MRI findings of cancers preoperatively diagnosed as pure DCIS at core needle biopsy

Magnetic resonance imaging insights from active surveillance of women with ductal carcinoma in situ

New approaches are needed to determine which ductal carcinoma in situ (DCIS) is at high risk for progression to invasive ductal carcinoma (IDC). We retrospectively studied DCIS patients who declined surgery (2002-2019), and received endocrine therapy (ET) and breast MRI. Baseline MRI and changes at 3 months and 6 months were analyzed by recursive partitioning to stratify IDC risk. Sixty-two patients (63 DCIS; 1 bilateral) with a mean follow-up of 8.5 years were included. Fifty-one percent remained on active surveillance (AS) without evidence of IDC, with a mean duration of 7.6 years. A decision tree based on MRI features of lesion distinctness and background parenchymal enhancement (BPE) at baseline and change after 3 months of ET stratified patients into low, intermediate, and high risk for progression to IDC. MRI imaging features in patients treated with ET and undergoing AS, may help determine which DCIS lesions are at low versus high risk for IDC.

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging

Objective

To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision.

Materials and methods

This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set.

Results

The model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619-0.829) and 0.742 (0.623-0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575-0.892) and 0.7 (0.558-0.842), 0.714 (0.608-0.820) and 0.767 (0.651-0.882), respectively.

Conclusion

Our study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.

The characteristics associated with upgrade on surgical pathology of conventional imaging occult DCIS diagnosed by MRI

PURPOSE

To characterize the clinical, pathological, and imaging features of DCIS occult on conventional imaging diagnosed on MRI-guided biopsy associated with increased risk of invasive disease on surgical excision.

MATERIALS AND METHODS

All consecutive patients with MRI-detected DCIS occult on conventional imaging between January 2009 and December 2018 were included. Women were divided into two groups based on final pathology: Pure DCIS or DCIS with invasive component. Clinical, imaging, and pathological risk factors for upgrade to invasion were evaluated.

RESULTS

Of 50 patients who met the inclusion criteria, 12 (24%) were upgraded to invasive malignancy in the final pathology. The only parameters that showed statistically significant association with upgrade were related to kinetic characteristics: 53% of patients with the combination of fast early upstroke and either plateau or washout curve were upgraded, compared to 12% of women without this combination (p = 0.006). The sensitivity of combined kinetic features for predicting upgrade was 67% (95% CI 35-90%), specificity was 84% (CI 95% 68-94%), positive predictive value was 57% (CI 95% 37-75%), negative predictive value was 89% (CI 95% 77-95%), and OR was 78% (64-88%).

CONCLUSION

Kinetic characteristics show the strongest association with upgrade to invasion in DCIS occult on mammogram and US. Larger studies should be encouraged to consolidate our findings, which may have implication for treatment planning.

The role of MRI and clinicopathologic features in predicting the invasive component of biopsy-confirmed ductal carcinoma in situ

Background

The upgrade rate of biopsy-confirmed ductal carcinoma in situ (DCIS) to invasive carcinoma is up to 50% on final pathology. We investigated MRI and clinicopathologic predictors of the invasive components of DCIS diagnosed by preoperative biopsy and then compared MRI features between patients with DCIS, microinvasive ductal carcinoma (mIDC), and invasive ductal carcinoma (IDC) diagnosed on final pathology.

Methods

Two hundred and one patients with 206 biopsy-confirmed DCIS lesions were enrolled. MRI and clinicopathologic features were used to predict either mIDC or IDC via a cumulative logistic regression analysis. For the lesions detected on MRI, morphologic and kinetic analyses were performed using the Chi-square, Fisher’s exact, and Kruskal-Wallis tests.

Results

Of all the lesions, 112 (54.4%) were diagnosed as DCIS, 50 (24.3%) were upgraded to mIDC, and 44 (21.4%) to IDC. The detection on MRI as mass (Odds ratio (OR) = 8.84, 95% confidence interval (CI) = 1.05–74.04, P = 0.045) or non-mass enhancement (NME; OR = 11.17, 95% CI = 1.35–92.36, P = 0.025), negative progesterone receptor (PR; OR = 2.40, 95% CI = 1.29–4.44, P = 0.006), and high Ki-67 level (OR = 2.42, 95% CI = 1.30–4.50, P = 0.005) were significant independent predictors of histologic upgrade. On MRI, 87 (42.2%) lesions appeared as mass and 107 (51.9%) as NME. Irregularly shaped, not-circumscribed, heterogeneous, or rim-enhancing masses with intratumoral high signal intensity or peritumoral edema, clumped or clustered ring-enhancing NMEs, and high peak enhancement were significantly associated with histologic upgrade (P < 0.001).

Conclusion

MRI detection, negative PR, and high Ki-67 levels are associated with a histologic upgrade in patients with biopsy-confirmed DCIS. Suspicious MRI features are more frequent in such patients.

Predictive factors for the presence of invasive components in patients diagnosed with ductal carcinoma in situ based on preoperative biopsy

Background

In patients diagnosed with ductal carcinoma in situ (DCIS) with needle biopsy before surgery, invasive component (IC) is often found in the postoperative tissue, which results in altered post-surgical care. However, there are no clinically available factors to predict IC, and few MRI studies are available for the detection of IC in DCIS patients. The purpose of this study was to evaluate which risk factors can predict IC preoperatively.

Methods

Patients with a DCIS diagnosis based on preoperative biopsy, who underwent breast surgery Kangbuk Samsung Hospital between Jan 2005 and June 2018, were retrospectively evaluated. Clinico-pathological and breast MRI factors were compared between DCIS and DCIS with IC in postsurgical specimens.

Results

Of the 431 patients with a preoperative diagnosis of DCIS, 34 (7.9%) showed IC during the postoperative pathological investigations, and 217 (50.3%) underwent breast MRI. Among MRI-related factors, Mass-like enhancement on MRI was the sole but significant predictor of IC (HR = 0.26, C.I. = 0.07–0.93, p = 0.038), while nipple-areolar complex invasion, enhancement peak and pattern were not statistically significant. Nuclear grade was the only significant predictor of IC in the analysis of other clinico-pathological factors (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).

Conclusions

Mass-like enhancement on MRI and high nuclear grade were associated with IC in patients with preoperative diagnosis of DCIS. Considering the high sensitivity of breast MRI for IC, further evaluation of the predictive value of MRI in preoperative DCIS patients is desirable.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?

PURPOSE

To assess the ability of algorithmically assessed magnetic resonance imaging (MRI) features to predict the likelihood of upstaging to invasive cancer in newly diagnosed ductal carcinoma in situ (DCIS).

MATERIALS AND METHODS

We identified 131 patients at our institution from 2000-2014 with a core needle biopsy-confirmed diagnosis of pure DCIS, a 1.5 or 3T preoperative bilateral breast MRI with nonfat-saturated T1 -weighted MRI sequences, no preoperative therapy before breast MRI, and no prior history of breast cancer. A fellowship-trained radiologist identified the lesion on each breast MRI using a bounding box. Twenty-nine imaging features were then computed automatically using computer algorithms based on the radiologist's annotation.

RESULTS

The rate of upstaging of DCIS to invasive cancer in our study was 26.7% (35/131). Out of all imaging variables tested, the information measure of correlation 1, which quantifies spatial dependency in neighboring voxels of the tumor, showed the highest predictive value of upstaging with an area under the curve (AUC) = 0.719 (95% confidence interval [CI]: 0.609-0.829). This feature was statistically significant after adjusting for tumor size (P < 0.001).

CONCLUSION

Automatically assessed MRI features may have a role in triaging which patients with a preoperative diagnosis of DCIS are at highest risk for occult invasive disease.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1332-1340.

Preoperative clinicopathologic factors and breast magnetic resonance imaging features can predict ductal carcinoma in situ with invasive components

PURPOSE

Ductal carcinoma in situ (DCIS) is a non-invasive cancerous breast lesion; however, from 10% to 50% of patients with DCIS diagnosed by core needle biopsy (CNB) or vacuum-assisted core biopsy (VACB) are shown to have invasive carcinoma after surgical excision. In this study, we evaluated whether preoperative clinicopathologic factors and breast magnetic resonance image (MRI) features are predictive of DCIS with invasive components before surgery.

MATERIALS AND METHODS

Patients comprised 128 adult women with a diagnosis of DCIS as determined by pathological analysis of CNB or VACB specimens and positive MRI findings who underwent breast surgery during the period January 2011 to December 2013 at the Changhua Christian Hospital. Clinicopathologic and breast MRI factors were compared between patients with postoperative pathology indicative of true DCIS and those with postoperative pathology showing DCIS with invasive components.

RESULTS

Of the 128 patients with a preoperative diagnosis of DCIS, 73 (57.0%) had postoperative histopathologic evidence of true DCIS and 55 (43.0%) showed evidence of DCIS with invasive components. Results of statistical analyses revealed that MRI evidence of a mass-like lesion (P=0.025), nipple-areolar complex (NAC) invasion (P=0.029), larger tumor volume (P=0.010), larger maximum measurable apparent diffusion coefficient (ADC) area (P=0.039), heterogenous or rim enhancement pattern (P=0.010), as well as immunohistochemical evidence of human epidermal growth factor receptor 2 (HER-2) overexpression (P=0.010) were predictive of DCIS with an invasive component in postoperative surgical specimens.

CONCLUSION

Invasive component should be considered in biopsy proven DCIS patients with preoperative MRI evidence of a mass-like lesion, nipple-areolar complex invasion, large tumor volume, a larger maximum measurable ADC area, or a rim or heterogenous enhancement pattern, as well as in patients with immunohistochemical evidence of HER-2 overexpression.

The diagnostic sensitivity of dynamic contrast-enhanced magnetic resonance imaging and breast-specific gamma imaging in women with calcified and non-calcified DCIS

BACKGROUND

Early detection of breast cancer reduces mortality. Therefore, diagnosis of ductal carcinoma in situ (DCIS) is important.

PURPOSE

To compare the sensitivities of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and breast-specific gamma imaging (BSGI) in pathologically proven calcified and non-calcified DCIS.

MATERIAL AND METHODS

Thirty-five patients with pathologically diagnosed DCIS from 1 June 2009 through 31 December 2011, underwent a protocol involving both breast MRI and BSGI. Each image was assessed by a separate dedicated breast radiologist. All lesions were divided into two groups; with or without microcalcifications on mammograms. In cases without microcalcifications, we recorded the mass, asymmetry, or negative findings on mammography. On MRI, the enhancement pattern was categorized as mass or non-mass-like enhancement. On BSGI, the uptake pattern was analyzed. The histopathological features of the lesions were obtained. Statistical analysis of the sensitivity of each modality was performed using McNemar's test.

RESULTS

Thirty-five women with a mean age of 48 years (range, 26-69 years) were enrolled in the study. The total sensitivities of MRI and BSGI in the 35 cases were 91.4% (32 of 35 DCIS) and 68.6% (24 of 35 DCIS), respectively. Eighteen cases with DCIS displayed microcalcifications on mammography, while 17 cases did not. Of these 17 cases without microcalcifications on mammography, 88.2% (15 of 17 DCIS) were detected by MRI and 52.9% (9 of 17 DCIS) by BSGI. Of 18 cases with microcalcifications on mammography, 94.4% (17 of 18 DCIS) were detected by MRI and 83.3% (15 of 19 DCIS) by BSGI.

CONCLUSION

MRI showed a higher sensitivity for the detection of calcified and non-calcified DCIS and is more helpful than BSGI in cases without microcalcifications on mammography.

Features of occult invasion in biopsy-proven DCIS at breast MRI

The purpose of this study is to determine if MRI BI-RADS criteria or radiologist perception correlate with presence of invasive cancer after initial core biopsy of ductal carcinoma in situ (DCIS). Retrospective search spanning 2000-2007 identified all core-biopsy diagnoses of pure DCIS that coincided with preoperative MRI. Two radiologists fellowship-trained in breast imaging categorized lesions according to ACR MRI BI-RADS lexicon and estimated likelihood of occult invasion. Semiquantitative signal enhancement ratio (SER) kinetic analysis was also performed. Results were compared with histopathology. 51 consecutive patients with primary core biopsy-proven DCIS and concurrent MRI were identified. Of these, 13 patients (25%) had invasion at excision. Invasion correlated significantly with presence of a mass for both readers (p = 0.012 and 0.001), rapid initial enhancement for Reader 1 (p = 0.001), and washout kinetics for Reader 2 (p = 0.012). Significant correlation between washout and invasion was confirmed by SER (p = 0.006) when threshold percent enhancement was sufficiently high (130%), corresponding to rapidly enhancing portions of the lesion. Radiologist perception of occult invasion was strongly correlated with true presence of invasion. These results provide evidence that certain BI-RADS MRI criteria, as well as radiologist perception, correlate with occult invasion after an initial core biopsy of DCIS.