Breast radiotherapy planning: A decision-making framework using deep learning.

Background: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D-CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints.

Purpose: This study aims to develop a decision-making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatment techniques.

Differential biological effect of low doses of ionizing radiation depending on the radiosensitivity in a cell line model.

Purpose: Exposure to low doses (LD) of ionizing radiation (IR), such as the ones employed in computed tomography (CT) examination, can be associated with cancer risk. However, cancer development could depend on individual radiosensitivity. In the present study, we evaluated the differences in the response to a CT-scan radiation dose of 20 mGy in two lymphoblastoid cell lines with different radiosensitivity.

Multi-centre evaluation of variation in cumulative dose assessment in reirradiation scenarios.

Background And Purpose: Safe reirradiation relies on assessment of cumulative doses to organs at risk (OARs) across multiple treatments. Different clinical pathways can result in inconsistent estimates. Here, we quantified the consistency of cumulative dose to OARs across multi-centre clinical pathways.

Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy.

To adopt a transfer learning approach and establish a convolutional neural network (CNN) model for the prediction of rectum and bladder dose-volume histograms (DVH) in prostate patients treated with a VMAT technique. One hundred forty-four VMAT patients with intermediate or high-risk prostate cancer were included in this study. Data were split into two sets: 120 and 24 patients, respectively.

Publication of interventional phase 3 and 4 clinical trials in radiation oncology: an observational study.

Objectives: Clinical trials produce the best data available for decision-making in modern evidence-based medicine. We aimed to determine the rate of non-publication of interventional phase 3 and 4 clinical trials involving patients with cancer undergoing radiotherapy.

Setting: The ClinicalTrials.