Diagnostic Value of Unenhanced CT Attenuation and CT Histogram Analysis in Differential Diagnosis of Adrenal Tumors

Evaluation of Adrenal Metastases in Prostate Cancer Patients with [68GA]GA-PSMA PET/CT Imaging

OBJECTIVES

This study aimed to evaluate the imaging and clinical characteristics of adrenal metastases detected by [68Ga]Ga-PSMA PET/CT in prostate cancer patients, with a focus on diagnostic accuracy and prognostic implications. Specifically, we examined the correlation between adrenal lesion characteristics and prognostic markers, such as prostate-specific antigen (PSA) levels and Gleason scores. This study also assessed the diagnostic performance of PSA, standardized uptake value maximum (SUVmax), and Hounsfield Unit (HU) values in differentiating adrenal metastases from benign adrenal adenomas.

MATERIALS AND METHODS

This retrospective study included 44 prostate cancer patients with adrenal lesions identified using [68Ga]Ga-PSMA PET/CT between January 2020 and October 2024. The patients were categorized into two groups: benign adrenal adenomas (n = 16) and adrenal metastases (n = 28). The PET/CT imaging was performed using a 5-ring Discovery IQ PET/CT scanner with QClear reconstruction, following the injection of 2.5 MBq/kg [68Ga]Ga-PSMA ligand and a standardized uptake time of 60 min. The imaging parameters (SUVmax and HU values), clinical characteristics (PSA levels, Gleason scores, and presence of lymphadenopathy), and patient outcomes were analyzed. A ROC analysis was conducted to evaluate the diagnostic performance of these key parameters.

RESULTS

Patients with adrenal metastases had significantly higher PSA levels (mean: 45.6 ± 12.4 ng/mL vs. 18.3 ± 6.7 ng/mL; p < 0.01) and Gleason scores (median: 8 vs. 6; p < 0.01) than those with benign adenomas. SUVmax values were significantly elevated in metastatic lesions (mean: 12.8 ± 4.3 vs. 3.4 ± 1.2; p < 0.001), and HU values were also higher (mean: 45 ± 15 vs. 18 ± 10; p < 0.01). The ROC analysis revealed that SUVmax had the highest diagnostic accuracy (AUC: 0.87), followed by PSA (AUC: 0.85) and HU (AUC: 0.80). Disease progression was observed in 67.9% of metastatic cases versus 18.8% in the adenoma group (p < 0.001), and median overall survival was shorter in metastatic cases (24 months vs. 38 months; p < 0.01).

CONCLUSIONS

[68Ga]Ga-PSMA PET/CT is a valuable imaging modality for distinguishing adrenal metastases from benign adenomas in prostate cancer patients. The integration of PSA, SUVmax, and HU values into diagnostic workflows enhances diagnostic precision and improves clinical decision-making. Future research should focus on the prospective validation of these findings in larger cohorts and explore artificial intelligence-based approaches for automated lesion characterization.

The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.

Can MDCT Enhancement Patterns Be Helpful in Differentiating Secretory from Non-Functional Adrenal Adenoma?

Background and Objectives: Primary adrenal tumors (AT) are a heterogeneous group of neoplasms due to their functional heterogeneity, which results in the diverse clinical presentation of these tumors. The purpose of this study was to examine cross-sectional imaging characteristics using multi-detector computed tomography (MDCT) to provide insight into the lesion characterization and functional status of these tumors. The radionuclide imaging using Technetium-99m radiolabeled hydrazinonicotinylacid-d-phenylalanyl1-tyrosine3-octreotide (99mTc-HYNIC-TOC), was also used in the diagnostic evaluation of these tumors. Materials and Methods: This cross-sectional study included 50 patients with confirmed diagnoses of AT (21 hormone-secreting and 29 non-functional) at the University Clinical Center, Kragujevac, Serbia, during the 2019-2022 year period. The morphological and dynamic characteristics using MDCT were performed, using qualitative, semi-quantitative, and quantitative analysis. Absolute washout (APW) and relative washout (RPW) values were also calculated. A semi-quantitative analysis of all visual findings with 99mTc-HYNIC-TOC was performed to compare the tumor to non-tumor tracer uptake. Results: A statistically significant difference was found in the MDCT values in the native phase (p < 0.05), the venous phase (p < 0.05), and the delayed phase (p < 0.001) to detect the existence of adrenal tumors. Most of these functional adrenocortical lesions (n = 44) can be differentiated using the delayed phase (p < 0.05), absolute percentage washout (APW) (p < 0.05), and relative percentage washout (RPW) (p < 0.001). Furthermore, 99mTc-HYNIC-TOC could have a high diagnostic yield to detect adrenal tumor existence (p < 0.001). There is a positive correlation between radionuclide imaging scan and APW to detect all AT (p < 0.01) and adrenocortical adenomas as well (p < 0.01). Conclusions: The results can be very helpful in a diagnostic algorithm to quickly and precisely diagnose the expansive processes of the adrenal glands, as well as to learn about the advantages and limitations of the mentioned imaging modalities.

18F-FDG-PET/CT-based machine learning model evaluates indeterminate adrenal nodules in patients with extra-adrenal malignancies

BACKGROUND

To assess the value of an 18F-FDG-positron emission tomography/computed tomography (PET/CT)-based machine learning model for distinguishing between adrenal benign nodules (ABNs) and adrenal metastases (AMs) in patients with indeterminate adrenal nodules and extra-adrenal malignancies.

METHODS

A total of 303 patients who underwent 18F-FDG-PET/CT with indeterminate adrenal nodules and extra-adrenal malignancies from March 2015 to June 2021 were included in this retrospective study (training dataset (n = 182): AMs (n = 97), ABNs (n = 85); testing dataset (n = 121): AMs (n = 68), ABNs (n = 55)). The clinical and PET/CT imaging features of the two groups were analyzed. The predictive model and simplified scoring system for distinguishing between AMs and ABNs were built based on clinical and PET/CT risk factors using multivariable logistic regression in the training cohort. The performances of the predictive model and simplified scoring system in both the training and testing cohorts were evaluated by the areas under the receiver operating characteristic curves (AUCs) and calibration curves. The comparison of AUCs was evaluated by the DeLong test.

RESULTS

The predictive model included four risk factors: sex, the ratio of the maximum standardized uptake value (SUVmax) of adrenal lesions to the mean liver standardized uptake value, the value on unenhanced CT (CTU), and the clinical stage of extra-adrenal malignancies. The model achieved an AUC of 0.936 with a specificity, sensitivity and accuracy of 0.918, 0.835, and 0.874 in the training dataset, respectively, while it yielded an AUC of 0.931 with a specificity, sensitivity, and accuracy of 1.00, 0.735, and 0.851 in the testing dataset, respectively. The simplified scoring system had comparable diagnostic value to the predictive model in both the training (AUC 0.938, sensitivity: 0.825, specificity 0.953, accuracy 0.885; P = 0.5733) and testing (AUC 0.931, sensitivity 0.735, specificity 1.000, accuracy 0.851; P = 1.00) datasets.

CONCLUSIONS

Our study showed the potential ability of a machine learning model and a simplified scoring system based on clinical and 18F-FDG-PET/CT imaging features to predict AMs in patients with indeterminate adrenal nodules and extra-adrenal malignancies. The simplified scoring system is simple, convenient, and easy to popularize.

Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis

Objectives

(1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.

Methods

PubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman’s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks’ funnel plot.

Results

Twenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93–0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85–0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.

Conclusions

The methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .

Optimal and novel imaging of the adrenal glands

PURPOSE OF REVIEW

Adrenal imaging forms an important role in the workup of adrenal masses. The purpose of this review is to briefly review the traditional role of imaging in adrenal diseases and highlight the most recent research and new applications aimed to improve diagnostic accuracy.

RECENT FINDINGS

The current review will focus on new applications of computed tomography (CT), MRI and PET/CT imaging, addressing the implications of artificial intelligence and radiomics in progressing diagnostic accuracy.

SUMMARY

The new applications of adrenal imaging are improving diagnostic accuracy and expanding the role of imaging, particularly with novel PET radiotracers and the use of artificial intelligence.

Differential diagnostic value of plain CT scan in adrenal adenoma and non-adenoma: A two-center control study of mean attenuation value, minimum attenuation value, and CT histogram

OBJECTIVES

To investigate the value of mean attenuation value (AV_mean), minimum attenuation value (AV_min), and CT histogram (CTH) for the differential diagnosis of adrenal adenoma and non-adenoma in two medical centers.

METHODS

The plain CT data of 403 cases of adrenal adenoma and 141 cases of non-adenoma in center A were retrospectively analyzed, and compared with data of 86 cases of adenoma and 71 cases of non-adenoma in center B. All cases were confirmed by pathology or clinical follow-up. The diagnostic efficacy of AV_mean ≤ 10 Hounsfield units (HU), AV_min ≤ 0 HU, and CTH negative pixels ≥ 10% for adrenal adenoma, and AV_min and CTH for adenoma with AV_mean > 10Hu were compared between the two medical centers.

RESULTS

In medical centers A and B, the AUC of AV_mean for the differential diagnosis of adenoma and non-adenoma was 0.956 and 0.956, respectively, and the corresponding sensitivity, specificity, and accuracy were, 0.591 and 0.663, 1.000 and 1.000, 0.697, and 0.815, respectively, when the threshold was ≤ 10 HU. The AUC of AV_min was 0.941 and 0.958, respectively, and the corresponding sensitivity, specificity, and accuracy were 0.869 and 0.826, 0.986, and 0.972, 0.899, and 0.892, respectively, when the threshold was ≤ 0 HU. The AUC of CTH negative pixels was 0.948 and 0.952, respectively, and the corresponding sensitivity, specificity, and accuracy were 0.759 and 0.674, 1.000 and 1.000, 0.822, and 0.822, respectively, when the threshold was ≥ 10%. Among adenoma with AV_mean >10 HU, the best threshold of AV_min in center A and center B were -0.250HU and 2.375HU, and the corresponding AUC, sensitivity and specificity were 0.858 and 0.846, 0.691 and 0.586, 0.986 and 0.958; the best threshold of CTH in center A and center B were 0.895% and 0.775%, and the corresponding AUC, sensitivity and specificity were 0.873 and 0.822, 0.818 and 0.724, 0.837 and 0.915.

CONCLUSION

AV_mean, AV_min, and CTH are all important parameters for differentiating adrenal adenoma from non-adenoma. Even for adenomas with AV_mean > 10 HU, AV_min and CTH still had high diagnostic efficiency. The three parameters are complementary, assisting clinicians to develop personalized treatments.