Breast Cancer

Predicting neoadjuvant therapy response in breast cancer from preoperative biopsy via spatial-semantic-differential learning and interpretable clinicopathological-guided fusion.

Predicting pathological complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer remains challenging due to high tumor heterogeneity and disparities across data modalities. This study introduces a multimodal learning framework that integrates whole-slide image (WSI) from preoperative biopsy with clinicopathological (CP) variables to predict pCR. The framework is built on two novel com...

Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial ...

European Society of Urogenital Radiology (ESUR) perspectives on the role of prostate MRI in active surveillance.

MRI has a central role in the diagnosis and management of prostate cancer, including active surveillance (AS) of low- and favourable intermediate-risk...

Generative Artificial Intelligence for Medical Summarization in Prostate Cancer: Comparative Evaluation by Physicians and Patient Advocates-A Pilot Study.

PURPOSE: The exponential growth of scientific publications presents increasing challenges for clinicians and patients seeking to access up-to-date med...

Development and Clinical Validation of a Protocol-Agnostic Machine Learning Platform for Automated Treatment Planning in External Beam Radiation Therapy.

PURPOSE: To develop and validate a protocol-agnostic machine learning platform ("Predictive Planning") for knowledge-based planning (KBP) in external ...

Explainable machine learning to predict muscle loss during radiotherapy for oral cavity cancer.

BACKGROUND AND PURPOSE: Muscle loss during adjuvant radiotherapy is associated with poor survival outcomes in patients with oral cavity cancer (OCC). ...

Multimodality treatment and survival in primary cardiac malignancies: evidence from competing-risk and machine learning analyses.

BACKGROUND: Primary cardiac malignancies are rare and highly aggressive, with limited clinical evidence to guide optimal treatment. This study evaluat...

Single-Cell Transcriptomic Analysis of Chemotherapy-Induced Changes in Osteosarcoma With a Pyroptosis-Related Gene-Based Prognostic Model.

Osteosarcoma, the most common primary malignant bone tumour, presents significant treatment challenges due to its complex tumour microenvironment and ...

Head radiotherapy positioning guidance system based on feature recognition and automatic annotation: Clinical validation and error analysis.

BACKGROUND: Positioning accuracy in radiotherapy is critical for treatment outcomes, especially in head tumor radiotherapy, where the target area is s...

Automated identification of radiotherapy treatment sites from unstructured physician notes.

PURPOSE: Ambiguous or incomplete documentation is a recurrent bottleneck in radiation oncology workflows, leading to inefficiencies in communication a...

Arrhythmias and cardiac devices management in oncology patients.

Cardiac arrhythmia is increasingly encountered in patients with cancer, not only as a result of shared risk factors but also as a direct consequence o...

Explainable Machine Learning for Performance Assessment of Fiber-Nanoparticle Concrete in High-Altitude Cold Environments.

The construction of concrete structures in high-altitude cold regions faces unique challenges, including intense radiation, low atmospheric pressure, ...

Testosterone-Sparing Treatment Strategies for Biochemically Recurrent Prostate Cancer After Radical Prostatectomy.

PURPOSE OF REVIEW: Biochemical recurrence (BCR) after radical prostatectomy occurs in up to one-third of patients and increases the risk of metastasis...

AI-assessed sarcopenia as an independent predictor of neoadjuvant chemotherapy outcomes in muscle-invasive bladder cancer.

PURPOSE: Sarcopenia has already been widely investigated as a potential indicator of negative outcomes in oncology patients. Our aim was to evaluate t...

Integration of protein and metabolomic blood biomarkers enhances classification of radiation exposure in X-irradiated humanized mice.

PURPOSE: The general population is at risk of exposure to ionizing radiation due to nuclear warfare, terrorism, or radiological accidents. Such exposu...

Cardioprotection in oncology: Mechanisms, risks and therapeutic strategies.

Objectives of this review is to conduct an analysis of current data of cardiotoxicity of anticancer drugs and radiation therapy, methods of cardiovasc...

Reinforcement learning for real-time adaptive radiotherapy.

State-of-the-art radiotherapy machines with integrated magnetic resonance (MR) imaging, known as MR-Linacs, provide the capability to track tumors in ...

Fast 3D whole-body occupational dose estimation in interventional radiology using physics-informed deep learning.

Occupational radiation exposure in interventional radiology is spatially heterogeneous and inadequately captured by conventional point-based dosimetry...

PTBP1 knockdown reprograms glioma stem cells into neuronal-like cells and suppresses tumorigenesis via the DUSP5-ERK1/2 signaling pathway.

BACKGROUND: Glioblastoma (GBM), the most prevalent and aggressive primary brain tumor in adults, has a median survival of merely 14 months. Current th...

Potential of SERS and proteomics for biomarker detection in cancer cells.

This study investigates the use of quantitative LC-MS/MS-based proteomics and surface-enhanced Raman spectroscopy (SERS) for biomarker detection in cl...