A retrospective analysis of the clinical characteristics of 207 hospitalized children with adrenal masses

Clinical features and treatment options for pediatric adrenal incidentalomas: a retrospective single center study

BACKGROUND

The aim of this study was to investigate the clinical features and treatment options for pediatric adrenal incidentalomas(AIs) to guide the diagnosis and treatment of these tumors.

METHODS

The clinical data of AI patients admitted to our hospital between December 2016 and December 2022 were collected and retrospectively analyzed. All patients were divided into neonatal and nonneonatal groups according to their age at the time of the initial consultation.

RESULTS

In the neonatal group, 13 patients were observed and followed up, and the masses completely disappeared in 8 patients and were significantly reduced in size in 5 patients compared with the previous findings. Four patients ultimately underwent surgery, and the postoperative pathological diagnosis was neuroblastoma in three patients and teratoma in one patient. In the nonneonatal group, there were 18 cases of benign tumors, including 9 cases of ganglioneuroma, 2 cases of adrenocortical adenoma, 2 cases of adrenal cyst, 2 cases of teratoma, 1 case of pheochromocytoma, 1 case of nerve sheath tumor, and 1 case of adrenal hemorrhage; and 20 cases of malignant tumors, including 10 cases of neuroblastoma, 9 cases of ganglioneuroblastoma, and 1 case of adrenocortical carcinoma.

CONCLUSIONS

Neuroblastoma is the most common type of nonneonatal AI, and detailed laboratory investigations and imaging studies are recommended for aggressive evaluation and treatment in this population. The rate of spontaneous regression of AI is high in neonates, and close observation is feasible if the tumor is small, confined to the adrenal gland and has no distant metastasis.

Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images

Background

The aim of the study was to employ a combination of radiomic indicators based on computed tomography (CT) imaging and machine learning (ML), along with deep learning (DL), to differentiate between adrenal hematoma and adrenal neuroblastoma in neonates.

Methods

A total of 76 neonates were included in this retrospective study (40 with neuroblastomas and 36 with adrenal hematomas) who underwent CT and divided into a training group (n = 38) and a testing group (n = 38). The regions of interest (ROIs) were segmented by two radiologists to extract radiomics features using Pyradiomics package. ML classifications were done using support vector machine (SVM), AdaBoost, Extra Trees, gradient boosting, multi-layer perceptron (MLP), and random forest (RF). EfficientNets was employed and classified, based on radiometrics. The area under curve (AUC) of the receiver operating characteristic (ROC) was calculated to assess the performance of each model.

Results

Among all features, the least absolute shrinkage and selection operator (LASSO) logistic regression selected nine features. These radiomics features were used to construct radiomics model. In the training cohort, the AUCs of SVM, MLP and Extra Trees models were 0.967, 0.969 and 1.000, respectively. The corresponding AUCs of the test cohort were 0.985, 0.971 and 0.958, respectively. In the classification task, the AUC of the DL framework was 0.987.

Conclusion

ML decision classifiers and DL framework constructed from CT-based radiomics features offered a non-invasive method to differentiate neonatal adrenal hematoma from neuroblastoma and performed better than the clinical experts.