Anne L . Martel

Background: Although DCIS has a relatively low recurrence rate, many patients still receive adjuvant radiotherapy or endocrine therapy, raising concerns about overtreatment. Reliable biomarkers are therefore needed to predict an individual patient’s risk and guide treatment decisions. Recent studies suggest that the composition of the tumour-associated stroma (TAS) affects progression and outcome, highlighting TAS-derived biomarkers as promising candidates for further investigation. Methods: We trained AI models for cell and tumour segmentation using whole slide digital pathology images acquired as part of a retrospective cohort study. We investigated the effects of cell density within both the tumour and the TAS to determine how they correlated with recurrence in the ipsilateral breast. Results: We found that the concentration of DCIS lesions on the slide and the density of mitotic figures inside the TAS region were significantly associated with recurrence risk. Additionally, we found some predictive value in the lymphocyte and red blood cell densities in different tumour regions. Stromal composition was shown to associate with recurrence risk, and density-based biomarkers were identified and used to cluster patients into phenotypes with significantly different risk profiles. Conclusion: Our findings highlight the prognostic relevance of stromal composition in DCIS, and we identify novel density-based biomarkers that can be used to identify patients who are more likely to experience a local recurrence after breast-conserving surgery alone. These results may aid in developing future risk-stratification tools for breast cancer patients, thereby reducing overtreatment and improving patient care.

PurposeThe scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence systems to breast cancer diagnosis. Deep models for mammogram analysis and mass (or micro-calcification) detection require training with a large volume of labeled images, which are often expensive and time-consuming to collect. To reduce this challenge, we proposed a method that leverages self-supervised learning (SSL) and a deep hybrid model, named HybMNet, which combines local self-attention and fine-grained feature extraction to enhance breast cancer detection on screening mammograms.ApproachOur method employs a two-stage learning process: (1) SSL pretraining: We utilize Efficient Self-Supervised Vision Transformers, an SSL technique, to pretrain a Swin Transformer (Swin-T) using a limited set of mammograms. The pretrained Swin-T then serves as the backbone for the downstream task. (2) Downstream training: The proposed HybMNet combines the Swin-T backbone with a convolutional neural network (CNN)-based network and a fusion strategy. The Swin-T employs local self-attention to identify informative patch regions from the high-resolution mammogram, whereas the CNN-based network extracts fine-grained local features from the selected patches. A fusion module then integrates global and local information from both networks to generate robust predictions. The HybMNet is trained end-to-end, with the loss function combining the outputs of the Swin-T and CNN modules to optimize feature extraction and classification performance.ResultsThe proposed method was evaluated for its ability to detect breast cancer by distinguishing between benign (normal) and malignant mammograms. Leveraging SSL pretraining and the HybMNet model, it achieved an area under the ROC curve of 0.864 (95% CI: 0.852, 0.875) on the Chinese Mammogram Database (CMMD) dataset and 0.889 (95% CI: 0.875, 0.903) on the INbreast dataset, highlighting its effectiveness.ConclusionsThe quantitative results highlight the effectiveness of our proposed HybMNet and the SSL pretraining approach. In addition, visualizations of the selected region of interest patches show the model’s potential for weakly supervised detection of microcalcifications, despite being trained using only image-level labels.

PurposeBreast density (BD) and background parenchymal enhancement (BPE) are important imaging biomarkers for breast cancer (BC) risk. We aim to evaluate longitudinal changes in quantitative BD and BPE in high-risk women undergoing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), focusing on the effects of age and transition into menopause.ApproachA retrospective cohort study analyzed 834 high-risk women undergoing breast DCE-MRI for screening between 2005 and 2020. Quantitative BD and BPE were derived using deep-learning segmentation. Linear mixed-effects models assessed longitudinal changes and the effects of age, menopausal status, weeks since the last menstrual period (LMP-wks), body mass index (BMI), and hormone replacement therapy (HRT) on these imaging biomarkers.ResultsBD decreased with age across all menopausal stages, whereas BPE declined with age in postmenopausal women but remained stable in premenopausal women. HRT elevated BPE in postmenopausal women. Perimenopausal women exhibited decreases in both BD and BPE during the menopausal transition, though cross-sectional age at menopause had no significant effect on either measure. Fibroglandular tissue was positively associated with BPE in perimenopausal women.ConclusionsWe highlight the dynamic impact of menopause on BD and BPE and correlate well with the known relationship between risk and age at menopause. These findings advance the understanding of imaging biomarkers in high-risk populations and may contribute to the development of improved risk assessment leading to personalized chemoprevention and BC screening recommendations.

Suicide risk is substantially elevated following discharge from a psychiatric hospitalization. Caring Contact (CC) messages are brief messages of hope, support and information sent post-discharge that can improve mental health outcomes, including suicidal ideation and behaviours. Patients who received CCs in previous studies indicated a desire for increased personalization. To support personalization in an efficient and scalable manner, we will pilot the training of a Large Language Model (LLM) that constructs tailored CCs using information extracted from patients’ electronic health records (EHRs) created during the index psychiatric hospitalization. This is a three-phase, mixed-methods study. In phase 1, psychiatric inpatient clinical staff members will be recruited to generate personalized CC messages from representative, de-identified samples of EHRs. This will be considered the control CCs. Clinical staff focus groups will then identify key components of a successful CC message. In phase 2, the control CCs and focus group feedback will be used to train an LLM on a large number of EHRs to produce personalized CC messages. The LLM will be given the same EHRs used in phase 1 to produce CC messages. In phase 3, we will recruit 30 patients with lived experiences (PWLE) of psychiatric hospitalization to evaluate and compare the control CCs with the LLM-generated messages to determine the acceptability of AI-generated CC messages. We hypothesize that LLM-generated CC messages will achieve acceptability ratings at least as positive as the control CC messages. Since CC messages are modifiable and can be altered to suit the needs of various clinical settings, the findings of this study can potentially be broadly generalizable.

Early detection of breast cancer is important for improving survival rates. Based on accurate and tissue-specific risk factors, such as breast density and background parenchymal enhancement (BPE), risk-stratified screening can help identify high-risk women and provide personalized screening plans, ultimately leading to better outcomes. Measurements of density and BPE are carried out through image segmentation, but volumetric measurements may not capture the qualitative scale of these tissue-specific risk factors. This study aimed to create deep regression models that estimate the interval scale underlying the BI-RADS density and BPE categories. These models incorporate a 3D convolutional encoder and transformer layers to comprehend time-sequential data in DCE-MRI. The correlation between the models and the BI-RADS categories was evaluated with Spearman coefficients. Using 1024 patients with a BI-RADS assessment score of 3 or less and no prior history of breast cancer, the models were trained on 50% of the data and tested on 50%. The density and BPE ground truth labels were extracted from the radiology reports using BI-RADS BERT. The ordinal classes were then translated to a continuous interval scale using a linear link function. The density regression model is strongly correlated to the BI-RADS category with a correlation of 0.77, slightly lower than segmentation %FGT. The BPE regression model with transformer layers shows a moderate correlation with radiologists at 0.52, similar to the segmentation %BPE. The deep regression transformer has an advantage over segmentation as it doesn’t need time-point image registration, making it easier to use.

Deep-learning-based models have been proposed as an automated second reader for mammograms that might help reduce radiologists’ workload and improve screening accuracy. However, the inherent traits of mammograms, characterized by significantly higher resolutions and smaller regions of interest (ROIs) in comparison to natural images, impose constraints on the adaptability of deep neural networks that are well-suited for natural image analysis to the domain of mammogram analysis. In this work, we propose a novel neural network to effectively detect breast cancer on screening mammograms and address the above issues. First, we use a local self-attention-based Swin Transformer as the backbone to select the most informative patch regions from the whole mammogram. We then utilize a second CNN based network to further extract the fine-grained features of the selected patches. Finally, we employ a fusion module that aggregates global and local information to make a prediction. The final loss function is the combination of the prediction from both the transformer and CNN modules. With local self-attention and a hierarchical structure, our backbone can effectively model the relationships between ROIs (e.g., masses or micro-calcifications) of different sizes and their surrounding tissues. Thus introduces meaningful contextual information for robust feature extraction. The experimental results show that our model can achieve state-of-the-art performance, in terms of classification AUC of 0.856 on a public mammogram dataset.

Immune phenotype data, specifically the description of densities and spatial distribution of immune cells are now frequently included in the clinical pathology report as these features of the cells in the tumor microenvironment (TME) have shown to be associated with prognosis. In addition, immune-therapeutics, which aim at manipulating the patients’ immune system to kill cancer cells, have recently been approved for treatment of triple-negative breast cancers (TNBCs). Thus, quantifying the immune phenotype of the cancer could be important both for prognostication, and for prediction of therapy response. We have studied the immune phenotype of 42 breast cancers using immunofluorescence protein multiplexing and quantitative image analysis. After sectioning, formalin-fixed paraffin-embedded tissues were sequentially stained with a panel of fluorescently-labelled antibodies and imaged with the multiplexer (Cell DIVE, Leica Biosystems). Composite images of antibody-stained sections were then analysed using specialized digital pathology software (HALO, Indica Labs). Binary thresholding was conducted to identify and quantify densities of various immune lineage subsets (T lymphocytes and macrophages). Their cellular localisation was mapped and the spatial features of cellular arrangement were evaluated using a k-nearest neighbor graph (KNNG) method and Louvain community-proximity clustering. The spatial relationship of various immune and cancer cell types was quantified to assess whether cellular arrangements and structures differed among breast cancer subtypes. Our work demonstrates the use of molecular and cellular imaging in quantifying features of the tumor microenvironment in breast cancer classification, and the application of KNNG in studying spatial biology.