radiology. However, important privacy issues and the reliable applicability of these models in medicine remain to be addressed.

Changes in aortic diameter induced by weight loss: The HELENA trial- whole-body MR imaging in a dietary intervention trial.

Obesity-related metabolic disorders such as hypertension, hyperlipidemia and chronic inflammation have been associated with aortic dilatation and resulting in aortic aneurysms in many cases. Whether weight loss may reduce the risk of aortic dilatation is not clear. In this study, the diameter of the descending thoracic aorta, infrarenal abdominal aorta and aortic bifurcation of 144 overweight or obese non-smoking adults were measured by MR-imaging, at baseline, and 12 and 50 weeks after weight loss by calorie restriction. Changes in aortic diameter, anthropometric measures and body composition and metabolic markers were evaluated using linear mixed models. The association of the aortic diameters with the aforementioned clinical parameters was analyzed using Spearman`s correlation. Weight loss was associated with a reduction in the thoracic and abdominal aortic diameters 12 weeks after weight loss (predicted relative differences for Quartile 4: 2.5% ± 0.5 and -2.2% ± 0.8, p < 0.031; respectively). Furthermore, there was a nominal reduction in aortic diameters during the 50-weeks follow-up period. Aortic diameters were positively associated with weight, visceral adipose tissue, glucose, HbA1c and with both systolic and diastolic blood pressure. Weight loss induced by calorie restriction may reduce aortic diameters. Future studies are needed to investigate, whether the reduction of aortic diameters via calorie restriction may help to prevent aortic aneurysms.

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports.

Purpose: To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms. Materials and Methods: In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors-related TRCs for SOR for ground truth definition. The deep NLP bidirectional encoder representations from transformers (BERT) model and three feature-rich algorithms were trained on SOR to predict TRCs in FTOR. Models' F1 scores were compared against scores of radiologists, medical students, and radiology technologist students. Lexical and semantic analyses were conducted to investigate human and model performance on FTOR. Results: Oncologic findings and TRCs were accurately mined from 9653 of 12 833 (75.2%) queried SOR, yielding oncology reports from 10 455 patients (mean age, 60 years ± 14 [SD]; 5303 women) who met inclusion criteria. On 802 FTOR in the test set, BERT achieved better TRC classification results (F1, 0.70; 95% CI: 0.68, 0.73) than the best-performing reference linear support vector classifier (F1, 0.63; 95% CI: 0.61, 0.66) and technologist students (F1, 0.65; 95% CI: 0.63, 0.67), had similar performance to medical students (F1, 0.73; 95% CI: 0.72, 0.75), but was inferior to radiologists (F1, 0.79; 95% CI: 0.78, 0.81). Lexical complexity and semantic ambiguities in FTOR influenced human and model performance, revealing maximum F1 score drops of -0.17 and -0.19, respectively. Conclusion: The developed deep NLP model reached the performance level of medical students but not radiologists in curating oncologic outcomes from radiology FTOR.Keywords: Neural Networks, Computer Applications-Detection/Diagnosis, Oncology, Research Design, Staging, Tumor Response, Comparative Studies, Decision Analysis, Experimental Investigations, Observer Performance, Outcomes Analysis Supplemental material is available for this article. © RSNA, 2022.

Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep-learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two datasets: our institutional DE-CTPA dataset D1, comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism CT Dataset D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naïve approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep-learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.

OBJECTIVE: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)-based classification in a multi-demographic setting. METHODS: This multi-institutional review boards-approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18-100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS-based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. RESULTS: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the "wavelet_(LH)_GLCM_Imc1" feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. CONCLUSION: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. KEYPOINTS: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92.

Protocol of a prospective study investigating the association of PAncreatic parenchymal RISk factors with postoperative pancreatic fistula after partial pancreaticoduodenectomy (PARIS trial).

INTRODUCTION: Partial pancreatoduodenectomy (PD) is the treatment of choice for many malignant and benign diseases of the pancreatic head. Postoperative complication rates of up to 40% are regularly reported. One of the most common and potentially life-threatening complication is the postoperative pancreatic fistula (POPF). Parenchymal risk factors like main pancreatic duct diameter or texture of the pancreatic gland have already been identified in retrospective studies. The aim of this study is to evaluate the diagnostic value of parenchymal risk factors on POPF in a prospective manner. METHODS AND ANALYSIS: All patients scheduled for elective PD at the Department of General, Visceral and Transplantation Surgery of the University of Heidelberg will be screened for eligibility. As diagnostic factors, diameter and texture of the pancreatic gland as well as radiological and histopathological features will be recorded. Furthermore, the new four class risk classification system by the International Study Group of Pancreatic Surgery (ISGPS) will be recorded. The postoperative course will be monitored prospectively. The primary endpoint will be the association of the main pancreatic duct size and the texture of the pancreatic gland on POPF according to the updated ISGPS definition. The diagnostic value of the above-mentioned factors for POPF will be evaluated in a univariable and multivariable analysis. ETHICS AND DISSEMINATION: PARIS is a monocentric, prospective, diagnostic study to evaluate the association of parenchymal risk factors and the development of POPF approved by the Ethics Committee of the medical faculty of Heidelberg University (S-344/2019). Results will be available in 2022 and will be published at national and international meetings. With this knowledge, the intraoperative and perioperative decision-making process could be eased and improve the individual outcome of patient. TRIAL REGISTRATION NUMBER: DRKS00017184.

Prospective Study of Low-Radiation and Low-Iodine Dose Aortic CT Angiography in Obese and Non-Obese Patients: Image Quality and Impact of Patient Characteristics.

The purpose of this study was to prospectively analyse image quality and radiation dose of body mass index (BMI)-adapted low-radiation and low-iodine dose CTA of the thoracoabdominal aorta in obese and non-obese patients. This prospective, single-centre study included patients scheduled for aortic CTA between November 2017 and August 2020 without symptoms of high-grade heart failure. A BMI-adapted protocol was used: Group A/Group B, BMI < 30/≥ 30 kg/m2, tube potential 80/100 kVp, total iodine dose 14.5/17.4 g. Intraindividual comparison with the institutional clinical routine aortic CTA protocol was performed. The final study cohort comprised 161 patients (mean 71.1 ± 9.4 years, 32 women), thereof 126 patients in Group A (mean BMI 25.4 ± 2.8 kg/m2) and 35 patients in Group B (34.0 ± 3.4 kg/m2). Mean attenuation over five aortoiliac measurement positions for Group A/B was 354.9 ± 78.2/262.1 ± 73.0 HU. Mean effective dose for Group A/B was 3.05 ± 0.46/6.02 ± 1.14 mSv. Intraindividual comparison in 50 patients demonstrated effective dose savings for Group A/B of 34.4 ± 14.5/25.4 ± 14.1% (both p < 0.001), and iodine dose savings for Group A/B of 54/44.8%. Regression analysis showed that female sex and increasing age were independently associated with higher vascular attenuation. In conclusion, BMI-adapted, low-radiation and low-iodine dose CTA of the thoracoabdominal aorta delivers diagnostic image quality in non-obese and obese patients without symptoms of high-grade heart failure, with superior image quality in females and the elderly.

Utility of Automated Cardiac Chamber Volumetry by Nongated CT Pulmonary Angiography for Detection of Pulmonary Hypertension Using the 2018 Updated Hemodynamic Definition.

BACKGROUND. Noninvasive tests for pulmonary hypertension (PH) are needed to help select patients for diagnostic right heart catheterization (RHC). CT pulmonary angiography (CTPA) is commonly performed for suspected PH. OBJECTIVE. The purpose of this study was to assess the utility of CTPA-based cardiac chamber volumetric measurements for the diagnosis of PH in comparison with echocardiographic and conventional CTPA parameters, with the 2018 updated hemodynamic definition used as reference. METHODS. This retrospective study included 109 patients (72 women and 37 men; median age, 68 years) who underwent nongated CTPA, transthoracic echocardiography, and RHC for the workup of suspected PH between August 2013 and February 2016. Two radiologists independently used automated 3D segmentation software to determine the volumes of the right ventricle (RV), right atrium (RA), left ventricle (LV), and left atrium (LA) and also measured the axial diameters of the cardiac chambers, main pulmonary artery, and ascending aorta. Interobserver agreement was assessed, and mean values were obtained; one observer repeated volumetric measurements to assess intraobserver agreement. ROC analysis was used to assess diagnostic performance for the detection of PH. A multivariable binary logistic regression model was established. RESULTS. A total of 60 of 109 patients had PH. Intra- and interobserver agreements were excellent for all volume measurements (intraclass correlation coefficients, 0.935-0.999). In patients with PH versus those without PH, RV volume was 172.6 versus 118.1 mL, and RA volume was 130.2 versus 77.0 mL (both p < .05). Cardiac chamber measurements with the highest AUC for PH were the RV/LV volume ratio and RA volume (both 0.791). Significant predictors of PH20 (as defined using the 2018 hemodynamic definition from the Sixth World Symposium on Pulmonary Hypertension) after adjustment for age, sex, and body surface area included RV volume per 10 mL (odds ratio [OR], 1.21), RA volume per 10 mL (OR, 1.27), RV/LV volume ratio (OR, 2.91), and RA/LA volume ratio (OR, 11.22). Regression analysis yielded a predictive model for PH that contained two independent predictors: echocardiographic pulmonary arterial systolic pressure and CTPA-based RA volume; the model had an AUC of 0.898, sensitivity of 83.3%, and specificity of 85.7%. CONCLUSION. Automated cardiac chamber volumetry using nongated CTPA, particularly of the RA, provides incremental utility relative to echocardiographic and conventional CTPA parameters for diagnosis of PH. CLINICAL IMPACT. Automated volumetry of cardiac chambers based on nongated CTPA may facilitate early noninvasive detection of PH, identifying patients who warrant further evaluation by RHC.

CT Angiography Clot Burden Score from Data Mining of Structured Reports for Pulmonary Embolism.

Background Many studies emphasize the role of structured reports (SRs) because they are readily accessible for further automated analyses. However, using SR data obtained in clinical routine for research purposes is not yet well represented in literature. Purpose To compare the performance of the Qanadli scoring system with a clot burden score mined from structured pulmonary embolism (PE) reports from CT angiography. Materials and Methods In this retrospective study, a rule-based text mining pipeline was developed to extract descriptors of PE and right heart strain from SR of patients with suspected PE between March 2017 and February 2020. From standardized PE reporting, a pulmonary artery obstruction index (PAOI) clot burden score (PAOICBS) was derived and compared with the Qanadli score (PAOIQ). Scoring time and confidence from two independent readings were compared. Interobserver and interscore agreement was tested by using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. To assess conformity and diagnostic performance of both scores, areas under the receiver operating characteristic curve (AUCs) were calculated to predict right heart strain incidence, as were optimal cutoff values for maximum sensitivity and specificity. Results SR content authored by 67 residents and signed off by 32 consultants from 1248 patients (mean age, 63 years ± 17 [standard deviation]; 639 men) was extracted accurately and allowed for PAOICBS calculation in 304 of 357 (85.2%) PE-positive reports. The PAOICBS strongly correlated with the PAOIQ (r = 0.94; P < .001). Use of PAOICBS yielded overall time savings (1.3 minutes ± 0.5 vs 3.0 minutes ± 1.7), higher confidence levels (4.2 ± 0.6 vs 3.6 ± 1.0), and a higher ICC (ICC, 0.99 vs 0.95), respectively, compared with PAOIQ (each, P < .001). AUCs were similar for PAOICBS (AUC, 0.75; 95% CI: 0.70, 0.81) and PAOIQ (AUC, 0.77; 95% CI: 0.72, 0.83; P = .68), with cutoff values of 27.5% for both scores. Conclusion Data mining of structured reports enabled the development of a CT angiography scoring system that simplified the Qanadli score as a semiquantitative estimate of thrombus burden in patients with pulmonary embolism. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Hunsaker in this issue.

PIM1 Inhibition Affects Glioblastoma Stem Cell Behavior and Kills Glioblastoma Stem-like Cells.

Despite comprehensive therapy and extensive research, glioblastoma (GBM) still represents the most aggressive brain tumor in adults. Glioma stem cells (GSCs) are thought to play a major role in tumor progression and resistance of GBM cells to radiochemotherapy. The PIM1 kinase has become a focus in cancer research. We have previously demonstrated that PIM1 is involved in survival of GBM cells and in GBM growth in a mouse model. However, little is known about the importance of PIM1 in cancer stem cells. Here, we report on the role of PIM1 in GBM stem cell behavior and killing. PIM1 inhibition negatively regulates the protein expression of the stem cell markers CD133 and Nestin in GBM cells (LN-18, U-87 MG). In contrast, CD44 and the astrocytic differentiation marker GFAP were up-regulated. Furthermore, PIM1 expression was increased in neurospheres as a model of GBM stem-like cells. Treatment of neurospheres with PIM1 inhibitors (TCS PIM1-1, Quercetagetin, and LY294002) diminished the cell viability associated with reduced DNA synthesis rate, increased caspase 3 activity, decreased PCNA protein expression, and reduced neurosphere formation. Our results indicate that PIM1 affects the glioblastoma stem cell behavior, and its inhibition kills glioblastoma stem-like cells, pointing to PIM1 targeting as a potential anti-glioblastoma therapy.

Lower extremity CT angiography in peripheral arterial disease: from the established approach to evolving technical developments.

With the advent of multidetector computed tomography (CT), CT angiography (CTA) has gained widespread popularity for noninvasive imaging of the arterial vasculature. Peripheral extremity CTA can nowadays be performed rapidly with high spatial resolution and a decreased amount of both intravenous contrast and radiation exposure. In patients with peripheral artery disease (PAD), this technique can be used to delineate the bilateral lower extremity arterial tree and to determine the amount of atherosclerotic disease while differentiating between acute and chronic changes. This article provides an overview of several imaging techniques for PAD, specifically discusses the use of peripheral extremity CTA in patients with PAD, clinical indications, established technical considerations and novel technical developments, and the effect of postprocessing imaging techniques and structured reporting.

Acute pulmonary embolism multimodality imaging prior to endovascular therapy.

The manuscript discusses the application of CT pulmonary angiography, ventilation-perfusion scan, and magnetic resonance angiography to detect acute pulmonary embolism and to plan endovascular therapy. CT pulmonary angiography offers high accuracy, speed of acquisition, and widespread availability when applied to acute pulmonary embolism detection. This imaging modality also aids the planning of endovascular therapy by visualizing the number and distribution of emboli, determining ideal intra-procedural catheter position for treatment, and signs of right heart strain. Ventilation-perfusion scan and magnetic resonance angiography with and without contrast enhancement can also aid in the detection and pre-procedural planning of endovascular therapy in patients who are not candidates for CT pulmonary angiography.

Imaging-Based 4D Aortic Pressure Mapping in Marfan Syndrome Patients: A Matched Case-Control Study.

BACKGROUND: Marfan syndrome predisposes to aortic aneurysm, dissection, and rupture. We sought to investigate aortic 4-dimensional (4D) relative pressure maps derived from 4D flow cardiovascular magnetic resonance to identify disease characteristic alterations of the intraaortic pressure field in Marfan patients with aortic root dilation compared with age- and sex-matched healthy controls. METHODS: This prospective case-control study included 11 Marfan patients with aortic root dilation (31 ± 5 years, 5 female) and 11 age- and sex-matched healthy controls (31 ± 8 years, 5 female) undergoing 4D flow cardiovascular magnetic resonance of the thoracic aorta. 4D relative pressure maps were computed and compared between groups for 8 aortic regions. RESULTS: Aortic root diameters were significantly larger in patients compared with controls (43 vs 31 mm, P < .001), but not in the proximal descending aorta (23 vs 21 mm, P = .19). Regional pressure gradients over the cardiac cycle were significantly altered in Marfan patients with significantly higher minimum pressure gradients in the proximal ascending aorta (-44.3 vs -97.0 mm Hg/m, P < .001) and significantly lower maximum pressure gradients in the proximal descending aorta (55.1 vs 82.3 mm Hg/m, P < .01). The latter finding was associated with pathologic vortical flow patterns. Regional pressure gradient at mid systole significantly correlated with aortic diameter (proximal ascending aorta: r = 0.73, P < .001; proximal descending aorta: r = -0.59, P = .004). CONCLUSIONS: Noninvasive 4D pressure mapping derived from 4D flow cardiovascular magnetic resonance revealed significant alterations of spatiotemporal pressure characteristics in the thoracic aorta of Marfan patients. These alterations were most pronounced in the proximal ascending aorta and the proximal descending aorta, corresponding to the regions where aortic dissections often originate in Marfan patients.

L-Carnitine-Mediated Tumor Cell Protection and Poor Patient Survival Associated with OCTN2 Overexpression in Glioblastoma Multiforme.

PURPOSE: Apoptotic dysregulation, redox adaptive mechanisms, and resilience to hypoxia are major causes of glioblastoma (GBM) resistance to therapy. Commonly known as crucial factors in energy metabolism, OCTN2 (SLC22A5) and its substrate L-carnitine (LC) are increasingly recognized as actors in cytoprotection. This study provides a comprehensive expression and survival analysis of the OCTN2/LC system in GBM and clarifies the system's impact on GBM progression. EXPERIMENTAL DESIGN: OCTN2 expression and LC content were measured in 121 resected human GBM specimens and 10 healthy brain samples and analyzed for prognostic significance. Depending on LC administration, the effects of hypoxic, metabolic, and cytotoxic stress on survival and migration of LN18 GBM cells were further studied in vitro. Finally, an orthotopic mouse model was employed to investigate inhibition of the OCTN2/LC system on in vivo GBM growth. RESULTS: Compared with healthy brain, OCTN2 expression was increased in primary and even more so in recurrent GBM on mRNA and protein level. High OCTN2 expression was associated with a poor overall patient survival; the unadjusted HR for death was 2.7 (95% CI, 1.47-4.91; P < 0.001). LC administration to GBM cells increased their tolerance toward cytotoxicity, whereas siRNA-mediated OCTN2 silencing led to a loss of tumor cell viability. In line herewith, OCTN2/LC inhibition by meldonium resulted in reduced tumor growth in an orthotopic GBM mouse model. CONCLUSIONS: Our data indicate a potential role of the OCTN2/LC system in GBM progression and resistance to therapy, and suggests OCTN2 as a prognostic marker in patients with primary GBM.

Association of Glioblastoma Multiforme Stem Cell Characteristics, Differentiation, and Microglia Marker Genes with Patient Survival.

Patients with glioblastoma multiforme (GBM) are at high risk to develop a relapse despite multimodal therapy. Assumedly, glioma stem cells (GSCs) are responsible for treatment resistance of GBM. Identification of specific GSC markers may help to develop targeted therapies. Here, we performed expression analyses of stem cell (ABCG2, CD44, CD95, CD133, ELF4, Nanog, and Nestin) as well as differentiation and microglia markers (GFAP, Iba1, and Sparc) in GBM compared to nonmalignant brain. Furthermore, the role of these proteins for patient survival and their expression in LN18 stem-like neurospheres was analyzed. At mRNA level, ABCG2 and CD95 were reduced, GFAP was unchanged; all other investigated markers were increased in GBM. At protein level, CD44, ELF4, Nanog, Nestin, and Sparc were elevated in GBM, but only CD133 and Nestin were strongly associated with survival time. In addition, ABCG2 and GFAP expression was decreased in LN18 neurospheres whereas CD44, CD95, CD133, ELF4, Nanog, Nestin, and Sparc were upregulated. Altogether only CD133 and Nestin were associated with survival rates. This raises concerns regarding the suitability of the other target structures as prognostic markers, but makes both CD133 and Nestin candidates for GBM therapy. Nevertheless, a search for more specific marker proteins is urgently needed.