Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer

INTRODUCTION

Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT.

METHODS

In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC.

RESULTS

A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively.

CONCLUSION

A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

ReFs: A hybrid pre-training paradigm for 3D medical image segmentation

Self-supervised learning (SSL) has achieved remarkable progress in medical image segmentation. The application of an SSL algorithm often follows a two-stage training process: using unlabeled data to perform label-free representation learning and fine-tuning the pre-trained model on the downstream tasks. One issue of this paradigm is that the SSL step is unaware of the downstream task, which may lead to sub-optimal feature representation for a target task. In this paper, we propose a hybrid pre-training paradigm that is driven by both self-supervised and supervised objectives. To achieve this, a supervised reference task is involved in self-supervised learning, aiming to improve the representation quality. Specifically, we employ the off-the-shelf medical image segmentation task as reference, and encourage learning a representation that (1) incurs low prediction loss on both SSL and reference tasks and (2) leads to a similar gradient when updating the feature extractor from either task. In this way, the reference task pilots SSL in the direction beneficial for the downstream segmentation. To this end, we propose a simple but effective gradient matching method to optimize the model towards a consistent direction, thus improving the compatibility of both SSL and supervised reference tasks. We call this hybrid pre-training paradigm reference-guided self-supervised learning (ReFs), and perform it on a large-scale unlabeled dataset and an additional reference dataset. The experimental results demonstrate its effectiveness on seven downstream medical image segmentation benchmarks.

Coronary Atherosclerotic Plaque Activity and Future Coronary Events

Importance

Recurrent coronary events in patients with recent myocardial infarction remain a major clinical problem. Noninvasive measures of coronary atherosclerotic disease activity have the potential to identify individuals at greatest risk.

Objective

To assess whether coronary atherosclerotic plaque activity as assessed by noninvasive imaging is associated with recurrent coronary events in patients with myocardial infarction.

Design, Setting, and Participants

This prospective, longitudinal, international multicenter cohort study recruited participants aged 50 years or older with multivessel coronary artery disease and recent (within 21 days) myocardial infarction between September 2015 and February 2020, with a minimum 2 years' follow-up.

Intervention

Coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography.

Main Outcomes and Measures

Total coronary atherosclerotic plaque activity was assessed by 18F-sodium fluoride uptake. The primary end point was cardiac death or nonfatal myocardial infarction but was expanded during study conduct to include unscheduled coronary revascularization due to lower than anticipated primary event rates.

Results

Among 2684 patients screened, 995 were eligible, 712 attended for imaging, and 704 completed an interpretable scan and comprised the study population. The mean (SD) age of participants was 63.8 (8.2) years, and most were male (601 [85%]). Total coronary atherosclerotic plaque activity was identified in 421 participants (60%). After a median follow-up of 4 years (IQR, 3-5 years), 141 participants (20%) experienced the primary end point: 9 had cardiac death, 49 had nonfatal myocardial infarction, and 83 had unscheduled coronary revascularizations. Increased coronary plaque activity was not associated with the primary end point (hazard ratio [HR], 1.25; 95% CI, 0.89-1.76; P = .20) or unscheduled revascularization (HR, 0.98; 95% CI, 0.64-1.49; P = .91) but was associated with the secondary end point of cardiac death or nonfatal myocardial infarction (47 of 421 patients with high plaque activity [11.2%] vs 19 of 283 with low plaque activity [6.7%]; HR, 1.82; 95% CI, 1.07-3.10; P = .03) and all-cause mortality (30 of 421 patients with high plaque activity [7.1%] vs 9 of 283 with low plaque activity [3.2%]; HR, 2.43; 95% CI, 1.15-5.12; P = .02). After adjustment for differences in baseline clinical characteristics, coronary angiography findings, and Global Registry of Acute Coronary Events score, high coronary plaque activity was associated with cardiac death or nonfatal myocardial infarction (HR, 1.76; 95% CI, 1.00-3.10; P = .05) but not with all-cause mortality (HR, 2.01; 95% CI, 0.90-4.49; P = .09).

Conclusions and Relevance

In this cohort study of patients with recent myocardial infarction, coronary atherosclerotic plaque activity was not associated with the primary composite end point. The findings suggest that risk of cardiovascular death or myocardial infarction in patients with elevated plaque activity warrants further research to explore its incremental prognostic implications.

Normal periocular anthropometric measurements in an Australian population

PURPOSE

To report the normative ocular and periocular anthropometric measurements in an Australian cohort and investigate how these may be affected age, gender, and ethnicity.

METHODS

Prospective study of patients presenting to the Royal Adelaide Hospital. Patient with orbital or eyelid disease, previous surgery, craniofacial abnormalities, pupil abnormalities, strabismus, and poor image quality was excluded. Standardised photographs were taken in a well-illuminated room. A green dot with a diameter of 24 mm was placed on the participant's foreheads for calibration between pixels and millimetres. Ocular and periocular landmarks were segmented to calculate the periorbital measurements. Independent sample t test was used to compare male and female subjects, Pearson's correlation was used to correlate periocular dimensions with age, and ANOVA with Bonferroni was used to compare periocular dimension between ethnic groups.

RESULTS

Seven hundred and sixty eyes from 380 participants (215 female, mean age 58 ± 18 years) were included. The mean marginal reflex distance (MRD) 1 was 3.5 mm and decreased with increasing age (r =  - 0.09, p = 0.01) and MRD 2 was 5.2 mm. Compared to Caucasians, African subjects had a significantly larger interpupillary distance and outer intercanthal distance, whereas East Asians had a significantly larger inner intercanthal distance (p < 0.05). The values of marginal reflex distance 2, palpebral fissure height, horizontal palpebral aperture, inner intercanthal distance, interpupillary distance and outer intercanthal distance were significantly higher in male subjects than female subjects (p < 0.05).

CONCLUSIONS

Normative periocular dimensions may vary according to age, gender, and ethnicity. An understanding of normal periocular dimensions is important in the evaluation of orbital disease across different ethnic groups and may serve as reference points for oculoplastic surgery and industry.

Baseline gut microbiota composition is associated with oral mucositis and tumour recurrence in patients with head and neck cancer: a pilot study

PURPOSE

Mounting evidence suggests that the gut microbiome influences radiotherapy efficacy and toxicity by modulating immune signalling. However, its contribution to radiotherapy outcomes in head and neck cancer (HNC) is yet to be investigated. This study, therefore, aimed to uncover associations between an individual's pre-therapy gut microbiota and (i) severity of radiotherapy-induced oral mucositis (OM), and (ii) recurrence risk in patients with HNC.

METHODS

In this prospective pilot study, 20 patients with HNC scheduled to receive radiotherapy or chemoradiotherapy were recruited. Stool samples were collected before treatment and microbial composition was analysed using 16S rRNA gene sequencing. OM severity was assessed using the NCI-CTCAE scoring system. Patients were also followed for 12 months of treatment completion to assess tumour recurrence.

RESULTS

Overall, 80% of the patients were male with a median age of 65.5 years. Fifty-three percent experienced mild/moderate OM while 47% developed severe OM. Furthermore, 18% experienced tumour relapse within 1 year of treatment completion. A pre-treatment microbiota enriched of Eubacterium, Victivallis, and Ruminococcus was associated with severe OM. Conversely, a higher relative abundance of immunomodulatory microbes Faecalibacterium, Prevotella, and Phascolarctobacterium was associated with a lower risk of tumour recurrence.

CONCLUSION

Our results indicate that a patient's gut microbiota composition at the start of treatment is linked to OM severity and recurrence risk. We now seek to validate these findings to determine their ability to predict treatment outcomes in HNC, with the goal of using this data to inform second-generation microbial therapeutics to optimise treatment outcomes for patients with HNC.

Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN)

Purpose

Convolutional neural networks (CNNs) are increasingly being developed for automated fracture detection in orthopaedic trauma surgery. Studies to date, however, are limited to providing classification based on the entire image—and only produce heatmaps for approximate fracture localization instead of delineating exact fracture morphology. Therefore, we aimed to answer (1) what is the performance of a CNN that detects, classifies, localizes, and segments an ankle fracture, and (2) would this be externally valid?

Methods

The training set included 326 isolated fibula fractures and 423 non-fracture radiographs. The Detectron2 implementation of the Mask R-CNN was trained with labelled and annotated radiographs. The internal validation (or ‘test set’) and external validation sets consisted of 300 and 334 radiographs, respectively. Consensus agreement between three experienced fellowship-trained trauma surgeons was defined as the ground truth label. Diagnostic accuracy and area under the receiver operator characteristic curve (AUC) were used to assess classification performance. The Intersection over Union (IoU) was used to quantify accuracy of the segmentation predictions by the CNN, where a value of 0.5 is generally considered an adequate segmentation.

Results

The final CNN was able to classify fibula fractures according to four classes (Danis-Weber A, B, C and No Fracture) with AUC values ranging from 0.93 to 0.99. Diagnostic accuracy was 89% on the test set with average sensitivity of 89% and specificity of 96%. External validity was 89–90% accurate on a set of radiographs from a different hospital. Accuracies/AUCs observed were 100/0.99 for the ‘No Fracture’ class, 92/0.99 for ‘Weber B’, 88/0.93 for ‘Weber C’, and 76/0.97 for ‘Weber A’. For the fracture bounding box prediction by the CNN, a mean IoU of 0.65 (SD ± 0.16) was observed. The fracture segmentation predictions by the CNN resulted in a mean IoU of 0.47 (SD ± 0.17).

Conclusions

This study presents a look into the ‘black box’ of CNNs and represents the first automated delineation (segmentation) of fracture lines on (ankle) radiographs. The AUC values presented in this paper indicate good discriminatory capability of the CNN and substantiate further study of CNNs in detecting and classifying ankle fractures.

Level of evidence

II, Diagnostic imaging study.

Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images

Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based registration methods have demonstrated superior per-formance compared to iterative methods in just a fraction of the time. Most of the learning-based methods have focused on mono-modal image registration. The extension to multi-modal registration depends on the use of an appropriate similarity function, such as the mutual information (MI). We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network. Our results show that a small, 2-layer network produces competitive results in both mono- and multi-modal registration, with sub-second run-times. Comparisons to both iterative and deep learning-based methods show that our MI-based method produces topologically and qualitatively superior results with an extremely low rate of non-diffeomorphic transformations. Real-time clinical application will benefit from a better visual matching of anatomical structures and less registration failures/outliers.

Intra- and Inter-Pair Consistency for Semi-Supervised Gland Segmentation

Accurate gland segmentation in histology tissue images is a critical but challenging task. Although deep models have demonstrated superior performance in medical image segmentation, they commonly require a large amount of annotated data, which are hard to obtain due to the extensive labor costs and expertise required. In this paper, we propose an intra- and inter-pair consistency-based semi-supervised (I²CS) model that can be trained on both labeled and unlabeled histology images for gland segmentation. Considering that each image contains glands and hence different images could potentially share consistent semantics in the feature space, we introduce a novel intra- and inter-pair consistency module to explore such consistency for learning with unlabeled data. It first characterizes the pixel-level relation between a pair of images in the feature space to create an attention map that highlights the regions with the same semantics but on different images. Then, it imposes a consistency constraint on the attention maps obtained from multiple image pairs, and thus filters low-confidence attention regions to generate refined attention maps that are then merged with original features to improve their representation ability. In addition, we also design an object-level loss to address the issues caused by touching glands. We evaluated our model against several recent gland segmentation methods and three typical semi-supervised methods on the GlaS and CRAG datasets. Our results not only demonstrate the effectiveness of the proposed due consistency module and Obj-Dice loss, but also indicate that the proposed I²CS model achieves state-of-the-art gland segmentation performance on both benchmarks.

Impact of COVID-19 Pandemic on Cardiovascular Testing in Asia

Background

The coronavirus disease-2019 (COVID-19) pandemic significantly affected management of cardiovascular disease around the world. The effect of the pandemic on volume of cardiovascular diagnostic procedures is not known.

Objectives

This study sought to evaluate the effects of the early phase of the COVID-19 pandemic on cardiovascular diagnostic procedures and safety practices in Asia.

Methods

The International Atomic Energy Agency conducted a worldwide survey to assess changes in cardiovascular procedure volume and safety practices caused by COVID-19. Testing volumes were reported for March 2020 and April 2020 and were compared to those from March 2019. Data from 180 centers across 33 Asian countries were grouped into 4 subregions for comparison.

Results

Procedure volumes decreased by 47% from March 2019 to March 2020, showing recovery from March 2020 to April 2020 in Eastern Asia, particularly in China. The majority of centers cancelled outpatient activities and increased time per study. Practice changes included implementing physical distancing and restricting visitors. Although COVID testing was not commonly performed, it was conducted in one-third of facilities in Eastern Asia. The most severe reductions in procedure volumes were observed in lower-income countries, where volumes decreased 81% from March 2019 to April 2020.

Conclusions

The COVID-19 pandemic in Asia caused significant reductions in cardiovascular diagnostic procedures, particularly in low-income countries. Further studies on effects of COVID-19 on cardiovascular outcomes and changes in care delivery are warranted.

Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.

The role of intracoronary imaging in translational research

Atherosclerotic cardiovascular disease is a key public health concern worldwide and leading cause of morbidity, mortality and health economic costs. Understanding atherosclerotic plaque microstructure in relation to molecular mechanisms that underpin its initiation and progression is needed to provide the best chance of combating this disease. Evolving vessel wall-based, endovascular coronary imaging modalities, including intravascular ultrasound (IVUS), optical coherence tomography (OCT) and near-infrared spectroscopy (NIRS), used in isolation or as hybrid modalities, have been advanced to allow comprehensive visualization of the pathological substrate of coronary atherosclerosis and accurately measure temporal changes in both the vessel wall and plaque characteristics. This has helped further our appreciation of the natural history of coronary artery disease (CAD) and the risk for major adverse cardiovascular events (MACE), evaluate the responsiveness to conventional and experimental therapeutic interventions, and assist in guiding percutaneous coronary intervention (PCI). Here we review the use of different imaging modalities for these purposes and the lessons they have provided thus far.

3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease

Structural heart disease (SHD) is a new field within cardiovascular medicine. Traditional imaging modalities fall short in supporting the needs of SHD interventions, as they have been constructed around the concept of disease diagnosis. SHD interventions disrupt traditional concepts of imaging in requiring imaging to plan, simulate, and predict intraprocedural outcomes. In transcatheter SHD interventions, the absence of a gold-standard open cavity surgical field deprives physicians of the opportunity for tactile feedback and visual confirmation of cardiac anatomy. Hence, dependency on imaging in periprocedural guidance has led to evolution of a new generation of procedural skillsets, concept of a visual field, and technologies in the periprocedural planning period to accelerate preclinical device development, physician, and patient education. Adaptation of 3-dimensional (3D) printing in clinical care and procedural planning has demonstrated a reduction in early-operator learning curve for transcatheter interventions. Integration of computation modeling to 3D printing has accelerated research and development understanding of fluid mechanics within device testing. Application of 3D printing, computational modeling, and ultimately incorporation of artificial intelligence is changing the landscape of physician training and delivery of patient-centric care. Transcatheter structural heart interventions are requiring in-depth periprocedural understanding of cardiac pathophysiology and device interactions not afforded by traditional imaging metrics.

Sema3A promotes the resolution of cardiac inflammation after myocardial infarction

Optimal healing after myocardial infarction requires not only the induction of inflammation, but also its timely resolution. In patients, 30 days post myocardial infarction, circulating monocytes have increased expression of Semaphorin3A (Sema3A) as compared to directly after admission. This increased expression coincides with increased expression of Cx3CR1—a marker of non-classical monocytes that are important for immune resolution hence proper wound healing. In mice, the expression of Sema3A also increases in response to myocardial ischemia being expressed by infiltrating leukocytes. Comparing Sema3A heterozygote (HZ) and wild type (WT) mice post myocardial infarction, revealed increased presence of leukocytes in the cardiac tissues of HZ mice as compared to WT, with no differences in capillary density, collagen deposition, cardiomyocyte surface area, chemokine—or adhesion molecules expression. Whilst infarct sizes were similar 14 days after myocardial infarction in both genotypes, Sema3A HZ mice had thinner infarcts and reduced cardiac function as compared to their WT littermates. In vitro experiments were conducted to study the role of Sema3A in inflammation and resolution of inflammation as a potential explanation for the differences in leukocyte recruitment and cardiac function observed in our in vivo experiments. Here, recombinant Sema3A protein was able to affect the pro-inflammatory state of cultured bone marrow derived macrophages. First, the pro-inflammatory state was altered by the induced apoptosis of classical macrophages in the presence of Sema3A. Second, Sema3A promoted the polarization of classical macrophages to resolution-phase macrophages and enhanced their efferocytotic ability, findings that were reflected in the infarcted cardiac tissue of the Sema3A HZ mice. Finally, we demonstrated that besides promoting resolution of inflammation, Sema3A was also able to retard the migration of monocytes to the myocardium. Collectively our data demonstrate that Sema3A reduces cardiac inflammation and improves cardiac function after myocardial infarction by promoting the resolution of inflammation.

Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging

Intravascular optical coherence tomography (IVOCT) is a well-established method for the high-resolution investigation of atherosclerosis in vivo. Intravascular near-infrared fluorescence (NIRF) imaging is a novel technique for the assessment of molecular processes associated with coronary artery disease. Integration of NIRF and IVOCT technology in a single catheter provides the capability to simultaneously obtain co-localized anatomical and molecular information from the artery wall. Since NIRF signal intensity attenuates as a function of imaging catheter distance to the vessel wall, the generation of quantitative NIRF data requires an accurate measurement of the vessel wall in IVOCT images. Given that dual modality, intravascular OCT-NIRF systems acquire data at a very high frame-rate (>100 frames/s), a high number of images per pullback need to be analyzed, making manual processing of OCT-NIRF data extremely time consuming. To overcome this limitation, we developed an algorithm for the automatic distance-correction of dual-modality OCT-NIRF images. We validated this method by comparing automatic to manual segmentation results in 180 in vivo images from six New Zealand White rabbit atherosclerotic after indocyanine-green injection. A high Dice similarity coefficient was found (0.97 ± 0.03) together with an average individual A-line error of 22 µm (i.e., approximately twice the axial resolution of IVOCT) and a processing time of 44 ms per image. In a similar manner, the algorithm was validated using 120 IVOCT clinical images from eight different in vivo pullbacks in human coronary arteries. The results suggest that the proposed algorithm enables fully automatic visualization of dual modality OCT-NIRF pullbacks, and provides an accurate and efficient calibration of NIRF data for quantification of the molecular agent in the atherosclerotic vessel wall.

Adenine nucleotide translocase 1 deficiency results in dilated cardiomyopathy with defects in myocardial mechanics, histopathological alterations, and activation of apoptosis

OBJECTIVES

the aim of this study was to test the hypothesis that chronic mitochondrial energy deficiency causes dilated cardiomyopathy, we characterized the hearts of age-matched young and old adenine nucleotide translocator (ANT)1 mutant and control mice.

BACKGROUND

ANTs export mitochondrial adenosine triphosphate into the cytosol and have a role in the regulation of the intrinsic apoptosis pathway. Mitochondrial energy deficiency has been hypothesized, on the basis of indirect evidence, to be a factor in the pathophysiology of dilated cardiomyopathies. Ant1 inactivation should limit adenosine triphosphate for contraction and calcium transport, thereby resulting in early cardiac dysfunction with later dilation and heart failure.

METHODS

we conducted a multiyear study of 73 mutant (Ant1-/-) and 57 control (Ant1+/+) mice, between the ages of 2 and 21 months. Hearts were characterized by cardiac anatomy, echocardiographic imaging with velocity vector analysis, histopathology, and apoptosis assays.

RESULTS

the Ant1-/- mice developed a distinctive concentric dilated cardiomyopathy, characterized by substantial myocardial hypertrophy and ventricular dilation, with cardiac function declining earlier in age as compared to control mice. Left ventricular circumferential, radial, and rotational mechanics were reduced even in the younger mutants with preserved systolic function. Histopathologic analysis demonstrated increased myocyte hypertrophy, fibrosis, and calcification in the mutant mice as compared with control mice. Furthermore, increased cytoplasmic cytochrome c levels and caspase 3 activation were observed in the mutant mice.

CONCLUSIONS

our results demonstrate that mitochondrial energy deficiency is sufficient to cause dilated cardiomyopathy, confirming that energy defects are a factor in this disease. Energy deficiency initially leads to early mechanical dysfunction before a decline in left ventricular systolic function. Chronic energy deficiency with age then leads to heart failure. Our results now allow us to use the Ant1-/- mouse model for testing new therapies for ANT1 mutant patients.

Early molecular imaging of interstitial changes in patients after myocardial infarction: comparison with delayed contrast-enhanced magnetic resonance imaging

INTRODUCTION

The clinical feasibility of noninvasive imaging of interstitial alterations after myocardial infarction (MI) was assessed using a technetium-99m-labeled RGD imaging peptide (RIP). In experimental studies, RIP has been shown to target integrins associated with collagen-producing myofibroblasts (MFB).

METHODS AND RESULTS

Ten patients underwent myocardial perfusion imaging (MPI) within the first week after MI. At 3 and 8 weeks after MI, RIP was administered intravenously and SPECT images acquired for interstitial imaging. RIP imaging was compared to initial MPI and to the extent of scar formation defined by late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) imaging 1 year after MI. RIP uptake was observed in 7 of the 10 patients at both 3 and 8 weeks. Although, RIP uptake corresponded to areas of perfusion defects, it usually extended beyond the infarct zone to a variable extent; 2 of 7 patients showed tracer uptake throughout myocardium. In all positive cases, RIP uptake was similar to the extent of scar observed at 1 year by LGE-CMR imaging.

CONCLUSION

This study demonstrates that RGD-based imaging early after MI may predict the eventual extent of scar formation, which often exceeds initial MPI deficit but colocalizes with LGE in CMR imaging performed subsequently.

Molecular imaging of interstitial alterations after myocardial infarction

To prevent progression of heart failure (HF), it is necessary to understand the cellular and molecular processes that precede more irreversible stages. Molecular imaging of HF has hitherto focused on myocytic and neuronal components. More recently, interstitial changes, particularly interstitial fibrosis, have been targeted both for diagnostic and therapeutic purposes. Molecular imaging of interstitial changes may identify patients at risk to develop HF, before adverse remodeling has occurred. In addition, it may also serve as a surrogate endpoint for the assessment of treatment efficacy.

Resolution of apoptosis in atherosclerotic plaque by dietary modification and statin therapy

Although apoptosis within atherosclerotic plaques is associated with plaque vulnerability and rupture, the role of inhibition of the apoptotic process is not clear. We evaluated the impact of dietary modification and statin therapy (measures known to favorably influence outcomes in coronary disease) on the incidence of apoptosis in experimental atherosclerotic lesions.

METHODS

A total of 30 animals were studied; 1 group of 6 animals served as the controls (group 1), and the remaining 24 animals were subjected to balloon de-endothelialization of the abdominal aorta and a high-cholesterol diet. These atherosclerotic animals were randomized as follows: high-cholesterol diet for 4 mo (n=6; untreated atherosclerotic group [group 2]), high-cholesterol diet for 3 mo and normal chow diet for 1 mo (n=6; diet withdrawal group [group 3]), and high-cholesterol diet for 4 mo and simvastatin orally every day of the last month (n=6; statin therapy group [group 4]). 99mTc-Annexin A5 was used for noninvasive detection of apoptosis in groups 1-4. The remaining 6 rabbits on a high-cholesterol diet for 4 mo were studied with radiolabeled mutant annexin A5 (n=6; nonspecific control group [group 5]). Quantitative annexin A5 uptake in the abdominal aorta was determined and compared with the histologic and immunohistochemical characteristics of the atherosclerotic lesions.

RESULTS

Maximum annexin A5 uptake (mean+/-SD, 0.051+/-0.009 percentage injected dose per gram [%ID/g] of tissue) was observed in the untreated atherosclerotic animals. The uptake was substantially reduced in the diet withdrawal (0.03+/-0.006%ID/g; P<0.0001) and statin therapy (0.03+/-0.006%ID/g; P<0.0001) groups. The plaques in the untreated high-cholesterol group demonstrated advanced atherosclerotic lesions. On the other hand, the diet withdrawal and statin therapy groups showed histologic characteristics of stabilization, including the resolution of macrophage infiltration and an increase in smooth muscle cell content. There was a marked reduction in the apoptosis of macrophages. No significant uptake of annexin A5 or mutant annexin A5 was seen in rabbits on the normal chow diet or atherosclerotic rabbits, respectively.

CONCLUSION

Dietary modification and statin therapy in atherosclerosis lead to a reduction in apoptosis and contribute to plaque stabilization. It can be hypothesized that a reduction in apoptosis is a favorable process in atherosclerotic disease.