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.

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.

Multimodal integration of radiology and pathology signatures for distinguishing between aldosterone-producing adenomas and nonfunctional adrenal adenomas.

OBJECTIVE: To develop and validate a nomogram combining radiomics and pathology features to distinguish between aldosterone-producing adenomas (APAs) and nonfunctional adrenal adenomas (NF-AAs). METHODS: Consecutive patients diagnosed with adrenal adenomas via computed tomography (CT) or pathologic analysis between January 2011 and November 2022 were eligible for inclusion in this retrospective study. CT images and hematoxylin & eosin-stained slides were used for annotation and feature extraction. The selected radiomics and pathology features were used to develop a risk model using various machine learning models, and the area under the receiver operating characteristic curve (AUC) was determined to evaluate diagnostic performance. The predicted results from radiomics and pathology features were combined and visualized using a nomogram. RESULTS: A total of 211 patients (APAs, n = 59; NF-AAs, n = 152) were included in this study, with patients randomly divided into either the training set or the testing set at a ratio of 8:2. The ExtraTrees model yielded a sensitivity of 0.818, a specificity of 0.733, and an accuracy of 0.756 (AUC = 0.817; 95% confidence interval [CI]: 0.675-0.958) in the radiomics testing set and a sensitivity of 0.999, a specificity of 0.842, and an accuracy of 0.867 (AUC = 0.905, 95% CI: 0.792-1.000) in the pathology testing set. A nomogram combining radiomics and pathology features demonstrated a strong performance (AUC = 0.912; 95% CI: 0.807-1.000). CONCLUSION: A nomogram combining radiomics and pathology features demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians to distinguish between APAs and NF-AAs.

Fully-automated CT derived body composition analysis reveals sarcopenia in functioning adrenocortical carcinomas.

Determination of body composition (the relative distribution of fat, muscle, and bone) has been used effectively to assess the risk of progression and overall clinical outcomes in different malignancies. Sarcopenia (loss of muscle mass) is especially associated with poor clinical outcomes in cancer. However, estimation of muscle mass through CT scan has been a cumbersome, manually intensive process requiring accurate contouring through dedicated personnel hours. Recently, fully automated technologies that can determine body composition in minutes have been developed and shown to be highly accurate in determining muscle, bone, and fat mass. We employed a fully automated technology, and analyzed images from a publicly available cancer imaging archive dataset (TCIA) and a tertiary academic center. The results show that adrenocortical carcinomas (ACC) have relatively sarcopenia compared to benign adrenal lesions. In addition, functional ACCs have accelerated sarcopenia compared to non-functional ACCs. Further longitudinal research might shed further light on the relationship between body component distribution and ACC prognosis, which will help us incorporate more nutritional strategies in cancer therapy.

Distinguishing between aldosterone-producing adenomas and non-functional adrenocortical adenomas using the YOLOv5 network.

BACKGROUND: You Only Look Once version 5 (YOLOv5), a one-stage deep-learning (DL) algorithm for object detection and classification, offers high speed and accuracy for identifying targets. PURPOSE: To investigate the feasibility of using the YOLOv5 algorithm to non-invasively distinguish between aldosterone-producing adenomas (APAs) and non-functional adrenocortical adenomas (NF-ACAs) on computed tomography (CT) images. MATERIAL AND METHODS: A total of 235 patients who were diagnosed with ACAs between January 2011 and July 2022 were included in this study. Of the 215 patients, 81 (37.7%) had APAs and 134 (62.3%) had NF-ACAs' they were randomly divided into either the training set or the validation set at a ratio of 9:1. Another 20 patients, including 8 (40.0%) with APA and 12 (60.0%) with NF-ACA, were collected for the testing set. Five submodels (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) of YOLOv5 were trained and evaluated on the datasets. RESULTS: In the testing set, the mAP_0.5 value for YOLOv5x (0.988) was higher than the values for YOLOv5n (0.969), YOLOv5s (0.965), YOLOv5m (0.974), and YOLOv5l (0.983). The mAP_0.5:0.95 value for YOLOv5x (0.711) was also higher than the values for YOLOv5n (0.587), YOLOv5s (0.674), YOLOv5m (0.671), and YOLOv5l (0.698) in the testing set. The inference speed of YOLOv5n was 2.4 ms in the testing set, which was the fastest among the five submodels. CONCLUSION: The YOLOv5 algorithm can accurately and efficiently distinguish between APAs and NF-ACAs on CT images, especially YOLOv5x has the best identification performance.

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.

Differentiation between adrenocortical carcinoma and lipid-poor adrenal adenoma using a multiparametric MRI-based diagnostic algorithm.

PURPOSE: The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC). MATERIALS AND METHODS: Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard. RESULTS: Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 ± 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 ± 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87). CONCLUSION: A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC.

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.

Prognostic role of quantitative [18F]FDG PET/CT parameters in adrenocortical carcinoma.

PURPOSE: We aimed to evaluate the prognostic potential of baseline [18F]FDG PET/CT for overall survival (OS) in patients with adrenocortical carcinoma (ACC). METHODS: We performed a retrospective analysis of 67 treatment-naïve ACC patients with available [18F]FDG PET/CT at time of initial diagnosis. Pretherapeutic PETs of primary tumors were manually segmented and quantitative parameters (maximum/mean/peak standardized uptake value (SUVmax/mean/peak), metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG, defined as TV*SUVmean) were derived. Based on a visual read, absence (M0) or presence of metastatic disease (M1) were evaluated. Kaplan-Meier and Cox regression analyses were used to determine the prognostic value of the above mentioned markers on overall survival adjusted for established prognostic markers. RESULTS: 24/67 patients (36%) presented with M0 based on PET/CT, while the remaining 43/67 (64%) had M1-status. 32/67 patients died during follow-up and median OS was 48 months. In 12% of patients FDG-PET detected additional metastatic lesion not clearly visible by CT only. In univariable analysis, all quantitatively derived PET parameters failed to reach significance (P ≥ 0.1), and only PET/CT-based M1-status and Ki-67 were associated with increased mortality (M1: HR 13.89, 95% CI 4.15-86.32, P < 0.001; Ki-67 HR 1.29, 95% CI 1.16-1.42; P < 0.0001). Using multivariable Cox regression analyses, M1-status (HR 9.69, 95% CI 2.82-60.99) and Ki-67 index (HR 1.29, 95% CI 1.13-1.04; P < 0.05) remained significant associated with OS. CONCLUSION: In treatment-naïve ACC patients, the quantitative PET parameter failed to predict OS, but presence of metastases detected by [18F]FDG PET/CT and Ki-67 index were independently associated with shorter OS. Therefore, a simple visual PET-based read-out is of prognostic value at initial diagnosis, while time-consuming PET-based quantification can be omitted.

Contrast CT radiomic features add value to prediction of prognosis in adrenal cortical carcinoma.

OBJECTIVE: Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with poor prognosis due to high postoperative recurrence rates. The aim of this study is to develop a contrast CT radiomic feature-based prognosis prediction model for ACC and evaluate its performance by comparison with ENSAT staging system and S-GRAS score. METHODS: Included in this study were 39 ACC patients, from which we extracted 1411 radiomic features. Using cross-validated least absolute shrinkage and selection operator regression (cv-LASSO regression), we generated a radiomic index. Additionally, we further validated the radiomic index using both univariate and multivariate Cox regression analyses. We constructed a radiomic nomogram that incorporated the radiomic signature and compared it with ENSAT stage and S-GRAS score in terms of calibration, discrimination and clinical usefulnes. RESULTS: In this study, the average progression free survival (PFS) of 39 patients was 20.4 (IQR 9.1-60.1) months and the average overall survival (OS) was 57.8 (IQR 32.4-NA). The generated radiomic features were significantly associated with PFS, OS, independent of clinical-pathologic risk factors (HR 0.16, 95%CI 0.02-0.99, p = 0.05; HR 0.20, 95%CI 0.04-1.07, p = 0.06, respectively). The radiomic index, ENSAT stage, resection status, and Ki67% index incorporated nomogram exhibited better performance for both PFS and OS prediction as compared with the S-GRAS and ENSAT nomogram (C-index: 0.75 vs. C-index: 0.68, p = 0.030 and 0.67, p = 0.025; C-index: 0.78 vs. C-index: 0.72, p = 0.003 and 0.73, p = 0.006). Calibration curve analysis showed that the radiomics-based model performs best in predicting the two-year PFS and the three-year OS. Decision curve analysis demonstrated that the radiomic index nomogram outperformed the S-GRAS and ENSAT nomogram in predicting the two-year PFS and the three-year OS. CONCLUSION: The contrast CT radiomic-based nomogram performed better than S-GRAS or ENSAT in predicting PFS and OS in ACC patients.

Right Adrenocortical Carcinoma Coexisting With Left Adrenal Sarcomatoid Carcinoma on FDG PET/CT.

ABSTRACT: Bilateral adrenal glands synchronously involved by different types of pathologies are uncommon. An 80-year-old man underwent FDG PET/CT to evaluate bilateral adrenal masses, which were initially discovered by ultrasonography and confirmed by MRI. The images demonstrated elevated FDG activity in both lesions, which were subsequently diagnosed as concurrent right adrenocortical carcinoma and left adrenal sarcomatoid carcinoma respectively by histopathological examination.

[A Case of Aldosterone-Producing Adrenocortical Carcinoma].

A 59-year-old man presented with high blood pressure, hypokalemia and muscle weakness. His aldosterone/renin ratio was high and plasma renin activity was low. Computed tomography (CT) showed a heterogeneous left adrenal mass. Primary aldosteronism was diagnosed and laparoscopic left adrenalectomy was performed. The pathological diagnosis was adrenocortical carcinoma with positive surgical margins. He underwent radiotherapy and mitotane as adjuvant therapies. Subsequently, CT revealed multiple metastases, in the liver and retroperitoneum. After six courses of EDP (a combination of etoposide, doxorubicin and cisplatin), CT showed widespread metastases in the retroperitoneum and he chose to receive the best supportive care. Aldosterone-producing adrenocortical carcinoma is exceedingly rare. To the best of our knowledge, only67 cases have been reported. Complete resection is needed to improve prognosis and this was not achieved in our case. We therefore recommend careful selection of the operative procedure.