Pure Ductal Carcinoma In Situ Research Articles

Abstract PO3-20-08: Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation

Abstract Purpose: To introduce clinical cases of ductal carcinoma in situ (DCIS) underestimation focusing on additional benefits of contrast-enhanced spectral mammography (CESM). Background: DCIS, a noninvasive breast cancer, is often underestimated during diagnosis. Predicting which cases will upgrade to invasive cancer is crucial. CESM offers superior diagnostic performance compared to mammography and mammography with targeted ultrasound (US). Its low-energy images improve microcalcification conspicuity, enable accurate tumor extent measurement, and provide information on enhancements associated with nonmass microcalcifications. Enhanced areas on CESM can be easily compared with mammographic microcalcifications, for they are obtained during the same session. Indicators heralding DCIS underestimation 1. Detectability: underestimated DCIS is more likely to be detected on CESM and mammogram than pure DICS, whereas detection rates do not statistically differ on US and MRI. 2. Lesion type: underestimated DCIS mostly presents as enhancing mass or nonmass lesions on CESM and calcifications on low-energy conventional mammography. 3. Disease extent: on CESM, pure DCIS show smaller tumor extent than the underestimated ones. 4. CESM gray value (CGV) of the lesion on recombined image: the pure DCIS types show lower mean, standard deviation, maximum, and difference values, whereas the underestimated types demonstrate lower minimum values, and vice versa. Case 1: Screen detected pure DCIS without postsurgical upgrade in an asymptomatic 44-year-old woman. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 1.5 cm pure ductal carcinoma in situ, intermediate nuclear grade with necrosis. (A) US shows a 1.3 cm irregular hypoechoic mass in the right breast. (B) Routine mammography demonstrates no definite abnormality in the right breast. (C) CESM shows a 1.2 cm irregular enhancing mass in the upper central portion of right breast and CGV was 2034 AU. (D) Contrast-enhanced MR images show a 2.1 cm irregular enhancing mass in 12 o’clock direction of the right breast. Case 2: Screen detected DCIS with postsurgical upgrade to microinvasive ductal carcinoma in a 64-year-old woman with nonspecific discomfort in her right breast. US-guided core biopsy yielded DCIS, high nuclear grade with necrosis. Postsurgical pathology confirmed a 2.8 cm sized microinvasive ductal carcinoma, high nuclear grade, histologic grade 3 with necrosis. (A) US demonstrates a 1.9 cm irregular hypoechoic mass with calcifications in 9 o’clock direction 2.1 cm from the nipple in the right breast. (B) Right magnification view shows a 2.5 cm-extent segmentally distributed fine pleomorphic calcifications in the right breast. (C) CESM shows a 3 cm irregular enhancing mass with calcifications in the right breast, and CGV was 2045 AU. (D) Contrast-enhanced MR images shows a 2.3 cm irregular enhancing mass in the right breast. Case 3: Screen detected DCIS with postsurgical upgrade to invasive ductal carcinoma with DCIS in a 47-year-old woman who had a family history of breast cancer. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 0.2 cm invasive ductal carcinoma with 2.4 cm DCIS. (A) US demonstrates a 2.3 cm irregular hypoechoic mass in the left breast. (B) Routine mammography shows asymmetry in the upper portion of the left breast. (C) CESM shows a 3 cm enhancing mass in the left breast, and CGV of the lesion on MLO-recombined image was 2072 AU. (D) Contrast-enhanced MR image shows a 6 cm irregular enhancing mass in the upper portion of the left breast. Conclusion: CESM provides added advantages over mammography and breast US in detecting underestimated DCIS, and it is comparable to breast MRI. Key indicators to consider include detectability, enhancing lesion, larger tumor extent, higher mean CGV on CESM. Citation Format: Minji Song, Hee Jung Shin. Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-08.

Abstract PO3-28-10: MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ

Abstract INTRODUCTION Ductal carcinoma in situ (DCIS) is a non-lethal pre-invasive breast cancer that can co-exist with invasive disease. Dynamic contrast-enhanced (DCE) MRI is sensitive for the detection of high-grade DCIS and invasive cancer while Oncotype DCIS Score is a 12-gene assay that can assess recurrence risk. In practice, it is difficult to distinguish low- from high-risk DCIS, which leads to overtreatment for up to half of women diagnosed with DCIS. We hypothesize that radiomic phenotypes of DCIS derived from DCE-MRI data may serve as prognostic markers to improve risk stratification by capturing disease heterogeneity. Here we evaluate the ability of these phenotypes to predict DCIS Score and upstaging of DCIS to invasive disease on wide local excision in a multicenter trial. METHODS Data: DCE-MRI data from the ECOG-ACRIN E4112 trial were retrospectively analyzed. Primary analysis focused on participants with data on disease upstaging (N=295), with secondary analysis in a subset (N=174) of participants with DCIS Scores (dichotomized as >55 and 55) and pure DCIS. Clinical information included patient demographics, lesion morphology on MRI, background parenchymal enhancement, DCIS grade, central necrosis, and hormone receptor status. Data analysis: Radiologist-drawn lesion segmentations and publicly available software, CaPTk, were used to compute 64 radiomic features from first post-contrast images for each participant. Radiomic phenotypes were identified using hierarchical clustering on the extracted features. A Chi-square test was used to evaluate the association between radiomic phenotypes and each outcome. The likelihood ratio test was used to compare two logistic regression models: 1)clinical model using only clinical information as predictors and 2) clinical+phenotypes model using clinical information and phenotype assignment as predictors. Each model was used for the prediction of upstaging to invasive disease and DCIS score. Model performance was evaluated as the 10-fold cross-validated area under the receiver operator characteristic curve (AUC). A p< 0.05 was considered significant. RESULTS A total of 45 (15%) cases upstaged to invasive disease. Two radiomic phenotypes were identified: Phenotype 1 indicated greater lesion signal heterogeneity, while Phenotype 2 indicated lower heterogeneity. Radiomic phenotype was strongly associated with disease upstaging (p=0.0034) – with a higher rate of upstaging for Phenotype 1 – but not with DCIS score (p=0.1174, Table 1). For predicting disease upstaging, the clinical+phenotypes model yielded a higher AUC=0.72 and a significantly better fit to the data (p=0.0022) compared to the clinical model parameterized by clinical information alone (AUC=0.69). For predicting DCIS Score, the clinical+phenotypes model (AUC=0.77) showed similar performance compared to the clinical model (AUC=0.76) and no significant improvement in fit to the data (p=0.2920). CONCLUSION Radiomic phenotypes capturing disease heterogeneity show promise as prognostic predictors for predicting disease upstaging in DCIS compared to clinical information alone and may enable more efficient disease management. We observed that phenotypes did not have independent predictive outcome for DCIS score, suggesting that MRI and DCIS Score offer independent information and could be combined in future models to better predict disease recurrence or progression. Clinical applications of radiomic phenotypes may improve risk stratification and potentially result in decreased overtreatment of women diagnosed with DCIS. Table 1: Association between radiomic phenotypes and DCIS outcomes using a Chi square test. Citation Format: Kalina Slavkova, Ruya Kang, Vivian Belenky, Anum Kazerouni, Debosmita Biswas, Hannah Horng, Rhea Chitalia, Michael Hirano, Jennifer Xiao, Ralph Corsetti, Sarah Javid, Derrick Spell, Antonio Wolff, Joseph Sparano, Seema Khan, Christopher Comstock, Justin Romanoff, Jon Steingrimsson, Constantine Gatsonis, Constance Lehman, Savannah Partridge, Despina Kontos, Habib Rahbar. MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-28-10.

Abstract PO2-28-07: Artificial Intelligence-based Computer-aided Diagnosis for Mammography: Predicting Occult Invasive Cancer in Women with Biopsy-proven Ductal Carcinoma in Situ

Abstract Background: Ductal carcinoma in situ (DCIS) is a noninvasive breast cancer confined within the basement membrane. Due to the increase in breast cancer screening, DCIS is increasingly being diagnosed on biopsy. Active surveillance is a novel and evolving treatment strategy for DCIS to reduce overtreatment. Currently ongoing clinical trials of active surveillance include only mammographic findings in their eligibility criteria, however mammography has limited ability to detect occult invasive cancer. The objective of this study was to investigate whether an artificial intelligence-based computer-aided diagnosis (AI-CAD) application for mammography can improve the prediction of occult invasive cancer in women with percutaneous needle biopsy-proven DCIS. Methods: A retrospective search of our database identified consecutive women with percutaneous needle biopsy-proven DCIS who underwent surgery at Seoul National University Hospital (Seoul, Korea) between June 2019 and May 2021. Two board-certified breast radiologists reviewed preoperative mammographic findings of the primary tumor in consensus according to the Breast Imaging Reporting and Data System. Using a commercially available AI-CAD (Lunit INSIGHT for Mammography, Lunit Inc.), quantitative abnormality scores of 0 to 100% of the ipsilateral breast were obtained. Data were collected on age at diagnosis, symptoms, biopsy methods, DCIS grade on biopsy, and presence or absence of invasive cancer and axillary lymph node metastases on surgical pathology. Multivariable logistic regression models were used to evaluate factors associated with prediction of occult invasive cancer. Results: Of 692 biopsy-proven DCIS in 692 women (mean age, 53 years ± 11 [standard deviation]), 298 (43%) presented as calcifications only, 160 (23%) as mass or asymmetry, and 111 (16%) as masses with calcifications. The other 123 (18%) cancers were not demonstrable on mammography. Surgical pathology revealed occult invasive cancer in 278 (40%) women and 18 (3%) had axillary metastasis. Women with occult invasive cancer showed higher abnormality scores on AI-CAD than women with pure DCIS (mean, 70% vs. 51%; P < .001), and women with axillary metastasis had higher abnormality scores on AI-CAD than women without axillary metastasis (mean, 88% vs. 58%; P = .002). High ( >75%) abnormality score on AI-CAD (Odds ratio [OR], 1.8 [95% CI: 1.3, 2.6]; P < .001), age of 55 years or higher (OR, 1.6 [95% CI: 1.2, 2.3]; P =.005), present symptoms in the ipsilateral breast (OR, 2.7 [95% CI: 1.9, 4.0]; P < .001), biopsy performed with a 14-gauge automated gun (OR, 2.5 [95% CI: 1.7, 3.6]; P < .001), and high-grade DCIS at biopsy (OR, 2.5 [95% CI: 1.7, 3.5]; P < .001] were independent predictors of occult invasive cancer with a C-index of 0.75 (95% CI: 0.72, 0.78). Conclusion: The quantitative score of preoperative mammography obtained from a commercially available AI-CAD was able to predict occult invasive cancer in an easy and simple manner. AI-CAD applications for preoperative mammography may be useful in predicting occult invasive cancer in women with biopsy-proven DCIS, especially in those without preoperative breast MRI. Citation Format: Su Hyun Lee, Hajung Kim, Su Min Ha, Soo-Yeon Kim, Jung Min Chang, Nariya Cho, Woo Kyung Moon. Artificial Intelligence-based Computer-aided Diagnosis for Mammography: Predicting Occult Invasive Cancer in Women with Biopsy-proven Ductal Carcinoma in Situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-07.

Abstract PO4-13-02: Molecular Expression Assays Improve the Prediction of Local and Invasive Local Recurrence after Breast Conserving Surgery for DCIS

Abstract Introduction Ductal carcinoma in situ (DCIS) is routinely treated with adjuvant radiotherapy (RT) after breast conserving surgery (BCS) in order to reduce the risk of local recurrence (LR) and invasive LR. Nomograms based on clinicopathological features (CPF) and molecular expression assays have been developed in an effort to provide individualized risk estimates and personalize decision-making. However, molecular assays are costly and it remains unclear if they provide more accurate recurrence risk estimates compared to algorithms based on CPF alone. We examined the impact of the 12-Gene DCIS Score (DS) and the 21-Gene Recurrence Score (RS) molecular expression assays, in addition to CPF, on the accuracy of predicting 10-year LR and invasive LR risk compared to predicted estimates based on CPF alone. In addition, we examined if a model including the 21-Gene RS improves the 10-year predicted risks of invasive LR after BCS for DCIS compared to estimates based on the DS+CPF or CPF alone. Methods We used a population-based cohort diagnosed with pure DCIS treated with BCS +/- RT from 1994-2003. All cases had expert pathology review providing contemporary assessment of diagnosis, margin status, margin width, multifocality, presence and extent of comedo necrosis, subtype, nuclear grade, and tumor size. For each case, a representative tissue block or unstained slide was sent to measure the 12-Gene DS and 21-Gene RS. Predictive models were developed using multivariable Cox regression analyses with backward selection and included all CPF, treatment with RT, and interactions. The performance of each model was evaluated based on c-statistic, -2log likelihood estimate (-2LLE), and Akaike information criterion (AIC). Calibration was performed using bootstrap resamples, with replacement. We compared the performance of the best model derived from CPF alone, the 12-Gene DS with CPF, and the 21-Gene RS with CPF on their ability to predict the 10 year risks of LR and invasive LR measured against outcomes observed in the cohort. Results The population-based cohort includes 1226 women, 514 were treated with BCS alone and 712 were treated with BCS + RT. Median age was 56 years. Median follow-up was 10 years. Fifty-two percent of tumors were between 1 and 2.5 cm, 35% were ≤1cm, and 13% were >2.5 cm. Comedo necrosis was present in 68%, and nuclear grade was low, moderate, and high in 7%, 54%, and 39%, respectively. Margins were negative in 90.5% of cases (N=1109). The 12-Gene DS was low, intermediate, and high in 53.5%, 20.9%, and 25.6% and the 21-Gene RS was >25 in 30% of patients. 194 women (15.8%) experienced ipsilateral LR as a first event; 112 were invasive LR. Models including either the DS or RS expression assays performed better in predicting the 10-year risk of LR after BCS compared to the model based on CPFs alone, demonstrating higher c-statistics (0.705, 0.699, and 0.662, respectively), lower AIC and lower -2LLE. The two molecular-based predictive models also performed better in predicting the risk of invasive LR compared to CPF model, although with smaller differences in c-statistics (0.684, 0.683, and 0.667, respectively), AIC or -2LLE. The predictive model based on the 21-Gene RS with CPF did not perform better in the prediction of the 10 year risk of invasive LR compared the 12-Gene DS + CPF model. All models were well calibrated. Conclusion The predictive model based on the 12-Gene DS with CPF more accurately predicted the 10-year risk of LR and invasive LR after BCS compared to model based on CPF alone. Inclusion of the 21-Gene RS with CPF did not improve the prediction of the 10-year risk of LR or invasive LR. This suggests that nomograms that include the 12-Gene assay with CPF provide more accurate individualized estimates of recurrence risk after BCS and can help improve personalized decision-making in the management of DCIS. Citation Format: Ezra Hahn, Rinku Sutradhar, Lawrence Paszat, Lena Nguyen, Danielle Rodin, Sharon Nofech-Mozes, Sabina Trebinjac, Cindy Fong, Eileen Rakovitch. Molecular Expression Assays Improve the Prediction of Local and Invasive Local Recurrence after Breast Conserving Surgery for DCIS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-02.

Clinical and Imaging Characteristics of Contrast-enhanced Mammography and MRI to Distinguish Microinvasive Carcinoma from Ductal Carcinoma In situ

Rationale and ObjectivesThe prognosis of ductal carcinoma in situ with microinvasion (DCISM) is more similar to that of small invasive ductal carcinoma (IDC) than to pure ductal carcinoma in situ (DCIS). It is particularly important to accurately distinguish between DCISM and DCIS. The present study aims to compare the clinical and imaging characteristics of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) between DCISM and pure DCIS, and to identify predictive factors of microinvasive carcinoma, which may contribute to a comprehensive understanding of DCISM in clinical diagnosis and support surveillance strategies, such as surgery, radiation, and other treatment decisions. Materials and MethodsForty-seven female patients diagnosed with DCIS were included in the study from May 2019 to August 2023. Patients were further divided into two groups based on pathological diagnosis: DCIS and DCISM. Clinical and imaging characteristics of these two groups were analyzed statistically. The independent clinical risk factors were selected using multivariate logistic regression and used to establish the logistic model [Logit(P)]. The diagnostic performance of independent predictors was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test. ResultsIn CEM, the maximum cross-sectional area (CSAmax), the percentage signal difference between the enhancing lesion and background in the craniocaudal and mediolateral oblique projection (%RSCC, and %RSMLO) were found to be significantly higher for DCISM compared to DCIS (p = 0.001; p < 0.001; p = 0.008). Additionally, there were noticeable statistical differences in the patterns of enhancement morphological distribution (EMD) and internal enhancement pattern (IEP) between DCIS and DCISM (p = 0.047; p = 0.008). In MRI, only CSAmax (p = 0.012) and IEP (p = 0.020) showed significant statistical differences. The multivariate regression analysis suggested that CSAmax (in CEM or MR) and %RSCC were independent predictors of DCISM (all p < 0.05). The area under the curve (AUC) of CSAmax (CEM), %RSCC (CEM), Logit(P) (CEM), and CSAmax (MR) were 0.764, 0.795, 0.842, and 0.739, respectively. There were no significant differences in DeLong's test for these values (all p > 0.10). DCISM was significantly associated with high nuclear grade, comedo type, high axillary lymph node (ALN) metastasis, and high Ki-67 positivity compared to DCIS (all p < 0.05). ConclusionThe tumor size (CSAmax), enhancement index (%RS), and internal enhancement pattern (IEP) were highly indicative of DCISM. DCISM tends to express more aggressive pathological features, such as high nuclear grade, comedo-type necrosis, ALN metastasis, and Ki-67 overexpression. As with MRI, CEM has the capability to help predict when DCISM is accompanying DCIS.

Expression of immune-related genes and breast cancer recurrence in women with ductal carcinoma in situ

Background and aimDuctal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer with highly variable clinical behavior, but risk stratification is still challenging. We sought to identify immune-related gene expression signatures of pure DCIS associated with different risks of breast cancer recurrence. MethodsA retrospective nested case-control study of 143 pure DCIS was performed including 70 women with subsequent ipsilateral breast event (IBE, in situ or invasive; cases) and 73 DCIS women with no IBE and matched for age, tumor size, treatment, hormone receptors/HER2 status, and follow-up time (controls). RNA was extracted from DCIS samples and subjected to next-generation sequencing gene expression analysis of 395 immune-related genes. Correlations between DCIS immune-related gene expression and IBE were analyzed using weighted Cox regression for nested case-control data. ResultsEight immune-related genes were differentially expressed between cases and controls. MAGEA10 expression (present vs. absent) and high expression levels of IFNA17 and CBLB (Q4 vs. Q1) were observed more frequently in DCIS of women with subsequent IBE, mainly invasive (p-valueFDR < 0.05). Conversely, expression of IL3RA1, TAGAP, TNFAIP8, and high expression levels of CCL2 and LRP1 were associated with a lower risk of IBE (p-valueFDR < 0.05). ConclusionThis exploratory analysis of pure DCIS showed significant differences in immune-related gene expression profiles between women with and with no subsequent IBE, particularly as invasive IBE. These results, after additional validation, could improve risk stratification and management of DCIS patients.

Abstract NG02: Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Abstract NG02: Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ.

Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

ObjectiveTo investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment. MethodsConsecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established. ResultsA total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867. ConclusionMulti-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

Abstract A012: Molecular profiling of neoductgenesis in early stage breast cancer for the development of novel diagnostic biomarkers

Abstract Ductal carcinoma in situ (DCIS) is the most common precancerous finding in mammography screens. DCIS generally has a good prognosis, and only 20-25% of the cases progress to invasive breast carcinoma (IBC) even if left untreated. Breast pathologists and radiologists have suggested that there are currently more than one kind of DCIS under the same diagnosis. A subset of DCIS cases with reduced survival rate are associated with casting-type calcifications that can be histologically detected within closely packed, contorted, newly formed ducts, a phenomenon called neoductgenesis. Tumors with neoductgenesis are often erroneously diagnosed as DCIS, while in fact, they are associated with higher degree of metastasis and should be considered invasive carcinomas. This tumor category currently lacks specific histopathological markers and the biological processes leading to metastasis via neoductgenesis remain poorly characterised. To reveal new information of this breast tumor type, we are currently conducting comprehensive characterization of FFPE tissue samples of DCIS cases with and without neoductgenesis using imaging mass cytometry analysis and immunohistochemistry of specific tumor areas. Preliminary results on immunofluorescence staining suggest that neoductgenesis is associated with changes in the expression of specific myoepithelial marker proteins compared to areas with pure DCIS. In addition, we are analysing the three-dimensional ductal architecture of these lesions. The information obtained from this study can lead to development of new diagnostic tools for the identification of early breast cancers with a higher risk of spreading. Early detection of these cases is important to ensure the effectiveness and timeliness of treatment, and at the same time to improve the patients' quality of life. Citation Format: Laura Lehtinen, Leena Koskinen, Markus Peurla, Emilia Huotari, Otto Kauko, Peter Dean, Pauliina Hartiala, Pia Boström, Emilia Peuhu. Molecular profiling of neoductgenesis in early stage breast cancer for the development of novel diagnostic biomarkers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A012.

Abstract PR05: Spatial transcriptomics of ductal carcinoma in situ reveal subtype specific differences in tumoral and stromal cell compartments

Abstract Up to 50% of patients diagnosed with ductal carcinoma in situ (DCIS) never experience progression to invasive disease, even if left untreated. Current knowledge of what makes DCIS become invasive is limited and we lack diagnostic tools to predict which patients can be spared treatment. Escape of tumor cells from the breast ducts is influenced by characteristics of the tumor cells, the microenvironment surrounding the ducts, and the interplay between the two. Intraductal tumor cells in DCIS are not physically in contact with extraductal stromal cells. Until now, analyses of DCIS tissue have mainly been performed on bulk tissue, however, this approach does not take into consideration the unique morphology of DCIS. We used the Nanostring GeoMX® digital spatial profiling platform to determine the transcriptome of DCIS tumor cells and the surrounding stromal cells separately. From a large cohort of &amp;gt;500 DCIS cases from Akershus and Oslo University Hospitals, we selected 23 pure DCIS cases, grade 3, of different molecular subtypes: triple negative (TN), Luminal A (LumA) and HER2-enriched. At least four tumor-stroma pairs were selected from each case. Bioinformatic analyses were used to explore both tumor and stroma expression data and the interplay between the two cellular compartments. There were distinct gene expression differences between DCIS of different subtypes. Intertumoral heterogeneity was larger than intratumoral heterogeneity in all subtypes. This was also apparent in stromal cell compartments, although less pronounced. Gene ontology analyses of DCIS tumor cells from each of the three subtypes showed several overlapping biological processes, suggesting common mechanisms for regulating tumor cell growth in DCIS. We also found subtype specific differences: Tumor cells of the TN subtype showed upregulation of protein synthesis. Processes associated with RNA splicing were upregulated in tumor cells of the LumA subtype, whereas the HER2-enriched subtype showed increased DNA repair activity. The immune microenvironment differed between the subtypes: LumA DCIS were characterized by lower immune cell infiltration than TN and HER2-enriched, however, there was variation between and within the cases. Using in silico cell deconvolution, we found that B-cells were more abundant in HER2-enriched tumors, while T-cells were more common in TN and LumA. T-regulatory cells were found in all subtypes. The GeoMX® digital spatial profiling platform is well suited for exploring the transcriptome of DCIS samples. We found biologically relevant differences between tumor cells of the different molecular subtypes already at the DCIS stage in breast cancer progression. The immune cell composition surrounding DCIS lesions also differed between the subtypes. Further studies of the tumor-stroma interplay are required to understand the processes involved in progression of DCIS to invasive disease. Citation Format: Helga Bergholtz, Jens Henrik Norum, Tonje G Lien, Hossein Schandiz, Torill Sauer, Jürgen Geisler, Therese Sørlie. Spatial transcriptomics of ductal carcinoma in situ reveal subtype specific differences in tumoral and stromal cell compartments [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr PR05.

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis. To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome. Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004). Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.

Survival Outcomes Based on Axillary Surgery in Ductal Carcinoma In Situ: A Nationwide Study From the Korean Breast Cancer Society.

In total mastectomy (TM), sentinel lymph node biopsy (SLNB) is recommended but can be omitted for breast-conserving surgery (BCS) in patients with ductal carcinoma in situ (DCIS). However, concerns regarding SLNB-related complications and their impact on quality of life exist. Consequently, further research is required to evaluate the role of axillary surgeries, including SLNB, in the treatment of TM. We aimed to explore the clinicopathological factors and outcomes associated with axillary surgery in patients with a final diagnosis of pure DCIS who underwent BCS or TM. We retrospectively analyzed large-scale data from the Korean Breast Cancer Society registration database, highlighting on patients diagnosed with pure DCIS who underwent surgery and were categorized into two groups: BCS and TM. Patients were further categorized into surgery and non-surgery groups according to their axillary surgery status. The analysis compared clinicopathological factors and outcomes according to axillary surgery status between the BCS and TM groups. Among 18,196 patients who underwent surgery for DCIS between 1981 and 2022, 11,872 underwent BCS and 6,324 underwent TM. Both groups leaned towards axillary surgery more frequently for large tumors. In the BCS group, clinical lymph node status was associated with axillary surgery (odds ratio, 11.101; p = 0.003). However, in the TM group, no significant differences in these factors were observed. Survival rates did not vary between groups according to axillary surgery performance. The decision to perform axillary surgery in patients with a final diagnosis of pure DCIS does not affect the prognosis, regardless of the breast surgical method. Furthermore, regardless of the breast surgical method, axillary surgery, including SLNB, should be considered for high-risk patients, such as those with large tumors. This may reduce unnecessary axillary surgery and enhance the patients' quality of life.

Factors Associated With Node-Positive Disease in Estrogen Receptor-Positive Breast Cancer Patients

IntroductionLarger tumor size and shorter tumor-to-nipple distance at diagnosis are associated with greater risk of lymph node involvement in breast cancer. However, the relationship between receptor subtype status and lymph node metastasis remains unclear. Our objective was to examine the association between primary tumor size, location, and nodal metastasis across estrogen receptor (ER)+/ progesterone receptor (PR)+/ human epidermal growth factor receptor 2 (HER2)-, ER+/PR-/HER2-, ER+/PR+/HER2+, and ER+/PR-/HER2+ tumors. MethodsA single-institution retrospective chart review was conducted of breast cancer patients diagnosed between 1998 and 2019 who underwent nodal evaluation during primary surgery. Neoadjuvant chemotherapy, pure ductal carcinoma in situ, inflammatory, recurrent, metastatic, bilateral, multicentric, and multifocal disease were excluded. Descriptive statistics (proportions and frequencies for categorical variables and medians [Q1-Q3] for continuous variables) were used to summarize patient characteristics. Kruskal–Wallis test was applied to test the association of outcome variables and continuous variables. Chi-square test or Fisher exact test was applied to test the association of outcome variables and categorical variables. ResultsSix hundred eighteen ER + patients had a median tumor size of 1.7 cm (1.1-2.5 cm). Two hundred ninety six out of 618 (47.9%) were node-positive and 188/618 (30.4%) had axillary dissection. Eighty four point three percent of patients were ER+/PR+/HER2-, 6.31% were ER+/PR-/HER2-, 6.96% were ER+/PR+/HER2+, and 1.13% were ER+/PR-/HER2+. Median tumor size was significantly larger in node-positive cases compared to node-negative cases in ER+/PR+/HER2-, ER+/PR+/HER2+, and ER+/PR-/HER2- subgroups. In ER+/PR+/HER2-patients, median tumor-nipple distance was significantly shorter in node-positive patients compared to node-negative patients. Upper outer quadrant location was significantly associated with nodal positivity in ER+/PR-/HER2- patients. ConclusionsAcross ER + patients, the significance between tumor size, location, and lymph node positivity varied significantly when differentiating by PR and HER2 status.

Preoperative breast MRI positively impacts surgical outcomes of needle biopsy-diagnosed pure DCIS: a patient-matched analysis from the MIPA study.

To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS). The MIPA observational study database (7245 patients) was searched for patients aged 18-80years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs). Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111). Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation. When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies. • Confounding factors cause imbalance when investigating the influence of preoperative MRI on surgical outcomes of pure DCIS. • When patient matching is applied to women with pure unilateral DCIS, reoperation rates are significantly reduced in women who underwent preoperative MRI. • The reduction of reoperations brought about by preoperative MRI is more than double the increase in overall mastectomies.

The Feasibility of Omitting Postoperative Radiotherapy in Japanese Patients With Ductal Carcinoma In Situ of Breast Treated With Breast-Conserving Surgery.

Background To analyze the feasibility of omitting postoperative radiotherapy (PORT) after breast-conserving surgery (BCS) in Japanese patients with ductal carcinoma in situ (DCIS). Materials and methods We retrospectively analyzed 88 patients with small pure DCIS (median diameter 1.1 cm, ≤ 4 cm) who underwent BCS with (n = 39) or without (n = 49) PORT.The primary and secondary endpoints were ipsilateral breast tumor recurrence (IBTR) and overall survival (OS), respectively, between the groups that received PORT and those that did not. Results The PORT group included a high number of margin-positive cases. The incidence of IBTR was 2.4% (95% confidence interval (CI), 0.3-15.7%) and 2.8% (95% CI, 0.4-18.2%) at five years and 5.5% (95% CI, 1.4-20.6%) and 2.8% (95% CI, 0.4-18.2%) at 10 years in patients without and with PORT, respectively (p = 0.686). In the margin-negative group, only one patient showed IBTR without RT (2.3%), whereas no patient with PORT experienced IBTR (0%). To date, there have been no regional or distant metastases; therefore, no patient has experienced breast cancer-related deaths.The OS rates were 97.7% (95% CI, 84.9-99.6%) and 100% at 10 years in patients without and with PORT, respectively (p = 0.372). Conclusion This study suggests that the omission of PORT after BCS could be a feasible option for selected Japanese patientsbut requires further investigation to identify the low-risk factor in patients who can omit PORT.

Ductal Carcinoma Arising in a Squamous Epithelial Inclusion Cyst within an Axillary Lymph Node: A Challenging Nodal Metastasis

Introduction. Assessment of axillary lymph nodes in breast carcinoma is an important part of staging to guide appropriate clinical management. Lymph node inclusions of different types, including nevoid, squamous, and glandular, are rare but have been reported in multiple different anatomic locations including the axilla. These can result in diagnostic challenges and pose risks of misdiagnoses. Rarely, malignancies may arise intrinsic to otherwise incidental benign nodal inclusions. Case Presentation. We report a case of ductal carcinoma diagnosed within a squamous epithelial inclusion cyst within an axillary lymph node in a patient with pure ductal carcinoma in situ (DCIS) of the ipsilateral right breast. To our knowledge, this is the fifth report in the literature of breast carcinoma confirmed within an axillary inclusion in a patient with pure DCIS. Evaluation of the primary DCIS and lymph node inclusions, by routine and immunohistochemical stains, was performed for assessment. Discussion. The presence of lymph node inclusions can pose a challenge in assessment of benignity and malignancy, on frozen and permanent histologic sections. Pathologists should carefully evaluate lymph node inclusions to ensure that intrinsic malignancies are not missed within rare otherwise benign appearing incidental epithelial rests.

Role of contrast-enhanced mammography in the preoperative detection of ductal carcinoma in situ of the breasts: a comparison with low-energy image and magnetic resonance imaging.

To compare contrast-enhanced mammography (CEM) with low-energy image (LEI) alone and with magnetic resonance imaging (MRI) in the preoperative diagnosis of ductal carcinoma in situ (DCIS). In this single-center retrospective study, we reviewed 98 pure DCIS lesions in 96 patients who underwent CEM and MRI within 2weeks preoperatively. The diagnostic performances of each imaging modality, lesion morphology, and extent were evaluated. The sensitivity of CEM to DCIS was similar to that of MRI (92.9% vs. 93.9%, p = 0.77) and was significantly higher than that of LEI alone (76.5%, p = 0.002). The sensitivity of CEM to calcified DCIS (92.4%) was not significantly different from LEI alone (92.4%) and from MRI (93.9%, p = 1.00). However, CEM contributed to the simultaneous comparison of calcifications with enhancements. CEM had considerably higher sensitivity compared with LEI alone (93.8% vs. 43.8%, p < 0.001) and performed similarly to MRI (93.8%, p = 1.00) for noncalcified DCIS. All DCIS lesions were enhanced in MRI, whereas 94.9% (93/98) were enhanced in CEM. Non-mass enhancement was the most common presentation (CEM 63.4% and MRI 66.3%). The difference between the lesion size on each imaging modality and the histopathological size was smallest in MRI, followed by CEM, and largest in LEI. CEM was more sensitive than LEI alone and comparable to MRI in DCIS diagnosis. The enhanced morphology of DCIS in CEM was consistent with that in MRI. CEM was superior to LEI alone in size measurement of DCIS. This study investigated the value of CEM in the diagnosis and evaluation of DCIS, aiming to offer a reference for the selection of examination methods for DCIS and contribute to the early diagnosis and precise treatment of DCIS. • DCIS is an important indication for breast surgery. Early and accurate diagnosis is crucial for DCIS treatment and prognosis. • CEM overcomes the deficiency of mammography in noncalcified DCIS diagnosis, exhibiting similar sensitivity to MRI; and CEM contributes to the comparison of calcification and enhancement of calcified DCIS, thereby outperforming MRI. • CEM is superior to LEI alone and slightly inferior to MRI in the size evaluation of DCIS.

Clinical presentations and outcomes of young women aged

Ductal carcinoma in situ (DCIS) in young women is rare and not well studied. Since they do not qualify for mammographic screening, they are more likely to present with symptoms. Young women have also been associated with poorer outcomes, but it is unknown whether presentation mode affects outcome. We aimed to compare characteristics of DCIS patients <40 years of age presenting with symptoms versus those without, and determine whether presentation mode affects recurrence. Pure DCIS patients aged <40 years were retrospectively analyzed. Clinical presentation, pathology and recurrence data were collected. Statistical analysis was performed to investigate the correlation of presentation mode with outcomes. 40 patients with 41 cases were included. The mean age at diagnosis was 32.3 years (range 17-39). 73.2% and 26.8% presented with symptoms or abnormal imaging respectively. Of the cases who presented with symptoms, 86.7%, 10.0% and 3.3% had palpable lump, nipple discharge or breast pain, respectively. The average tumor size was 22.0 mm (range 2.0-86.9) and 12.2 mm (range 3-25) for patients who presented with symptoms and non-symptomatic group, respectively. Cases presenting with symptoms were statistically associated with higher grade (p =0.0090). On median follow-up of 85 months, there were 3 (7.3%) recurrences, which were not statistically associated with presentation mode. Young women with DCIS tend to present with symptoms, with breast lump as the commonest symptom. Symptomatic patients tend to be associated with grade III tumours, compared to non-symptomatic patients. On long-term follow-up, mode of presentation was not statistically associated with recurrence.

Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging.

Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.