Analysis of re-recurrent rectal cancer after curative treatment of locally recurrent rectal cancer.

Purpose: Substantiating data guiding clinical decision making in locally recurrent rectal cancer (LRRC) is lacking, specifically in target volume (TV) definition for chemoradiotherapy (CRT). A case-by-case review of local re-recurrences (re-LRRC) after multimodal treatment for LRRC was performed, to determine location of re-LRRC and assess whether treatment could have been improved.

Methods: All patients treated with curative intent for LRRC at the Catharina Hospital Eindhoven from October 2016 onwards, in whom complete imaging of (re-)LRRC and radiotherapy was available, were retrieved.

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time.

Inter-observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients.

Introduction: In the Library-of-Plans (LoP) approach, correct plan selection is essential for delivering radiotherapy treatment accurately. However, poor image quality of the cone-beam computed tomography (CBCT) may introduce inter-observer variability and thereby hamper accurate plan selection. In this study, we investigated whether new techniques to improve the CBCT image quality and improve consistency in plan selection, affects the accuracy of LoP selection in cervical cancer patients.

Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography.

Background And Purpose: To improve cone-beam computed tomography (CBCT), deep-learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT evaluation is mainly focused on image quality and CT number accuracy. However, correct representation of daily anatomy of the CBCT is also important for sCTs in adaptive radiotherapy.

A mask-compatible, radiolucent, 8-channel head and neck receive array for MRI-guided radiotherapy treatments and pre-treatment simulation.

Immobilization masks are used to prevent patient movement during head and neck (H&N) radiotherapy. Motion restriction is beneficial both during treatment, as well as in the pre-treatment simulation phase, where magnetic resonance imaging (MRI) is often used for target definition. However, the shape and size of the immobilization masks hinder the use of regular, close-fitting MRI receive arrays.