Multimodal deep learning for liver cancer applications: a scoping review.

Background: Hepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients. Objective: This scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Methodology: A comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis. Results: Ten studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis. Conclusion: The use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.

Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.

BACKGROUND: Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. OBJECTIVE: This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. METHODS: This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. RESULTS: Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer-based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. CONCLUSIONS: It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.

Wnt10b protects cardiomyocytes against doxorubicin-induced cell death via MAPK modulation.

BACKGROUND: Doxorubicin, an anthracycline chemotherapeutic known to incur heart damage, decreases heart function in up to 11% of patients. Recent investigations have implicated the Wnt signaling cascade as a key modulator of cardiac tissue repair after myocardial infarction. Wnt upregulation in murine models resulted in stimulation of angiogenesis and suppression of fibrosis after ischemic insult. However, the molecular mechanisms of Wnt in mitigating doxorubicin-induced cardiac insult require further investigation. Identifying cardioprotective mechanisms of Wnt is imperative to reducing debilitating cardiovascular adverse events in oncologic patients undergoing treatment. METHODS: Exposing human cardiomyocyte AC16 cells to varying concentrations of Wnt10b and DOX, we observed key metrics of cell viability. To assess the viability and apoptotic rates, we utilized MTT and TUNEL assays. We quantified cell and mitochondrial membrane stability via LDH release and JC-1 staining. To investigate how Wnt10b mitigates doxorubicin-induced apoptosis, we introduced pharmacologic inhibitors of key enzymes involved in apoptosis: FR180204 and SB203580, ERK1/2 and p38 inhibitors. Further, we quantified apoptotic executor enzymes, caspase 3/7, via immunofluorescence. RESULTS: AC16 cells exposed solely to doxorubicin were shrunken with distorted morphology. Cardioprotective effects of Wnt10b were demonstrated via a reduction in apoptosis, from 70.1% to 50.1%. LDH release was also reduced between doxorubicin and combination groups from 2.27-fold to 1.56-fold relative to the healthy AC16 control group. Mitochondrial membrane stability was increased from 0.67-fold in the doxorubicin group to 5.73 in co-treated groups relative to control. Apoptotic protein expression was stifled by Wnt10b, with caspase3/7 expression reduced from 2.4- to 1.3-fold, and both a 20% decrease in p38 and 40% increase in ERK1/2 activity. CONCLUSION: Our data with the AC16 cell model demonstrates that Wnt10b provides defense mechanisms against doxorubicin-induced cardiotoxicity and apoptosis. Further, we explain a mechanism of this beneficial effect involving the mitochondria through simultaneous suppression of pro-apoptotic p38 and anti-apoptotic ERK1/2 activities.

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review.

BACKGROUND: Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. OBJECTIVE: This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. METHODS: In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data. RESULTS: Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs. CONCLUSION: It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .

Identifying the role of vision transformer for skin cancer-A scoping review.

Introduction: Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance. Objective: This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection. Methods: The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included. Results and discussions: The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.

Deep Learning in Colorectal Cancer Classification: A Scoping Review.

Colorectal cancer (CRC) is one of the most common cancers worldwide, and its diagnosis and classification remain challenging for pathologists and imaging specialists. The use of artificial intelligence (AI) technology, specifically deep learning, has emerged as a potential solution to improve the accuracy and speed of classification while maintaining the quality of care. In this scoping review, we aimed to explore the utilization of deep learning for the classification of different types of colorectal cancer. We searched five databases and selected 45 studies that met our inclusion criteria. Our results show that deep learning models have been used to classify colorectal cancer using various types of data, with histopathology and endoscopy images being the most common. The majority of studies used CNN as their classification model. Our findings provide an overview of the current state of research on deep learning in the classification of colorectal cancer.

Predicting Overall Survival in METABRIC Cohort Using Machine Learning.

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that presents very high relapse and mortality. However, due to differences in the genetic architecture associated with TNBC, patients have different outcomes and respond differently to available treatments. In this study, we predicted the overall survival of TNBC patients in the METABRIC cohort employing supervised machine learning to identify important clinical and genetic features that are associated with better survival. We achieved a slightly higher Concordance index than the state of art and identified biological pathways related to the top genes considered important by our model.

Impact of Biopsy Proven Liver Fibrosis on Patients Undergoing Evaluation and Treatment for Advanced Heart Failure Surgical Therapies.

There is a paucity of data regarding the impact of liver fibrosis on patients with stage D heart failure (HF). We conducted a retrospective study (January 1, 2017 to December 12, 2020) in patients with stage D HF who underwent liver biopsy as part of their advanced HF therapy evaluation. Baseline characteristics and 1-year outcomes were compared between no- or mild-to-moderate-fibrosis (grade 0 to 2) and advanced-fibrosis (grade 3 to 4) groups. Of 519 patients with stage D HF, 136 who underwent liver biopsy (113 [83%] no or mild-to-moderate fibrosis and 23 [17%] advanced fibrosis) were included. A total of 71 patients (52%) received advanced HF therapies (23 heart transplantation, 48 left ventricular assist devices). One-year mortality was higher among patients with advanced fibrosis (52% vs 18%, p <0.001). Further subgroup analysis suggested a trend toward increased 1-year mortality among patients with advanced fibrosis who underwent advanced therapies (37% vs 13%, p = 0.09). There was a trend of lower likelihood of receiving advanced HF therapies in the advanced-fibrosis group, only 1 heart transplantation and 7 left ventricular assist devices, but it did not reach statistical significance (35% vs 56%, p = 0.06). After adjustment for confounders, degree of liver fibrosis was an independent predictor of mortality (odds ratio 6.2; 95% 1.27 to 30.29, p = 0.02). We conclude that advanced liver fibrosis is common among patients with stage D HF who undergo evaluation for advanced HF surgical therapies and significantly increases 1-year mortality. Further larger studies are needed to support our findings.

A new generative adversarial network for medical images super resolution.

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.

The role of generative adversarial networks in brain MRI: a scoping review.

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.

ALLD: Acute Lymphoblastic Leukemia Detector.

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.

Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review.

Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.

Early and aggressive presentation of wild-type transthyretin amyloid cardiomyopathy: A case report.

BACKGROUND: Wild-type transthyretin amyloidosis (ATTRwt) is the most common form of transthyretin amyloid cardiomyopathy, occurring mostly over age of 60 years (mean age of 80 years). Mean survival without treatment is 3.6 years, making early detection imperative. We report an unusual case of a 58-year-old patient with ATTRwt cardiomyopathy requiring heart transplantation. CASE SUMMARY: A 58-year-old male presented with progressive fatigue, shortness of breath, weight gain, leg swelling, orthopnoea, and paroxysmal nocturnal dyspnoea for several months. Approximately ten months before this clinical presentation, the patient had first received a diagnosis of heart failure with reduced ejection fraction (EF) of 15% to 20%. The patient was started on appropriate guideline-directed medical therapy with only mild improvement in his EF. Upon further investigation, echocardiogram, technetium pyrophosphate scan (Tc PYP), and cardiac magnetic resonance imaging (cMRI) suggested a diagnosis of amyloidosis, and ATTRwt was subsequently confirmed with native heart tissue biopsy, congo red staining, liquid chromatography-tandem mass spectrometry, and genetic testing. The patient was successfully treated with heart transplantation and is doing well post-transplant. CONCLUSION: Wild-type ATTR amyloidosis should be kept on differentials in all patients (even less than 60 years old) with non-ischemic cardiomyopathy, especially in the setting of increased ventricular wall thickness and other classic echocardiogram, cMRI, and Tc PYP findings. Early diagnosis and management can be consequential in improving patient outcomes.

Nationwide study of six-month readmissions in critical limb ischemia: Predictors and impact of revascularization strategies.

OBJECTIVE: There is a paucity of data regarding six-month readmissions in critical limb ischemia patients and the influence of management strategy during index-admission [endovascular, surgical, hybrid procedure, medical therapy, and amputation]. We aimed to investigate the incidence, predictors, and impact of management strategies on six-month readmission in patients with critical limb ischemia. METHODS: A secondary analysis of the Nationwide Readmissions Database (2016-2017) was conducted. Propensity score matching was performed for subgroup analysis. RESULTS: We identified 50,058 patients with primary diagnosis of critical limb ischemia. Six-month all-cause and critical limb ischemia-related readmission rate was 52.36% and 10.86%, respectively. The risk of all-cause readmission was lower with amputation but was similar among other subgroups. Patients receiving surgical [HR 0.62, CI(0.48-0.79), p < 0.001] and hybrid procedure [HR 0.65 (0.46-0.93), p = 0.02] had lower risk of unplanned critical limb ischemia-related readmission compared to endovascular, though the risk of unplanned revascularization/amputation during readmission was similar between the three strategies. The risk of non-critical limb ischemia-related readmission was higher with surgical [HR 1.13, CI(1.04-1.23), p = 0.003] and hybrid procedure [HR 1.17, CI(1.08-1.28), p < 0.001], driven by increased procedure-related/wound complications. Eventhough endovascular patients were older with more severe critical limb ischemia presentation, a lower proportion received home-health or placement upon discharge from index-admission. This could account for higher readmission without higher repeat revascularization in endovascular group. CONCLUSION: The risk of critical limb ischemia and non-critical limb ischemia-related readmission differ according to the management strategy. Significant differences in discharge disposition exist depending on revascularization strategy. Study findings identify opportunities for reducing readmissions by focusing on nonprocedural aspects like wound-care, discharge planning and placement.

Clinical comparison of V122I genotypic variant of transthyretin amyloid cardiomyopathy with wild-type and other hereditary variants: a systematic review.

V122I genotype variant (pV142I) is the most common hereditary transthyretin amyloidosis (hATTR) in the USA, with 3-3.5% of African-Americans being the carriers of this mutation. We aimed to compare baseline clinical features, cardiac parameters, and mortality in V122I-ATTR with the wild-type ATTR and other hATTR subtypes. We systematically searched PubMed/Medline and Google Scholar databases to identify relevant studies from inception to 10th September, 2020 reporting phenotypic, echocardiographic, and/or laboratory parameters in patients with hereditary and wild types of cardiac amyloidoses. A total of 2843 patients from 7 individual studies with 67-100% males and an overall follow-up duration of 51.6 ± 30.4 months were identified. The mean age of diagnosis among wild-type ATTR patients was 77 years, followed by 71.2 and 65 years in V122I and T60A group patients, respectively. V122I patients were mostly black, had a poor quality of life, and highest mortality risk compared with other subtypes. Merely, the presence of V122I mutation was identified as an independent predictor of mortality. V30M subtype correlated with the least severe cardiac disease and a median survival duration comparable with T60A subtype. V122I ATTR is an aggressive disease, prevalent in African-Americans, and is associated with a greater morbidity and mortality, which is partly attributed to its misdiagnosis and/or late diagnosis. Current advances in non-invasive studies to diagnose hATTR coupled with concurrent drug therapies have improved quality of life and provide a survival benefit to these patients.

Prognostic Role of Cardiopulmonary Exercise Testing in Wild-Type Transthyretin Amyloid Cardiomyopathy Patients Treated With Tafamidis.

BACKGROUND: The prognostic value of cardiopulmonary exercise testing (CPET) in patients with wild-type transthyretin cardiac amyloidosis treated with tafamidis is unknown. METHODS AND RESULTS: This retrospective study included patients with wtATTR who underwent baseline cardiopulmonary exercise testing and were treated with tafamidis from August 31, 2018, until March 31, 2020. Univariate logistic and multivariate cox-regression models were used to predict the occurrence of the primary outcome (composite of mortality, heart transplant, and palliative inotrope initiation). A total of 33 patients were included (median age 82 years, interquartile range [IQR] 79-84 years), 84% were Caucasians and 79% were males). Majority of patients had New York Heart Association functional class III disease at baseline (67%). The baseline median peak oxygen consumption (VO2) and peak circulatory power (CP) were 11.35 mL/kg/min (IQR 8.5-14.2 mL/kg/min) and 1485.8 mm Hg/mL/min (IQR 988-2184 mm Hg/mL/min), respectively, the median ventilatory efficiency was 35.7 (IQR 31-41.2). After 1 year of follow-up, 11 patients experienced a primary end point. Upon multivariate analysis, the low peak VO2 (hazard ratio [HR] 0.43, 95% confidence interval [CI] 0.23-0.79, P = .007], peak CP (HR 0.98, 95% CI 0.98-0.99, P = .02), peak oxygen pulse (HR 0.62, 95% CI 0.39-0.97, P = .03), and exercise duration of less than 5.5 minutes (HR 5.82, 95% CI 1.29-26.2, P = .02) were significantly associated with the primary outcome. CONCLUSIONS: Tafamidis-treated patients with wtATTR who had baseline low peak VO2, peak CP, peak O2 pulse, and exercise duration of less than 5.5 minutes had worse outcomes.

Vitamin D3 attenuates doxorubicin-induced senescence of human aortic endothelial cells by upregulation of IL-10 via the pAMPKα/Sirt1/Foxo3a signaling pathway.

The toxicity of doxorubicin to the cardiovascular system often limits its benefits and widespread use as chemotherapy. The mechanisms involved in doxorubicin-induced cardiovascular damage and possible protective interventions are not well-explored. Using human aortic endothelial cells, we show vitamin D3 strongly attenuates doxorubicin-induced senescence and cell cycle arrest. We further show the protective effects of vitamin D3 are mediated by the upregulation of IL-10 and FOXO3a expression through fine modulation of pAMPKα/SIRT1/FOXO3a complex activity. These results have great significance in finding a target for mitigating doxorubicin-induced cardiovascular toxicity.

Comparison of Hospitalization Trends and Outcomes in Acute Myocardial Infarction Patients With Versus Without Opioid Use Disorder.

Discrepancies in medical care are well known to adversely affect patients with opioid abuse disorders (OUD), including management and outcomes of acute myocardial infarction (AMI) in patients with OUD. We used the National Inpatient Sample was queried from January 2006 to September 2015 to identify all patients ≥18 years admitted with a primary diagnosis of AMI (weighted N = 13,030; unweighted N = 2,670) and concomitant OUD. Patients using other nonopiate illicit drugs were excluded. Propensity matching (1:1) yielded 2,253 well-matched pairs in which intergroup comparison of invasive revascularization strategies and cardiac outcomes were performed. The prevalence of OUD patients with AMI over the last decade has doubled, from 163 (2006) to 326 cases (2015) per 100,000 admissions for AMI. The OUD group underwent less cardiac catheterization (63.2% vs 72.2%; p <0.001), percutaneous coronary intervention (37.0% vs 48.5%; p <0.001) and drug-eluting stent placement (32.3% vs 19.5%; p <0.001) compared with non-OUD. No differences in in-hospital mortality/cardiogenic shock were noted. Among subgroup of ST-elevation myocardial infarction patients (26.2% of overall cohort), the OUD patients were less likely to receive percutaneous coronary intervention (67.9% vs 75.5%; p = 0.002), drug-eluting stent (31.4% vs 47.9%; p <0.001) with a significantly higher mortality (7.4% vs 4.3%), and cardiogenic shock (11.7% vs 7.9%). No differences in the frequency of coronary bypass grafting were noted in AMI or its subgroups. In conclusion, OUD patients presenting with AMI receive less invasive treatment compared with those without OUD. OUD patients presenting with ST-elevation myocardial infarction have worse in-hospital outcomes with increased mortality and cardiogenic shock.