Breast Cancer

Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies.

Inclusion of physiologically relevant clearance mechanisms into organ-on-a-chip models is essential to reproduce tissue exposure and predict therapeutic efficacy, especially for local therapies and drug delivery applications that are already common in the clinic for ocular and cancer treatments. There remains a need for clearance-enabled organ-on-a-chips amenable to high throughput screening, espe...

AI-Assisted Chromosome Aberration Analysis for Rapid Biological Dose Estimation of Ionizing Radiation.

Radiation dose assessment in exposed individuals relies on the dicentric assay, the gold-standard cytogenetic biodosimeter that quantifies radiation-induced chromosomal aberrations. Although fully automated methods have been proposed, their accuracy remains suboptimal for large-scale emergency response. Here, we present a hybrid, semi-automatic framework that couples a deep-learning-based dicentri...

Evaluating dose distribution in prostate IMRT patients using deep learning: the influence of loss function on model performance.

To estimate the influence of various loss functions on the performance of deep learning (DL) models for dose prediction in intensity-modulated radioth...

Interpretable MRI-Based Machine Learning Model for Noninvasive Prediction of Axillary Lymph Node Metastasis After Neoadjuvant Chemotherapy in Breast Cancer.

RATIONALE AND OBJECTIVES: Accurate prediction of axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) remains challenging in bre...

EXPRESS: Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. T...

SH3BP5L triggers the RAB11A-regulated integrin recycling network implicated in breast cancer metastasis.

Metastatic progression in aggressive breast cancer (BC) depends on a tightly controlled vesicular recycling network regulated by RAB11, a small guanos...

Molecular classification of radiation-induced meningiomas.

PURPOSE: Radiation-induced meningiomas (RIMs) are an uncommon late complication of cranial irradiation that frequently display aggressive behavior. Al...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

Single‑center weakly supervised deep learning prediction of KRAS, NRAS, BRAF, and HER2 status in colorectal cancer from histopathology images using internal cross‑validation.

Machine learning-based prognostic model for local control prediction in lung cancer patients undergoing stereotactic body radiation therapy: a preliminary study.

PURPOSE: Predicting local recurrence after stereotactic body radiation therapy (SBRT) for lung cancer remains challenging. This study aims to develop ...

Integrating network toxicology, machine learning, and molecular dynamics simulations to reveal tanshinone iia's dual mechanisms in TNBC and doxorubicin-induced cardiotoxicity.

Doxorubicin (Dox)-induced cardiotoxicity remains a critical barrier to optimizing breast cancer (BC) treatment, highlighting the urgent need to dissec...

Overcoming Resistance in EGFR-Mutant Cancers: A Comprehensive Review of Inhibitor Evolution and SAR-Based Design.

The epidermal growth factor receptor (EGFR) is a key target in cancer therapy, mainly in non-small cell lung cancer (NSCLC). Though, the efficacy of E...

Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.

OBJECTIVE: Dosiomics and radiomics elaborate the low-and high-order features extracted from images to predict clinical outcomes. Whole-brain radiother...

Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening.

PURPOSE: Low-dose CT (LDCT) is increasingly being adopted as a preferred method for lung cancer screening. However, the accompanying rise in image noi...

Machine learning for optimizing mAs in KUB radiography with metal implants.

BACKGROUND AND PURPOSE: Kidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased...

Radiation-free Cobb angle estimation in adolescent scoliosis using surface topography and a linear regression model.

Scoliosis, the most common spinal deformity in adolescents, requires frequent radiographic follow-up, exposing patients to cumulative ionizing radiati...

Predicting progression-free survival in hormone-receptor positive (HR+/HER2-) metastatic breast cancer (MBC) treated with CDK4/6 inhibitors: A machine learning approach.

BACKGROUND: In HR+/HER2- metastatic breast cancer (MBC), CDK4/6 inhibitors combined with endocrine therapy (ET) significantly improve progression-free...

Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.

BACKGROUND: Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse condi...

An interpretable machine learning model for predicting survival in pancreatic cancer via SHAP: a multicenter study.

BACKGROUND: Existing pancreatic cancer prediction models still have significant limitations until now. This multicenter retrospective study aimed to i...

Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution.

PURPOSE: Radiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scolios...