Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer.

Aim: To train and validate a comprehensive deep-learning (DL) segmentation model for loco-regional breast cancer with the aim of clinical implementation.

Methods: DL segmentation models for 7 clinical target volumes (CTVs) and 11 organs at risk (OARs) were trained on 170 left-sided breast cancer cases from two radiotherapy centres in Norway. Another 30 patient cases were used for validation, which included the evaluation of Dice similarity coefficient and Hausdorff distance, qualitative scoring according to clinical usability, and relevant dosimetric parameters.

Dosimetric evaluation of manually and inversely optimized treatment planning for high dose rate brachytherapy of cervical cancer.

Background: To compare five inverse treatment planning methods with the conventional manually optimized planning approach for brachytherapy of cervical cancer with respect to dosimetric parameters.

Material And Methods: Eighteen cervical cancer patients treated with magnetic resonance imaging (MRI)-guided high dose rate (HDR) brachytherapy were included in this study. Six plans were created for each of the 4 HDR brachytherapy fractions for each patient: 1 manually optimized and 5 inversely planned.

Effects of growth factors on growth and radiation sensitivity of the human breast cancer cell line T-47D.

Different growth factors are supposed to be involved in evolution of breast cancer. Radiation therapy is used in treatment of breast cancer patients, and the possible relationships between the influence of growth factors on cells and their radiation sensitivity are therefore of interest. Cell growth and radiation sensitivity of the human oestrogen and progesterone receptor positive cell line T-47D were investigated following exposure to the growth factors IGF-1, TGF-alpha and TGF-beta.