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Abstract Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. Results Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10–7, 3 × 10–4, 4 × 10–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. Conclusion Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. Relevance statement Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. Key points • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines. Graphical Abstract

Abstract High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Background: Immune checkpoint inhibitors (ICI) are one of the most effective therapies in oncology, albeit associated with various immune-related adverse events also affecting the cardiovascular system. Methods: We aimed to investigate the effect of ICI on arterial 2-[18F]FDG uptake by using 2-[18F]FDG PET/CT imaging pre/post treatment in 47 patients with lung cancer. Maximum 2-[18F]FDG standardized uptake values (SUVmax) and target-to-background ratios (TBRs) were calculated along six arterial segments. We classified the arterial PET lesions by pre-existing active inflammation (cut-off: TBRpre ≥ 1.6). 2-[18F]FDG metabolic activity pre/post treatment was also quantified in bone marrow, spleen, and liver. Circulating blood biomarkers were additionally collected at baseline and after immunotherapy. Results: ICI treatment resulted in significantly increased arterial inflammatory activity, detected by increased TBRs, in all arterial PET lesions analyzed. In particular, a significant elevation of arterial 2-[18F]FDG uptake was only recorded in PET lesions without pre-existing inflammation, in calcified as well as in non-calcified lesions. Furthermore, a significant increase in arterial 2-[18F]FDG metabolic activity after immunotherapy was solely observed in patients not previously treated with chemotherapy or radiotherapy as well as in those without CV risk factors. No significant changes were recorded in either 2-[18F]FDG uptake of bone marrow, spleen and liver after treatment, or the blood biomarkers. Conclusions: ICI induces vascular inflammation in lung cancer patients lacking pre-existing arterial inflammation.

BackgroundPathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.MethodsOmental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).ResultsThe performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.ConclusionsCT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

Acute myocardial infarction (AMI) is a result of cardiac non-perfusion and leads to cardiomyocyte necrosis, inflammation, and compromised cardiac performance. Here, we showed that the secretome of γ-irradiated peripheral blood mononuclear cells (PBMCsec) improved heart function in a porcine AMI model and displayed beneficial long- and short-term effects. As an AMI is known to strongly affect gene regulation of the ischemia non-affected heart muscle and distal organs, we employed a transcriptomics approach to further study the immediate molecular events orchestrated using the PBMCsec in myocardium, liver, and spleen 24 h post ischemia. In the infarcted area, the PBMCsec mainly induced genes that were essential for cardiomyocyte function and simultaneously downregulated pro-inflammatory genes. Interestingly, genes associated with pro-inflammatory processes were activated in the transition zone, while being downregulated in the remote zone. In the liver, we observed a pronounced inhibition of immune responses using the PBMCsec, while genes involved in urea and tricarboxylic cycles were induced. The spleen displayed elevated lipid metabolism and reduced immunological processes. Together, our study suggested several types of pharmacodynamics by which the PBMCsec conferred immediate cardioprotection. Furthermore, our data supported the assumption that an AMI significantly affects distal organs, suggesting that a holistic treatment of an AMI, as achieved by PBMCsec, might be highly beneficial.

Simple Summary: Radiomics is the extraction of high-dimensional quantitative features from medical images. These features have shown potential to provide diagnostic and prognostic information in various oncological settings, but their reproducibility and stability can be affected by multiple physiological and technical factors. In this study, we assessed the effect of contrast agent timing on radiomics features using dynamic contrast-enhanced CT or MRI in prostate and lung cancers, whereby multiple images are acquired with a high temporal resolution after contrast agent application. We observed that a substantial number of radiomics features are contrast perfusion dependent and prone to bias. Therefore, when using contrast-enhanced scans for radiomics studies, patient-specific perfusion timing (e.g., test bolus protocol) should be applied. Background: The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or MRI (dceMRI) in prostate and lung cancers. Methods: Radiomics features were extracted from dceCT or dceMRI images in patients with biopsy-proven peripheral prostate cancer (pzPC) or biopsy-proven non-small cell lung cancer (NSCLC), respectively. Features that showed significant differences between contrast phases were identified using linear mixed models. An L2-penalized logistic regression classifier was used to predict class labels for pzPC and unaffected prostate regions-of-interest (ROIs). Results: Nine pzPC and 28 NSCLC patients, who were imaged with dceCT and/or dceMRI, were included in this study. After normalizing for individual enhancement patterns by defining seven individual phases based on a reference vessel, 19, 467 and 128 out of 1204 CT features showed significant temporal dynamics in healthy prostate parenchyma, prostate tumors and lung tumors, respectively. CT radiomics-based classification accuracy of healthy and tumor ROIs was highly dependent on contrast agent phase. For dceMRI, 899 and 1027 out of 1118 features were significantly dependent on time after contrast agent injection for prostate and lung tumors. Conclusions: CT and MRI radiomics features in both prostate and lung tumors are significantly affected by interindividual differences in contrast agent dynamics. [ABSTRACT FROM AUTHOR]

Summary: Hughes-Stovin syndrome (HSS) is a rare vasculitis of unknown etiology. The disease is characterized by pronounced inflammation and damage to the vessel walls, with subsequent widespread vascular thrombosis and the formation of pulmonary artery aneurysms that can lead to fatal hemoptysis. This disorder can be mistaken for other conditions, such as chronic thromboembolic pulmonary disease (CTEPD) without or with pulmonary hypertension at rest (CTEPH). We report the case of a 20-year-old female with HSS, which was misdiagnosed as CTEPH and subsequently treated with anticoagulants, which led to severe hemoptysis and eventually death of the patient. This case highlights the challenges of diagnosing HSS at early stages of the disease. HSS should be considered in young patients with signs of large vessel vasculitis in combination with thrombotic occlusions of pulmonary arteries, with or without aneurysms of the pulmonary arteries, and particularly, if there are no risk factors for thromboembolic disease. [ABSTRACT FROM AUTHOR]

Due to the treatment-induced immune cell infiltration and induction of fibrosis, the pre-surgical tumour size assessed with CT does not (always) correlate well with the extension of viable tumour cells in pathology. However, in daily practice, pathologists face several challenges regarding the pathologic workup, as it is (1) time consuming - especially in large tumours - and (2) prone to bias as the tumour regression bed (the area of immune-mediated tumour clearance) is difficult to quantify [[5]]. This comment refers to the article available at https://doi.org/10.1007/s00330-023-09922-4 Neo-adjuvant immune checkpoint inhibitor plus chemotherapy has led to a paradigm shift in treatment of patients with non-small cell lung cancer (NSCLC). [Extracted from the article]

Background: Immune checkpoint inhibitors (ICI) are one of the most effective therapies in oncology, albeit associated with various immune-related adverse events also affecting the cardiovascular system. Methods: We aimed to investigate the effect of ICI on arterial 2-[18F]FDG uptake by using 2-[18F]FDG PET/CT imaging pre/post treatment in 47 patients with lung cancer. Maximum 2-[18F]FDG standardized uptake values (SUVmax) and target-to-background ratios (TBRs) were calculated along six arterial segments. We classified the arterial PET lesions by pre-existing active inflammation (cut-off: TBRpre ≥ 1.6). 2-[18F]FDG metabolic activity pre/post treatment was also quantified in bone marrow, spleen, and liver. Circulating blood biomarkers were additionally collected at baseline and after immunotherapy. Results: ICI treatment resulted in significantly increased arterial inflammatory activity, detected by increased TBRs, in all arterial PET lesions analyzed. In particular, a significant elevation of arterial 2-[18F]FDG uptake was only recorded in PET lesions without pre-existing inflammation, in calcified as well as in non-calcified lesions. Furthermore, a significant increase in arterial 2-[18F]FDG metabolic activity after immunotherapy was solely observed in patients not previously treated with chemotherapy or radiotherapy as well as in those without CV risk factors. No significant changes were recorded in either 2-[18F]FDG uptake of bone marrow, spleen and liver after treatment, or the blood biomarkers. Conclusions: ICI induces vascular inflammation in lung cancer patients lacking pre-existing arterial inflammation. [ABSTRACT FROM AUTHOR]

This document provides concise summaries of several research studies related to medical imaging techniques and their applications in diagnosing and treating various diseases. The studies cover a wide range of topics, including the use of AI algorithms in breast cancer detection, the effectiveness of diffusion tensor imaging in diagnosing brain tumors, and the use of PET imaging in detecting neurodegenerative diseases. The studies offer valuable insights into the potential benefits and limitations of these imaging techniques, but further research and validation are needed to confirm these findings. [Extracted from the article]

Purpose: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. Results: Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10–7, 3 × 10–4, 4 × 10–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. Conclusion: Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. Relevance statement: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. Key points: • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires specialized knowledge. In addition, inappropriate annotation processes can increase model bias. Self-supervised learning (SSL) is a type of unsupervised learning method that extracts image representations. Thus, SSL can be an effective method to reduce the number of labeled images. In this study, we investigated the feasibility of reducing the number of labeled images in a limited set of unlabeled medical images. The unlabeled chest X-ray (CXR) images were pretrained using the SimCLR framework, and then the representations were fine-tuned as supervised learning for the target task. A total of 2000 task-specific CXR images were used to perform binary classification of coronavirus disease 2019 (COVID-19) and normal cases. The results demonstrate that the performance of pretraining on task-specific unlabeled CXR images can be maintained when the number of labeled CXR images is reduced by approximately 40%. In addition, the performance was significantly better than that obtained without pretraining. In contrast, a large number of pretrained unlabeled images are required to maintain performance regardless of task specificity among a small number of labeled CXR images. In summary, to reduce the number of labeled images using SimCLR, we must consider both the number of images and the task-specific characteristics of the target images., (© 2024. The Author(s).)

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC. Response to treatment in high grade serous ovarian carcinoma (HGSOC) is highly variable. Here, the authors leverage a radiogenomic model to predict neoadjuvant chemotherapy response in HGSOC, including clinical data, medical imaging, and blood-based biomarkers such as CA-125 and ctDNA features. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Die Radiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. Methods: Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). Results: The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. Conclusions: CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application. [ABSTRACT FROM AUTHOR]

Background: The purpose was to determine whether tumor response to CPI varies by organ and to characterize response patterns in a group of surgically treated metastatic RCC patients treated with Nivolumab. Methods: A retrospective analysis was undertaken between January 2016 and March 2020 on patients receiving Nivolumab for metastatic RCC, following first-line therapy and having at least one baseline and two follow-up scans. A Fisher's exact test was used to compare categorical variables, and a Kruskal–Wallis test was used to compare continuous variables. Results: Twenty-one out of thirty patients evaluated were eligible, and they were divided into two groups: responders (n = 11) and non-responders (n = 10). According to all iRECIST standards, 18 (85.7 percent) of the 21 patients had PD (10 patients), PR (3 patients), or SD (8 patients). At baseline, 7, 15, 4, 13, 7, and 7 patients, respectively, had detectable hepatic metastasis and lung, brain, lymph node, soft tissue, and other intra-abdominal metastases; these patients were evaluated for organ-specific response. The ORRs for hepatic metastasis and lung, brain, lymph node, soft tissue, adrenals, and other intraperitoneal metastases were correspondingly 10%, 20%, 35%, 0%, and 25%. In total, 13 (61.9%) of them demonstrated varied responses to CPI therapy, with 6 (28.5%) demonstrating intra-organ differential responses. The lymph nodes (35%) had the best objective response (BOR), followed by the adrenals and peritoneum (both 25%), the brain (20%), and the lung (20%). The response rate was highest in adrenal gland lesions (2/4; 50%), followed by lymph nodes (13/19; 68.4 percent) and liver (5/10; 50%), whereas rates were lowest for lesions in the lung (9/25; 36%), intraperitoneal metastases (1/4; 25%), and brain (1/5; 20%). Conclusions: In renal cell carcinoma, checkpoint inhibitors have a variable response at different metastatic sites, with the best response occurring in lymph nodes and the least occurring in soft tissue. [ABSTRACT FROM AUTHOR]

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: We evaluated renal cancer with varying aggressive appearances on histology, using an emerging form of non-invasive metabolic MRI. This imaging technique assesses the uptake and metabolism of a breakdown product of glucose (pyruvate) labelled with hyperpolarized carbon-13. We show that pyruvate metabolism is dependent on the aggressiveness of an individual tumor and we provide a mechanism for this finding from tissue analysis of molecules influencing pyruvate metabolism, suggesting a role for its membrane transporter. Differentiating aggressive clear cell renal cell carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of renal tumors using carbon-13 MRI following injection of hyperpolarized [1-13C]pyruvate (HP-13C-MRI) and validated these findings with histopathology. Nine patients with treatment-naïve renal tumors (6 ccRCCs, 1 liposarcoma, 1 pheochromocytoma, 1 oncocytoma) underwent pre-operative HP-13C-MRI and conventional proton (1H) MRI. Multi-regional tissue samples were collected using patient-specific 3D-printed tumor molds for spatial registration between imaging and molecular analysis. The apparent exchange rate constant (kPL) between 13C-pyruvate and 13C-lactate was calculated. Immunohistochemistry for the pyruvate transporter (MCT1) from 44 multi-regional samples, as well as associations between MCT1 expression and outcome in the TCGA-KIRC dataset, were investigated. Increasing kPL in ccRCC was correlated with increasing overall tumor grade (ρ = 0.92, p = 0.009) and MCT1 expression (r = 0.89, p = 0.016), with similar results acquired from the multi-regional analysis. Conventional 1H-MRI parameters did not discriminate tumor grades. The correlation between MCT1 and ccRCC grade was confirmed within a TCGA dataset (p < 0.001), where MCT1 expression was a predictor of overall and disease-free survival. In conclusion, metabolic imaging using HP-13C-MRI differentiates tumor aggressiveness in ccRCC and correlates with the expression of MCT1, a predictor of survival. HP-13C-MRI may non-invasively characterize metabolic phenotypes within renal cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Acute myocardial infarction is characterized by impaired coronary blood flow, which leads to cardiac ischemia and, ultimately, compromised heart function. Damage and cellular responses are not limited to the non-perfused area, but rather affect the entire heart, as well as distal organs, such as the liver and spleen. We found that the therapeutic secretome of stressed white blood cells improved short-term and long-term cardiac performance in a porcine infarction model. In order to unravel the molecular events governing secretome-mediated tissue regeneration, we performed transcriptional analyses of the non-perfused, transition, and perfused heart, as well as the liver and spleen 24 h after myocardial infarction. We observed a highly tissue-specific effect of the secretome and, except for the transition zone, a uniform downregulation of pro-inflammatory factors and pathways. Simultaneously, the secretome strongly promoted the expression of genes that are essential for heart function in the non-perfused area. In the liver and spleen, different metabolic processes were induced. Together, our data suggest several plausible mechanisms by which the secretome improves heart function after cardiac ischemia. Deepening our understanding of the molecular processes identified here might uncover further pharmacologic strategies aiming at delimiting adverse cardiac remodeling and sequelae after myocardial infarction. Acute myocardial infarction (AMI) is a result of cardiac non-perfusion and leads to cardiomyocyte necrosis, inflammation, and compromised cardiac performance. Here, we showed that the secretome of γ-irradiated peripheral blood mononuclear cells (PBMCsec) improved heart function in a porcine AMI model and displayed beneficial long- and short-term effects. As an AMI is known to strongly affect gene regulation of the ischemia non-affected heart muscle and distal organs, we employed a transcriptomics approach to further study the immediate molecular events orchestrated using the PBMCsec in myocardium, liver, and spleen 24 h post ischemia. In the infarcted area, the PBMCsec mainly induced genes that were essential for cardiomyocyte function and simultaneously downregulated pro-inflammatory genes. Interestingly, genes associated with pro-inflammatory processes were activated in the transition zone, while being downregulated in the remote zone. In the liver, we observed a pronounced inhibition of immune responses using the PBMCsec, while genes involved in urea and tricarboxylic cycles were induced. The spleen displayed elevated lipid metabolism and reduced immunological processes. Together, our study suggested several types of pharmacodynamics by which the PBMCsec conferred immediate cardioprotection. Furthermore, our data supported the assumption that an AMI significantly affects distal organs, suggesting that a holistic treatment of an AMI, as achieved by PBMCsec, might be highly beneficial. [ABSTRACT FROM AUTHOR]