Volumetric evaluation of CT images of adrenal glands in primary aldosteronism

Clinical value of 68Ga-pentixafor PET/CT in patients with primary aldosteronism and bilateral lesions: preliminary results of a single-centre study

BACKGROUND

Subtype diagnosis of primary aldosteronism (PA) is used to determine treatment, and the potential utility of 68Ga-pentixafor PET/CT for investigation of PA has long been recognized. The study aimed to evaluate the clinical value of 68Ga-pentixafor PET/CT in the diagnosis and prognosis of patients with bilateral lesions identified by CT.

METHODS

In total, 25 patients with PA and bilateral lesions on CT were retrospectively evaluated. All patients underwent 68Ga-Pentixafor PET/CT and adrenal vein sampling. The analysis focused on establishing the relationship between bilateral adrenal lesions SUVmax and the ratio of bilateral adrenal lesions SUVmax (CON) and clinical diagnosis, treatment outcomes, and KCNJ5 gene status.

RESULTS

The concordance rate between 68Ga-Pentixafor PET/CT and adrenal venous sampling was 65.2% (15/23). The lateralization results of 68Ga-pentixafor PET/CT supported the clinical decisions of 20 patients with PA, 90% of whom showed effectiveness in treatment. The SUVmax on the dominant side of the surgically treated patients was higher than that of patients treated with drugs. The SUVmax of the KCNJ5 mutant group was higher than that of the KCNJ5 wild group, and 68Ga-Pentixafor uptake was correlated with KCNJ5 gene status.

CONCLUSIONS

68Ga-Pentixafor PET/CT proves beneficial for patients with PA with bilateral lesions on CT. The treatment is generally effective based on the results of PET lateralization. Simultaneously, a certain relationship exists between 68Ga-Pentixafor PET/CT and KCNJ5 gene status, warranting further analysis.

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.

Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation

OBJECTIVE

This study aims to evaluate the morphometrics of normal adrenal glands in adult patients semiautomatically using a deep learning-based segmentation model.

MATERIALS AND METHODS

A total of 520 abdominal CT image series with normal findings, from January 1, 2016, to March 14, 2019, were retrospectively collected for the training of the adrenal segmentation model. Then, 1043 portal venous phase image series of inpatient contrast-enhanced abdominal CT examinations with normal adrenal glands were included for analysis and grouped by every 10-year gap. A 3D U-Net-based segmentation model was used to predict bilateral adrenal labels followed by manual modification of labels as appropriate. Quantitative parameters (volume, CT value, and diameters) of the bilateral adrenal glands were then analyzed.

RESULTS

In the study cohort aged 18-77 years old (554 males and 489 females), the left adrenal gland was significantly larger than the right adrenal gland [all patients, 2867.79 (2317.11-3499.89) mm³ vs. 2452.84 (1983.50-2935.18) mm³, P < 0.001]. Male patients showed a greater volume of bilateral adrenal glands than females in all age groups (all patients, left: 3237.83 ± 930.21 mm³ vs. 2646.49 ± 766.42 mm³, P < 0.001; right: 2731.69 ± 789.19 mm³ vs. 2266.18 ± 632.97 mm³, P = 0.001). Bilateral adrenal volume in male patients showed an increasing then decreasing trend as age increased that peaked at 38-47 years old (left: 3416.01 ± 886.21 mm³, right: 2855.04 ± 774.57 mm³).

CONCLUSIONS

The semiautomated measurement revealed that the adrenal volume differs as age increases. Male patients aged 38-47 years old have a peaked adrenal volume.

The performance of left/right adrenal volume ratio and volume difference in predicting unilateral primary aldosteronism

PURPOSE

The role of computed tomography (CT) in the diagnosis of primary aldosteronism (PA) warrants attention, since the success application of adrenal venous sampling (AVS) remains limited. We aimed to investigate the value of CT-based volumetric indicators, including left-versus-right-adrenal-volume ratio (L/Rv) and left-subtract-right-adrenal-volume difference (L - Rv), in the diagnosis of unilateral primary aldosteronism (UPA).

METHODS

A retrospective case-control study included 153 patients with PA and 1272 controls. AVS was used to classify patients into bilateral disease, left-sided disease, and right-sided disease groups.

RESULTS

Adrenal gland volume on both sides of PA patients was significantly larger than controls. The optimal cutoff values of L/Rv and L - Rv were 1.417 [area under the curve (AUC) 0.864] and 1.185 (AUC 0.827), respectively, for the diagnosis of left-sided PA, and 1.030 (AUC 0.767) and 0.220 (AUC 0.769), respectively, for the diagnosis of right-sided PA. The mean AUC for subsequent cross-validation ranged from 0.77 ± 0.03 to 0.86 ± 0.02. Based on the optimal cutoff values, the combination of L/Rv and L - Rv detected 69.6% of patients with left-sided PA and 74.3% of patients with right-sided PA, with a specificity of 93.5% and 89.0%, respectively. For a better clinical application, we reported the sub-optimal cutoffs corresponding to a specificity of 95%. A L/Rv higher than 1.431 and a L - Rv higher than 3.185 as sub-optimal cutoff values was detected in 26.1% of patients with left-sided PA (specificity: 97.2%). A L/Rv smaller than 0.892 and a L - Rv smaller than -0.640 could detect 48.6% of patients with right-sided PA (specificity: 97.5%).

CONCLUSIONS

CT-based L/Rv and L - Rv performed well in predicting UPA. The combination of L/Rv and L - Rv may serve as a potential indicator for guiding surgical decision making in centers without AVS programs.

Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia

OBJECTIVES

To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia.

METHODS

This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification.

RESULTS

The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000.

CONCLUSION

The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia.

KEY POINTS

• A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

Clinical, pathophysiologic, genetic and therapeutic progress in Primary Bilateral Macronodular Adrenal Hyperplasia

Patients with primary bilateral macronodular adrenal hyperplasia (PBMAH) usually present bilateral benign adrenocortical macronodules at imaging and variable levels of cortisol excess. PBMAH is a rare cause of primary overt Cushing's syndrome, but may represent up to one third of bilateral adrenal incidentalomas with evidence of cortisol excess. The increased steroidogenesis in PBMAH is often regulated by various G-protein coupled receptors aberrantly expressed in PBMAH tissues; some receptor ligands are ectopically produced in PBMAH tissues creating aberrant autocrine/paracrine regulation of steroidogenesis. The bilateral nature of PBMAH and familial aggregation, led to the identification of germline heterozygous inactivating mutations of the ARMC5 gene, in 20-25% of the apparent sporadic cases and more frequently in familial cases; ARMC5 mutations/pathogenic variants can be associated with meningiomas. More recently, combined germline mutations/pathogenic variants and somatic events inactivating the KDM1A gene were specifically identified in patients affected by GIP-dependent PBMAH. Functional studies demonstrated that inactivation of KDM1A leads to GIP-receptor (GIPR) overexpression and over or down-regulation of other GPCRs. Genetic analysis is now available for early detection of family members of index cases with PBMAH carrying identified germline pathogenic variants. Detailed biochemical, imaging, and co-morbidities assessment of the nature and severity of PBMAH is essential for its management. Treatment is reserved for patients with overt or mild cortisol/aldosterone or other steroid excesses taking in account co-morbidities. It previously relied on bilateral adrenalectomy; however recent studies tend to favor unilateral adrenalectomy, or less frequently, medical treatment with cortisol synthesis inhibitors or specific blockers of aberrant GPCR.