Study population
This retrospective study (clinical trial ChiCTR1900027676) was approved by the Institutional Review Board of the participant hospitals, and informed consent was waived. Between June 2016 and May 2019, consecutive patients with primary breast cancer in hospital #1 (Tongji Hospital of Huazhong University of Science and Technology, training cohort) and hospital #2 (Hunan Provincial Tumour Hospital, validation cohort) referred for 2D SWE examination with subsequent biopsy and surgical treatment were evaluated.
Patient’s inclusion criteria including: (1) pathologically confirmed primary breast cancer; (2) all patients underwent mastectomy with SLND or ALND (in case SLN biopsy was positive); (3) SWE image of breast displayed with the same BMUS image in split-screen mode was carried out within two weeks prior to operation; and (4) availability of clinical data. The exclusion criteria including: (1) preoperative therapy (neoadjuvant radiotherapy or chemotherapy) before US examination; (2) with multifocal lesions or bilateral disease; (3) little or no shear wave signal was obtained in the region of interest (ROI) of SWE (masses deeper than 3 cm in depth will lead to the attenuation of SWE); (4) missing important histopathological results, such as immunohistochemical (IHC) results or post-operative pathological ALN status. In total, 303 patients from Tongji Hospital comprised the training cohort (age, 51.11 ± 10.70 years; range, 27–85 years) were enrolled from Jun 2016 to April 2019. From March 2017 to May 2019, an independent external validation cohort of 130 patients (age, 50.98 ± 10.06 years; range, 33–82 years) from Hunan Provincial Tumour Hospital was enrolled with the same criteria. A flowchart describing the patient screening process is shown in Figure 1.
The US-reported LN status was obtained from the US reports, and axillary images containing important features of suspicious LNs were documented into the Picture Archiving and Communication Systems (PACS). It was retrospectively reviewed and verified by two radiologists (X.M.L and S.C.T, with 15 and 31 years of experience respectively). US features of LN used to assess suspicion for malignancy were as follows: 1) irregular cortical thickness ≥3 mm; 2) longest/shortest axes ratio < 2; or 3) absence of fatty hilum [21].
The baseline clinicopathological data were derived from the patient medical records, including age, clinical tumour size, pathological type, IHC results and post-operative ALN status. According to the 2017 St Gallen International Expert Consensus, the breast cancers were classified into four molecular subtypes based on preoperative biopsy: human epidermal growth factor receptor-2 positive (HER2+), triple-negative, Luminal A, and Luminal B [22]. The status of HER2, progesterone receptor (PR), estrogenic receptor (ER) and Ki-67 was assessed by IHC examination.
US image acquisition
BMUS and SWE examinations were performed with a Supersonic Aixplorer system (SuperSonic Imagine, Aix-en-Provence, France) using a 4-14 MHz linear transducer by five radiologists from Tongji Hospital and two radiologists from Hunan Tumour Hospital experienced in breast US according to standard protocols. After standard conventional BMUS, SWE was performed. The ROI was set to include the whole breast cancer and adjacent normal parenchyma for SWE acquisition, and stiffness was shown as a colour map on the ROI. On the colour map, blue and red regions reflect comparatively soft (low kPa) and stiff (high kPa) tissues, respectively [23]. The detail US examination procedures are presented in Additional file 1.
Tumour segmentation and radiomic feature extraction
Figure 2 shows the flowchart of the radiomics workflow. One image per tumour was used for analysis. The ROI for feature extraction was manually delineated on the largest cross section of B-model image using ITK-SNAP software (3.8.0; http://www.itksnap.org). All the manual segmentations were performed by two experienced breast US radiologists (with 11 and 9 years of experience, respectively) and each twice who were blinded from the final pathological diagnosis (for interobserver and intraobserver reproducibility evaluation). Because the contour of the lesion on SWE was indefinite, the same region on BMUS was copied and pasted to the corresponding SWE image, and was expanded to include the "stiff rim" sign if it existed. SWE is a combination of B-model image and pseudocolor elasticity layer, algorithms presented in previous studies were employed to produce clean quantitative images that mapping the tissue stiffness as grey levels [24-26]. The radiomic features were extracted automatically from each BMUS and SWE image by Pyradiomics (https://pyradiomics.readthedocs.io/en/latest/index.html) (Additional file 2, Supplemental Table 1-6) [27]. The stable features with interclass correlation coefficient (ICC) > 0.8 were selected to adapt different segmentations.
Radiomic signature building
We used Spearman’s correlation coefficient to evaluate the relevance and redundancy of the features, and eliminated redundant features that with a Spearman’s correlation coefficient ≥ 0.8. Then, the minimum redundancy maximum relevance (MRMR) algorithm and least absolute shrinkage and selection operator (LASSO) regression method using 10-fold cross-validation was applied to select the most useful predictive ALN status-related features from the training data set [28]. The formulas for the SWE and BMUS radiomic signatures were built using the respective selected features.
Radiomics nomogram construction
Univariate analysis was conducted to select statistically significant clinical factors associated with ALN metastasis. We mainly considered SWE radiomic signature in our nomogram; however, the incremental predictive value of BMUS radiomic signature to the model was also investigated using the net reclassification index (NRI) and integrated discriminatory improvement (IDI). Proportional odds ordinal logistic regression was used to build the radiomics nomogram based on the radiomic signatures and clinical characteristics [29]. The total points (Nomo-score) of each patients were calculated based on predictors of the nomogram. The association of the Nomo-score with pathologic ALN status was assessed using Spearman's correlation analysis. In addition, a classification procedure was proposed based on cut-offs of the Nomo-score to split patients into three subgroups of ALN status. Furthermore, a model based on clinical characteristics only was developed using the same method for comparison.
Performance evaluation
Harrell's C-indexes of the radiomics nomogram and clinical model were compared in the training and validation cohorts with Delong test. Besides, we carried out subgroup analysis based on patient age and clinical N stage, and calculated pairwise C-indexes for discriminating N+(≥1) versus N0, and N+(≥3) versus N+(1–2). The calibration curve was also plotted to measure the model. Among the subgroup analysis, N+(≥1) versus N0 is of special concern since it would determine the axillary surgical strategy. Decision curve analysis (DCA) was conducted accordingly to evaluate the clinical usefulness of the model in guiding SLN biopsy by quantifying the net benefits.
Statistical analysis
Statistical analyses were conducted with R software 3.6.1 and SPSS20.0 software (SPSS Inc., Chicago, IL). A two-sided P value less than 0.05 was used as the standard of statistically significant difference. In the univariate analysis, the differences in clinical characteristics between the patients of different groups were compared using Mann–Whitney U test or independent t test for continuous variables, and chi-square test or Fisher's exact test for categorical variables, as appropriate. Analysis of variance and Kruskal–Wallis H test were used for comparing more than two groups. The detailed descriptions of the LASSO and DCA algorithm are provided in the Additional file 1.