In our study, we evaluated factors related to upstaging of the diagnosis of breast cancer from pre-operative DCIS to post-operative invasive carcinoma. In previous studies, the risk factors associated with this upstaging such as age, body mass index, mammographic Breast Imaging Reporting and Data System, palpability, nipple discharge, specimen number, extent of microcalcification, mass size, nuclear grade, axillary lymph node involvement, multicentric lesion, contralateral lesion, and the presence of HER–2 over expression, were reported as predictors of DCIS with an IC prior to surgery [1, 4–11, 15–28]. However, these studies were small in sample size and reported inconsistent results, and therefore, no predictable factors for IC were available clinically. In particular, few studies related to MRI, and the number of subjects in these studies was very small. By contrast, the scale of our research is relatively large.
There has been no consensus on age as a predictor for DCIS with an IC. Yen et al. found that 55 years of age or younger was the predictor for an IC in DCIS [16]. However, Nori et al. suggested that age greater than 55 years at diagnosis was significantly associated with a higher risk of an IC [10]. Considering inconsistent results between studies and few studies suggesting age as a significant predictor, age is not likely a predictor, and thus, consistent with our results.
Mixed results suggest nuclear grade as a predictor across the studies. In our analysis, high grade was the only pathological predictor for IC (HR = 2.39, C. I. = 1.05–5.42, p = 0.038 in univariate analysis, HR = 2.86, C. I. = 1.14–7.14, p = 0.025 in multivariate analysis). Guillot et al noted that high-grade DCIS was a risk factor of IC [4]. Brennan also reported that high-grade DCIS was significantly associated with a underestimation of DCIS [24]. However, several studies found no relationship between the high grade and the likelihood of upstaging [1, 16, 17, 19, 29]. This discrepancy is a reflection of the controversy excluding microinvasion from histologic grade; however, this pathological factor is still worthy of investigation in future studies along with other features such as comedo-necrosis.
A larger lesion size in imaging studies has been suggested a predictive factor for upstaging to invasive cancer. In mammography, the cutoff value of size ranged from 20 to 50 mm in previous studies [9, 27]. Lee et al reported that sonographic lesions larger than 20 mm were significantly related to invasion [30]. Park et al found that sonographic mass sizes larger than 32 mm were strong risk factors for DCIS with IC in conjunction with findings suggesting that mass sizes larger than 30 mm on an MRI were strong predictors for DCIS with IC [9].Although we evaluated US and MRI tumor size as risk factors for occult invasion, no statistically significant data were obtained with these variables. These varying results might be attributed to insufficient case numbers, use of different cut-off values and diagnostic modalities in each study, and interobserver variability.
The role of MRI in screening and diagnosis of invasive breast cancer is well known [10], but few studies investigated MRI features as preoperative risk factors for occult IC in DCIS patients. Previously, Goto el al. reported that higher signal intensity of the enhancing lesion on fat-suppressed (FS) T2W image was a good predictor of IC [5].Wisner et al. concluded that rapid early enhancement (p = 0.001) and washout kinetics correlated with occult IC in DCIS patients [8].In contrast to these reports, Park pointed out that a lower signal intensity on FS T2WI MRI, and heterogeneous or rim enhancement on an MRI were significant variables, but not the higher signal intensity of the enhancing lesion on FS T2WI or the time-signal intensity curve pattern [9]. In our analysis, MRI-related variables such as enhancement peak, initial enhancement peak of dynamic curve, and kinetic pattern, showed no significant differences in IC detection. NME was the only predictive factor for DCIS without IC (HR = 0.26, C. I. = 0.07–0.93, p = 0.038).
Based on breast MRI, lesion types were classified into NME or mass. NME on an MRI has been investigated previously as a predictor of DCIS with IC. However, most of them showed no significant correlation between NME and occult invasion [5–11, 26, 28].Only two studies suggested that mass-like enhancement was a predictor of DCIS with IC, which is consistent with our study [11, 22].It may be explained by previous studies about non-mass lesion (NML) on breast US [31–35]. Most breast malignancies with NML on US were DCIS and only 5.6% of them were associated with IC alone [35].Although our results were supported by larger case numbers than in previous studies, the lack of concordance between previous studies and our results suggests the need for re-evaluation of NME on a larger scale and for prospective studies to predict IC.
In kinetic curve assessments, no typical pattern differentiated invasive carcinoma from pure DCIS. Previously, rapid initial enhancement and the washout kinetic curve were reported as predictors of DCIS with IC by Wisner et al [8]. Another study by Goto el al., showed that kinetic descriptors for initial and delayed phase enhancement had no significant role [5]. A few additional studies also evaluated this enhancement pattern; however, they revealed no significant results [9, 11].Although no obvious conclusions can be drawn based on MRI enhancement patterns, considering the diagnostic value for breast malignancy, it appears that their significance as a predictor should be elucidated in additional trials in the future.
This study has some limitations. Our study was designed retrospectively, which may lead to bias between the group exposed to MRI scan and the unexposed group. In addition, the difference in tissue volume between CNB and VANB may have affected the diagnostic ability.
We suggest that high nuclear grade and mass-like enhancement on MRI are predictors of IC in patients with a preoperative diagnosis of DCIS. In clinical practice, no factors are available to predict IC yet. Considering the high sensitivity of IC for breast MRI, we expect further evaluation of the predictive value of MRI for IC in patients with preoperative DCIS. Therefore, a score system or a calculator that includes clinico-pathological factors and MRI factors is needed to predict DCIS with IC in the future, which can facilitate the treatment plan for patients with DCIS.