MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer

Radiomic features hold potential to improve prediction of disease-free survival (DFS) in triple-negative breast cancer (TNBC) and may show better performance if developed from TNBC patients. We aimed to develop a radiomics score based on MRI features to estimate DFS in patients with TNBC. A total of 228 TNBC patients who underwent preoperative MRI and surgery between April 2012 and December 2016 were included. Patients were temporally divided into the training (n = 169) and validation (n = 59) set. Radiomic features of the tumor were extracted from T2-weighted and contrast-enhanced T1- weighted MRI. Then a radiomics score was constructed with the least absolute shrinkage and selection operator regression in the training set. Univariate and multivariate Cox proportional hazards models were used to determine what associations the radiomics score and clinicopathologic variables had with DFS. A combined clinicopathologic-radiomic (CCR) model was constructed based on multivariate Cox analysis. The incremental values of the radiomics score were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC) and bootstrapping (n = 1000). The radiomics score, which consisted of 5 selected MRI features, was significantly associated with worse DFS in both the training and validation sets (p = 0.002, p = 0.033, respectively). In both the training and validation set, the radiomics score showed comparable performance with the clinicopathologic model. The CCR model demonstrated better performance than the clinicopathologic model in the training set (iAUC, 0.844; difference in iAUC, p < 0.001) and validation set (iAUC, 0.765, difference in iAUC, p < 0.001). In conclusion, MRI-based radiomic features can improve the prediction of DFS when integrated with clinicopathologic data in patients with TNBC.

independent prognostic factors for DFS 12 . However, the imaging features of TNBC differ from non-TNBC subtypes, and semantic MRI features that are associated with worse survival also differ between breast cancer subtypes 8,13,14 . Therefore, radiomic features that predict survival may differ between TNBC and non-TNBC turmors, and can potentially show better performance if they have been developed from a subgroup made up of only TNBC patients.
Therefore, the purpose of this study was to develop a radiomics score based on MRI features to estimate DFS in patients with TNBC. Results patient characteristics and survival outcomes. The median follow-up period was 48.0 months (range, 5.0-80.0 months). Disease recurred in 32 of 228 patients (14.0%) at a median 12.5 months (range, 2.5-60.2 months). Among these 32 patients, 16 (50.0%) had distant recurrence, 12 (37.5) had both distant and locoregional recurrence, and 4 (12.5%) had locoregional recurrence. Thirteen patients died during treatment for recurrence, with a median time to death of 16.5 months (range, 11.0-45.0 months).
The characteristics of the patients in the training and validation sets are shown in Table 1. None of the clinicopathologic variables differed between the two cohorts. feature selection, radiomics score building and validation. Of the extracted radiomic features, we selected 2412 radiomic features with a ICC value greater than 0.75. These features were reduced to five potential predictors with nonzero coefficients in the LASSO Cox regression model based on the 169 patients in the training set. The five predictors consisted of one feature from contrast-enhanced T1-weighted (CE T1W) images and four features from T2-weighted (T2W) images. The radiomics score (Rad-score) was constructed by combining these five features, with a Rad-score calculated for each patient as a linear combination of the selected features that were weighted by their respective coefficients (Supplementary Information).
Among clinicopathologic factors, tumor size on MRI, pathologic T category, pathologic N category, type of surgery, neoadjuvant chemotherapy, adjuvant chemotherapy, lymphovascular invasion were associated with worse DFS at univariate analysis ( factors and the Rad-score, confirmed that the Rad-score was an independent factor in both the training set (HR, 78.182, p = 0.002) and validation set (HR, 210.516, p = 0.033; Table 3). In the Rad-score-only model, the optimal cutoff of the Rad-score for differentiating patients into either the high-risk and low-risk group was obtained in the training data set based on the maximally selected log-rank statistic (log-rank test, p < 0.001,). When the validation set was stratified into the high-risk and low-risk groups using the cutoff derived from the training set, the survival curves of the two groups were significantly different (p = 0.013) (Fig. 1).

Development and validation of the ccR Model.
A clinicopathologic model and a CCR model that incorporated the radiomics score and significant clinicopathologic factors in the data set were also established. We evaluated the performance of the three models for prediction of DFS. In the training set, the radiomics score (

Discussion
In this study we showed that the radiomics score was associated with DFS in both the training and validation data set, and that it remained an independent prognostic factor at multivariate analysis. Furthermore, the CCR model showed a significant improvement over the clinicopathologic model. As the clinicopathologic model was determined by the entire data set (n = 228), this approach would have provided a larger advantage to the clinicopathologic model. Nonetheless, the radiomics score enabled further improvement in DFS prediction, showing that radiomic features do contain additional information that can potentially be used in risk assessment.
In the last few years, researchers have used various approaches when applying MRI-based radiomic features in breast imaging, but most studies have focused on the differential diagnosis of breast lesions, the prediction of pathological characteristics, and response to neoadjuvant chemotherapy [15][16][17][18][19][20][21][22] . Recently, one study reported that a MRI-based radiomics signature was an independent predictor of DFS 12 . However, only 17% of its validation set were TNBC, and the triple-negative subtype remained an independent prognostic factor 12 . TNBC shows different imaging features compared to other breast cancer subtypes, often presenting with areas of intratumoral high T2 signal intensity, lobulated shape, rim enhancement, and smooth margins 14 Table 3. Prognostic factors of disease-free survival for the training and validation set in the combined clinicopathologic and radiomic model. www.nature.com/scientificreports www.nature.com/scientificreports/ which investigated the role of radiomics in differentiating breast cancer subtypes have also shown that radiomic features related to lesion shape contribute the most among other features when discriminating TNBC from other subtypes 19,23 . Interestingly, whereas one shape-related radiomic feature was selected in the aforementioned study from Park et al. 12 , none of the shape-related features were selected in our study. Our study results may indicate that shape-related features may be less helpful in risk stratification for TNBC.
However, similar to the study of Park et al. in which all other radiomic features were T2W-imaging based features, we found that four of the five selected radiomic features in our study were also extracted from T2W images. Another previous study also reported that breast cancers that appear more heterogeneous on T2W images (higher entropy) exhibited poorer recurrence-free survival 24 . In addition, in one study which investigated the performance of a MRI-based radiomics classifier for predicting Ki-67 status in breast cancer, the T2W-imaging-based radiomics classifier significantly predicted Ki-67 status and outperformed the CE T1W-imaging-based rclassifier 16 . As Ki-67 is a well-established prognostic marker in breast cancer, especially in ER-positive breast cancer 25 , this may partially explain the high contribution of T2W images for predicting survival using radiomic features. Our results and previous studies highlight the importance of including T2W images for analysis in radiomics research regarding survival in breast cancer.
In an attempt to improve risk stratification in TNBC, previous studies have aimed to identify MRI features associated with survival, with mixed results 7,8,26 . Whereas rim enhancement was associated with poorer outcome only in TNBC in one study 26 , another reported that rim enhancement was associated with poor distant metastasis-free survival only in hormone receptor-positive or HER2 positive tumors 8 . Compared to semantic imaging features, quantitative radiomic features have the advantage of being less affected by interobserver variability. In our study, we found that the radiomics score was not only significantly associated with DFS in TNBC but also improved its prediction. As the selected independent clinicopathologic factors were based on peritumoral or non-tumor characteristics (pathological N category, lymphovascular invasion), it is likely that tumor-based radiomic features provided complementary information that could further improve the performance of both the clinicopathologic model or the radiomics score alone.
One difficulty with research on TNBC is its relatively small proportion among breast cancer (10-20%) 27 . This is especially true for studies related to survival, which require longer follow-up and thus, are more affected by follow-up loss. In order to construct both the training and validation data set, we included patients without regard to neoadjuvant chemotherapy. Although this approach has been used in previous studies and despite the fact that we included treatment variables in analysis 28,29 , it may be more desirable to construct a radiomics score separately based on neoadjuvant chemotherapy status. Furthermore, as we aimed to compare the performance of the radiomics score with a clinicopathological model for estimating DFS, patients achieving pCR were eventually excluded due to unavailable pathological data. Therefore, our results may not be transferable to patients with TNBC who achieve pCR. Yet, our preliminary study showed that the radiomics score can potentially improve risk stratification in TNBC, and that T2W-imaging-based features provide valuable information regarding survival, similar to other breast cancer subtypes. These results will aid in the design of future studies with larger study populations, more preferably of multi-center design, that will confirm the prognostic role of radiomics for TNBC.
Our study had several limitations. First, this was a retrospective, single-institution study, and selection bias is inevitable. As all images were obtained using the same MRI protocol and scanner, it may be difficult to generalise our results to different settings. In addition, as we did not perform external validation using an independent data set from different institutions, there is a possibility of overfitting. Second, this study had a relatively small sample size and relatively short follow-up period. As TNBC represents a small proportion of primary breast cancers, future multi-center studies with larger study populations and longer follow-up are needed to determine the role of radiomics as a prognostic tool in TNBC patients, including its association with overall survival. Third, lesion segmentation was performed by one radiologist using semiautomatic software. Although we discarded radiomic features with an ICC value of less than 0.75, future software that can perform fully automatic segmentation can help reduce interobserver variability and improve the feasibility of performing radiomics analysis in daily practice. Finally, as MRI is not universally performed for preoperative evaluation of breast cancer, the clinical utility of such MRI-based radiomics features may be potentially limited in actual clinical practice.
In conclusion, our preliminary study shows that the identified radiomics score has the potential to be used as a biomarker for risk stratification in patients with TNBC. The CCR model, which incorporated the radiomics score with clinicopathologic data, showed significant improvement in the prediction of DFS. However, further validation with larger study populations is required to confirm the prognostic role of radiomics in TNBC.  www.nature.com/scientificreports www.nature.com/scientificreports/ Methods patients. The institutional review board of Severance hospital approved this retrospective study and the requirement for informed consent was waived. All research was performed in accordance with relevant guidelines/regulations. We identified 342 consecutive women with triple-negative (ie., estrogen-receptor-negative, progesterone receptor-negative, and human epidermal growth factor receptor 2 [HER2]-negative) invasive breast cancer who underwent surgery following preoperative MRI using a 3-T scanner (Discovery MR750w; GE Healthcare) at our institution between April 2012 and December 2016 30,31 .
Of the initial 342 patients, we excluded 114 patients for the following reasons: patients with recurrent breast cancer (n = 23), patients presenting with systemic metastases (n = 5), patients with malignancy other than the primary breast cancer (n = 3), patients who received neoadjuvant chemotherapy prior to MRI (n = 2), patients in whom MRI was performed after vacuum-assisted or excisional biopsy (n = 9), patients with occult breast cancer (n = 1), patients with unavailable pathological variables (n = 61), patients with bilateral breast cancer (n = 5), and patients who were immediately loss to follow-up after surgery (n = 5). Finally, 228 patients (mean age, 53 years; range, 22-85 years) were included in this study. For independent temporal validation, patients who underwent surgery up to April 2015 were assigned to the training set (n = 169, mean age, 52 years [range, 25-85 years]) and subsequent patients were assigned to the validation set (n = 59; mean age, 54 years [range, 22-81 years]).
immunohistochemical Staining and interpretation. Immunohistochemical staining for the ER, PR and HER2 status was performed on tissue slices with standard methods 30,31 . A cut-off value of ≥ 1% positively stained nuclei was used to define ER and PR positivity using the Envision FLEX Kit (DAKO, Glostrup, Denmark). HER2 staining using the Hercep Test TM (DAKO, Glostrup, Denmark) was scored as 0, 1+, 2+, or 3+ according to the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines 31 . HER2 immunostaining was considered positive when strong (3+) membranous staining was observed whereas cases with 0-1+ were regarded as negative. In cases with a HER2 2+ result, silver in situ hybridization (SISH) was performed using the INFORM HER2 Dual ISH DNA Probe Cocktail Assay (Ventana Medical Systems, Tucson, AZ) with an automated slide stainer according to the manufacturer's protocols. HER2 gene amplification was defined with a HER2 gene/chromosome 17 copy number ratio ≥ 2.0 or a HER2 gene/chromosome 17 copy number ratio < 2.0 with an average HER2 copy number ≥ 6.0 signals/cell according to the ASCO/CAP guidelines. clinicopathologic evaluation and follow-up. Patient age, information on clinical follow-up and on treatment modalities including surgery, radiation therapy, and neoadjuvant or adjuvant chemotherapy were obtained from medical records. The final histopathological results of surgical specimens were reviewed to determine pathological T and N categories, histologic grade and lymphovascular invasion. Tumor size at initial preoperative MRI was obtained from the radiology report. The follow-up protocol for patients is described in the Supplementary Information.
The end point of our study was DFS, which was defined as the time interval from the date of surgery to development of the first evidence of events. Events for determining DFS were events of breast cancer recurrence (locoregional or distant recurrence) or the development of a new primary contralateral breast cancer.
MRi preparation for radiomic feature analysis. Lesion segmentation and image preprocessing. One breast radiologist (V.Y.P, with 5 years of subspecialty experience in breast imaging) semiautomatically segmented the tumor lesion in early contrast-enhanced T1-weighted images using MIPAV software (Medical Imaging Processing Analysis & Visualization, National Institutes of Health, mipav.cit.nih.gov) and the generated mask was used for CE T1W and T2W images. To evaluate interobserver reproducibility, another breast radiologist (M.J.K, with 16 years of subspecialty experience in breast imaging) independently performed tumor segmentation on 40 randomly chosen lesions. Further details are given in the Supplementary  For feature selection, radiomic features with ICC values of less than 0.75 were removed. We then used the least absolute shrinkage and selection operator (LASSO) method using 10-fold cross validation, to select the most significant features in the training data set 33 . The LASSO is a data analysis method that is suitable for the regression of high-dimensional data. The selected imaging features were then combined into a radiomics score (Rad-score). For each patient, a Rad-score was calculated through a linear combination of selected features weighted by their respective coefficients. (2020) 10:3750 | https://doi.org/10.1038/s41598-020-60822-9 www.nature.com/scientificreports www.nature.com/scientificreports/ We first assessed the potential association of the radiomics score with DFS in the training set, and then validated it in the validation set. Patients were classified into high-risk or low-risk groups according to the Rad-score, by the maximally selected log-rank statistic 34 Kaplan-Meier curves were used to analyse DFS between the high-risk and low-risk groups, and log-rank tests were used to compare differences in survival. In addition, to determine clinicopathologic variables associated with DFS, we used the univariate Cox proportional hazards model to analyze the association between clinicopathologic variables in the whole data set (n = 228). Multivariate Cox regression was performed for variables with a p value of < 0.1 in the univariate Cox regression analysis.
In addition to the radiomics score (Rad-score-only model), we built a clinicopathologic model and a combined clinicopathologic-radiomics (CCR) model to evaluate the prognostic performance of all three models and demonstrate the value of the radiomics score. The CCR model incorporated the radiomics score and independent clinicopathologic risk factors based on the multivariate Cox analysis. The performance of the CCR model was compared with that of both the Rad-score-only model and clinicopathologic model in the training set and validation set, respectively. Model performance was calculated by using the integrated area under the time-dependent ROC curve (iAUC) based on predicted risks from each model ( Supplementary Information).
The interobserver variability of the radiomic features was assessed with the intraclass correlation coefficient (ICC). An ICC value greater than 0.75 was considered to represent good reproducibility 35 . Changes in hazard ratio (HR) were calculated with a 0.1-unit difference in the Rad-score. All statistical and radiomic analyses were performed using the R software (version 3.3.1; R Foundation for Statistical Computing). A two-tailed p value of <0.05 was considered statistically significant.

Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.