Ductal Carcinoma In Situ of Breast: From Molecular Etiology to Therapeutic Management

Differentiation of histological calcification classifications in breast cancer using ultrashort echo time and chemical shift-encoded imaging MRI

Introduction

Ductal carcinoma in situ (DCIS) accounts for 25% of newly diagnosed breast cancer cases with only 14%-53% developing into invasive ductal carcinoma (IDC), but currently overtreated due to inadequate accuracy of mammography. Subtypes of calcification, discernible from histology, has been suggested to have prognostic value in DCIS, while the lipid composition of saturated and unsaturated fatty acids may be altered in de novo synthesis with potential sensitivity to the difference between DCIS and IDC. We therefore set out to examine calcification using ultra short echo time (UTE) MRI and lipid composition using chemical shift-encoded imaging (CSEI), as markers for histological calcification classification, in the initial ex vivo step towards in vivo application.

Methods

Twenty female patients, with mean age (range) of 57 (35-78) years, participated in the study. Intra- and peri-tumoural degree of calcification and peri-tumoural lipid composition were acquired on MRI using UTE and CSEI, respectively. Ex vivo imaging was conducted on the freshly excised breast tumour specimens immediately after surgery. Histopathological analysis was conducted to determine the calcification status, Nottingham Prognostic Index (NPI), and proliferative activity marker Ki-67.

Results

Intra-tumoural degree of calcification in malignant classification (1.05 ± 0.13) was significantly higher (p = 0.012) against no calcification classification (0.84 ± 0.09). Peri-tumoural degree of calcification in malignant classification (1.64 ± 0.10) was significantly higher (p = 0.033) against no calcification classification (1.41 ± 0.18). Peri-tumoural MUFA in malignant classification (0.40 ± 0.01) was significantly higher (p = 0.039) against no calcification classification (0.38 ± 0.02). Ki-67 showed significant negative correlation against peri-tumoural MUFA (p = 0.043, ρ = -0.457), significant positive correlation against SFA (p = 0.008, ρ = 0.577), and significant negative correlation against PUFA (p = 0.002, ρ = -0.653).

Conclusion

The intra- and peri-tumoural degree of calcification and peri-tumoural MUFA are sensitive to histological calcification classes supporting future investigation into DCIS prognosis.

Image Features and Diagnostic Value of Contrast-Enhanced Ultrasound for Ductal Carcinoma In Situ of the Breast: Preliminary Findings

To explore the image features and the diagnostic value of contrast-enhanced ultrasound (CEUS) for ductal carcinoma in situ (DCIS) of the breast. A total of 96 female patients with a solitary and histologically proven DCIS were analyzed retrospectively, and 100 female cases of invasive ductal carcinoma (IDC) lesions were used as the control group. The Breast Imaging Reporting and Data System (BI-RADS) category of breast lesions was assessed according to conventional ultrasound features. The DCIS lesions were classified into mass type and non-mass type. The CEUS characteristics of these breast lesions were retrospectively analyzed qualitatively and quantitatively. The final gold standard was biopsy or surgery with histo-pathological examination. Comparing the ultrasound images of DCIS with that of IDC, there were significant differences in echo pattern, calcification morphology, and calcification distribution (p < .05 for all). There was a significant difference between DCIS and IDC in enhancement intensity, perfusion defects, peripheral high enhancement, intratumoral vessels, and arrival time (AT) (p < .05 for all). In the logistic multivariate regression analysis, two indicators linked with DCIS were recognized: perfusion defects (p = .002) and peripheral high enhancement (p < .001). In forecasting DCIS, the logistic regression equation resulted in an AUC of 0.689, a specificity of 0.720, and a sensitivity of 0.563. CEUS showed differences in enhancement characteristics between DCIS and IDC, with perfusion defects and peripheral high enhancement being associated with DCIS.

Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients Treated With Mastectomy: Indications for Treatment and Oncological Outcomes

Objective

The aim of this study was to evaluate the clinical outcomes of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC) followed by mastectomy, focusing on cases achieving pathologic complete response (pCR). The implications of residual ductal carcinoma in situ (DCIS) on prognosis and survival were examined.

Materials and Methods

A retrospective cohort study included BC patients treated with NAC followed by mastectomy at the breast unit of IRCCS Humanitas Research Hospital between March 2010 and October 2021. Patients were sub-grouped into two: Those with residual DCIS (ypTis) and those with complete response without residual tumor (ypT0). Key variables such as demographics, tumor characteristics, treatment regimens, and survival outcomes were analyzed.

Results

Of 681 patients treated with NAC, 175 achieved pCR, with 60 undergoing mastectomy. Among these 60 patients, 24 had residual DCIS (ypTis) while 36 had no residual invasive or in situ disease (ypT0). Patients with ypTis had higher rates of multifocal disease (62.5% vs. 27.8%, p = 0.006) and stage III disease (37.5% vs. 11.1%, p = 0.046). Triple-negative breast cancer was more prevalent in the ypT0 group (55.6% vs. 20.8%, p = 0.005). During a mean follow-up of 47 months, 11 patients experienced recurrence, with no significant differences in disease-free survival (DFS) and overall survival (OS) between the groups (p = 0.781, p = 0.963, respectively).

Conclusion

Residual DCIS after NAC did not significantly impact DFS or OS compared to complete pathologic response without residual DCIS. This study underscores the need for further research to refine pCR definitions and improve NAC's prognostic and therapeutic roles in BC management.

Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis

Objective

To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis.

Methods

The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models.

Results

Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model.

Conclusion

The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system

Introduction

The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.

Methods

We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.

Results

Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.

Conclusions

Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.

MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97-97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865-0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.

Age and race/ethnicity differences in decisional conflict in women diagnosed with ductal carcinoma in situ

PURPOSE

Women diagnosed with ductal carcinoma in situ (DCIS) face confusion and uncertainty about treatment options. The objective of this study was to determine whether there are differences in decisional conflict about treatment by age and race/ethnicity.

METHODS

A cross-sectional survey was conducted of women (age ≥ 18) diagnosed with DCIS enrolled at Kaiser Permanente of Southern California. The Decisional Conflict Scale (DCS) measured personal perceptions of decision uncertainty, values clarity, and effective decision-making. We used a multivariable regression to study whether age, race, and ethnicity were associated with patient-reported DCS.

RESULTS

45% (N = 1395) of women who received the online survey, participated. The mean age was 56 (± 9.6) years, the majority were white. Compared to women younger than 50, women aged 60-69 reported lower overall DCS scores (-5.4; 95% CI -1.5 to -9.3). Women > 70 had lower values clarity scores (-9.0; 95% CI -2.8 to -15.2) about their treatment compared to women aged 50-59 and 60-69 (-7.1; 95% CI -2.9 to -11.3 and - 7.2; 95% CI -2.9 to -11.5) and likewise, lower effective decision-making scores (-5.4; 95% CI -1.7 to -9.2 and - 5.2; 95% CI -1.4 to -9.0) compared to women < 50. Compared to whites, blacks reported lower decision conflict (-4.4; 95% CI 0.04 to -8.8) and lower informed decision (-5.2; 95% CI -0.18 to -10.3) about DCIS treatment.

CONCLUSION

Younger women reported higher decisional conflict about DCIS treatment, compared to older women (> 70). Age based tailored discussions about treatment options, health education, and supportive decision-making interventions/tools may reduce decision conflict in future DCIS patients.

TRADE REGISTRATION

The IRB number is 10678.

KRT15 in early breast cancer screening and correlation with HER2 positivity, pathological grade and N stage

Objective: This study was designed to explore KRT15 dysregulation and its correlation with clinical characteristics among ductal carcinoma in situ (DCIS), DCIS with microinvasion (DCIS-MI) and invasive breast cancer (IBC) patients. Methods: KRT15 from lesion samples of 50 DCIS patients, 48 DCIS-MI patients and 50 IBC patients was detected by immunohistochemistry. Results: KRT15 discriminated IBC patients from DCIS patients (area under the curve [AUC] = 0.895; 95% CI = 0.836-0.954) and DCIS-MI patients (AUC = 0.707; 95% CI = 0.606-0.808). In DCIS patients, KRT15 was negatively correlated with pathological grade (p = 0.015). In DCIS-MI patients, KRT15 was positively related to estrogen receptor positivity but negatively associated with Ki-67 (both p < 0.05). In IBC patients, KRT15 was negatively linked to HER2 positivity, histological grade, N stage and tumor node metastasis stage (all p < 0.05). Conclusion: KRT15 assessment may help with early breast cancer screening.

Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model

Standard clinicopathological parameters (age, growth pattern, tumor size, margin status, and grade) have been shown to have limited value in predicting recurrence in ductal carcinoma in situ (DCIS) patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. Generative adversarial networks (GAN) are a class of DL models in which two adversarial neural networks, generator and discriminator, compete with each other to generate high quality images. In this work, we have developed a deep learning (DL) classification network that predicts breast cancer events (BCEs) in DCIS patients using hematoxylin and eosin (H & E) images. The DL classification model was trained on 67 patients using image patches from the actual DCIS cores and GAN generated image patches to predict breast cancer events (BCEs). The hold-out validation dataset (n = 66) had an AUC of 0.82. Bayesian analysis further confirmed the independence of the model from classical clinicopathological parameters. DL models of H & E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.

Breast cancer vaccines: New insights into immunomodulatory and nano-therapeutic approaches

Breast cancer (BC) is known to be a highly heterogeneous disease that is clinically subdivided into four primary molecular subtypes, each having distinct morphology and clinical implications. These subtypes are principally defined by hormone receptors and other proteins involved (or not involved) in BC development. BC therapeutic vaccines [including peptide-based vaccines, protein-based vaccines, nucleic acid-based vaccines (DNA/RNA vaccines), bacterial/viral-based vaccines, and different immune cell-based vaccines] have emerged as an appealing class of cancer immunotherapeutics when used alone or combined with other immunotherapies. Employing the immune system to eliminate BC cells is a novel therapeutic modality. The benefit of active immunotherapies is that they develop protection against neoplastic tissue and readjust the immune system to an anti-tumor monitoring state. Such immunovaccines have not yet shown effectiveness for BC treatment in clinical trials. In recent years, nanomedicines have opened new windows to increase the effectiveness of vaccinations to treat BC. In this context, some nanoplatforms have been designed to efficiently deliver molecular, cellular, or subcellular vaccines to BC cells, increasing the efficacy and persistence of anti-tumor immunity while minimizing undesirable side effects. Immunostimulatory nano-adjuvants, liposomal-based vaccines, polymeric vaccines, virus-like particles, lipid/calcium/phosphate nanoparticles, chitosan-derived nanostructures, porous silicon microparticles, and selenium nanoparticles are among the newly designed nanostructures that have been used to facilitate antigen internalization and presentation by antigen-presenting cells, increase antigen stability, enhance vaccine antigenicity and remedial effectivity, promote antigen escape from the endosome, improve cytotoxic T lymphocyte responses, and produce humoral immune responses in BC cells. Here, we summarized the existing subtypes of BC and shed light on immunomodulatory and nano-therapeutic strategies for BC vaccination. Finally, we reviewed ongoing clinical trials on BC vaccination and highlighted near-term opportunities for moving forward.