To develop a machine learning-based workflow for patient-specific breast radiation dosimetry in CT. Two hundred eighty-six chest CT examinations, with corresponding right and left breast contours, were retrospectively collected from the radiotherapy department at our institution to develop and validate breast segmentation U-Nets. Additionally, Monte Carlo simulations were performed for each CT scan to determine radiation doses to the breasts. The derived breast doses, along with predictors such as X-ray tube current and radiomic features, were then used to train deep neural networks (DNNs) for breast dose prediction. The breast segmentation models achieved a mean dice similarity coefficient of 0.92, with precision and sensitivity scores above 0.90 for both breasts, indicating high segmentation accuracy. The DNNs demonstrated close alignment with ground truth values, with mean predicted doses of 5.05 ± 0.50 mGy for the right breast and 5.06 ± 0.55 mGy for the left breast, compared to ground truth values of 5.03 ± 0.57 mGy and 5.02 ± 0.61 mGy, respectively. The mean absolute percentage errors were 4.01 % (range: 3.90 %-4.12 %) for the right breast and 4.82 % (range: 4.56 %-5.11 %) for the left breast. The mean inference time was 30.2 ± 4.3 s. Statistical analysis showed no significant differences between predicted and actual doses (p ≥ 0.07). This study presents an automated, machine learning-based workflow for breast radiation dosimetry in CT, integrating segmentation and dose prediction models. The models and code are available at: https://github.com/eltzanis/ML-based-Breast-Radiation-Dosimetry-in-CT.

Breast ultrasound is essential for evaluating breast nodules, with Breast Imaging Reporting and Data System (BI-RADS) providing standardized classification. However, interobserver variability among radiologists can affect diagnostic accuracy. Large language models (LLMs) like ChatGPT-4 have shown potential in medical imaging interpretation. This study explores its feasibility in improving BI-RADS classification consistency and malignancy prediction compared to radiologists. This study aims to evaluate the feasibility of using LLMs, particularly ChatGPT-4, to assess the consistency and diagnostic accuracy of standardized breast ultrasound imaging reports, using pathology as the reference standard. This retrospective study analyzed breast nodule ultrasound data from 671 female patients (mean 45.82, SD 9.20 years; range 26-75 years) who underwent biopsy or surgical excision at our hospital between June 2019 and June 2024. ChatGPT-4 was used to interpret BI-RADS classifications and predict benign versus malignant nodules. The study compared the model's performance to that of two senior radiologists (≥15 years of experience) and two junior radiologists (<5 years of experience) using key diagnostic metrics, including accuracy, sensitivity, specificity, area under the receiver operating characteristic curve, P values, and odds ratios with 95% CIs. Two diagnostic models were evaluated: (1) image interpretation model, where ChatGPT-4 classified nodules based on BI-RADS features, and (2) image-to-text-LLM model, where radiologists provided textual descriptions, and ChatGPT-4 determined malignancy probability based on keywords. Radiologists were blinded to pathological outcomes, and BI-RADS classifications were finalized through consensus. ChatGPT-4 achieved an overall BI-RADS classification accuracy of 96.87%, outperforming junior radiologists (617/671, 91.95% and 604/671, 90.01%, P<.01). For malignancy prediction, ChatGPT-4 achieved an area under the receiver operating characteristic curve of 0.82 (95% CI 0.79-0.85), an accuracy of 80.63% (541/671 cases), a sensitivity of 90.56% (259/286 cases), and a specificity of 73.51% (283/385 cases). The image interpretation model demonstrated performance comparable to senior radiologists, while the image-to-text-LLM model further improved diagnostic accuracy for all radiologists, increasing their sensitivity and specificity significantly (P<.001). Statistical analyses, including the McNemar test and DeLong test, confirmed that ChatGPT-4 outperformed junior radiologists (P<.01) and showed noninferiority compared to senior radiologists (P>.05). Pathological diagnoses served as the reference standard, ensuring robust evaluation reliability. Integrating ChatGPT-4 into an image-to-text-LLM workflow improves BI-RADS classification accuracy and supports radiologists in breast ultrasound diagnostics. These results demonstrate its potential as a decision-support tool to enhance diagnostic consistency and reduce variability.

Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, which assists radiologists in identifying abnormalities. However, training AI systems requires large and diverse datasets, which are often difficult to obtain due to privacy and ethical constraints. To address this issue, the paper introduces MAMmography ensemBle mOdel (MAMBO), a novel patch-based diffusion approach designed to generate full-resolution mammograms. Diffusion models have shown breakthrough results in realistic image generation, yet few studies have focused on mammograms, and none have successfully generated high-resolution outputs required to capture fine-grained features of small lesions. To achieve this, MAMBO integrates separate diffusion models to capture both local and global (image-level) contexts. The contextual information is then fed into the final patch-based model, significantly aiding the noise removal process. This thoughtful design enables MAMBO to generate highly realistic mammograms of up to 3840x3840 pixels. Importantly, this approach can be used to enhance the training of classification models and extended to anomaly detection. Experiments, both numerical and radiologist validation, assess MAMBO's capabilities in image generation, super-resolution, and anomaly detection, highlighting its potential to enhance mammography analysis for more accurate diagnoses and earlier lesion detection.

Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisitions can be cast as a limited-angle computed-tomography problem, with variational networks being a promising model-based deep learning solution. Some acquired data frames may, however, get corrupted by noise due to, e.g., motion, lack of contact, and acoustic shadows, which in turn negatively affects the resulting SoS reconstructions. We propose to use the uncertainty in SoS reconstructions to attribute trust to each individual acquired frame. Given multiple acquisitions, we then use an uncertainty-based automatic selection among these retrospectively, to improve diagnostic decisions. We investigate uncertainty estimation based on Monte Carlo Dropout and Bayesian Variational Inference. We assess our automatic frame selection method for differential diagnosis of breast cancer, distinguishing between benign fibroadenoma and malignant carcinoma. We evaluate 21 lesions classified as BI-RADS 4, which represents suspicious cases for probable malignancy. The most trustworthy frame among four acquisitions of each lesion was identified using uncertainty-based criteria. Selecting a frame informed by uncertainty achieved an area under curve of 76% and 80% for Monte Carlo Dropout and Bayesian Variational Inference, respectively, superior to any uncertainty-uninformed baselines with the best one achieving 64%. A novel use of uncertainty estimation is proposed for selecting one of multiple data acquisitions for further processing and decision making.

Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and developing strategies to enhance its effectiveness in breast cancer is crucial. A key resistance mechanism is the pronounced desmoplasia in the tumor microenvironment, which leads to dysfunction of tumor blood vessels and thus, to hypoperfusion, limited drug delivery and hypoxia. Ultrasound sonopermeation and agents that normalize the tumor stroma have been employed separately to restore vascular abnormalities in tumors with some success. Here, we performed in vivo studies in two murine, orthotopic breast tumor models to explore if combination of ultrasound sonopermeation with a stroma normalization drug can synergistically improve tumor perfusion and enhance the efficacy of nano-immunotherapy. We found that the proposed combinatorial treatment can drastically reduce primary tumor growth and in many cases tumors were no longer measurable. Overall survival studies showed that all mice that received the combination treatment survived and rechallenge experiments revealed that the survivors obtained immunological memory. Employing ultrasound elastography and contrast enhanced ultrasound along with proteomics analysis, flow cytometry and immunofluorescene staining, we found the combinatorial treatment reduced tumor stiffness to normal levels, restoring tumor perfusion and oxygenation. Furthermore, it increased infiltration and activity of immune cells and altered the levels of immunosupportive chemokines. Finally, using machine learning analysis, we identified that tumor stiffness, CD8⁺ T cells and M2-type macrophages were strong predictors of treatment response.

Breast cancer is the second deadliest cancer among women after lung cancer. Though the breast cancer death rate continues to decline in the past 20 years, the stages IV and III breast cancer death rates remain high. Therefore, an automated breast cancer diagnosis system is of great significance for early screening of breast lesions to improve the survival rate of patients. This paper proposes a deep learning-based network hybrid adaptive attention deep supervision-guided U-Net (HAA-DSUNet) for breast lesion segmentation of breast ultrasound computed tomography (BUCT) images, which replaces the traditionally sampled convolution module of U-Net with the hybrid adaptive attention module (HAAM), aiming to enlarge the receptive field and probe rich global features while preserving fine details. Moreover, we apply the contrast loss to intermediate outputs as deep supervision to minimize the information loss during upsampling. Finally, the segmentation prediction results are further processed by filtering, segmentation, and morphology to obtain the final results. We conducted the experiment on our two UCT image datasets HCH and HCH-PHMC, and the highest Dice score is 0.8729 and IoU is 0.8097, which outperform all the other state-of-the-art methods. It is demonstrated that our algorithm is effective in segmenting the legion from BUCT images.

Novel antibody-drug conjugates highlight the benefits for breast cancer patients with low human epidermal growth factor receptor 2 (HER2) expression. This study aims to develop and validate a Vision Transformer (ViT) model based on dynamic contrast-enhanced MRI (DCE-MRI) to classify HER2-zero, -low, and -positive breast cancer patients and to explore its interpretability. The model is trained and validated on early enhancement MRI images from 708 patients in the FUSCC cohort and tested on 80 and 101 patients in the GFPH cohort and FHCMU cohort, respectively. The ViT model achieves AUCs of 0.80, 0.73, and 0.71 in distinguishing HER2-zero from HER2-low/positive tumors across the validation set of the FUSCC cohort and the two external cohorts. Furthermore, the model effectively classifies HER2-low and HER2-positive cases, with AUCs of 0.86, 0.80, and 0.79. Transcriptomics analysis identifies significant biological differences between HER2-low and HER2-positive patients, particularly in immune-related pathways, suggesting potential therapeutic targets. Additionally, Cox regression analysis demonstrates that the prediction score is an independent prognostic factor for overall survival (HR, 2.52; p = 0.007). These findings provide a non-invasive approach for accurately predicting HER2 expression, enabling more precise patient stratification to guide personalized treatment strategies. Further prospective studies are warranted to validate its clinical utility.

The purpose of this study is to evaluate the diagnostic accuracy of an artificial intelligence (AI)-based Computer-Aided Diagnosis (CADx) system for breast ultrasound, compare its performance with radiologists, and assess the effect of AI-assisted diagnosis. This study aims to investigate the system's ability to differentiate between benign and malignant breast masses among Japanese patients. This retrospective study included 171 breast mass ultrasound images (92 benign, 79 malignant). The AI system, BU-CAD™, provided Breast Imaging Reporting and Data System (BI-RADS) categorization, which was compared with the performance of three radiologists. Diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were analyzed. Radiologists' diagnostic performance with and without AI assistance was also compared, and their reading time was measured using a stopwatch. The AI system demonstrated a sensitivity of 91.1%, specificity of 92.4%, and an AUC of 0.948. It showed comparable diagnostic performance to Radiologist 1, with 10 years of experience in breast imaging (0.948 vs. 0.950; p = 0.893), and superior performance to Radiologist 2 (7 years of experience, 0.948 vs. 0.881; p = 0.015) and Radiologist 3 (3 years of experience, 0.948 vs. 0.832; p = 0.001). When comparing diagnostic performance with and without AI, the use of AI significantly improved the AUC for Radiologists 2 and 3 (p = 0.001 and 0.005, respectively). However, there was no significant difference for Radiologist 1 (p = 0.139). In terms of diagnosis time, the use of AI reduced the reading time for all radiologists. Although there was no significant difference in diagnostic performance between AI and Radiologist 1, the use of AI substantially decreased the diagnosis time for Radiologist 1 as well. The AI system significantly improved diagnostic efficiency and accuracy, particularly for junior radiologists, highlighting its potential clinical utility in breast ultrasound diagnostics.

Various deep learning models have been developed and employed for medical image classification. This study conducted comprehensive experiments on 12 models, aiming to establish reliable benchmarks for research on breast dynamic contrast-enhanced magnetic resonance imaging image classification. Twelve deep learning models were systematically compared by analyzing variations in 4 key hyperparameters: optimizer (Op), learning rate, batch size (BS), and data augmentation. The evaluation criteria encompassed a comprehensive set of metrics including accuracy (Ac), loss value, precision, recall rate, F1-score, and area under the receiver operating characteristic curve. Furthermore, the training times and model parameter counts were assessed for holistic performance comparison. Adjustments in the BS within Adam Op had a minimal impact on Ac in the convolutional neural network models. However, altering the Op and learning rate while maintaining the same BS significantly affected the Ac. The ResNet152 network model exhibited the lowest Ac. Both the recall rate and area under the receiver operating characteristic curve for the ResNet152 and Vision transformer-base (ViT) models were inferior compared to the others. Data augmentation unexpectedly reduced the Ac of ResNet50, ResNet152, VGG16, VGG19, and ViT models. The VGG16 model boasted the shortest training duration, whereas the ViT model, before data augmentation, had the longest training time and smallest model weight. The ResNet152 and ViT models were not well suited for image classification tasks involving small breast dynamic contrast-enhanced magnetic resonance imaging datasets. Although data augmentation is typically beneficial, its application should be approached cautiously. These findings provide important insights to inform and refine future research in this domain.

To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics models for non-massive breast cancer in dense breast backgrounds. Manual segmentation of ultrasound images was performed to define the intratumoral region of interest (ROI), and five peritumoral ROIs were generated by extending the contours by 1 to 5 mm. A total of 851 radiomics features were extracted from these regions and filtered using statistical methods. Thirteen machine learning algorithms were employed to create radiomics models for the intratumoral and peritumoral areas. The best model was combined with clinical ultrasound predictive factors to form a joint model, which was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).Based on this model, a nomogram was developed, demonstrating high predictive performance, with C-index values of 0.982 and 0.978.The model incorporating the intratumoral and peritumoral 2 mm regions outperformed other models, indicating its effectiveness in distinguishing between benign and malignant breast lesions. This study concludes that ultrasound imaging, particularly in the intratumoral and peritumoral 2 mm regions, has significant potential for diagnosing non-massive breast cancer, and the nomogram can assist clinical decision-making.