Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Utility of a Smartphone-Based System (cvrPhone) in Estimating Minute Ventilation from Electrocardiographic Signals.

Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.

Poor patient compliance with instructions for continuous sialogogues after 131 I therapy.

OBJECTIVES: To analyze the role of patient compliance as a factor in evaluating the effectiveness of continuous sialogogues to prevent salivary side effects from 131 I therapy in differentiated thyroid cancer patients. METHODS: Differentiated thyroid cancer patients who were clinically scheduled for an 131 I therapy at MedStar Washington Hospital Center between 2012 and 2013 were given instructions for continuous sialogogues per standard clinical protocol. The prospective survey was given at multiple time points. RESULTS: Ninety-nine patients consented to participate of whom 94 participants had complete data. The mean prescribed 131 I activity was 121 ± 50 mCi (4.5 ± 1.9 GBq), range 27.5-288 mCi (1.0-10.7 GBq ). Overall, only 10% (9/94) of patients were compliant with continuous sialogogues. Even though all patients took sialogogues on the first day of post-therapy, 17% of participants did not continuously take sialogogues during the first day, 60% during the first night, and 72% on the second day despite rigorous instructions to continue for two days. CONCLUSION: Despite repetitive instructions to use sialogogues continuously, most patients (90%) were not compliant. In future studies, strict monitoring and evaluation of patient compliance will be crucial when assessing the effect of continuous versus intermittent or delayed initiation of sialogogues.

Altered ventricular repolarisation dynamic: the missing link between obstructive sleep apnoea and sudden death?

Research is needed to explore the broader links between oxygen desaturation episodes, ventricular repolarisation instability and genesis of malignant arrhythmic events https://bit.ly/3WeQNHy.

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms.

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.

Open-source software for respiratory rate estimation using single-lead electrocardiograms.

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

Redifferentiation of Differentiated Thyroid Cancer: Clinical Insights from a Narrative Review of Literature.

Background: Patients who have metastatic differentiated thyroid cancer (mDTC) frequently have negative diagnostic and/or post-therapy radioiodine scans. As a result, 131I therapy is frequently no longer considered a therapeutic option for these patients. However, with the knowledge of genomic alterations of patients with mDTC, the use of selected agents in specific patient groups may be used with the intention to re-establish 131I uptake (i.e., redifferentiation) and additional 131I therapy. The objectives of this narrative review are to present definitions of related terminology, a brief overview of the molecular mechanisms of redifferentiating agents, and a narrative review of the literature for redifferentiation in patients who have radioiodine refractory mDTC. Summary: We searched multiple electronic databases and reviewed the relevant English-language literature reported after 2010. Fourteen articles were included in this narrative review. Conclusions: Preliminary data suggest that select agents may offer potential for re-establishing 131I uptake in selected patients with radioiodine refractory mDTC (e.g., negative diagnostic and/or post-therapy radioiodine scans). These agents may also enhance uptake (e.g., uptake enhancement) in patients who have 131I uptake in mDTC on a diagnostic and/or post-therapy radioiodine scan. As a result, this may facilitate higher absorbed dose delivered (Gy (rad]) per 131I activity administered [GBq (mCi)]. This in turn may increase the likelihood of a better therapeutic effect for the planned administered 131I activity or a reduction in the originally planned administered 131I activity, while achieving the same intended therapeutic effect with potentially less untoward effects. Further studies are warranted to confirm these preliminary observations and to confirm acceptable subsequent 131I therapy responses after redifferentiation and/or uptake enhancement.

Can Stochastic Pacing Restore Heart Rate Variability in Diseased Hearts? An In-vivo Ovine Case Study.

Heart rate variability (HRV) is an important clinical parameter that depicts the autonomic balance. Diminished HRV has been associated with diseased hearts and incorporating stochasticity in pacing has been investigated as a potential mechanism for restoring the altered autonomic balance and preventing cardiac arrhythmias. We studied the change in HRV with the development of chronic myocardial infarction (MI) in adult sheep (n=16). Next, we investigated the utility of stochastic pacing in modulating HRV in-vivo in both sham and MI hearts. The propensity of the heart to the development of cardiac alternans, a known precursor to tachyarrhythmias, was studied under three different pacing techniques, namely periodic pacing, stochastic pacing and constant diastolic interval (DI) pacing in one sham and one MI sheep. Autonomic balance was observed to be altered after 6 weeks of chronic MI. Increased heart rate, QTc interval, standard deviation of the R-R intervals and LF/HF ratio was observed in MI hearts. Stochastic pacing was found to be proarrhythmic and increased T-wave alternans burden was observed with increase in stochasticity. Maintaining a constant DI on every beat demonstrated reduced alternans levels compared to both periodic and stochastic pacing.Clinical Relevance-Our results demonstrate that precise control of the diastolic interval may be more beneficial in inhibiting arrhythmias than stochastic pacing.

A comprehensive framework for evaluation of high pacing frequency and arrhythmic optical mapping signals.

Introduction: High pacing frequency or irregular activity due to arrhythmia produces complex optical mapping signals and challenges for processing. The objective is to establish an automated activation time-based analytical framework applicable to optical mapping images of complex electrical behavior. Methods: Optical mapping signals with varying complexity from sheep (N = 7) ventricular preparations were examined. Windows of activation centered on each action potential upstroke were derived using Hilbert transform phase. Upstroke morphology was evaluated for potential multiple activation components and peaks of upstroke signal derivatives defined activation time. Spatially and temporally clustered activation time points were grouped in to wave fronts for individual processing. Each activation time point was evaluated for corresponding repolarization times. Each wave front was subsequently classified based on repetitive or non-repetitive events. Wave fronts were evaluated for activation time minima defining sites of wave front origin. A visualization tool was further developed to probe dynamically the ensemble activation sequence. Results: Our framework facilitated activation time mapping during complex dynamic events including transitions to rotor-like reentry and ventricular fibrillation. We showed that using fixed AT windows to extract AT maps can impair interpretation of the activation sequence. However, the phase windowing of action potential upstrokes enabled accurate recapitulation of repetitive behavior, providing spatially coherent activation patterns. We further demonstrate that grouping the spatio-temporal distribution of AT points in to coherent wave fronts, facilitated interpretation of isolated conduction events, such as conduction slowing, and to derive dynamic changes in repolarization properties. Focal origins precisely detected sites of stimulation origin and breakthrough for individual wave fronts. Furthermore, a visualization tool to dynamically probe activation time windows during reentry revealed a critical single static line of conduction slowing associated with the rotation core. Conclusion: This comprehensive analytical framework enables detailed quantitative assessment and visualization of complex electrical behavior.

Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing.

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas.

Targeted Atrial Fibrillation Therapy and Risk Stratification Using Atrial Alternans.

Atrial fibrillation (AF) is the most persistent arrhythmia today, with its prevalence increasing exponentially with the rising age of the population. Particularly at elevated heart rates, a functional abnormality known as cardiac alternans can occur prior to the onset of lethal arrhythmias. Cardiac alternans are a beat-to-beat oscillation of electrical activity and the force of cardiac muscle contraction. Extensive evidence has demonstrated that microvolt T-wave alternans can predict ventricular fibrillation vulnerability and the risk of sudden cardiac death. The majority of our knowledge of the mechanisms of alternans stems from studies of ventricular electrophysiology, although recent studies offer promising evidence of the potential of atrial alternans in predicting the risk of AF. Exciting preclinical and clinical studies have demonstrated a link between atrial alternans and the onset of atrial tachyarrhythmias. Here, we provide a comprehensive review of the clinical utility of atrial alternans in identifying the risk and guiding treatment of AF.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

RespiCo: A novel, flexible, and stand-alone electronic respiratory coaching device.

Conscious respiratory pattern and rate control is desired by patients with some forms of pulmonary disease that are undergoing respiratory muscle conditioning and rehabilitation, by practitioners of meditation hoping to improve mindfulness and wellbeing, by athletes striving to obtain breathing control in order to increase competitiveness, and by engineers and scientists that wish to use the data from breathing subjects to test hypotheses and develop physiological monitoring systems. Although prerecorded audio sources and computer applications are available that guide breathing exercises, they often suffer from being inflexible and allow only limited customization of the breathing cues. Here we describe a small, lightweight, battery-powered, microprocessor-based respiratory coaching device (RespiCo), which through wireless or wired connections, can be easily customized to precisely guide subjects to breathe at desired respiratory rates using specific breathing patterns through visual, auditory, or haptic cues. Digital signals can also be captured from the device to document the breathing cues provided by the device for research purposes. It is anticipated that this device will have important utility for those who wish to be guided to breathe in a precise manner or in research and development of physiologic monitoring systems.

Investigating Electrophysiological Markers of Arrhythmogenesis in a Chronic Myocardial Infarction Ovine Model.

Cardiac alternans has been associated with an increased propensity to lethal tachyarrhythmias such as ventricular tachycardia and fibrillation (VT/VF). Myocardial infarction (MI), resulting from restricted oxygen supply to the heart, is a known substrate for VT/VF. Here, we investigate the utility of cardiac alternans as a predictor of tachyarrhythmias in a chronic MI ovine model. In-vivo electrophysiological studies were performed to assess the change in microvolt T-wave alternans (TWA) with induction of acute ischemia following coronary artery occlusion. 24-hour telemetry was performed in an ambulatory animal for 6 weeks to monitor the progression of TWA with chronic MI. At 6 weeks, ex-vivo optical mapping experiments were performed to assess the spatiotemporal evolution of alternans in sham (n=5) and chronic MI hearts (n=8). Our results demonstrate that chronic MI leads to significant electrophysiological changes in the cardiac substrate. Significant increase in TWA is observed post occlusion and a steady rise in alternans is seen with progression of chronic MI. Compared to sham, chronic MI hearts show significant presence of localized action potential amplitude alternans, which spatially evolve with an increase in pacing frequency. Clinical Relevance - Our results demonstrate that localized alternans underlie arrhythmogenesis in chronic MI hearts and microvolt TWA can serve as a biomarker of disease progression during chronic MI.

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit.

Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963-0.972), heart failure AUC of 0.838 (CI: 0.825-0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821-0.842), pulmonary embolism AUC of 0.802 (CI: 0.764-0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499-2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

Tissue Preparation Techniques for Contrast-Enhanced Micro Computed Tomography Imaging of Large Mammalian Cardiac Models with Chronic Disease.

Structural remodeling is a common consequence of chronic pathological stresses imposed on the heart. Understanding the architectural and compositional properties of diseased tissue is critical to determine their interactions with arrhythmic behavior. Microscale tissue remodeling, below the clinical resolution, is emerging as an important source of lethal arrhythmia, with high prevalence in young adults. Challenges remain in obtaining high imaging contrast at sufficient microscale resolution for preclinical models, such as large mammalian whole hearts. Moreover, tissue composition-selective contrast enhancement for three-dimensional high-resolution imaging is still lacking. Non-destructive imaging using micro-computed tomography shows promise for high-resolution imaging. The objective was to alleviate sufferance from X-ray over attenuation in large biological samples. Hearts were extracted from healthy pigs (N = 2), and sheep (N = 2) with either induced chronic myocardial infarction and fibrotic scar formation or induced chronic atrial fibrillation. Excised hearts were perfused with: a saline solution supplemented with a calcium ion quenching agent and a vasodilator, ethanol in serial dehydration, and hexamethyldisilizane under vacuum. The latter reinforced the heart structure during air-drying for 1 week. Collagen-dominant tissue was selectively bound by an X-ray contrast-enhancing agent, phosphomolybdic acid. Tissue conformation was stable in air, permitting long-duration microcomputed tomography acquisitions to obtain high-resolution (isotropic 20.7 µm) images. Optimal contrast agent loading by diffusion showed selective contrast enhancement of the epithelial layer and sub-endocardial Purkinje fibers in healthy pig ventricles. Atrial fibrillation (AF) hearts showed enhanced contrast accumulation in the posterior walls and appendages of the atria, attributed to greater collagen content. Myocardial infarction hearts showed increased contrast selectively in regions of cardiac fibrosis, which enabled the identification of interweaving surviving myocardial muscle fibers. Contrast-enhanced air-dried tissue preparations enabled microscale imaging of the intact large mammalian heart and selective contrast enhancement of underlying disease constituents.

Clinical Potential of Beat-to-Beat Diastolic Interval Control in Preventing Cardiac Arrhythmias.

Life-threatening ventricular arrhythmias and sudden cardiac death are often preceded by cardiac alternans, a beat-to-beat oscillation in the T-wave morphology or duration. However, given the spatiotemporal and structural complexity of the human heart, designing algorithms to effectively suppress alternans and prevent fatal rhythms is challenging. Recently, an antiarrhythmic constant diastolic interval pacing protocol was proposed and shown to be effective in suppressing alternans in 0-, 1-, and 2-dimensional in silico studies as well as in ex vivo whole heart experiments. Herein, we provide a systematic review of the electrophysiological conditions and mechanisms that enable constant diastolic interval pacing to be an effective antiarrhythmic pacing strategy. We also demonstrate a successful translation of the constant diastolic interval pacing protocol into an ECG-based real-time control system capable of modulating beat-to-beat cardiac electrical activity and preventing alternans. Furthermore, we present evidence of the clinical utility of real-time alternans suppression in reducing arrhythmia susceptibility in vivo. We provide a comprehensive overview of this promising pacing technique, which can potentially be translated into a clinically viable device that could radically improve the quality of life of patients experiencing abnormal cardiac rhythms.

Comparing surgical thoroughness and recurrence in thyroid cancer patients across race/ethnicity.

BACKGROUND: For patients with differentiated thyroid cancer who will receive postoperative radioactive iodine, thyroid remnant uptake can be calculated and may point to the thoroughness of the surgical resection. In the United States, outcome disparities exist among ethnic/racial minorities with differentiated thyroid cancer. Data about surgical thoroughness and recurrence rates across races/ethnicities do not exist. This study compared the amount of thyroid remnant uptake and cancer recurrence rates across race/ethnicity. METHODS: This was a retrospective analysis of adult patients with differentiated thyroid cancer who had postoperative radioactive iodine in 2017 and 2018 and were followed to 2020. We collected thyroid bed remnant uptake from postoperative radioactive iodine scans and analyzed it as a ratio of percent of uptake to dose of radioactive iodine received to control for varying radioactive iodine doses. Thyroid remnant, uptake to dose of radioactive iodine received, and recurrence were evaluated across race/ethnicity. RESULTS: Of 218 patients: 61% were White, 21% Black, 11% Asian, and 7% Hispanic; 72% were female. Seventy-one percent of patients had their surgery done by a high-volume surgeon, although volume data were not available for all. In White, Black, Asian, and Hispanic patients, median uptake was 0.68%, 0.44%, 1.5%, and 0.8%, respectively (P = .13). We did not observe differences in median uptake to dose of radioactive iodine received across groups (P = .41). Recurrence rate was 17.0% among White patients, 16.7% among Black patients, 17.6% among Asian patients, and 16.7% among Hispanic patients (P = 1.00). CONCLUSION: We did not observe differences across race/ethnicity in surgical thoroughness or rate of recurrence. These findings suggest that disparities may be mitigated when ethnic/racial minorities have similar access to quality surgical care.

Nonseminomatous Germ-Cell Tumor Presenting as Bilateral Adrenal Masses.

OBJECTIVE: Many tumors can metastasize to the adrenal glands, making the diagnosis of adrenal masses challenging. Awareness that rare primary tumors can metastasize to the adrenals and consideration of biopsy for their diagnosis, sometimes at extra-adrenal sites, is essential to prevent unnecessary adrenalectomies and facilitate the right treatment. We report a rare case of bilateral adrenal masses due to metastasis from a nonseminomatous germ-cell tumor of a retroperitoneal lymph node origin. METHODS: The diagnosis of the adrenal masses from the nonseminomatous germ-cell tumor of a retroperitoneal lymph node origin was based on a retroperitoneal lymph node core biopsy. An initial core biopsy of the adrenal gland revealed necrotic tissue and inflammatory cells without evidence of malignancy. Due to nondiagnostic findings, the core biopsy was repeated, which showed degenerating cells with a high mitotic index and immunohistochemical staining positive for vimentin, suggesting the possibility of a high-grade sarcoma. A retroperitoneal lymph node biopsy was performed. The patient was started on chemotherapy. RESULTS: A 34-year-old man presented with acute left upper-abdominal pain of 2 weeks and tenderness on the left upper quadrant of the abdomen, and he was found to have bilateral adrenal masses. Laboratory results showed the following: adrenocorticotropic hormone 41 pg/mL (7-69 pg/mL), metanephrine <0.1 nmol/L (0-0.49 nmol/L), normetanephrine 0.99 nmol/L (0-0.89 nmol/L), and morning cortisol 3.1 µg/dL after a 1-mg dexamethasone-suppression test. His dehydroepiandrosterone sulfate level was 62 µg/dL (120-520 µg/dL), and 17OH progesterone level was 36 ng/dL (<138 ng/dL); androstenedione and serum estradiol levels were normal. Laboratory tests for tumor markers revealed the following: testosterone 21 ng/dL (241-827 ng/dL), prostate-specific antigen 0.57 ng/mL (0-4 ng/mL), alpha-fetoprotein 1.9 IU/mL (0.6-6 IU/ml), and beta-human chorionic gonadotropin 134 mIU/mL (0-1 mIU/mL). CONCLUSION: We report a rare case of rapidly progressing adrenal masses in a young man, found to have metastasized from nonseminomatous germ-cell tumors. Histopathologic confirmation of the metastatic tumor was done, which prevented unnecessary adrenalectomy. The patient received appropriate chemotherapy.