Breast Cancer

Deep learning-based multi-modality image conversion for evaluating dose calculation and position correction accuracy in image-guided radiation therapy.

Cone-beam CT (CBCT) is widely used in image-guided radiation therapy (IGRT). However, CBCT has limited image quality, which can reduce dose calculation and position correction accuracy. This study aimed to propose a deep learning-based multi-modality image synthesis framework to enhance the feasibility of adaptive radiation therapy (ART) and IGRT. Two conditional generative adversarial network (CG...

AI-Assisted Risk Stratification in Stage II Colorectal Cancer: Multi-Institutional Validation of Semantically-Enhanced Deep Learning.

BACKGROUND AND AIMS: Accurate risk stratification in stage II colorectal cancer is essential for treatment decision-making, as current guidelines recommend adjuvant chemotherapy only for patients with a high-risk of relapse. We aimed to develop and validate an AI-based approach for automated invasive front assessment to improve prognostic stratification in this population. METHODS: We developed SÉ...

Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.

PURPOSE: The choice of adjuvant chemotherapy in pancreatic ductal adenocarcinoma (PDAC) is mainly guided by patients' general condition. We hypothesiz...

Tumour cell density quantified by artificial intelligence is associated with differential benefit from irinotecan-based chemo-radiotherapy in locally advanced rectal cancer: a post-hoc study of the phase 3 ARISTOTLE trial.

BACKGROUND: Tumour cells and tumour-associated stroma are key components of the tumour microenvironment, and their interaction impacts disease progres...

Artificial Intelligence for radiation protection in medical imaging and radiotherapy: A perspective from the AI Working Party of ICRP Committee 3.

PURPOSE: The increasing integration of Artificial Intelligence (AI) into clinical workflows for medical imaging and radiotherapy presents new opportun...

Radiation dose reduction in computed tomography perfusion of acute ischemic stroke patients using a denoising autoencoder.

BACKGROUND: Ischemic stroke results from the occlusion of a cerebral artery and is a leading cause of mortality and disability worldwide. Multimodal c...

Genetically predicted breastfeeding propensity and brain microstructural morphology: Insights from genome-wide association study summary statistics and neurogenomics.

BACKGROUND: Breastfeeding provides nutrients and bioactive components relevant to infant development, but observational associations with brain outcom...

Deep learning-based assessment of HER2-ultralow expression in breast cancer: a multi-center validation study across primary and metastatic lesions.

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is foundational for targeted therapy in breast cancer (BC). The rece...

AI Risk Prediction Tools for Autologous Breast Reconstruction.

BACKGROUND: Autologous breast reconstruction offers patients a durable and natural-appearing option after mastectomy. However, complication risks incl...

Overcoming therapeutic resistance in biliary tract cancer: from resistance mechanisms to dynamic precision oncology - a narrative review.

INTRODUCTION: Biliary tract cancer (BTC) is a biologically heterogeneous and highly aggressive malignancy with limited therapeutic options and poor su...

Chemical-Sensing Catheters for Long-Term and Personalized Therapeutic Management.

Long-term intravenous catheterization is vital for delivering chemotherapy to cancer patients and parenteral nutrition to those with intestinal failur...

DEGRO consensus framework for undergraduate radiation therapy teaching in Germany: a white paper with integrated guidance on artificial intelligence in medical education.

BACKGROUND AND PURPOSE: Undergraduate medical education is increasingly competency-based, digital, and interprofessional in accordance with the Nation...

Identification and functional validation of lipid droplet-associated prognostic biomarkers in breast cancer via integrative multi-omics and machine learning approaches.

BACKGROUND: Lipid droplet (LD)-associated metabolic reprogramming plays a critical role in breast cancer progression and immune modulation, yet robust...

Verification of the impact of differences between objective and subjective evaluation methods on the interpretation of artificial intelligence systems generating mammogram reports using vision-language model.

PURPOSE: Research on vision-language models (VLMs) in the medical field has recently increased. However, while multifaceted evaluation is necessary to...

Utilizing machine learning to identify multimodal signatures for patients who would benefit from the addition of tremelimumab to durvalumab and chemotherapy (TRIDENT).

PURPOSE: POSEIDON (NCT03164616) was a randomized, open-label, multicenter phase 3 trial comparing first-line durvalumab with or without tremelimumab i...

Impact of a silver filter with deep learning reconstruction on noncontrast abdominopelvic MDCT in terms of image quality, density values and radiation dose reduction.

INTRODUCTION: To assess how the use of a silver beam filtration with deep learning reconstruction (DLR) impacts the unenhanced abdominopelvic CT in te...

Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma.

Keratins (KRTs) are intermediate filament proteins expressed in epithelial cells and serve as diagnostic cancer biomarkers. They serve pivotal roles i...

Recent Advances in Immunotherapy for Breast Cancer: An Updated Review.

Immunotherapy has revolutionized the treatment landscape of breast cancer, particularly for triple-negative breast cancer (TNBC), yet primary and acqu...

Deep learning approaches for predicting response to neoadjuvant chemotherapy in muscle invasive bladder cancer: A systematic review.

Neoadjuvant chemotherapy (NAC) followed by radical cystectomy is the standard of care for muscle-invasive bladder cancer (MIBC), yet treatment respons...

Redefining breast cancer: therapeutic opportunities in HER2-low and emerging molecular subtypes.

Breast cancer classification has undergone significant evolution; however, the long-standing binary model distinguishing HER2-positive from HER2-negat...