Clinical Data and Participants
This study used anonymous data and received approval from the Institutional Ethics Committee of Sun Yat-sen University Cancer Center (No. B2019-016) .Written informed consent was obtained from all participating patients in the study. A database of 1328 consecutive patients who presented at Sun Yat-sen University Cancer Center for CBBCT examination from January 2019 to July 2020 was retrospectively reviewed. Their clinical records were reviewed, with informed consent obtained from all patients, and only patients initially diagnosed with T1-T2 stage invasive breast cancer were included in the study cohort. All patients had no history of additional systematic treatment, including chemotherapy, radiation, or ipsilateral breast surgery. Finally, a total of 387 patients who underwent surgery with SLNB or ALND after examination with histologically proven malignant breast tumors were enrolled (Figure 2). The eligible population was randomly divided into two independent cohorts at a ratio of 70.0% (271/387) and 30.0% (116/387).
Protocols for Dedicated Cone-Beam Breast CT
All participants underwent dedicated breast CT examination (KBCT 1000®, Koning Corporation) using a single coil for each breast in the prone position. CBBCT images were acquired for the affected side with precontrast injection, 60 s postcontrast injection, and 120 s postcontrast injection. For each examination, the injection of contrast enhancement materials was performed under generally accepted standard protocols. Iodine contrast agent was injected intravenously with a high-pressure injector at a concentration of 300-370 mg/ml and 1-2 ml per kilogram for the injection dose. The total amount did not exceed 100 ml for a single patient. During the injection procedure, close attention was paid to the patient's physical condition. The injection speed was set at 2.0-3.0 ml/s. Optimal scanning parameters were set automatically at a constant voltage of 49 kVp and tube current of 50-160 mA (calculated automatically according to the size and density of the breast). The standard reconstruction mode (1024×1024, 0.273 mm3) was selected to reconstruct multiplanar images. Dedicated three-dimensional visualization software was utilized (Visage CS Thin Client/Server, Visage Imaging), and the automatic skin removal option was used to better distinguish lesions from the surrounding gland tissue on 3D images.
Reassessment of Image Parameters
Two radiologists (with four to seven years of experience) who were blinded to any clinical and pathological information reviewed all images independently. Both noncontrast-enhanced and contrast-enhanced CBBCT images were reassessed in consensus. Discrepancies with reassessment were resolved by consulting experts in breast cancer diagnosis (Y.P.W.) to reach a final conclusion. ΔCT was computed using a formula proposed by Prionas et al. (24): ΔHU = (HUlesion - post-HUfat - post) - (HUlesion - pre-HUfat - pre). Distance to the nipple was measured manually on sagittal sections. The relationship between vessels and masses was visualized with specialized three-dimensional reconstruction software. (Figure 3)
AUS images were acquired at our hospital using IU22 (PHILIPS, The Netherlands) and ACUSON S2000 (SIEMENS, Germany) systems with a high-frequency transducer (12 to 15 MHz). Electronic reports were reviewed, and parameters including primary LN morphology, blood flow type and detection site were evaluated and recorded.
Biopsy and Pathology Assessments
For each patient undergoing a preoperative CT scan, concordance with subsequent pathology outcomes was determined after surgical excision, including the evaluation of “histological grade and types, number of metastatic LNs, bundle/vascular invasion and expression of molecular markers”. Histopathological subtypes of breast cancer were classified as invasive ductal carcinoma (IDC), mixed-type (IDC with other components), and special-type breast cancer. Tumor (nuclear) grade was categorized using the Nottingham grading system as a reference. The expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor type 2 (HER2), and Ki-67 was determined using IHC. Tumors with more than one positive tumor cell on the basis of the nuclear staining intensity were considered to be ER/PR positive (25). The HER2 staining intensity was scored from 0 to 3, and tumors with scores of 3+ or positive results of Fisher’s test were considered to exhibit HER2 overexpression. For the Ki-67 expression status, nuclear staining of at least 14% was considered to indicate a high level of proliferation. All LNs were surgically removed via SLNB/ALND and assessed with postoperative biopsy. The presence of micrometastases or macrometastases on SLNB was indicative of a positive LN status. Isolated tumor cells were classified as node-negative. According to the ACOSOG Z0011 criteria, two positive LNs was an optimal cutoff to select patients who can avoid traumatic axillary dissection surgery. Therefore, outcomes of the LN status were divided into three groups to develop two separate clinical prediction models.
Feature Selection and Statistical Analyses
Patients who met the inclusion criteria were retrospectively reviewed and treated as independent variables in statistical analysis. Two models were constructed using the LN status as outcome indicators: a) prediction of negativity and metastasis (N0 versus N+) and b) low burden and high burden (N<3 versus N≥3). All clinical and pathological primary lesion information was derived from a medical database. Continuous variables were analyzed using Levene’s test and the Mann-Whitney test, whereas differences in categorical variables were assessed using the chi-squared test, adjusted chi-squared test or Fisher’s exact test. Univariate analysis was performed on the primary feature set to choose model parameters. Then, a bidirectional elimination method was applied to modify the model by repeatedly adding in and removing features until an optimal equation was developed (22,23). Finally, multivariable logistic regression analysis was used to select statistically meaningful parameters and fit the best prediction model. Statistical analysis was completed by using IBM SPSS version 26 (IBM, Armonk, NY, USA) and R studio software (version 1.3 https://rstudio.com/products/rstudio/download/). All statistical hypothesis tests were two-sided, and p-values < 0.05 were considered to indicate significance. The randomization method was used to divide the primary cohort into two separate groups with a ratio of “0.7 * n”. The analysis of continuous and categorical variables was conducted with SPSS software. Feature selection was conducted using bidirectional stepwise regression with the “mass” package. Plotting of the nomogram, calibration curves and clinical impact curves was performed with the package “rms”. Decision curve analysis (DCA) construction was performed by means of library (rmda). The evaluation of the prediction ability of ROC curves was performed with “pROC” bag. The Hosmer-Lemeshow test and C-index were performed using the “ResourceSelection” and “Hmisc” packages.
Assessment and validation of Prediction Performance
Receiver operating characteristic curves (ROCs) were plotted to assess the discriminative performance of the two prediction models. Additionally, the predictive ability of the two models was compared between the combinative or independent use of two preoperative imaging modalities. The area under the curve (AUC) of both examinations was computed in both the primary and internal validation cohorts. DeLong's test, as a quantitative method for comparing the AUC of two curves, was calculated for overfitting risk between the training and validation cohorts.The predictive performance of the two models was assessed by calibration curves plotted with 1000 bootstrap samples to evaluate the agreement between the predictions and actual observations. The adjusted C-index was calculated to demonstrate the accuracy of the prediction models with bootstrap correction. Hosmer-Lemeshow tests were used to assess the fitness degree.
Clinical Utility of the Final Model
Axillary LN metastasis prediction was illustrated with nomograms integrating clinicopathological features to provide explicit models for image-assisted treatment planning.To determine the clinical significance of the final model, decision curve analysis and clinical impact curve analysis were performed.