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1. SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy

Author

Thummerer, Adrian, van der Bijl, Erik, Galapon, Arthur Jr, Verhoeff, Joost JC, Langendijk, Johannes A, Both, Stefan, Cornelis, Berg, AT van den, and Maspero, Matteo

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images to facilitate the development and evaluation of sCT generation for radiotherapy planning. Acquisition and validation methods: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in CSV files provided with the datasets. Data format and usage notes: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260705) under the SynthRAD2023 collection. The images for each subject are available in nifti format. Potential applications: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning., Comment: 15 pages, 4 figures, 9 tables, pre-print submitted to Medical Physics - dataset. The training dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7260705 from April, 1st 2023

Published
2023
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6. Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Author

Ma, Baoqiang, Li, Yan, Chu, Hung, Tang, Wei, De la O Arévalo, Luis Ricardo, Guo, Jiapan, van Ooijen, Peter, Both, Stefan, Langendijk, Johannes Albertus, van Dijk, Lisanne V., Sijtsema, Nanna Maria, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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7. Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach

Author

Chu, Hung, De la O Arévalo, Luis Ricardo, Tang, Wei, Ma, Baoqiang, Li, Yan, De Biase, Alessia, Both, Stefan, Langendijk, Johannes Albertus, van Ooijen, Peter, Sijtsema, Nanna Maria, van Dijk, Lisanne V., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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17. Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Author

Ma, Baoqiang, Guo, Jiapan, De Biase, Alessia, Sourlos, Nikos, Tang, Wei, van Ooijen, Peter, Both, Stefan, Sijtsema, Nanna Maria, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2022
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24. Proton beam reirradiation for locally recurrent pancreatic adenocarcinoma

Author

Boimel, Pamela J, Berman, Abigail T, Li, Jonathan, Apisarnthanarax, Smith, Both, Stefan, Lelionis, Kristi, Larson, Gary L, Teitelbaum, Ursina, Lukens, John N, Ben-Josef, Edgar, Metz, James M, and Plastaras, John P

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Pancreatic Cancer, Digestive Diseases, Cancer, Rare Diseases, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Proton, reirradiation, toxicity, pancreatic cancer, survival, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundLocal recurrence following definitive treatment for pancreatic adenocarcinoma is common and can be associated with significant morbidity and mortality. Retreatment options for these patients are limited. Proton beam reirradiation (PRT) may limit dose and toxicity to previously irradiated normal tissues in patients without evidence of metastatic disease.MethodsBetween 8/2010-2/2015, 15 patients with isolated, locally-recurrent pancreatic cancer were treated with PRT. Acute toxicity was graded using CTC v 4.0 and defined as occurring within 90 days. Kaplan-Meier survival analysis was performed from the start of PRT. A log-rank test was used to compare survival with or without concurrent chemotherapy.ResultsMedian follow-up was 15.7 months [2-48] from the start of PRT. The median clinical target volume (CTV) was 71 cc [15-200]. Ten (67%) patients received concurrent chemotherapy. Median PRT dose was 59.4 Gy (37.5-59.4 Gy). The median time interval from the prior treatment course was 26.7 months (7-461.3). There was a rate of 13% acute ≥ grade 3 toxicities attributed to PRT. The median overall survival (OS) was 16.7 months (95% CI, 4.7-36) and OS at 1 year was 67%. The "in-field" failure free survival at one year was 87%. The locoregional progression free survival (LPFS) and distant metastasis free survival (DMFS) at 1 year was 72% and 64% respectively. Concurrent chemotherapy was associated with a higher median survival.ConclusionsPRT was well tolerated, resulted in prolonged clinical outcomes compared to historical controls, and should be considered as a treatment option with concurrent chemotherapy in selected patients with locally-recurrent pancreatic cancer.

Published
2017

29. Salivary Gland Tumors

Author

Leeman, Jonathan E., Romesser, Paul, Melotek, James, Cahlon, Oren, Sine, Kevin, Both, Stefan, Lee, Nancy Y., Lee, Nancy Y., Series editor, Lu, Jiade J., Series editor, Leeman, Jonathan E., editor, Cahlon, Oren, editor, Sine, Kevin, editor, Jiang, Guoliang, editor, and Both, Stefan, editor

Published
2018
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30. Proton Treatment Planning

Author

Zeng, Chuan, Amos, Richard A., Winey, Brian, Beltran, Chris, Saleh, Ziad, Tochner, Zelig, Kooy, Hanne, Both, Stefan, Lee, Nancy Y., Series editor, Lu, Jiade J., Series editor, Leeman, Jonathan E., editor, Cahlon, Oren, editor, Sine, Kevin, editor, Jiang, Guoliang, editor, and Both, Stefan, editor

Published
2018
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31. Lower Gastrointestinal Malignancies

Author

Plastaras, John P., Both, Stefan, Lin, Haibo, Hawkins, Maria, Lee, Nancy Y., Series editor, Lu, Jiade J., Series editor, Leeman, Jonathan E., editor, Cahlon, Oren, editor, Sine, Kevin, editor, Jiang, Guoliang, editor, and Both, Stefan, editor

Published
2018
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32. Sarcoma

Author

Deville, Curtiland, Ladra, Matthew, Zhai, Huifang, Siddiqui, Moe, Both, Stefan, Lin, Haibo, Lee, Nancy Y., Series editor, Lu, Jiade J., Series editor, Leeman, Jonathan E., editor, Cahlon, Oren, editor, Sine, Kevin, editor, Jiang, Guoliang, editor, and Both, Stefan, editor

Published
2018
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34. Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy.

Author

Galapon, Arthur Villanueva, Thummerer, Adrian, Langendijk, Johannes Albertus, Wagenaar, Dirk, and Both, Stefan

Subjects
DEEP learning, PROTON therapy, PEARSON correlation (Statistics), PROTONS, COMPUTED tomography, PHOTON beams
Abstract

Background: Deep learning has shown promising results to generate MRI‐based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. Purpose: This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep‐learning‐based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. Methods: Two deep‐learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT‐MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU‐, range‐, WET‐, and dose‐error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90‐degree angle. To evaluate the dose distribution, a mask based on the 5%‐isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs Results: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty‐HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty‐range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty‐WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3 mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). Conclusion: The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD‐based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning‐based sCTs. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Partitioning of discrete proton arcs into interlaced subplans can bring proton arc advances to existing proton facilities

Author

Engwall, Erik, primary, Marthin, Otte, additional, Wase, Viktor, additional, Sundström, Johan, additional, Mikhalev, Victor, additional, de Jong, Bas A., additional, Langendijk, Johannes A., additional, Melbéus, Henrik, additional, Andersson, Björn, additional, Korevaar, Erik W., additional, Both, Stefan, additional, Bokrantz, Rasmus, additional, Glimelius, Lars, additional, and Fredriksson, Albin, additional

Published
2023
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45. CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Author

Ma, Baoqiang, primary, Guo, Jiapan, additional, Zhai, Tian‐Tian, additional, van der Schaaf, Arjen, additional, Steenbakkers, Roel J. H. M., additional, van Dijk, Lisanne V., additional, Both, Stefan, additional, Langendijk, Johannes A., additional, Zhang, Weichuan, additional, Qiu, Bingjiang, additional, van Ooijen, Peter M. A., additional, and Sijtsema, Nanna M., additional

Published
2023
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