This retrospective study was approved by the local institutional review board (No. 2023 (371)), with a waiver of informed consent. The inclusion and exclusion flowchart of the study cohort is shown in Fig. 1.

The inclusion and exclusion flowchart of patients and adrenal glands in the (A) training dataset, (B) external validation dataset 1, and (C) external validation dataset 2

The contrast-enhanced abdominal CT (including portal venous phase (PVP) (images of patients diagnosed with focal adrenal lesions upon comprehensive clinical diagnosis were included after the retrieval of the Hospital Information System. The lesions in these patients were not confirmed by pathological diagnosis. The PVP was selected to train the segmentation model of adrenal lesions. These images were acquired by GE Discovery CT750HD, GE LightSpeed VCT, GE Optima CT680 Expert, GE Revolution CT, Siemens SOMATOM Definition Flash, Siemens SOMATOM Force, Philips Brilliance 64, Philips Brilliance iCT 256, and Neusoft NeuViz Prime.

The inclusion criteria were as follows: (1) patients diagnosed with “adrenal metastasis” from January 1st, 2012, to September 30th, 2022, or “adrenal adenoma” from January 1st, 2021, to September 15th, 2022, upon discharge and (2) patients who underwent contrast-enhanced abdominal CT scans in our center. The exclusion criteria were as follows: (1) patients with a history of malignancy who were diagnosed with “adrenal adenoma”; (2) images could not be retrieved; (3) poor image quality; (4) non-standard scanning protocol; (5) insufficient scanning range; (6) patients included in external validation dataset 1; (7) postoperative or abnormal changes that made the adrenal gland unclear to be identified; and (8) patients with bilateral or exclusive unilateral ambiguous lesions. The image series of multiple reconstructions from the same PVP scan were kept. All these images were reviewed by 2 radiologists, and adrenals with non-focal, ambiguous, or undiscovered lesions were excluded. Finally, 1086 image series with 2138 adrenals were enrolled for training (213 metastases and 1047 adenomas, Fig. 1A).

Dataset 1: cases with pathologically confirmed focal adrenal lesions

Patients who underwent adrenalectomy or adrenal biopsy and were diagnosed with focal adrenal lesions pathologically were included, and the PVP image series of their most recent contrast-enhanced abdominal CT scans, as well as the corresponding radiology report before the procedure, were collected. These images were acquired by GE Discovery CT750HD, GE LightSpeed VCT, GE Optima CT680 Expert, GE Revolution CT, Siemens SOMATOM Definition Flash, Siemens SOMATOM Force, Philips Brilliance 64, Philips Brilliance iCT 256, and Neusoft NeuViz Prime.

The inclusion criteria were as follows: (1) patients diagnosed with focal adrenal lesions pathologically from January 1st, 2013, to October 11th, 2022, and (2) patients who underwent contrast-enhanced abdominal CT scans in our center. The exclusion criteria were those of the training set, plus: (1) multiple ipsilateral lesions with indistinguishable pathological results. Only one image series of the same patient was kept. Abnormal adrenal glands without pathological confirmation were excluded. After being reviewed by 2 radiologists, 959 image series (N = 944 from adrenalectomy, N = 15 from biopsy) with 1803 adrenals were enrolled for analysis (including 54 metastases and 647 adenomas, Fig. 1B).

Dataset 2: consecutive cases with a history of malignancy

The follow-up or baseline contrast-enhanced abdominal CT scans of patients who were diagnosed with malignancy that underwent or were in preparation for chemotherapy or radiotherapy were collected, and the PVP image series and the radiology report were consecutively included for analysis. These images were acquired by GE Discovery CT750HD, GE Optima CT680 Expert, GE Revolution CT, Siemens SOMATOM Definition Flash, Siemens SOMATOM Force, Philips Brilliance iCT 256, and Neusoft NeuViz Prime.

The inclusion criteria were as follows: (1) contrast-enhanced abdominal CT scans ordered by the Department of Medical Oncology or Department of Radiation Oncology and (2) exam date from May 1st, 2023, to June 30th, 2023. The exclusion criteria were those of the training set, plus: (1) patients without a history of malignancy according to the diagnosis recorded in the hospital information system and (2) patients included in the training dataset or the external validation dataset 1. After being reviewed by 2 radiologists, 479 image series with 934 adrenals were enrolled for analysis (Fig. 1C).

A contrast agent (iopromide 370 mgI/mL or iohexol 320 mgI/mL, Bayer AG) was injected through the venous system at a flow rate of 2.5 mL/s or 3 mL/s. The injection dose was 100 mL for patients weighing less than 75 kg, 120 mL for patients weighing 75–90 kg, and 150 mL for patients weighing more than 90 kg. All patients underwent a PVP scan (60–70 s after contrast injection). The tube voltage was 120 kVp, and auto-mAs were applied. Images were reconstructed with slice thickness of 1 mm or 1.25 mm and interval 1 mm.

The image series included in this study underwent autolabeling by the previously developed adrenal segmentation model [15] (Fig. 2). These bilateral adrenal labels were checked by a junior radiologist and a senior radiologist together, and unsatisfied labels were modified manually. Note that the adrenal labels of those after adrenalectomy were left empty. All CT images were reviewed by a junior radiologist and a senior radiologist together, and the normal/abnormal results were recorded after discussion. If no consensus could be reached, or they both could not determine whether any masses were on the adrenal gland, the result was given by a third senior radiologist (normal, abnormal, or ambiguous). If the third senior radiologist still did not have enough confidence to determine the lesion-containing status of the adrenal gland, it was marked as an “ambiguous gland” and excluded. The above procedure was used for all datasets in this study, including the training dataset and external validation datasets 1 and 2. In the training set or external validation dataset 1, patients with a unilateral ambiguous adrenal gland and another normal adrenal gland were excluded. Abnormal adrenal glands without pathological confirmation in external validation dataset 1 were not included for analysis. Then, focal adrenal lesions in these datasets were manually annotated within the adrenal label by one junior radiologist and checked by a senior radiologist if a focal lesion was discovered in the region of the adrenal gland.

Overview of the workflow of the present study. CT images underwent segmentation of adrenal glands and subsequent segmentation of adrenal lesions. *The segmentation model of adrenal glands is previously developed [15]

In the training dataset, the image series were randomly allocated into training set (N = 870), validation set (N = 108), and test set (N = 108) to train the segmentation model of adrenal lesions.

This study was based on a 3D V-Net architecture [14] equipped with Nvidia Tesla P100 16 G (Nvidia Corporation, Santa Clara, CA) GPU and PyTorch v1.7.1 + cu110 (https://pytorch.org/). The model inputs segmented adrenal glands from PVP CT images and outputs focal adrenal lesions. The output of a normal adrenal gland will be an empty label. Image preprocessing included window adjustment (center 30 HU, width 300 HU), resizing to 128px × 192px × 256px, and image augmentation by rotating, sheering, noise injection, denoising, etc. The training parameters were batch size 6, learning rate 0.0001, and epoch 400.

The 3D V-Net-based segmentation model for adrenal lesions was used to predict adrenal gland abnormalities. Adrenals with segmented regions that have overlapping voxels with their actual lesions were considered abnormal. Image series were grouped according to the diameter of the containing largest lesion, i.e., the contralateral adrenal glands (if not excluded) were included in the corresponding group as well. Detection performance was analyzed in each group.

Statistical analyses were performed using Python 3.7.6 with Scipy 1.4.1, Statsmodels 0.11.0, and Sklearn 0.22.1, unless otherwise specified. Dice similarity coefficient (DSC), Hausdorff distance (HD), and volumetric similarity (VS) were used to evaluate the segmentation performance. The reference standard was set as the annotation made by radiologists. Quantitative parameters of manually annotated adrenal gland and adrenal lesions were calculated programmatically. All analyses were two-sided, and the statistical significance was set at p < 0.05. Data normality was determined by the Kolmogorov‒Smirnov test. Continuous variables in univariate analysis were compared using Student’s t-test and the Mann–Whitney U-test, as appropriate. The reference standard for different classification methods (i.e., the model and the radiology report) was the result of image review by radiologists, and the McNemar test was used for comparisons between these classification methods.