Patients

Inclusion criteria were: (1) contrast-enhanced computed tomography (CECT) images of the patient’s venous phase were available; (2) patients underwent ALN dissection or SLNB; (3) available clinical data. Exclusion criteria were: (1) ALN diameter < 0.5 cm, which results in difficulties in delineating ROI in CECT images; (2) images of lymph nodes were ambiguous; (3) preoperative therapy (resection biopsy, neoadjuvant radiotherapy, or chemotherapy).

This study included 193 consecutive patients who had non-specific invasive breast cancer between February 2021 and February 2022. All patients were randomly divided into a training set (n ═ 135) and a validation set (n ═ 58), with a ratio of approximately 7:3. We obtained the patients’ baseline clinicopathological data from the medical records, and the radiologist made a comprehensive assessment based on the physical examination and CECT images, and the assessment results were cN0 (non-ALNM) and cN+ (ALNM). cN0 was defined as no regional lymph node metastases detected on CECT, whereas cN+ was defined as metastases to mobile ipsilateral level II ALNs, which depended on the radiologist’s clinical experience. The primary outcome of this study was the pathological node stage (pNx), which was determined by the results of SLNB or ALN dissection procedures.

Contrast-enhanced CT image acquisition

All patients received preoperative contrast-enhanced chest CT examination (Philips Medical Systems, the Netherlands). The scanning method was as follows: a contrast agent (Iohexol, 320 mg/mL) was injected into the patient’s median cubital vein using a two-barrel high-pressure syringe, at a dosage of 1.0 mL/kg and a flow rate of 3.0 mL/s. The CT value of blood vessels at the level of the aortic arch was monitored after injection of the contrast agent. Enhanced CT scans were automatically triggered when the CT value reached approximately 250 HU, whereas venous phase scans were performed after a delay of 30 s. The scanning range extended from the level of the lower neck to the bottom of the thorax in the supine position.

The workflow

The workflow of the radiomics analysis included ROI segmentation, feature extraction, feature selection, model development, and model validation (Figure 1).

Segmentation and features extraction

Contrast-enhanced 2D images of the venous phase were obtained from Digital Imaging and Communications in Medicine (DICOM). We selected images with a slice thickness of 5 mm and the ROI segmentation was performed on the original CECT images using ITK-SNAP software (http://www.itksnap.org) (Figure 2) [16]. All manual segmentation of the CECT images was performed by two practicing experienced radiologists (Y.W., with 11 years of CECT imaging experience; Y.T., with 2 years of CECT imaging experience) who were blinded to the patients’ clinical information. For the radiomics feature extraction of the ROI images, we used Pyradiomics, an open-source package based on Python.

Radiomics score construction and performance assessment

We normalized all variables and measured correlations between the features using Pearson algorithm. If the correlation coefficient between the two features was greater than 0.9, one of the features was excluded. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select the extracted features. The selected features were weighted by their respective LASSO coefficients and the linear combination was used to calculate the radiomics score. The radiomics score performance was assessed in both the training and validation set and the area under the curve (AUC) of the receiver operator characteristic (ROC) curve was used to assess the discriminative ability of the radiomics score.

Nomogram development and validation

The radiomics score and other clinical predictors were tested using a multivariate logistic regression algorithm in the training set. We then selected independent predictors for developing the nomogram. Calibration was assessed using a calibration curve along with the Hosmer–Lemeshow test to evaluate the goodness-of-fit of the nomogram. Additionally, the ROC curve and decision curve analysis were implemented to determine discriminative ability and clinical utility.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (protocol code KY2022216, 20 June 2022). The requirement for written informed consent was waived.

Statistical analysis

Normally distributed data were analyzed using an independent t-test, while categorical variables were evaluated using the chi-squared test. Except for the LASSO algorithm, which was performed using Python software, all statistical analyses were conducted using R statistical software, version 4.2.0. All statistical tests were two-tailed, and p < 0.05 was deemed statistically significant.

Patient clinical characteristics

The results of clinical and histopathological characteristics of patients with pN0 (n ═ 97) and pN+ (n ═ 96) are presented in Table 1. Univariate analysis revealed that when pathological outcome was the outcome variable, only clinical T stage, clinical N stage, and tumor stage showed statistical significance.

Features selection

We customized the mode of features extraction based on Pyradiomics and set preferences for image type, feature class, and setting. In total, 1,218 features were extracted from each CECT image, including 96 original features and 1,122 wavelet features. Original features included FirstOrder (n ═ 18), shape-based (2D; n ═ 10), gray level dependence matrix (n ═ 14), gray level run length matrix (n ═ 16), gray level cooccurrence matrix (n ═ 22), and gray level size zone matrix (n ═ 16). Wavelet features were based on the original features and were derived through various filters (Wavelet, LoG, etc.). Besides, the Pearson correlation coefficient analysis was performed on the wavelet features. Ultimately, 387 radiomics features were obtained for each patient. LASSO logistic regression was used to further select the optimal features, and we finally obtained 21 potential radiomics predictors. The details of the LASSO features selection are displayed in Figure 3.

Radiomics signature construction and performance assessment

The formula for the radiomics score was developed according to the feature coefficients, and the calculations are shown in Table S1. We discovered that when patients had different pathological outcomes, the radiomics score was statistically different in the training and validation set, thereby confirming the discriminative ability of the radiomics signature. We used the Raincloud plot to visualize the different distributions of the samples (Figure 4A) and plotted the ROC curve to express the performance of the radiomics score. The AUC value in the validation set reached 0.88 (95% CI 0.80–0.97; Figure 4B).

Nomogram development and validation

Univariate analysis was used to select statistically significant clinicopathological features (p < 0.05). Combined with the radiomics score for the multivariate logistic regression analysis, the results indicated that the clinical N stage and radiomics score were statistically significant (p < 0.001), as the nomogram consisted of the above factors. A goodness-of-fit test was performed on the nomogram and a calibration curve was drawn to show that the predicted probability of the nomogram for ALNM corresponded closely with actual observations (p > 0.05; Figure 5B and 5C).

We verified the predictive effect of the nomogram, which reached peak AUC values of 0.95 (95% CI 0.93–0.98) and 0.93 (95% CI 0.87–0.99) in the training and validation sets, respectively. This demonstrates the outstanding predictive ability of the nomogram. The detailed statistical results for discriminating pN0 and pN+ patients are summarized in Table 2, and the corresponding ROC curves are shown in Figure 6A. We plotted the decision curves of the models in the validation set and found that the nomogram had superior clinical benefits compared to the radiomics score (Figure 6B).