Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy.

Background: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically implemented in-house trained DL segmentation model for breast cancer to a previously performed pilot study to assess possible differences in performance or acceptability.

Breast conserving surgery with intraoperative electron beam radiation therapy for low-risk breast cancer: Five-year follow-up of 306 patients.

Recent studies have reported a higher than expected risk of ipsilateral breast tumor recurrence (IBTR) after breast conserving surgery (BCS) and a single dose of electron beam intra-operative radiotherapy (IORT). This finding was the rationale to perform a retrospective single center cohort study evaluating the oncologic results of consecutive patients treated with BCS and IORT. Women were eligible if they had clinical low-risk (N0, ≤2 cm unifocal, Bloom and Richardson grade 1-2), estrogen receptor-positive and human-epidermal-growth-factor-receptor-2-negative breast cancer.

Optimisation of Organ Preservation treatment strategies in patients with rectal cancer with a good clinical response after neoadjuvant (chemo)radiotherapy: Additional contact X-ray brachytherapy versus eXtending the observation period and local excision (OPAXX) - protocol for two multicentre, parallel, single-arm, phase II studies.

Introduction: Standard treatment for patients with intermediate or locally advanced rectal cancer is (chemo)radiotherapy followed by total mesorectal excision (TME) surgery. In recent years, organ preservation aiming at improving quality of life has been explored. Patients with a complete clinical response to (chemo)radiotherapy can be managed safely with a watch-and-wait approach.

Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer.

Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them.