Evaluation of magnetic resonance imaging derived synthetic computed tomography for proton therapy planning in prostate cancer.

Background And Purpose: Magnetic resonance imaging (MRI)-only workflow is used in photon radiotherapy (RT) today, but not yet for protons. To bring MRI-only proton RT into clinical use, proton dose calculation on MRI-derived synthetic CT (sCT) must be validated. We evaluated proton dose calculation accuracy of prostate cancer proton plans using a commercially available sCT generator already validated for photon planning.

Script-based automatic radiotherapy planning for cervical cancer.

Background: This study aimed to develop fully automated script-based radiotherapy treatment plans for cervical cancer patients, and evaluate them against clinically accepted plans, as validation before clinical implementation.

Material And Methods: In this retrospective planning study, treatment plans for 25 locally advanced cervical cancer (LACC) patients with up to three dose levels were included. Fully automated plans were created using an in-house developed Python script in RayStation, and compared to clinically accepted manually made plans.

Locoregional breast radiotherapy including IMN: optimizing the dose distribution using an automated non-coplanar VMAT-technique.

Background: Volumetric Modulated Arc Therapy (VMAT) offers better conformity, homogeneity and sparing of the heart and ipsilateral lung for locoregional radiotherapy in left-sided breast cancer compared to three-dimensional conformal radiotherapy (3D-CRT). However, conventional coplanar VMAT (cVMAT) can result in higher doses to the normal tissue on the contralateral side. This study investigates a non-coplanar VMAT-technique (ncVMAT) to mitigate this issue.

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.

A cross-sectional study examining the prevalence of cachexia and areas of unmet need in patients with cancer.

Purpose: The semantics of defining cancer cachexia over the last decade has resulted in uncertainty as to the prevalence. This has further hindered the recognition and subsequent treatment of this condition. Following the consensus definition for cancer cachexia in 2011, there is now a need to establish estimates of prevalence.