A clinical radiomics nomogram preoperatively to predict ductal carcinoma in situ with microinvasion in women with biopsy-confirmed ductal carcinoma in situ: a preliminary study

Breast cancer is one of the most common malignant tumors in women worldwide [1].With medical advances, the number of patients with ductal cancer in situ (DCIS) and DCIS with microinvasion (DCISMI) is increasing [2]. Malignant epithelial cell growth inside the mammary duct lumen but no penetration beyond the basement membrane is the histological hallmark of DCIS [3]. DCISMI is thought to be the transitional stage between DCIS and invasive ductal cancer (IDC) [4]. In this study, we used the AJCC’s definition of “microinvasion” as one foci (DCISMI-one) or more foci (DCISMI-more) of invasive carcinoma \(\le\) 1 mm in diameter inside an area of DCIS [5]. Numerous studies indicate that DCISMI’s prognosis and natural history are quite similar to those of DCIS [6‐10]. However, a large-scale clinical study [11] revealed that the prognosis of DCISMI is more like that of small invasive carcinoma than DCIS.

In comparison to mammography (MG) and ultrasonography (US), dynamic contrast enhanced MRI (DCE-MRI), a common procedure for breast MRI, offers the best sensitivity for identifying DCIS or DCIS coupled with invasive cancer [12]. It is critical to “identify MRI features that can be combined with clinical and biological characteristics to better stratify risk in patients with DCIS” [13]. Clinically, the majority of DCIS and DCISMI exhibit a clinically comparable morphological appearance. There have been a number of publications on characteristics helping to predict microinvasion and invasion, including clinical findings and findings on conventional imaging but with inconsistent results. However, there have been only a few publications that have compared MRI findings between pure DCIS and DCISMI cases [14, 15]. To the best of our knowledge, no previous report has researched the predictors of DCISMI using radiomics-based machine learning algorithms. A radiomics-based signature could provide a more thorough approach by combining both geographical and temporal data to define the tumor more fully. Thus, we speculate that radiomics characteristics obtained from DCE-MRI may represent cellular and molecular data and may be able to foretell upstaging in females with biopsy-proven DCIS.

In this study, we aimed to compare the performance of clinical-pathological characteristics, conventional breast MRI features, DCE-MRI radiomics signatures, and combined multiple features in predicting DCISMI and to construct a nomogram to better understand the risk factors.

Our preliminary analysis showed that the preoperative clinicopathologic, conventional breast MRI and DCE-MRI radiomics features could predict the preoperative histological upstage of DCIS and that DCE-MRI radiomics signatures may be different for DCISMI-more from DCISMI-one. The mixed model showed excellent predictive performance. The DCE-MRI radiomics model, which could be replaced by the traditional model, showed better than the conventional MRI model. A nomogram constructed by combining clinicopathologic, conventional MRI features and DCE-MRI radiomics signatures may be useful in predicting DCISMI from DICS, preoperatively.

DCISMI represents an intermediate state between DCIS and IDC [19]. Although DCIS, DCISMI, and T1a IDC (invasive tumor size \(>\) 0.1 cm but \(\le\) 0.5 cm in greatest dimension was classified as T1a) all have generally excellent prognosis, some studies revealed that DCISMI more closely resembles small invasive carcinoma than pure DCIS, and many practitioners are treating it as such [11, 20]. In comparison to women with pure DCIS or DCISMI-one, DCISMI-more is linked to a higher probability of invasive local recurrence in women with DCIS treated with breast conserving surgery [21]. The rate of axillary lymph node metastasis is approximately 0% to 20% in DCISMI [19]. According to approved treatment guidelines, sentinel lymph node biopsy was used in conjunction with mastectomy because of these relatively low but clinically significant rates [22]. A change in diagnosis from pure DCIS before surgery to DCISMI after surgery creates a great deal of patient anxiety and possibly a second surgery [23]. Therefore, an accurate prediction of the histological DCISMI and even DICSMI-more could help with preoperative risk stratification and the best choice of patients who could benefit from more extensive surgery while avoiding overtreatment of patients at low risk.

In this study, higher nuclear grade as the independent factor associated with histologic upgrade in the clinicopathologic model and both the peritumoral edema and heterogeneous enhancement as significant independent factors in the conventional MRI model, which were partly consistent with those of previous studies for predicting invasive breast cancer [15, 24‐27]. Several prior studies have described the MRI features [14, 15, 28‐32] that can help predict the invasive component of a biopsy-proven DCIS. However, there have been few reports that have compared MRI findings between pure DCIS and DCISMI, rarely by the features using machine learning algorithms based on the clinicopathologic, conventional breast MRI, and DCE-MRI radiomics signatures. In this study, the mixed model established by the LR classifier showed that heterogeneous enhancement pattern and Radiomics score were independent predictors of upstage. DCISMI lesions showed more heterogeneous enhancement, which is partly consistent with previous studies [15, 28, 33]. The main applications of breast radiomics investigations include the molecular categorization, lymph node metastases, and molecular markers of IDC and DCIS prediction [34]. There is only one report based on US radiomics to predict the molecular biomarkers of DICS [35]. Radiomics is a precision medical method for non-invasive diagnosis, evaluation of efficacy, and biological behavior [36]. Contrary to our prior hypothesis, it was found that the DCE-MRI radiomics model’s AUC (AUC = 0.74) was the lowest one below 0.8. This may be because the MRI radiomics mostly depend on a set of MRI sequences [35]. In our study, only DCE-MRI radiomics features were included rather than diffusion weighted imaging (DWI) or apparent diffusion coefficient (ADC) radiomics features. DWI may provide more accurate tumor microenvironment monitoring [37], which has been widely explored to differentiate benign from malignant breast lesions [38, 39], and showed its diagnostic ability in DCISMI [33] and predicting upstaging of invasive components in biopsy-proven DCIS [29, 40]. Because some lesions were not visualized on DWI, DWI sequences were not included in this study. In our investigation, the DCE-MRI radiomics model was able to distinguish between DCISMI-more and DCISMI-one with an AUC of 0.72 and an accuracy of 0.8 using a very small sample size of 11 DCISMI-more lesions and 34 DCISMI-one lesions. All patients underwent sentinel lymph node surgery in this study. Patients with a final diagnosis of pure DCIS and DCISMI-one have no axillary lymph node metastasis in our institution, while there were 2 cases (4.4%) of DCISMI-more patients with axillary lymph node metastasis. DCE-MRI radiomics potential prediction in DICSMI-more and the relationship between DCISMI-more and axillary lymph node metastasis should be verified by future prospective multicenter studies with larger samples.

Our research revealed that, with a histologic upgrade rate somewhat higher than that of other studies with 8.8%-51.5% of DCIS patients upstaged to invasive disease [15, 41], 51.72% of lesions having a preoperative diagnosis of DCIS were histopathologically upgraded to DCISMI. There might be a few reasons. Firstly, the study’s sample size was small. Secondly, patients with DCIS histopathologically upgraded to IDC were excluded from our analysis according to the study’s objectives. Thirdly, the lesions were all US and MRI visible which would bias the series toward a higher rate of microinvasive cancer. To date, few features have consistently surfaced as strong predictors of upstaging on excision.

We discovered that the mixed model performed exceptionally well for preoperatively predicting the histological upstage of DCISMI. In our investigation, they were combined into a unique clinicopathologic + conventional MRI + radiomics nomogram, which demonstrated sufficient prediction performance. The clinicopathologic prediction model reflected the role of clinical and pathological baseline information in upstage prediction. While the radiomics model based on DCE-MRI included quantification of pictures, the conventional MRI model based on T2WI-FS and DCE-MRI involved qualitative assessment of images. The mixed model nomogram has the potential to increase diagnostic effectiveness and net benefit over the whole spectrum of threshold probabilities in DCA, in addition to displaying and customizing the likelihood of DCISMI for each patient.

This study had several limitations that should be noted. First, this was a retrospective study with a relatively small sample size at a single center. Second, two different breast MRI protocols (3.0 T Siemens and GE) were used at our hospital during the study period. At the same time, this may reflect the stability of the models in our study. Third, the DWI results were not examined, which would have revealed more data. Fourth, it was difficult to draw the margin of some DCIS lesions with non-mass enhancement. Finally, we did not perform an external validation test using an independent data set, although a 3-fold cross-validation was used. To verify our nomogram, additional external validation utilizing several independent data sets would be required.

Our preoperative nomogram model specifically for DCISMI patients with clinicopathologic, conventional MRI, and DCE-MRI radiomics signatures enabled a more accurate prediction of upstaging in women with biopsy-proven DCIS. This could help to select women who were indicated for sentinel lymph node biopsy at initial surgery, thus avoiding unnecessary axillary surgery and preventing delayed secondary surgery. Although validation requires a larger sample size, DCE-MRI radiomics may discriminate between DCISMI-more and DCISMI-one.