Predicting malignancy in patients with adrenal tumors using 18 F-FDG-PET/CT SUVmax

False-Positive Metastatic Disease Due to Bilateral Ectopic Adrenal Tissue

ABSTRACT

A 60-year-old man was referred for staging of prostate cancer. Initial CT scan demonstrated soft tissue lesions in bilateral renal hila, which demonstrated mild 68 Ga-PSMA avidity and moderate FDG avidity on sequential PET scans suspicious for malignancy. Biopsy confirmed adrenal cortical tissue. This case highlights an exceedingly rare occurrence of ectopic adrenocortical tissue in both renal hila.

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.

The Diagnostic Value of 18F-FDG PET/CT Scan in Characterizing Adrenal Tumors

CONTEXT

Imaging plays an important role in the characterization of adrenal tumors, but findings might be inconclusive. The clinical question is whether 18F fluodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is of diagnostic value in this setting.

OBJECTIVE

This meta-analysis was aimed at the diagnostic value of 18F-FDG PET/CT in differentiating benign from malignant adrenal tumors discovered either as adrenal incidentaloma or during staging or follow-up of oncologic patients.

DATA SOURCES

PubMed, EMBASE, Web of Science, and Cochrane Library were searched to select articles between 2000 and 2021.

STUDY SELECTION

We included studies describing the diagnostic value of 18F-FDG PET/CT in adult patients with an adrenal tumor. Exclusion criteria were 10 or fewer participants, insufficient data on histopathology, clinical follow-up, or PET results. After screening of title and abstract by 2 independent reviewers, 79 studies were retrieved, of which 17 studies met the selection criteria.

DATA EXTRACTION

Data extraction using a protocol and quality assessment according to QUADAS-2 was performed independently by at least 2 authors.

DATA SYNTHESIS

A bivariate random-effects model was applied using R (version 3.6.2.). Pooled sensitivity and specificity of 18F-FDG PET/CT for identifying malignant adrenal tumors was 87.3% (95% CI, 82.5%-90.9%) and 84.7% (95% CI, 79.3%-88.9%), respectively. The pooled diagnostic odds ratio was 9.20 (95% CI, 5.27-16.08; P < .01). Major sources of heterogeneity (I2, 57.1% [95% CI, 27.5%-74.6%]) were in population characteristics, reference standard, and interpretation criteria of imaging results.

CONCLUSIONS

18F-FDG PET/CT had good diagnostic accuracy for characterization of adrenal tumors. The literature, however, is limited, in particular regarding adrenal incidentalomas. Large prospective studies in well-defined patient populations with application of validated cutoff values are needed.

Using CT radiomic features based on machine learning models to subtype adrenal adenoma

BACKGROUND

Functioning and non-functioning adrenocortical adenoma are two subtypes of benign adrenal adenoma, and their differential diagnosis is crucial. Current diagnostic procedures use an invasive method, adrenal venous sampling, for endocrinologic assessment.

METHODS

This study proposes establishing an accurate differential model for subtyping adrenal adenoma using computed tomography (CT) radiomic features and machine learning (ML) methods. Dataset 1 (289 patients with adrenal adenoma) was collected to develop the models, and Dataset 2 (54 patients) was utilized for external validation. Cuboids containing the lesion were cropped from the non-contrast, arterial, and venous phase CT images, and 1,967 features were extracted from each cuboid. Ten discriminative features were selected from each phase or the combined phases. Random forest, support vector machine, logistic regression (LR), Gradient Boosting Machine, and eXtreme Gradient Boosting were used to establish prediction models.

RESULTS

The highest accuracies were 72.7%, 72.7%, and 76.1% in the arterial, venous, and non-contrast phases, respectively, when using radiomic features alone with the ML classifier of LR. When features from the three CT phases were combined, the accuracy of LR reached 83.0%. After adding clinical information, the area under the receiver operating characteristic curve increased for all the machine learning methods except for LR. In Dataset 2, the accuracy of LR was the highest, reaching 77.8%.

CONCLUSION

The radiomic features of the lesion in three-phase CT images can potentially suggest the functioning or non-functioning nature of adrenal adenoma. The resulting radiomic models can be a non-invasive, low-cost, and rapid method of minimizing unnecessary testing in asymptomatic patients with incidentally discovered adrenal adenoma.

The F-18 FDG PET/CT evaluation of the metastatic adrenal lesions of the non-lung cancer tumors compared with pathology results

Background

The aim of this study was to evaluate the role of F-18 FDG PET/CT imaging in the diagnosis of primary and metastatic adrenal tumors that originate from non-lung cancer primary tumors.

Results

F-18 FDG PET/CT images of patients (8 male and 6 female; mean: 55.36 ± 16.2 years old) who attended with the diagnosis of primary or adrenal metastatic lesions other than lung cancer metastasis were evaluated in a retrospective manner. The diameter of the adrenal lesions was mean: 23.93 ± 36.6 mm with SUVmax levels of mean: 9.98 ± 7.8. The primary site of 2/3 of the patients with unknown primary was the adrenal gland, and in one of the patients primary tumor remained unknown during follow-up.

Conclusions

According to the results of this study, F-18 FDG PET/CT has high diagnostic performance in the diagnosis of the adrenal gland primary and metastatic lesions, which originate from non-lung cancer tumors. Further studies in the larger series are warranted.

PET-CT in Clinical Adult Oncology: III. Gastrointestinal Malignancies

Simple Summary

Positron emission tomography (PET), typically combined with computed tomography (CT), has become a critical advanced imaging technique in oncology. With PET-CT, a radioactive molecule (radiotracer) is injected in the bloodstream and localizes to sites of tumor because of specific cellular features of the tumor that accumulate the targeting radiotracer. The CT scan, performed at the same time, provides information to facilitate the characterization of radioactivity from deep or dense structures, and to provide detailed anatomic information. PET-CT has a variety of applications in oncology, including staging, therapeutic response assessment, restaging and surveillance. This series of six review articles provides an overview of the value, applications, and imaging interpretive strategies of PET-CT in the more common adult malignancies. The third report in this series provides a review of PET-CT imaging in gastrointestinal malignancies.

Abstract

PET-CT is an advanced imaging modality with many oncologic applications, including staging, assessment of response to therapy, restaging and longitudinal surveillance for recurrence. The goal of this series of six review articles is to provide practical information to providers and imaging professionals regarding the best use of PET-CT for specific oncologic indications, and the potential pitfalls and nuances that characterize these applications. In the third of these review articles, key tumor-specific clinical information and representative PET-CT images are provided to outline the role that PET-CT plays in the management of patients with gastrointestinal malignancies. The focus is on the use of ¹⁸F fluorodeoxyglucose (FDG), rather than on research radiopharmaceuticals under development. Many different types of gastrointestinal tumors exist, both pediatric and adult. A discussion of the role of FDG PET-CT for all of these is beyond the scope of this review. Rather, this article focuses on the most common adult gastrointestinal malignancies that may be encountered in clinical practice. The information provided here will provide information outlining the appropriate role of PET-CT in the clinical management of patients with gastrointestinal malignancies for healthcare professionals caring for adult cancer patients. It also addresses the nuances and provides interpretive guidance related to PET-CT for imaging providers, including radiologists, nuclear medicine physicians and their trainees.

Oncocytic Adrenocortical Carcinoma With Low 18F-FDG Uptake and the Absence of Glucose Transporter 1 Expression

Adrenocortical carcinoma (ACC) is a rare tumor, and some histological variants (oncocytic, myxoid, and sarcomatoid ACCs) have been reported in addition to the conventional ACC. Among these subtypes, oncocytic ACC is histologically characterized by the presence of abundant eosinophilic granular cytoplasm in the carcinoma cells owing to the accumulation of mitochondria, which generally yields high ¹⁸F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET). Herein, we report the case of a 21-year-old woman with oncocytic ACC with low FDG uptake on PET scan. Her circulating levels of androgens were high, and androgen-synthesis enzymes were detected in carcinoma cells. The patient also had hypocholesterolemia. However, glucose transporter 1 (GLUT1) was not detected in the tumor, which was considered to account for the low FDG uptake by the tumor. To the best of our knowledge, this is the first case of low FDG uptake by oncocytic ACC without GLUT1 expression. Additionally, since hypocholesterolemia was reported in 3 previous reports of androgen-producing tumors, a possible correlation between androgenicity in adrenal tumors and the development of hypocholesterolemia could be postulated; however, further investigations are needed for clarification. This case highlights important information regarding the diversity of ACC and its impact on hypocholesterolemia.