Publications by authors named "K Haustermans"
Background: Radiotherapy is a frequently utilized palliative treatment for cancer patients. Electronic Patient-Reported Outcome Measures (ePROMs) offer a method for patients to communicate their symptoms and concerns to healthcare providers (HCPs) remotely. While ePROMs have demonstrated significant benefits for oncology patient care, their integration into routine clinical practice of palliative radiotherapy (PRT) poses challenges.
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- ProtOnART is a technique that improves proton therapy for esophageal cancer by adapting to changes in patient anatomy during treatment, focusing on effective autodelineation methods for target and risk areas.
- A study of 15 patients compared various autodelineation methods and their effectiveness in creating adaptive treatment plans, finding that deformation techniques yielded better results for organs at risk and clinical target volumes.
- The results showed that while most adaptive treatment plans met initial evaluation goals, significant challenges remained in ensuring adequate coverage of clinical targets, necessitating manual intervention for clinical acceptance.
Background: The addition of an integrated focal boost to the intraprostatic lesion is associated with improved biochemical disease-free survival (bDFS) in patients with intermediate- and high-risk prostate cancer (PCa) in conventionally fractionated radiotherapy. Furthermore, whole gland stereotactic body radiotherapy (SBRT) demonstrated to be non-inferior to conventional radiotherapy for low- and intermediate-risk PCa. To investigate the combination of ultra-hypofractionated prostate SBRT with iso-toxic focal boosting for intermediate- and high-risk PCa, we performed the hypo-FLAME trial.
View Article and Find Full Text PDFTo demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These strategies leverage existing deformable image registration (DIR) algorithms and state-of-the-art deep learning (DL) networks for organ segmentation and proton dose prediction.Four OAPT strategies featuring automatic segmentation and robust optimization were evaluated on a cohort of 17 patients, each undergoing a repeat CT scan.
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