arXiv subjects

Jonas Willmann

Publications and source records attributed to Jonas Willmann.

4 recordsLinked to original sources

arXiv2025

Simultaneous optimization of non-coplanar beam orientations and cumulative EQD2 distribution for high-dose reirradiation of locoregionally recurrent non-small cell lung cancer

Background and Purpose: Reirradiation for non-small cell lung cancer (NSCLC) is commonly delivered using coplanar techniques. In this study, we developed a beam orientation optimization algorithm for reirradiation planning to investigate whether the selection of favorable non-coplanar beam orientations may limit cumulative doses to critical organs-at-risk (OARs) and thus improve the therapeutic window. Materials and Methods: Fifteen cases of challenging high-dose reirradiation for locoregionally recurrent NSCLC were included in this in-silico study. For each patient, the dose distribution from the previous treatment was first mapped to the reirradiation planning CT using rigid dose registration, and subsequently converted to equivalent dose in 2 Gy fractions (EQD2). A 2-arc non-coplanar reirradiation plan, combining dynamic gantry and couch rotation, was then generated using an EQD2-based direct aperture optimization algorithm, which allows for the simultaneous optimization of the dynamic gantry-couch path and the cumulative EQD2 distribution. Non-coplanar reirradiation plans were benchmarked against 2-arc coplanar VMAT plans, which mimic state-of-the-art practice for reirradiation of NSCLC. Results: Non-coplanar reirradiation plans could reduce the maximum cumulative EQD2 to critical OARs such as bronchial tree, esophagus, thoracic wall and trachea by at least 5 Gy2 for 6 out of 15 patients compared to coplanar reirradiation plans. At the same time, target coverage and lung EQD2 metrics were comparable for both methods. Conclusions: The automated selection of favorable non-coplanar beam orientations may reduce the maximum cumulative EQD2 to critical OARs in challenging thoracic reirradiation cases. This allows to explore either better OAR sparing or dose-escalation in future clinical studies.

physics.med-ph↗

arXiv2025

Transformer-based cardiac substructure segmentation from contrast and non-contrast computed tomography for radiotherapy planning

Accurate segmentation of cardiac substructures on computed tomography (CT) scans is essential for radiotherapy planning but typically requires large annotated datasets and often generalizes poorly across imaging protocols and patient variations. This study evaluated whether pretrained transformers enable data-efficient training using a fixed architecture with balanced curriculum learning. A hybrid pretrained transformer-convolutional network (SMIT) was fine-tuned on lung cancer patients (Cohort I, N $=$ 180) imaged in the supine position and validated on 60 held-out Cohort I patients and 65 breast cancer patients (Cohort II) imaged in both supine and prone positions. Two configurations were evaluated: SMIT-Balanced (32 contrast-enhanced CTs and 32 non-contrast CTs) and SMIT-Oracle (180 CTs). Performance was compared with nnU-Net and TotalSegmentator. Segmentation accuracy was assessed primarily using the 95th percentile Hausdorff distance (HD95), with radiation dose and overlap-based metrics evaluated as secondary endpoints. SMIT-Balanced achieved accuracy comparable to SMIT-Oracle despite using 64$\%$ fewer training scans. On Cohort I, HD95 was 6.6 $\pm$ 4.3 mm versus 5.4 $\pm$ 2.6 mm, and on Cohort II, 10.0 $\pm$ 9.4 mm versus 9.4 $\pm$ 9.8 mm, respectively, demonstrating robustness to patient, imaging, and data variations. Radiation dose metrics derived from SMIT segmentations were equivalent to those from manual delineations. Although nnU-Net improved over the publicly trained TotalSegmentator, it showed reduced cross-domain robustness compared to SMIT. Balanced curriculum training reduced labeled data requirements without compromising accuracy relative to the oracle model and maintained robustness across patient and imaging variations. Pretraining reduced dependence on data domain and obviated the need for data-specific architectural reconfiguration required by nnU-Net.

eess.IV↗

arXiv2024

Clinical utility of automatic treatment planning for proton therapy of head-and-neck cancer patients using JulianA

Background: Automatic treatment planning promises many benefits for both research and clinical environments. For clinics, autoplanning promises to reduce planning time and achieve more comparable treatment plans and thereby reduce inter-planner variability. Further, it can assist clinicians in quality assurance by providing a minimum plan quality standard. Finally, autoplanning is an essential part of patient selection, which is crucial for the advancement of proton therapy itself. Methods: A retrospective planning study using a cohort of 17 head-and-neck cancer patients treated at our institute. The clinically accepted plans created by dosimetrists (d-plans) were compared to automatically generated JulianA plans (j-plans). Both methods used the same beam arrangement. The plans were analysed by two expert reviewers without knowing how each plan was created. They assessed the plan quality and stated a preference. Results: All of the j-plans were deemed rather or clearly acceptable, resulting in a higher acceptability than the d-plans. The j-plan was considered superior in 14 (82.4%) cases, of equal quality for 1 (5.9%) and inferior to the d-plan for only 2 (11.8%) of the cases. The reviewers concluded that JulianA achieves more conformal dose distributions for the 15 (88.2%) cases where the j-plans were at least as good as the d-plans. Conclusions: The results show that the JulianA is ready to be used as a clinical quality assurance tool and research platform at our institute. While these results are encouraging, further research is needed to reduce the number of spots further and introduce robustness considerations into the optimisation algorithm in order to employ it on a daily basis for patient treatment.

physics.med-ph↗

arXiv2023

JulianA: An automatic treatment planning platform for intensity-modulated proton therapy and its application to intra- and extracerebral neoplasms

Creating high quality treatment plans is crucial for a successful radiotherapy treatment. However, it demands substantial effort and special training for dosimetrists. Existing automated treatment planning systems typically require either an explicit prioritization of planning objectives, human-assigned objective weights, large amounts of historic plans to train an artificial intelligence or long planning times. Many of the existing auto-planning tools are difficult to extend to new planning goals. A new spot weight optimisation algorithm, called JulianA, was developed. The algorithm minimises a scalar loss function that is built only based on the prescribed dose to the tumour and organs at risk (OARs), but does not rely on historic plans. The objective weights in the loss function have default values that do not need to be changed for the patients in our dataset. The system is a versatile tool for researchers and clinicians without specialised programming skills. Extending it is as easy as adding an additional term to the loss function. JulianA was validated on a dataset of 19 patients with intra- and extracerebral neoplasms within the cranial region that had been treated at our institute. For each patient, a reference plan which was delivered to the cancer patient, was exported from our treatment database. Then JulianA created the auto plan using the same beam arrangement. The reference and auto plans were given to a blinded independent reviewer who assessed the acceptability of each plan, ranked the plans and assigned the human-/machine-made labels. The auto plans were considered acceptable in 16 out of 19 patients and at least as good as the reference plan for 11 patients. Whether a plan was crafted by a dosimetrist or JulianA was only recognised for 9 cases. The median time for the spot weight optimisation is approx. 2 min (range: 0.5 min - 7 min).

physics.med-ph↗