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1. Image-to-Image Translation Based on Deep Generative Modeling for Radiotherapy Synthetic Dataset Creation

Author

Glazunova, Olga, Wolfs, Cecile J. A., and Verhaegen, Frank

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Objective: Radiotherapy uses precise doses of radiation to treat cancer, requiring accurate verification, e.g. using the Electronic Portal Imaging Device (EPID), to guide treatment. To develop an effective artificial intelligence (AI) model for error detection and treatment verification, a large and well-annotated dataset of EPID images is needed, however, acquiring such high quality real data is difficult. While synthetic EPID data could be a viable alternative, it is critical to ensure that this data is as realistic as possible to effectively train an accurate and reliable AI model. The measurement uncertainty that is not modeled in EPID predictions but is present on real measured EPID images can hinder downstream tasks such as error detection and classification. Our research aims to improve synthetic EPID data through image-to-image (I2I) translation based on deep generative modeling. Approach: A dataset of 989 predicted EPID images and corresponding measured EPID images was used. We evaluate both paired and unpaired generative modeling approaches for this task. For the former, we introduce a novel modification of Variational Autoencoder (VAE) to I2I, a method that, to the best of our knowledge, has not been previously explored for this task. For the latter, we use UNsupervised Image-to-Image Translation Networks (UNIT). Results: Our results show that both models achieved some degree of I2I translation, with our novel modification of the VAE model outperforming the UNIT model in improving key metrics (mean absolute error: 4.1 cGy vs 6.4 cGy; relative dose difference in-field: 2.5% vs 5.5%; absolute dose difference in-field: 5.3 cGy vs 10.8 cGy). Significance: This enhanced synthetic data is expected to improve downstream tasks such as training neural networks for automated error detection and error classification in radiotherapy.

Published
2024

10. Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography.

Author

Daenen, Lars H B A, van de Worp, Wouter R P H, Rezaeifar, Behzad, de Bruijn, Joël, Qiu, Peiyu, Webster, Justine M, Peeters, Stéphanie, De Ruysscher, Dirk, Langen, Ramon C J, Wolfs, Cecile J A, and Verhaegen, Frank

Subjects
CONE beam computed tomography, NON-small-cell lung carcinoma, GENERATIVE adversarial networks, MUSCLE mass, BODY composition
Abstract

Objective. Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles. Approach. Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated. Main results. Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice similarity coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of −0.84 for the pCT dataset and −0.89 for the sCT dataset. Significance. This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Targeted nutritional intervention attenuates experimental lung cancer cachexia

Author

Worp, Wouter R. P. H., Theys, Jan, Wolfs, Cecile J. A., Verhaegen, Frank, Schols, Annemie M. W. J., Helvoort, Ardy, and Langen, Ramon C. J.

Abstract

Cachexia, a syndrome with high prevalence in non‐small cell lung cancer patients, impairs quality of life and reduces tolerance and responsiveness to cancer therapy resulting in decreased survival. Optimal nutritional care is pivotal in the treatment of cachexia and a recommended cornerstone of multimodal therapy. Here, we investigated the therapeutic effect of an intervention diet consisting of a specific combination of high protein, leucine, fish oil, vitamin D, galacto‐oligosaccharides, and fructo‐oligosaccharides on the development and progression of cachexia in an orthotopic lung cancer mouse model. Eleven‐week‐old male 129S2/Sv mice were orthotopically implanted with 344P lung epithelial tumour cells or vehicle (control). Seven days post‐implantation tumour‐bearing (TB) mice were allocated to either intervention‐ or isocaloric control diet. Cachexia was defined as 5 days of consecutive body weight loss, after which mice were euthanized for tissue analyses. TB mice developed cachexia accompanied by significant loss of skeletal muscle mass and epididymal fat mass compared with sham operated mice. The cachectic endpoint was significantly delayed (46.0 ± 15.2 vs. 34.7 ± 11.4 days), and the amount (−1.57 ± 0.62 vs. −2.13 ± 0.57 g) and progression (−0.26 ± 0.14 vs. −0.39 ± 0.11 g/day) of body weight loss were significantly reduced by the intervention compared with control diet. Moreover, systemic inflammation (pentraxin‐2 plasma levels) and alterations in molecular markers for proteolysis and protein synthesis, indicative of muscle atrophy signalling in TB‐mice, were suppressed in skeletal muscle by the intervention diet. Together, these data demonstrate the potential of this multinutrient intervention, targeting multiple components of cachexia, as integral part of lung cancer management.

Published
2024
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19. Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Author

Lustermans, Didier, Fonseca, Gabriel Paiva, Taasti, Vicki Trier, van de Schoot, Agustinus, Petit, Steven, van Elmpt, Wouter, Verhaegen, Frank, Lustermans, Didier, Fonseca, Gabriel Paiva, Taasti, Vicki Trier, van de Schoot, Agustinus, Petit, Steven, van Elmpt, Wouter, and Verhaegen, Frank

Abstract

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively. Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom. Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97-0.99) and decreasing the RMSE (62-55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02-0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU). Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the poten

Published
2024

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