Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Abstract

Background: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs. Methods: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance. Results: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05). Conclusions: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study

“…Some studies have shown that the performance of models using multiphase images is higher than that of those using single-phase images ( 37 - 39 ). In this experiment, we used AIMS to build radiomics models with features extracted from multiphase images for ccRCC Fuhrman grading.…”

Quantitative energy spectrum CT in differential diagnosis of aldosterone-producing adenoma and cortisol-producing adenoma

“…Subclinical CPA is the most common functional adrenal tumors ( 7 ), while as many as 30% of APA patients manifest the similar clinical symptoms as CPA because of hypokalemia ( 8 , 9 ); which makes it particularly difficult to distinguish between the two from a clinical perspective. Furthermore, 10–20% of ACAs occur bilaterally or with multiple lesions ( 3 , 10 ), functional diagnosis is challenging when the responsible lesion needs to be accurately located ( 3 , 8 , 11 ). Percutaneous cannulation of the adrenal vein is the “gold standard” for APA diagnosis, but the difficulty, cost and invasiveness of the procedure limit its widespread use in clinical practice ( 12 ).…”

“…In traditional imaging examinations, the evaluation is mainly based on the size, density, border and enhancement degree of the tumor, helping radiologists and clinicians distinguish whether adrenal masses are adenomas or non-adenomas. Further differentiating the function of ACA as APA or CPA is not available ( 3 ).…”

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

“…With the advancement of imaging techniques and the popularization of abdomen CT scanning for physical examination, the detection rate of adrenal incidentalomas is increasing [ 2 – 4 ]. Adrenal adenomas account for the majority of adrenal incidentalomas (41–52%), followed by metastatic tumors (19%), myelolipoma (9%), and pheochromocytoma (8%) [ 5 ]. Although up to 80% of these tumors are benign, sPHEO can cause life-threatening hypertension during surgery, which can lead to serious cardiovascular events [ 6 , 7 ].…”