Breast Cancer

Integrative multi-omics and machine learning framework identifies PRDX4 as a redox-EMT regulator and predictive marker in bone-metastatic breast cancer.

Bone metastasis is a major cause of morbidity and mortality in breast cancer, yet effective prognostic models and targeted therapies remain limited. Here, a machine learning (ML)-driven multi-omics framework integrating epithelial-mesenchymal transition (EMT) and nucleotide metabolism (NM) signatures is presented to uncover prognostic biomarkers and guide rational drug discovery. Using gene expres...

Semi-supervised learning for dose prediction in targeted radionuclide therapy: a synthetic data study.

Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds promise for this purpose. However, current DL-based dosimetry methods require large-scale supervised data, which is scarce in clinical practice.Approach.To address this challenge, we propose exploring semi-supervised learning (SSL) framework that leve...

iMCN: information compression-based multimodal confidence-guided fusion network for cancer survival prediction.

The rapid development of deep learning-based computational pathology and genomics has demonstrated the significant promise of effectively integrating ...

Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit.

OBJECTIVES: Artificial intelligence (AI) has been applied in a number of breast screening settings with favourable results. While there are a limited ...

Pathomics Signature for Prognosis and CA19-9 Interception in Pancreatic Ductal Adenocarcinoma: A Real-Life, Multi-Center Study.

Histopathological hematoxylin and eosin (H&E) slides contain valuable prognostic information for pancreatic ductal adenocarcinoma (PDAC), yet systemat...

Translational and real-world evidence of trastuzumab biosimilar CT-P6 plus pertuzumab in neoadjuvant HER2-positive early breast cancer.

BACKGROUND: Data on neoadjuvant treatment with trastuzumab biosimilars, particularly CT-P6, in combination with pertuzumab, are limited. This study ev...

Domain adaptation, self-supervision, and generative augmentation enhance GNNs for breast cancer prediction.

Breast cancer presents substantial molecular heterogeneity, requiring accurate subtype classification, receptor-status prediction, and survival estima...

Intratumoral and Peritumoral Fat CT‑Based Radiomics for Predicting Recurrence Risk in Non-Muscle-Invasive Bladder Cancer: A Two-Center Study.

BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) has a high risk of recurrence, and multiple surgeries increase the disease burden on patients. ...

Loss of biomechanical features reveals smooth muscle disruption and disease progression in prostate cancer.

BACKGROUND: Biomechanical features show notable heterogeneity in tumor risk stratification, yet their role in prostate cancer (PCa) progression remain...

Validation of uPath HER2 dual-colour dual in-situ hybridisation image analysis tool for HER2/neu testing in breast cancer.

AIMS: HER2/neu gene is amplified in 15%-20% of invasive breast cancers (IBCs), serving as critical prognostic and predictive marker. HER2-targeted the...

Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.

BACKGROUND: Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods f...

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-lea...

Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches.

Mammographic density is associated with the risk of developing breast cancer and can be predicted using deep learning methods. Model uncertainty estim...

A prognostic signature of ferroptosis and lipid metabolism related genes predicts survival and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. There is an interplay between ferroptosis and lipid metabolism i...

MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.

PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HE...

Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.

Transcriptomic assays such as the PAM50-based ROR-P score guide recurrence risk stratification in non-metastatic, ER-positive, HER2-negative breast ca...

TomoRay cranial: synthesis of cranial CT imaging from biplanar radiographs using a generative adversarial network.

OBJECTIVES: Besides clinical examination, cranial CT plays a critical role in diagnostics in neurosurgery. In trauma cases or perioperatively, having ...

Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.

OBJECTIVE: Pediatric scoliosis is the most prevalent spinal disorder, often leading to abnormal curvature and deformation of the spine. Early detectio...

BreastDCEDL: A standardized deep learning-ready breast DCE-MRI dataset of 2,070 patients.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is essential for monitoring breast cancer treatment response, yet deep learning progres...