1.1 General information
The research was approved by the ethics committee of the affiliated hospital of Southwest Medical University (KY2020063). All patient information was completely anonymous before analysis. According to Helsinki Declaration (2000 edition), this study is a retrospective study, the institutional review committee (IRB) of our hospital waived informed consent.
The clinical, pathology and imaging data were collected for 75 patients with ACA confirmed by surgery and pathology at our hospital from November 2018 to November 2020. There were 47 functional adenomas and 28 nonfunctional adenomas, including 36 males and 39 females.
The inclusion criteria are as follows: (1) adrenal cortical adenoma confirmed by clinical diagnosis and postoperative pathological examination; (2) multi-slice spiral CT used for imaging with complete imaging data and clear images; and (3) complete clinical data reviewed by an endocrinologist. The exclusion criteria were as follows: (1) partial clinical and imaging data; (2) a lesion diameter ≥ 4 cm; and (3) image is unable to be processed by imaging software.
1.2.1 Classification standard
This study used clinical symptoms and endocrine function evaluation results to distinguish functional ACA from non-functional ACA. Non-functional ACA patients were identified by extensive diagnosis. These standards include, but are not limited to: (1) no full moon face, buffalo back, hair, purple lines and ecchymosis, no sudden severe hypertension, paroxysmal flaccid paralysis, nocturia, hypokalemia and other symptoms. (2) ACTH-F rhythm is normal, 1mg DST cortisol level at midnight is less than 50nmol/L, and 24h urinary free cortisol (24h UFC) is normal, urinary catecholamine, norepinephrine (ne), epinephrine (E) and dopamine (DA) are within normal limits for at least two 24 hour intervals; spironolactone/mineralocorticoid antagonist discontinued for at least 4 weeks; diuretics, angiotensin-converting enzyme inhibitors, angiotensin II receptor antagonists and beta receptor blockers discontinued for at least 2 weeks; urine aldosterone is within normal limits for 24 hours; and the ratio of aldosterone (pmol/L)/ renin activity in resting plasma [µg/(L.h)] (ARR) < 30. 
1.2.2 Grouping method
According to the clinical symptoms and endocrine evaluation results, patients were divided into functional (F) and non-functional (N) groups. The image data for each group were extracted and analyzed according to four categories: plain scan, artery enhancement, vein enhancement and delayed scan.
1.2.3 Image segmentation and feature extraction
We used MaZda image analysis software (Version 4.6, http//www.eletel.p.londz.pl/program/Mazda/) to segment images and extract features. A radiologist with experience using this software sketched a region of interest (ROI) of the collected image data. All patients' image data was stored in the radiation work system.
The texture analysis process began with the radiologist selecting the image of the largest slice of the lesion (Figure 1a-d), saving it in a bitmap (BMP) format, importing the image into the MaZda software and sketching the ROI. We ensured that the distance between the sketching line and the edge of the lesion was approximately 1-2 mm to avoid including free fat outside the lesion and reduce the error caused by edge samples (Figure 2). After sketching the ROI, the MaZda software automatically analyzed the texture feature parameters of the selected area. Lastly, between 295 and 334 texture features were extracted from each image (Figure 3).
To evaluate the reproducibility of this approach, we randomly selected 20 samples from which the first radiologist and a second radiologist also with software experience simultaneously completed the same process described above. Each worked independently and was blinded to the order and grouping of sample data. By comparing the texture features extracted by two radiologists from the ROI description, the consistency between observers was evaluated. An intraclass correlation coefficient (ICC) was used to evaluate the consistency within and between observers. An ICC>0.75 indicates good reliability between observers.
1.2.4 Data feature processing
Using principal component analysis (PCA) dimensionality reduction, 300 effective texture features were selected from all the extracted texture features. First, 300 features of the four categories (plain scan, artery enhancement, vein enhancement and delayed scan) were normalized to eliminate the adverse image effects caused by outlier data. For this study, we defined normalization as (each data-minimum value)/ (maximum value-minimum value). A value of 0 was assigned for negative samples (non-functionality) and a value of 1 was assigned for positive samples (functionality). For the plain scan period, the number of negative samples (19 cases), was approximately equal to the number of positive samples (22 cases). Thus, there was no need to balance samples. However, for the arterial phase, negative samples (28 cases) and positive samples (43 cases) were imbalanced. To reduce the imbalance of samples, we applied the Synthetic Minority Over-sampling Technique (SMOTE), resulting in 84 negative samples and 86 positive samples.
1.2.5 Model establishment and testing
Based on the SVM, a single group of the four categories (models 1-4, which represent plain scan, artery enhancement, vein enhancement and delayed scan, respectively) was created by combining the texture features to predict the performance of four omics models. We randomly selected 80% of the total samples as the training set and designated the remaining 20% as the test set. We used this training set to train the SVM model, tested with the trained model and repeated the testing 100 times. We calculated the AUC by drawing the ROC curve of the 100 test results.
The discrimination of training model performance mainly depends on the AUC of the ROC. We also calculated the average accuracy, maximum accuracy, minimum accuracy, average negative predictive value (NPV), average positive predictive value (PPV), average sensitivity and average specificity of the 100 tests. Thus, the performance of the established model was evaluated according to all indicators of the validation sample.