Rare correlation of somatic PRKACA mutations with pregnancy-associated aldosterone- and cortisol-producing adenomas: a case report and literature review.

BACKGROUND: Somatic mutations have been observed to induce aldosterone-producing adenomas (APAs). These may be accelerated during pregnancy. Somatic PRKACA mutations are common in cortisol-producing adenomas (CPAs). However, their role in APAs, particularly aldosterone- and cortisol-producing adenomas (A/CPAs), is not well understood. This study aims to investigate the association between PRKACA mutations and the accelerated development of A/CPAs during pregnancy. CASE PRESENTATION: A patient with primary aldosteronism (PA) associated with severe Cushing's syndrome (CS) underwent surgical resection of an adrenal tumor one year after delivery. Pathologic examination revealed an adrenocortical adenoma characterized primarily by zona glomerulosa hyperplasia. Somatic mutation analysis revealed the presence of the somatic PRKACA mutation, which was validated as a deleterious mutation by various computational databases. Immunohistochemical results showed positive staining for cytochrome P450 family 11 subfamily B member 1 (CYP11B1), cytochrome P450 family 11 subfamily B member 2 (CYP11B2), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). Our study included a review of 20 previously documented cases of aldosterone- and cortisol-producing adenomas (A/CPAs), two of which were concurrently positive for both CYP11B1 and CYP11B2, consistent with our findings. CONCLUSION: Somatic mutations in PRKACA may correlate with the upregulation of LHCGR, which synergistically drives the accelerated growth of co-secretion tumors during pregnancy, thereby exacerbating disease progression.

Ectopic adrenocortical adenoma characterized by hypogonadism: a case report and review of the literature.

BACKGROUND: Currently, there is a scarcity of cases and diagnostic data regarding ectopic adrenocortical adenomas, particularly in relation to their impact on gonadal function and localization diagnostic techniques. We report a typical case of ectopic adrenocortical adenomas and the data of treatment follow-up, and review the literature of 31 available cases of ectopic adrenocortical adenomas. CASE PRESENTATION: A 27-year-old Chinese female patient was admitted to our hospital for hypertension, hyperglycaemia and primary amenorrhea. The patient was functionally diagnosed with ACTH-independent CS and hypogonadotropic hypogonadism. Radiological evaluations, including Computed Tomography (CT) and functional imaging, identified a mass at the left renal hilum. Histological assessments post-surgical excision confirmed the mass to be an ectopic adrenocortical adenoma. A subsequent 3-month follow-up showed no signs of disease recurrence, a swift recovery of the cortisol axis was observed, with a partial recuperation of the gonadal axis. REVIEW: Our literature review shows that the most common ectopic areas of cortisol adenomas are renal hilum and hepatic region. The most positive biomarker is Melan A, and only a few cases have been diagnosed with functional localization. CONCLUSION: Ectopic adrenocortical adenomas may be asymptomatic in the early stage and can impact gonadal function. Physicians who treat hypogonadism must be aware of the need to test cortisol levels and perform functional localization in patients with lumps present.

Clinical and imaging presentations are associated with function in incidental adrenocortical adenomas: a retrospective cohort study.

OBJECTIVE: The aim of this study is to assess whether clinical and imaging characteristics are associated with the hormonal subtype, growth, and adrenalectomy for incidental adrenal cortical adenomas (ACAs). DESIGN: This is a single-center cohort study. METHODS: Consecutive adult patients with incidental ACA were diagnosed between 2000 and 2016. RESULTS: Of the 1516 patients with incidental ACA (median age 59 years, 62% women), 699 (46%) had nonfunctioning adenomas (NFAs), 482 (31%) had mild autonomous cortisol secretion (MACS), 62 (4%) had primary aldosteronism (PA), 39 (3%) had Cushing syndrome, 18 (1%) had PA and MACS, and 226 (15%) had incomplete work-up. Age, sex, tumor size, and tumor laterality, but not unenhanced computed tomography Hounsfield units (HU), were associated with hormonal subtypes. In a multivariable analysis, ≥1 cm growth was associated with younger age (odds ratio [OR] = 0.8 per 5-year increase, P = .0047) and longer imaging follow-up (OR = 1.2 per year, P < .0001). Adrenalectomy was performed in 355 (23%) patients, including 38% of MACS and 15% of NFA. Adrenalectomy for NFA and MACS was more common in younger patients (OR = 0.79 per 5-year increase, P = .002), larger initial tumor size (OR = 2.3 per 1 cm increase, P < .0001), ≥1 cm growth (OR = 15.3, P < .0001), and higher postdexamethasone cortisol (OR = 6.6 for >5 vs <1.8 µg/dL, P = .002). CONCLUSIONS: Age, sex, tumor size, and laterality were associated with ACA hormonal subtype and can guide diagnosis and management. Tumor growth was more common with younger age and longer follow-up. Unenhanced HU did not predict hormonal subtype or growth. Adrenalectomy for MACS and NFA was mainly performed in younger patients with larger tumor size, growth, and elevated postdexamethasone cortisol.

Oncocytic adrenal cortical adenoma: a benign lesion mimicking malignancy.

Adrenocortical tumours are rare in children and account for only 0.3%-0.4% of all neoplasms in childhood. They present with variable signs and symptoms, depending on the type of hormonal hypersecretion. The majority of the adrenocortical tumours in children are functional (90%) and malignant (88%). Here, we describe a functional plurihormonal oncocytic adrenal cortical adenoma in a young girl, that mimicked a malignant adrenal lesion, clinically as well as on imaging and biochemical features. This report bears the objective of being aware of the atypical biochemical as well as imaging characteristics of oncocytic adrenal tumours.

A Comparison of Near-Infrared Autofluorescence Findings in Benign Versus Malignant Adrenal Tumors.

BACKGROUND: Many adrenal tumors are deemed radiologically indeterminate and surgically removed. Adrenal tissue, like parathyroid glands, exhibits near-infrared autofluorescence (NIRAF) properties. This study was designed to investigate the potential of NIRAF to differentiate benign versus malignant adrenal tumors. METHODS: Patients undergoing adrenalectomy between October 2021 and May 2023 were prospectively studied. Adrenalectomy specimens were inspected with NIRAF imaging. Specimen autofluorescence (AF) characteristics were recorded. Comparisons were made between different tumor types and a logistic regression model was constructed to differentiate benign versus malignant tumors. A receiver operating characteristic curve was used to identify an optimal AF threshold differentiating benign versus malignant tumors. RESULTS: A total of 108 adrenal specimens were examined: adrenocortical adenomas/other benign lesions (n = 72), pheochromocytomas (n = 18), adrenocortical neoplasms of uncertain behavior (n = 4), and malignant tumors (n = 14). A significant difference in normalized AF intensity was identified when comparing adrenocortical adenomas (3.08 times background) with pheochromocytomas (1.95, p = 0.001) and malignant tumors (1.11, p < 0.0001). The Area Under the Curve differentiating benign vs malignant tumors was 0.87, with an optimal normalized AF threshold at 1.93. CONCLUSIONS: Different adrenal pathologies exhibit diverse AF properties. These findings suggest a potential intraoperative utility of NIRAF in predicting benign versus malignant nature for radiologically indeterminate adrenal tumors.

Intracellular Calcium Dynamics in Primary Human Adrenocortical Cells Deciphered with a Novel Pipeline.

INTRODUCTION: The fluctuations of the intracellular Ca2+ concentration ([Ca2+]i) are key physiological signals for cell function under normal conditions and can undergo profound alterations in disease states, as high blood pressure due to endocrine disorders like primary aldosteronism (PA). However, when assessing such fluctuations several parameters in the Ca2+ signal dynamics need to be considered, which renders their assessment challenging. AIM: Aim to develop an observer-independent custom-made pipeline to analyze Ca2+ dynamics in terms of frequency and peak parameters, as amplitude, full width at half maximum (FWHM) and area under the curve (AUC). METHODS: We applied a custom-made methodology to aldosterone-producing adenoma (APA) and APA adjacent cells (AAC) and found this pipeline to be suitable for monitoring and processing a wide-range of [Ca2+]i events in these cell types delivering reproducible results. CONCLUSION: The designed pipeline can provide a useful tool for [Ca2+]i signal analysis that allows comparisons of Ca2+ dynamics not only in PA, but in other cell phenotypes that are relevant for the regulation of blood pressure.

Diagnostic and prognostic assessments of adrenocortical carcinomas by pathological features, immunohistochemical markers and reticular histochemistry staining.

BACKGROUND: Current diagnostic criteria of adrenocortical neoplasms are mostly based on morphology. The utility of immunohistochemistry (IHC) and histochemistry is limited. MATERIALS AND METHODS: To evaluate the diagnostic and prognostic utility of clinicopathological features, morphology, ancillary biomarkers, and reticular histochemistry in adrenocortical neoplasms. We examined 28 adrenocortical carcinomas (ACCs) and 50 adrenocortical adenomas (ACAs) obtained from pathology archives. Clinical data were retrieved from medical records. Two pathologists independently assessed hematoxylin and eosin-stained slides, employing modified Weiss criteria for all tumors and Lin-Weiss-Bisceglia criteria for oncocytic variants. Immunohistochemical markers (Calretinin, alpha-inhibin, MelanA, SF-1, Ki-67, PHH3, IGF-2, ß-catenin, P53, CYP11B1, CYP11B2, MLH1, MSH2, MSH6, PMS2, EPCAM) and Gomori's Silver histochemistry were applied. Statistical analysis utilized SPSS Statistics 26. RESULTS: ACCs exhibited larger tumor sizes (P<0.001) and symptomatic presentations (P = 0.031) compared to ACAs. Parameters of modified Weiss criteria and angioinvasion demonstrated diagnostic value for ACCs. Six immunohistochemical antibodies((MelanA, Ki-67, IGF-2, ß-catenin, P53 and CYP11B1) and reticulin framework alterations showed diagnostic value. Notably, Ki-67 and reticulin staining were most recommended. Evident reticulin staining was frequently present in ACCs (P<0.001). Ki-67 was significantly higher in ACCs (P<0.001). Twenty-one conventional and seven oncocytic entities showed different necrosis frequencies. Symptoms and Ki-67 index ≥ 30% were prognostic for ACCs, correlating with shorter survival. CONCLUSIONS: This study emphasizes the diagnostic value of reticulin framework alterations and a high Ki-67 index. Markers such as CYP11B1, IGF2, P53, ß-catenin and MelanA also contribute to the diagnosis of ACCs. Symptoms and Ki-67 index ≥ 30% predict shorter survival. These findings encourges the use of ancillary markers such as reticulin histochemistry and Ki-67 in the workup of evaluations of adrenocortical neoplasms.

Steroids-producing nodules: a two-layered adrenocortical nodular structure as a precursor lesion of cortisol-producing adenoma.

BACKGROUND: The human adrenal cortex consists of three functionally and structurally distinct layers; zona glomerulosa, zona fasciculata (zF), and zona reticularis (zR), and produces adrenal steroid hormones in a layer-specific manner; aldosterone, cortisol, and adrenal androgens, respectively. Cortisol-producing adenomas (CPAs) occur mostly as a result of somatic mutations associated with the protein kinase A pathway. However, how CPAs develop after adrenocortical cells acquire genetic mutations, remains poorly understood. METHODS: We conducted integrated approaches combining the detailed histopathologic studies with genetic, RNA-sequencing, and spatially resolved transcriptome (SRT) analyses for the adrenal cortices adjacent to human adrenocortical tumours. FINDINGS: Histopathological analysis revealed an adrenocortical nodular structure that exhibits the two-layered zF- and zR-like structure. The nodular structures harbour GNAS somatic mutations, known as a driver mutation of CPAs, and confer cell proliferative and autonomous steroidogenic capacities, which we termed steroids-producing nodules (SPNs). RNA-sequencing coupled with SRT analysis suggests that the expansion of the zF-like structure contributes to the formation of CPAs, whereas the zR-like structure is characterised by a macrophage-mediated immune response. INTERPRETATION: We postulate that CPAs arise from a precursor lesion, SPNs, where two distinct cell populations might contribute differently to adrenocortical tumorigenesis. Our data also provide clues to the molecular mechanisms underlying the layered structures of human adrenocortical tissues. FUNDING: KAKENHI, The Uehara Memorial Foundation, Daiwa Securities Health Foundation, Kaibara Morikazu Medical Science Promotion Foundation, Secom Science and Technology Foundation, ONO Medical Research Foundation, and Japan Foundation for Applied Enzymology.

Adrenocortical Adenoma Arising from Adrenohepatic Fusion: A Mimic of Hepatocellular Carcinoma-Case Report.

AIM: We present a case of adrenocortical adenoma originating from the adrenohepatic fusion (AHF) region, accompanied by advanced hepatosteatosis in the liver tissue, and discuss its distinction from hepatocellular carcinoma.  Case Experience: A 68-year-old male patient was admitted to the hospital following a fall from a height. He was referred to our hospital after an incidental discovery of a liver mass during an abdominal ultrasound examination. Subsequently, magnetic resonance imaging (MRI) imaging was conducted, followed by segmental liver resection with right adrenalectomy, and histological analysis of a biopsy from the lesion.  Results: Upon histologic examination, the case was determined to be an adrenocortical adenoma originating from the AHF.  Discussion: Adrenohepatic fusion (AHF) denotes the histological amalgamation of cells from the right adrenal cortex and right hepatic parenchyma. Only a limited number of cases of neoplasia originating from this region have been documented. These rare instances often present a diagnostic challenge, with preoperative imaging frequently misidentifying them as primary malignancies of either hepatic or adrenal origin, potentially leading to unnecessary extensive resections. The integration of immunohistochemical staining alongside clinical and radiological data proves helpful for accurately diagnosing this condition.  Conclusion: Awareness among clinicians, radiologists, and pathologists regarding the tumors that may arise from this region can mitigate the risk of performing extensive resections unnecessarily.

Steroid profiling in adrenal disease.

The measurement of steroid hormones in blood and urine, which reflects steroid biosynthesis and metabolism, has been recognized as a valuable tool for identifying and distinguishing steroidogenic disorders. The application of mass spectrometry enables the reliable and simultaneous analysis of large panels of steroids, ushering in a new era for diagnosing adrenal diseases. However, the interpretation of complex hormone results necessitates the expertise and experience of skilled clinicians. In this scenario, machine learning techniques are gaining worldwide attention within healthcare fields. The clinical values of combining mass spectrometry-based steroid profiles analysis with machine learning models, also known as steroid metabolomics, have been investigated for identifying and discriminating adrenal disorders such as adrenocortical carcinomas, adrenocortical adenomas, and congenital adrenal hyperplasia. This promising approach is expected to lead to enhanced clinical decision-making in the field of adrenal diseases. This review will focus on the clinical performances of steroid profiling, which is measured using mass spectrometry and analyzed by machine learning techniques, in the realm of decision-making for adrenal diseases.

Single-Nucleus Analysis Reveals Tumor Heterogeneity of Aldosterone-Producing Adenoma.

BACKGROUND: Recent advances in omics techniques have allowed detailed genetic characterization of aldosterone-producing adenoma (APA). The pathogenesis of APA is characterized by tumorigenesis-associated aldosterone synthesis. The pathophysiological intricacies of APAs have not yet been elucidated at the level of individual cells. Therefore, a single-cell level analysis is speculated to be valuable in studying the differentiation process of APA. METHODS: We conducted single-nucleus RNA sequencing of APAs with KCNJ5 mutation and nonfunctional adenomas obtained from 3 and 2 patients, respectively. RESULTS: The single-nucleus RNA sequencing revealed the intratumoral heterogeneity of APA and identified cell populations consisting of a shared cluster of nonfunctional adenoma and APA. In addition, we extracted 2 cell fates in APA and obtained a cell population specialized in aldosterone synthesis. Genes related to ribosomes and neurodegenerative diseases were upregulated in 1 of these fates, whereas those related to the regulation of glycolysis were upregulated in the other fate. Furthermore, the total RNA reads in the nucleus were higher in hormonally activated clusters, indicating a marked activation of transcription per cell. CONCLUSIONS: The single-nucleus RNA sequencing revealed intratumoral heterogeneity of APA with KCNJ5 mutation. The observation of 2 cell fates in KCNJ5-mutated APAs provides the postulation that a heterogeneous process of cellular differentiation was implicated in the pathophysiological mechanisms underlying APA tumors.

The new histological system for the diagnosis of adrenocortical cancer.

Introduction: Adrenocortical cancer (ACC) is a rare malignant tumor that originates in the adrenal cortex. Despite extensive molecular-genetic, pathomorphological, and clinical research, assessing the malignant potential of adrenal neoplasms in clinical practice remains a daunting task in histological diagnosis. Although the Weiss score is the most prevalent method for diagnosing ACC, its limitations necessitate additional algorithms for specific histological variants. Unequal diagnostic value, subjectivity in evaluation, and interpretation challenges contribute to a gray zone where the reliable assessment of a tumor's malignant potential is unattainable. In this study, we introduce a universal mathematical model for the differential diagnosis of all morphological types of ACC in adults. Methods: This model was developed by analyzing a retrospective sample of data from 143 patients who underwent histological and immunohistochemical examinations of surgically removed adrenal neoplasms. Statistical analysis was carried out on Python 3.1 in the Google Colab environment. The cutting point was chosen according to Youden's index. Scikit-learn 1.0.2 was used for building the multidimensional model for Python. Logistical regression analysis was executed with L1-regularization, which is an effective method for extracting the most significant features of the model. Results: The new system we have developed is a diagnostically meaningful set of indicators that takes into account a smaller number of criteria from the currently used Weiss scale. To validate the obtained model, we divided the initial sample set into training and test sets in a 9:1 ratio, respectively. The diagnostic algorithm is highly accurate [overall accuracy 100% (95% CI: 96%-100%)]. Discussion: Our method involves determining eight diagnostically significant indicators that enable the calculation of ACC development probability using specified formulas. This approach may potentially enhance diagnostic precision and facilitate improved clinical outcomes in ACC management.

Deep learning approach for differentiating indeterminate adrenal masses using CT imaging.

PURPOSE: Distinguishing stage 1-2 adrenocortical carcinoma (ACC) and large, lipid poor adrenal adenoma (LPAA) via imaging is challenging due to overlapping imaging characteristics. This study investigated the ability of deep learning to distinguish ACC and LPAA on single time-point CT images. METHODS: Retrospective cohort study from 1994 to 2022. Imaging studies of patients with adrenal masses who had available adequate CT studies and histology as the reference standard by method of adrenal biopsy and/or adrenalectomy were included as well as four patients with LPAA determined by stability or regression on follow-up imaging. Forty-eight (48) subjects with pathology-proven, stage 1-2 ACC and 43 subjects with adrenal adenoma >3 cm in size demonstrating a mean non-contrast CT attenuation > 20 Hounsfield Units centrally were included. We used annotated single time-point contrast-enhanced CT images of these adrenal masses as input to a 3D Densenet121 model for classifying as ACC or LPAA with five-fold cross-validation. For each fold, two checkpoints were reported, highest accuracy with highest sensitivity (accuracy focused) and highest sensitivity with the highest accuracy (sensitivity focused). RESULTS: We trained a deep learning model (3D Densenet121) to predict ACC versus LPAA. The sensitivity-focused model achieved mean accuracy: 87.2% and mean sensitivity: 100%. The accuracy-focused model achieved mean accuracy: 91% and mean sensitivity: 96%. CONCLUSION: Deep learning demonstrates promising results distinguishing between ACC and large LPAA using single time-point CT images. Before being widely adopted in clinical practice, multicentric and external validation are needed.

A Rare Case of Primary Adrenal Epithelioid Angiosarcoma.

BACKGROUND Primary adrenal epithelioid angiosarcoma (PAEA) is a very uncommon primary adrenal gland tumor that usually occurs around the age of 60 years and is more common among males. Owing to its rarity and histopathological features, PAEA could be misdiagnosed as adrenal cortical adenoma, adrenal cortical carcinoma, or other metastatic cancers, such as metastatic malignant melanoma and epithelioid hemangioendothelioma. CASE REPORT A 59-year-old male patient presented to our hospital with a complaint of abdominal bloating that started 2 months prior. His vital signs and the results of his physical and neurological examinations were unremarkable. A computed tomography scan showed a lobulated mass arising from the hepatic limb of the right adrenal gland but no evidence of metastasis to the chest or abdomen. The patient underwent right adrenalectomy, and the macroscopic pathological findings from a right adrenalectomy specimen revealed atypical tumor cells with an epithelioid appearance in the background of an adrenal cortical adenoma. Immunohistochemical staining was performed to confirm the diagnosis. The final diagnosis was epithelioid angiosarcoma involving the right adrenal gland with a background adrenal cortical adenoma. The patient had no postoperative complications, pain in the surgical wound, or fever. Therefore, he was discharged with a schedule for followup appointments. CONCLUSIONS PAEA may be misinterpreted as adrenal cortical carcinoma, metastatic carcinoma, or malignant melanoma radiologically and histologically. Immunohistochemical stains are essential for diagnosing PAEA. Surgery and strict monitoring are the main treatments. In addition, early diagnosis is essential for patient recovery.

[Adrenohepatic fusion - an unknown but not uncommon phenomenon of clinical importance]. / Adrenohepatisk fusion ­ ett okänt fenomen med klinisk betydelse.

Adrenohepatic fusion (AHF) is a union of the right adrenal gland and the liver with intermingling of parenchymal adrenal and liver cells. The phenomenon can be of clinical importance when evaluating patients with adrenal tumors. Using conventional imaging techniques such as computed tomography, a benign adrenal adenoma developing in an adrenohepatic fusion may mimic an invasive hepatocellular carcinoma or adrenal cortical carcinoma. This study presents a comprehensive review of the literature and shows a prevalence of 5.6 percent in autopsy studies. Moreover, 19 patients with adrenal masses in AHF are presented together with their clinical data.

Diagnostic accuracy of using multiple cytokines to predict aldosterone-producing adenoma.

Here, we aimed to study the important cytokines in plasma to identify the aldosterone-producing adenoma (APA). 19 unilateral primary aldosteronism (UPA) patients and 19 healthy people were divided into UPA group and Control group, and the serum of bilateral adrenal veins and inferior vena cava collected by adrenal blood sampling (AVS) in UPA patients and the serum from the healthy subjects were all used to detect multiple cytokines by Luminex immunoassays. Additionally, The UPA patients subjected to laparoscopic adrenalectomy were divided into different groups by pathological results for further study. According our results, IP-10, CXCL9 and RANTES were significantly higher in UPA group compared with control group, and the combination of the three cytokines have significant predictive power for predicting UPA, while the correlational analyses demonstrated that IP-10 and CXCL9 were positively correlated with BP and HR, while EGF was positively correlated with HDL. Additionally, IL-1b was suggested to be the most potential diagnostic biomarker to discriminate the APA and unilateral adrenal hyperplasia (UAH). The present findings might suggest a possibility of IP-10, CXCL9 and RANTES served as a sign to help UPA diagnosis and finally used to assist the diagnosis of APA, while IL-1b was suggested to be the most potential diagnostic biomarker to identify the APA from the UAH patients.

Rare benign adrenal lesions.

While most benign lesions of the adrenal glands represent either an adrenocortical adenoma or a myelolipoma, the advent and frequent use of high-resolution radiological investigations have led to relatively increased incidental discovery of rare adrenal lesions, specifically benign adrenal cysts, adrenal ganglioneuromas, adrenal schwannomas, adrenal hemorrhage, and adrenal calcifications. Radiological characteristics of the different rare benign adrenal lesions could vary from distinct to indeterminate. Though typically nonfunctional, these rare lesions require evaluation for adrenal hormone excess, as they may phenotypically appear similar to pheochromocytoma or adrenocortical carcinoma and could sometimes be associated with or conceal an underlying functional adrenal tumor. In this review, we discuss the various rare benign adrenal lesions, emphasizing a practical perspective.

Simplified urinary steroid profiling by LC-MS as diagnostic tool for malignancy in adrenocortical tumors.

OBJECTIVES: Preoperative identification of malignant adrenal tumors is challenging. 24-h urinary steroid profiling by LC-MS/MS and machine learning has demonstrated high diagnostic power, but the unavailability of bioinformatic models for public use has limited its routine application. We here aimed to increase usability with a novel classification model for the differentiation of adrenocortical adenoma (ACA) and adrenocortical carcinoma (ACC). METHODS: Eleven steroids (5-pregnenetriol, dehydroepiandrosterone, cortisone, cortisol, α-cortolone, tetrahydro-11-deoxycortisol, etiocholanolone, pregnenolone, pregnanetriol, pregnanediol, and 5-pregnenediol) were quantified by LC-MS/MS in 24-h urine samples from 352 patients with adrenal tumor (281 ACA, 71 ACC). Random forest modelling and decision tree algorithms were applied in training (n = 188) and test sets (n = 80) and independently validated in 84 patients with paired 24-h and spot urine. RESULTS: After examining different models, a decision tree using excretions of only 5-pregnenetriol and tetrahydro-11-deoxycortisol classified three groups with low, intermediate, and high risk for malignancy. 148/217 ACA were classified as being at low, 67 intermediate, and 2 high risk of malignancy. Conversely, none of the ACC demonstrated a low-risk profile leading to a negative predictive value of 100% for malignancy. In the independent validation cohort, the negative predictive value was again 100% in both 24-h urine and spot urine with a positive predictive value of 87.5% and 86.7%, respectively. CONCLUSIONS: This simplified LC-MS/MS-based classification model using 24-h-urine provided excellent results for exclusion of ACC and can help to avoid unnecessary surgeries. Analysis of spot urine led to similarly satisfactory results suggesting that cumbersome 24-h urine collection might be dispensable after future validation.