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Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Aims: Sodium restriction was not associated with improved outcomes in heart failure patients in recent trials. The skin might act as a sodium buffer, potentially explaining tolerance to fluctuations in sodium intake without volume overload, but this is insufficiently understood. Therefore, we studied the handling of an increased sodium load in patients with heart failure with reduced ejection fraction (HFrEF)., Methods and Results: Twenty-one ambulatory, stable HFrEF patients and 10 healthy controls underwent a 2-week run-in phase, followed by a 4-week period of daily 1.2 g (51 mmol) sodium intake increment. Clinical, echocardiographic, 24-h urine collection, and bioelectrical impedance data were collected every 2 weeks. Blood volume, skin sodium content, and skin glycosaminoglycan content were assessed before and after sodium loading. Sodium loading did not significantly affect weight, blood pressure, congestion score, N-terminal pro-brain natriuretic peptide, echocardiographic indices of congestion, or total body water in HFrEF (all p > 0.09). There was no change in total blood volume (4748 ml vs. 4885 ml; p = 0.327). Natriuresis increased from 150 mmol/24 h to 173 mmol/24 h (p = 0.024), while plasma renin decreased from 286 to 88 μU/L (p = 0.002). There were no significant changes in skin sodium content, total glycosaminoglycan content, or sulfated glycosaminoglycan content (all p > 0.265). Healthy controls had no change in volume status, but a higher increase in natriuresis without any change in renin., Conclusions: Selected HFrEF patients can tolerate sodium loading, with increased renal sodium excretion and decreased neurohormonal activation., (© 2024 European Society of Cardiology.)

Background: Lung cancer can be detected by measuring the patient's plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient's metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown., Patients and Methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1 H-NMR spectroscopy. NMR spectra ( s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset., Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86-100]) and a sensitivity of 92% (95% CI [81-98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77-97]) and an AUC value of 0.97 (95% CI [0.93-1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82-99]) and an AUC value of 0.97 (95% CI [0.93-1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile., Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor's presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3-62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose ( 18 F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.

Lung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients.

Acquisition time and injected activity of 18 F-fluorodeoxyglucose ( 18 F-FDG) PET should ideally be reduced. However, this decreases the signal-to-noise ratio (SNR), which impairs the diagnostic value of these PET scans. In addition, 89 Zr-antibody PET is known to have a low SNR. To improve the diagnostic value of these scans, a Convolutional Neural Network (CNN) denoising method is proposed. The aim of this study was therefore to develop CNNs to increase SNR for low-count 18 F-FDG and 89 Zr-antibody PET. Super-low-count, low-count and full-count 18 F-FDG PET scans from 60 primary lung cancer patients and full-count 89 Zr-rituximab PET scans from five patients with non-Hodgkin lymphoma were acquired. CNNs were built to capture the features and to denoise the PET scans. Additionally, Gaussian smoothing (GS) and Bilateral filtering (BF) were evaluated. The performance of the denoising approaches was assessed based on the tumour recovery coefficient (TRC), coefficient of variance (COV; level of noise), and a qualitative assessment by two nuclear medicine physicians. The CNNs had a higher TRC and comparable or lower COV to GS and BF and was also the preferred method of the two observers for both 18 F-FDG and 89 Zr-rituximab PET. The CNNs improved the SNR of low-count 18 F-FDG and 89 Zr-rituximab PET, with almost similar or better clinical performance than the full-count PET, respectively. Additionally, the CNNs showed better performance than GS and BF.

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1 H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1 H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort ( n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort ( n = 72).

Background: Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset., Materials and Methods: The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume., Results: The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%)., Conclusion: When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model., (© 2020 Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV-including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge., Methods: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test-retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUV MAX , and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability., Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUV MAX : 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUV MAX : 0.68)., Conclusion: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

Metabolism encompasses the biochemical processes that allow healthy cells to keep energy, redox balance and building blocks required for cell development, survival, and proliferation steady. Malignant cells are well-documented to reprogram their metabolism and energy production networks to support rapid proliferation and survival in harsh conditions via mutations in oncogenes and inactivation of tumor suppressor genes. Despite the histologic and genetic heterogeneity of tumors, a common set of metabolic pathways sustain the high proliferation rates observed in cancer cells. This review with a focus on lung cancer covers several fundamental principles of the disturbed glucose metabolism, such as the "Warburg" effect, the importance of the glycolysis and its branching pathways, the unanticipated gluconeogenesis and mitochondrial metabolism. Furthermore, we highlight our current understanding of the disturbed glucose metabolism and how this might result in the development of new treatments., (Copyright © 2019 Vanhove, Graulus, Mesotten, Thomeer, Derveaux, Noben, Guedens and Adriaensens.)

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by 18 F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between 1 H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose ( 18 F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUV max ), total body metabolic active tumor volumes (MATV WTB ) and total body total lesion glycolysis (TLG WTB ) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATV WTB and TLG WTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.

Lung cancer cells are well-documented to rewire their metabolism and energy production networks to support rapid survival and proliferation. This metabolic reorganization has been recognized as a hallmark of cancer. The increased uptake of glucose and the increased activity of the glycolytic pathway have been extensively described. However, over the past years, increasing evidence has shown that lung cancer cells also require glutamine to fulfill their metabolic needs. As a nitrogen source, glutamine contributes directly (or indirectly upon conversion to glutamate) to many anabolic processes in cancer, such as the biosynthesis of amino acids, nucleobases, and hexosamines. It plays also an important role in the redox homeostasis, and last but not least, upon conversion to α-ketoglutarate, glutamine is an energy and anaplerotic carbon source that replenishes tricarboxylic acid cycle intermediates. The latter is generally indicated as glutaminolysis. In this review, we explore the role of glutamine metabolism in lung cancer. Because lung cancer is the leading cause of cancer death with limited curative treatment options, we focus on the potential therapeutic approaches targeting the glutamine metabolism in cancer.

Introduction: To predict the outcome of patients with non-small cell lung cancer (NSCLC) the currently used prognostic system (TNM) is not accurate enough. The prognostic significance of the SUV max measured by PET remains controversial. This study aims to evaluate the prognostic value in overall survival and progression free survival of SUV max , the total lesion glycolysis (TLG) and the mean metabolic active volume (MATV) in NSCLC., Methods: We retrospectively reviewed 105 patients (72 males, 33 females) with a new diagnosis of NSCLC (TNM stage I: 27.6%, II: 10.5%, III: 40.9% and IV: 21.0%) who underwent scanning with a PET/CT. For VOI definition a semi-automatic delineation tool was used. On PET images SUV max , SUV mean and MATV of the primary tumor and the whole tumor burden were measured. TLG and MATV were measured by using a threshold of 50% of SUV max ., Results: OS and PFS are found to be higher in patients with low-SUV Tmax and low-TLG T values. OS and PFS were significantly higher for low-SUV WTBmax , low-MATV WTB and low-TLG WTB values of the whole-tumor burden. Multivariate analysis of the whole-tumor burden revealed that the most important prognostic factors for OS are high MATV WTB and TLG WTB values, increasing stage and male gender. TLG WTB and stage are also independent prognosticators in PFS., Conclusion: Only whole-body TLG is of prognostic value in NSCLC for both OS and PFS. Stratification of patients by TLG WTB might complement outcome prediction but the TNM stage remains the most important determinant of prognosis., Microabstract: In order to predict the outcome of patients with non-small cell lung cancer (NSCLC) the currently used prognostic system (TNM) is not accurate enough. The prognostic significance of the standard uptake value (SUV) measured by PET remains controversial. This study aims to evaluate the prognostic value in overall survival (OS) and progression free survival (PFS) of the standard uptake value (SUV), the total lesion glycolysis (TLG) and the mean metabolic active volume (MATV) in NSCLC. The study reveals that TLG of the whole-tumor burden is an independent prognostic factor for OS and PFS in patients with NSCLC., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Accurate identification and quantification of human plasma metabolites can be challenging in crowded regions of the NMR spectrum with severe signal overlap. Therefore, this study describes metabolite spiking experiments on the basis of which the NMR spectrum can be rationally segmented into well-defined integration regions, and this for spectrometers having magnetic field strengths corresponding to 1 H resonance frequencies of 400 MHz and 900 MHz. Subsequently, the integration data of a case-control dataset of 69 lung cancer patients and 74 controls were used to train a multivariate statistical classification model for both field strengths. In this way, the advantages/disadvantages of high versus medium magnetic field strength were evaluated. The discriminative power obtained from the data collected at the two magnetic field strengths is rather similar, i.e. a sensitivity and specificity of respectively 90 and 97% for the 400 MHz data versus 88 and 96% for the 900 MHz data. This shows that a medium-field NMR spectrometer (400-600 MHz) is already sufficient to perform clinical metabolomics. However, the improved spectral resolution (reduced signal overlap) and signal-to-noise ratio of 900 MHz spectra yield more integration regions that represent a single metabolite. This will simplify the unraveling and understanding of the related, disease disturbed, biochemical pathways. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Introduction: Low-dose computed tomography, the currently used tool for lung cancer screening, is characterized by a high rate of false-positive results. Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. Therefore, this study aims to evaluate whether the metabolic phenotype of blood plasma allows detection of lung cancer., Methods: The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 233 patients with lung cancer and 226 controls. The validity of the model was examined by classifying an independent cohort of 98 patients with lung cancer and 89 controls., Results: The model makes it possible to correctly classify 78% of patients with lung cancer and 92% of controls, with an area under the curve of 0.88. Important moreover is the fact that the model is convincing, which is demonstrated by validation in the independent cohort with a sensitivity of 71%, a specificity of 81%, and an area under the curve of 0.84. Patients with lung cancer have increased glucose and decreased lactate and phospholipid levels. The limited number of patients in the subgroups and their heterogeneous nature do not (yet) enable differentiation between histological subtypes and tumor stages., Conclusions: Metabolic phenotyping of plasma allows detection of lung cancer, even in an early stage. Increased glucose and decreased lactate levels are pointing to an increased gluconeogenesis and are in accordance with recently published findings. Furthermore, decreased phospholipid levels confirm the enhanced membrane synthesis., (Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Aims: Cardiomyocyte apoptosis is known to occur in infarct border zones, where cardiomyocyte dedifferentiation, as seen in hibernating myocardium, can also be observed. The aim of the study is to determine whether dedifferentiated cardiomyocytes represent a population of cells stably surviving or undergoing apoptosis., Methods and Results: Microinfarctions were induced in sheep (n=8) by intracoronary injection of polymer macrobeads. The sheep were killed when cardiac function was gradually decreased (ejection fraction 37+/-6%, mean+/-SEM), but not earlier than 6 weeks after embolization. Transmural biopsies were taken from embolized and remote areas, based on flow measurements with positron emission tomography. Cells were classified as dedifferentiated when sarcomere content was depleted by >10% and glycogen content increased. Apoptosis was detected using the Tdt-mediated nick-end labelling (TUNEL) method and activated caspase-3 immunolabelling. Dedifferentiated cardiomyocytes were identified by morphology and by immunohistochemical evaluation of dedifferentiation related expression patterns of desmin, titin, cardiotin and alpha-smooth muscle actin. Cardiomyocyte apoptosis was detected in both the infarction border zones and remote areas. Dedifferentiated cardiomyocytes accounted for up to 30% of the cells in embolized areas and were almost exclusively non-apoptotic., Conclusion: In embolization induced microinfarcted tissue, dedifferentiated cardiomyocytes are preferentially spared to undergo apoptosis. It is hypothesized that dedifferentiated cardiomyocytes and apoptotic cardiomyocytes represent two different cell populations. The dedifferentiated cells can be considered as stable surviving cells., (Copyright 2001 The European Society of Cardiology. Published by Elsevier Science Ltd. All rights reserved.)

The aim of this study was to evaluate changes of flow, metabolism and left ventricular function in patients revealing a "reversed mismatch" pattern (reduced glucose uptake relative to perfusion) on positron emission tomography (PET) early after myocardial infarction. In 19 out of 68 patients (28%), prospectively included in the GUSTO-I or STAR studies, a PET reversed mismatch pattern in the infarct-related region was found. All patients received thrombolytic therapy within 3 h after onset of pain and coronary angiography 90 min later. 2-[18F]fluoro-2-deoxy-D-glucose (18F-FDG)/nitrogen-13-labelled ammonia (13NH3) PET was performed after 5 days and 3 months. In 12 of the 19 patients, functional recovery was investigated with two-dimensional echocardiography at the same time points. In the infarct-related region, normalized 13NH3 uptake was 76% +/- 11% at 5 days and 85% +/- 10% at 3 months (P < 0.00001). Absolute blood flow in this region was 75 +/- 25 ml/min per 100 g at 5 days and 80 +/- 19 ml/min per 100 g at 3 months. At 5 days, normalized 18F-FDG uptake in the infarct-related region was decreased (51% +/- 12%). At 3 months, 18F-FDG uptake in this region had significantly recovered (75% +/- 11%, P < 0.00001). In the infarct-related region, absolute FDG metabolism was 17 +/- 6 mumol/min per 100 g at 5 days and 26 +/- 9 mumol/min per 100 g at 3 months (P < 0.0001). At 5 days, normalized 18F-FDG uptake was more severely decreased as compared to the normalized 13NH3 uptake (P < 0.00001) in the infarct-related region, resulting in a reversed mismatch pattern (25% +/- 13% of the left ventricle). At 3 months, 18F-FDG metabolism had partially recovered, giving rise to a change into a PET match pattern. Reversed mismatch regions were present in only 7% +/- 7% of the left ventricle at that time. The ratio of 18F-FDG uptake to 13NH3 uptake in the infarct-related region increased from 0.67 +/- 0.8 at 5 days to 0.88 +/- 0.09 at 3 months (P < 0.00001). No functional recovery was observed in the infarct-related region (the 5-day and 3-month wall motion scores were both 2.5 +/- 0.5). In patients with a myocardial infarction showing a PET reversed mismatch pattern 5 days after thrombolytic therapy, recovery of 18F-FDG uptake was found but no functional recovery was observed at 3-month follow-up.

The aim of this study was to evaluate the relationship between flow/metabolism, histology and functional follow-up in a sheep model of subacute myocardial infarction. In eight juvenile sheep, a myocardial infarction was induced by intracoronary injection of macrobeads. Left ventricular function was evaluated using echocardiography. 2-[18F]fluoro-2-deoxy-D-glucose (18F-FDG)/nitrogen-13-labelled ammonia (13NH3) positron emission tomography (PET) was performed at 6 weeks and 16 weeks after embolization. In five sheep, a dynamic carbon-11 acetate study was performed. In each animal, two regions of interest were defined on the polar map, corresponding to the embolized and the non-embolized region. After the final measurements, the hearts were processed for histological evaluation. PET revealed a moderately decreased flow and oxidative metabolism in the embolized region at 6 weeks, without significant changes at follow-up. At 6 weeks, 18F-FDG uptake in the embolized area was more severely decreased as compared to the flow index in the embolized area (P < 0.05). At 16 weeks, 18F-FDG metabolism had significantly recovered (P < 0.05). Serial echocardiography showed a persistent decrease in global and regional left ventricular function. Histology revealed a mix of micro-infarcted and viable tissue in the embolized region. In this model of subacute myocardial infarction, a PET "reversed mismatch" pattern was observed, with partial recovery of 18F-FDG uptake at follow-up. The histological counterpart of this PET pattern appears to be patchy necrosis.

Objectives: The aim of this study was to investigate the prognostic value of carbon-11-acetate (acetate) positron emission tomography (PET) after successful reperfusion of myocardial infarction (MI)., Background: Acetate PET allows the measurement of both myocardial flow and oxidative metabolism. The prognostic value of acetate measurements performed early (within 24 h) after Thrombolysis In Myocardial Infarction (TIMI) flow grade 3 reperfused MI is unknown., Methods: In 18 patients with TIMI flow grade 3 reperfusion of their first MI, a dynamic acetate study was performed within 24 h of the acute event. At five days, nitrogen-13-NH3 (NH3) and fluorine-18-labeled fluorodeoxyglucose (FDG) PET studies were performed. Infarct-related areas were classified as "PET viable" or "PET nonviable," as assessed with NH3 and FDG, according to previously established criteria. At five days and three months, radionuclide angiography was performed for evaluation of left ventricular (LV) function., Results: In infarct-related regions, myocardial blood flow, FDG uptake and oxygen consumption were decreased, compared with remote regions. However, oxygen consumption values, as measured with acetate in both PET-viable and PET-nonviable areas, as assessed with NH3 and FDG, were not significantly different (p = NS). A significant linear correlation was observed between global LV ejection fraction at three months and oxidative metabolism in the infarct-related area (r = 0.8, p < 0.0001). Multivariate analysis revealed that oxidative metabolism measurements in reperfused myocardium was the only significant predictor for recovery of LV function at three months (p < 0.05)., Conclusions: Measurement of oxidative metabolism early after TIMI flow grade 3 reperfusion of MI offers important prognostic value concerning LV function at follow-up.

BACKGROUND: Although several short-term animal models of stunning and hibernation have been studied extensively, it has been difficult to produce a consistent animal model of chronic hibernation. The aim of the present study was to develop a nonsurgical porcine stent model of coronary stenosis in order to investigate the relationship between chronic dysfunctional myocardium and viability using 2D-echo, dobutamine stress echo (DSE) and positron emission tomography (PET). METHODS AND RESULTS: Focal progressive coronary stenosis was induced by implantation of an oversized stent in the left anterior descending (LAD) and/or circumflex (LCX) coronary artery in a total of 115 pigs, according to various experimental protocols: copper stent in the LAD (group I, n = 5); noncoated stainless steel stent in the LAD combined with balloon overstretch (group II, n = 7); poly(organo)phosphazene-coated stent in the LAD (group III, n = 77); and poly(organo)phosphazene-coated stent in both the LAD and the LCX (group IV, n = 26). Occurrence of left ventricular dysfunction was evaluated weekly by 2D-echo. At the time of left ventricular dysfunction the presence of viable myocardium within the dysfunctional region was investigated with DSE and PET, and confirmed by histology. The degree of coronary artery stenosis was measured by quantitative coronary angiography and morphometry. Severe coronary artery stenosis in the presence of dysfunctional, but viable, myocardium was induced in groups III and IV (47% and 11% of the animals, respectively). CONCLUSIONS: The authors developed a nonsurgical porcine stent model of progressive coronary stenosis using an oversized polymer-coated stent resulting in chronically decreased myocardial function, with residual inotropic reserve and viable myocardium. This condition may arise from repetitive periods of ischemia, or from sustained hypoperfusion, or a combination of these processes eventually leading to myocardial hibernation.

This is the first article of a four-part series on nuclear cardiology. This article introduces and reviews the anatomy and function of the normal heart. Future articles will develop the contribution of nuclear medicine techniques in evaluating myocardial perfusion, function and viability. This article describes the external and internal features of the heart and its vascularization, conducting system and physiological function. After reading this article, the reader should understand the anatomy and the function of the normal heart.