Early prediction of treatment response can facilitate personalized treatment for breast cancer patients. Studies on the I-SPY 2 clinical trial demonstrate that multi-time point dynamic contrast-enhanced magnetic resonance (DCEMR) imaging improves the accuracy of predicting pathological complete response (pCR) to chemotherapy. However, previous image-based prediction models usually rely on mid- or post-treatment images to ensure the accuracy of prediction, which may outweigh the benefit of response-based adaptive treatment strategy. Accurately predicting the pCR at the early time point is desired yet remains challenging. To improve prediction accuracy at the early time point of treatment, we proposed a two-stage dual-task learning strategy to train a deep neural network for early prediction using only early-treatment data. We developed and evaluated our proposed method using the I-SPY 2 dataset, which included DCEMR images acquired at three time points: pretreatment (T0), after 3 weeks (T1) and 12 weeks of treatment (T2). At the first stage, we trained a convolutional long short-term memory (LSTM) model using all the data to predict pCR and extract the latent space image representation at T2. At the second stage, we trained a dual-task model to simultaneously predict pCR and the image representation at T2 using images from T0 and T1. This allowed us to predict pCR earlier without using images from T2. By using the conventional single-stage single-task strategy, the area under the receiver operating characteristic curve (AUROC) was 0.799. By using the proposed two-stage dual-task learning strategy, the AUROC was improved to 0.820. Our proposed two-stage dual-task learning strategy can improve model performance significantly (p=0.0025) for predicting pCR at the early time point (3rd week) of neoadjuvant chemotherapy for high-risk breast cancer patients. The early prediction model can potentially help physicians to intervene early and develop personalized plans at the early stage of chemotherapy.

The development of robust deep learning models for breast ultrasound (BUS) image analysis is significantly constrained by the scarcity of expert-annotated data. To address this limitation, we propose a clinically controllable generative framework for synthesizing BUS images. This framework integrates clinical descriptions with structural masks to generate tumors, enabling fine-grained control over tumor characteristics such as morphology, echogencity, and shape. Furthermore, we design a semantic-curvature mask generator, which synthesizes structurally diverse tumor masks guided by clinical priors. During inference, synthetic tumor masks serve as input to the generative framework, producing highly personalized synthetic BUS images with tumors that reflect real-world morphological diversity. Quantitative evaluations on six public BUS datasets demonstrate the significant clinical utility of our synthetic images, showing their effectiveness in enhancing downstream breast cancer diagnosis tasks. Furthermore, visual Turing tests conducted by experienced sonographers confirm the realism of the generated images, indicating the framework's potential to support broader clinical applications.

Predicting the risk of breast cancer recurrence is crucial for guiding therapeutic strategies, including enhanced surveillance and the consideration of additional treatment after surgery. In this study, we developed a deep convolutional neural network (DCNN) model to predict recurrence within six years after surgery using preoperative contrast-enhanced computed tomography (CECT) images, which are widely available and effective for detecting distant metastases. This retrospective study included preoperative CECT images from 133 patients with invasive ductal carcinoma. The images were classified into recurrence and no-recurrence groups using ResNet-101 and DenseNet-201. Classification performance was evaluated using the area under the receiver operating curve (AUC) with leave-one-patient-out cross-validation. At the optimal threshold, the classification accuracies for ResNet-101 and DenseNet-201 were 0.73 and 0.72, respectively. The median (interquartile range) AUC of DenseNet-201 (0.70 [0.69-0.72]) was statistically higher than that of ResNet-101 (0.68 [0.66-0.68]) (p < 0.05). These results suggest the potential of preoperative CECT-based DCNN models to predict breast cancer recurrence without the need for additional invasive procedures.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-based retrospective pharmacokinetic quantification (RoQ) of early-treatment dynamic contrast-enhanced (DCE) MRI. Materials and Methods This multicenter retrospective study included breast MRI data from four publicly available datasets of patients with breast cancer acquired from May 2002 to November 2016. RoQ was performed using a previously developed DL model for clinical multiphasic DCE-MRI datasets. Radiomic analysis was performed on RoQ maps and conventional enhancement maps. These data, together with clinicopathologic variables and shape-based radiomic analysis, were subsequently applied in pCR prediction using logistic regression. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC). Results A total of 1073 female patients with breast cancer were included. The proposed method showed improved consistency and generalizability compared with the reference method, achieving higher AUCs across external datasets (0.82 [CI: 0.72-0.91], 0.75 [CI: 0.71-0.79], and 0.77 [CI: 0.66-0.86] for Datasets A2, B, and C, respectively). On Dataset A2 (from the same study as the training dataset), there was no significant difference in performance between the proposed method and reference method (<i>P</i> = .80). Notably, on the combined external datasets, the proposed method significantly outperformed the reference method (AUC: 0.75 [CI: 0.72- 0.79] vs 0.71 [CI: 0.68-0.76], <i>P</i> = .003). Conclusion This work offers a novel approach to improve the generalizability and predictive accuracy of pCR response in breast cancer across diverse datasets, achieving higher and more consistent AUC scores than existing methods. ©RSNA, 2025.

This study aimed to develop an early predictive model for neoadjuvant therapy (NAT) response in breast cancer by integrating multimodal ultrasound (conventional B-mode, shear-wave elastography, and contrast-enhanced ultrasound) and radiomics with clinical-pathological data, and to evaluate its predictive accuracy after two cycles of NAT. This retrospective study included 239 breast cancer patients receiving neoadjuvant therapy, divided into training (n = 167) and validation (n = 72) cohorts. Multimodal ultrasound-B-mode, shear-wave elastography (SWE), and contrast-enhanced ultrasound (CEUS)-was performed at baseline and after two cycles. Tumors were segmented using a U-Net-based deep learning model with radiologist adjustment, and radiomic features were extracted via PyRadiomics. Candidate variables were screened using univariate analysis and multicollinearity checks, followed by LASSO and stepwise logistic regression to build three models: a clinical-ultrasound model, a radiomics-only model, and a combined model. Model performance for early response prediction was assessed using ROC analysis. In the training cohort (n = 167), Model_Clinic achieved an AUC of 0.85, with HER2 positivity, maximum tumor stiffness (Emax), stiffness heterogeneity (Estd), and the CEUS "radiation sign" emerging as independent predictors (all P < 0.05). The radiomics model showed moderate performance at baseline (AUC 0.69) but improved after two cycles (AUC 0.83), and a model using radiomic feature changes achieved an AUC of 0.79. Model_Combined demonstrated the best performance with a training AUC of 0.91 (sensitivity 89.4%, specificity 82.9%). In the validation cohort (n = 72), all models showed comparable AUCs (Model_Combined ~ 0.90) without significant degradation, and Model_Combined significantly outperformed Model_Clinic and Model_RSA (DeLong P = 0.006 and 0.042, respectively). In our study, integrating multimodal ultrasound and radiomic features improved the early prediction of NAT response in breast cancer, and could provide valuable information to enable timely treatment adjustments and more personalized management strategies.

Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can significantly reduce mortality rates. Mammograms are widely used for screening, but manual readings can lead to misdiagnosis. Computer-assisted diagnosis can help physicians make faster, more accurate judgments, which benefits patients. However, segmenting and classifying breast masses in mammograms is challenging due to their similar shapes to the surrounding glands. Current target detection algorithms have limited applications and low accuracy. Automated segmentation of breast masses on mammograms is a significant research challenge due to its considerable classification and contouring. This study introduces the Bi-Contextual Breast Mass Segmentation Framework (Bi-CBMSegNet), a novel paradigm that enhances the precision and efficiency of breast mass segmentation within full-field mammograms. Bi-CBMSegNet employs an advanced encoder-decoder architecture comprising two distinct modules: the Global Feature Enhancement Module (GFEM) and the Local Feature Enhancement Module (LFEM). GFEM aggregates and assimilates features from all positions within the mammogram, capturing extensive contextual dependencies that facilitate the enriched representation of homogeneous regions. The LFEM module accentuates semantic information pertinent to each specific position, refining the delineation of heterogeneous regions. The efficacy of Bi-CBMSegNet has been rigorously evaluated on two publicly available mammography databases, demonstrating superior computational efficiency and performance metrics. The findings advocate for Bi-CBMSegNet to effectuate a significant leap forward in medical imaging, particularly in breast cancer screening, thereby augmenting the accuracy and efficacy of diagnostic and treatment planning processes.

To predict the normalized glandular dose (DgN) coefficients and the related uncertainty in mammography and digital breast tomosynthesis (DBT) using a machine learning algorithm and patient-like digital breast models. 126 patient-like digital breast phantoms were used for DgN Monte Carlo ground truth calculations. An Automatic Relevance Determination Regression algorithm was used to predict DgN from anatomical breast features. These features included compressed breast thickness, glandular fraction by volume, glandular volume, center of mass and standard deviation of the glandular tissue distribution in the cranio-caudal direction. An algorithm for data imputation was explored to account for avoiding the use of the latter two features. 5-fold cross validation showed that the predictive model provides an estimation of DgN with 1% average difference from the ground truth; this difference was less than 3% in 50% of the cases. The average uncertainty of the estimated DgN values was 9%. Excluding the information related to the glandular distribution increased this uncertainty to 17% without inducing a significant discrepancy in estimated DgN values, with half of the predicted cases differing from the ground truth by less than 9%. The data imputation algorithm reduced the estimated uncertainty, without restoring the original performance. Predictive performance improved by increasing tube voltage. The proposed methodology predicts the DgN in mammography and DBT for patient-derived breasts with an uncertainty below 9%. Predicting test evaluations reported 1% average difference from the ground truth, with 50% of the cohort cases differing by less than 5%.

Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for tumor detection. This study proposes an automated deep learning system for robust binary classification of breast density (low: A/B vs. high: C/D) using the VinDr-Mammo dataset. We implemented and compared four advanced convolutional neural networks: ResNet18, ResNet50, EfficientNet-B0, and DenseNet121, each enhanced with channel attention mechanisms. To address the inherent class imbalance, we developed a novel Combined Focal Label Smoothing Loss function that integrates focal loss, label smoothing, and class-balanced weighting. Our preprocessing pipeline incorporated advanced techniques, including contrast-limited adaptive histogram equalization (CLAHE) and comprehensive data augmentation. The individual models were combined through an optimized ensemble voting approach, achieving superior performance (AUC: 0.963, F1-score: 0.952) compared to any single model. This system demonstrates significant potential to standardize density assessments in clinical practice, potentially improving screening efficiency and early cancer detection rates while reducing inter-observer variability among radiologists.

Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for tumor detection. This study proposes an automated deep learning system for robust binary classification of breast density (low: A/B vs. high: C/D) using the VinDr-Mammo dataset. We implemented and compared four advanced convolutional neural networks: ResNet18, ResNet50, EfficientNet-B0, and DenseNet121, each enhanced with channel attention mechanisms. To address the inherent class imbalance, we developed a novel Combined Focal Label Smoothing Loss function that integrates focal loss, label smoothing, and class-balanced weighting. Our preprocessing pipeline incorporated advanced techniques, including contrast-limited adaptive histogram equalization (CLAHE) and comprehensive data augmentation. The individual models were combined through an optimized ensemble voting approach, achieving superior performance (AUC: 0.963, F1-score: 0.952) compared to any single model. This system demonstrates significant potential to standardize density assessments in clinical practice, potentially improving screening efficiency and early cancer detection rates while reducing inter-observer variability among radiologists.

Abbreviated breast MRI protocols are advocated for breast screening as they limit acquisition duration and increase resource availability. However, radiologists' specificity may be slightly lowered when only such short protocols are evaluated. An adaptive approach, where a full protocol is performed only when abnormalities are detected by artificial intelligence (AI)-based models in the abbreviated protocol, might improve and speed up MRI screening. This study explores the potential benefits of such an approach. To assess the potential impact of adaptive breast MRI scanning based on AI detection of malignancies. Mathematical model. Breast cancer screening protocols. Theoretical upper and lower limits on expected protocol duration and recall rate were determined for the adaptive approach, and the influence of the AI model and radiologists' performance metrics on these limits was assessed, under the assumption that any finding on the abbreviated protocol would, in an ideal follow-up scenario, prompt a second MRI with the full protocol. Estimated most likely scenario. Theoretical limits for the proposed adaptive AI-based MRI breast cancer screening showed that the recall rates of the abbreviated and full screening protocols always constrained the recall rate. These abbreviated and full protocols did not fully constrain the expected protocol duration, and an adaptive protocol's expected duration could thus be shorter than the abbreviated protocol duration. Specificity, either from AI models or radiologists, has the largest effect on the theoretical limits. In the most likely scenario, the adaptive protocol achieved an expected protocol duration reduction of ~47%-60% compared with the full protocol. The proposed adaptive approach may offer a reduction in expected protocol duration compared with the use of the full protocol alone, and a lower recall rate relative to an abbreviated-only approach could be achieved. Optimal performance was observed when AI models emulated radiologists' decision-making behavior, rather than focusing solely on near-perfect malignancy detection. Not applicable. Stage 6.