ArticleMedical physics2021
Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- A systematic review of volumetric image guidance in proton therapy.Physical and engineering sciences in medicine · 2023Pooled it
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Equivariant conditional diffusion model for head and neck CT image synthesis from CBCT.Medical physics · 2026Article
- Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Automated target misalignment correction for cone beam computed tomography-based online adaptive radiotherapy of locally advanced lung cancer patients.Physics and imaging in radiation oncology · 2025Article
- Proton radiography interpretation with artificial intelligence for treatment deviation detection in proton therapy.Physics and imaging in radiation oncology · 2025Article
- Transfer learning for DL-based Synthetic CT after reconstruction algorithm upgrade in a proton therapy clinic.Medical physics · 2025Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Deep learning based super-resolution for CBCT dose reduction in radiotherapy.Medical physics · 2025Article
- Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients.Sensors (Basel, Switzerland) · 2024Article
- Are offline ART decisions for NSCLC impacted by the type of dose calculation algorithm?Technical innovations & patient support in radiation oncology · 2024Article
- Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients.Physics and imaging in radiation oncology · 2023Article
- Emerging technologies for cancer therapy using accelerated particles.Progress in particle and nuclear physics · 2023Article
- Image-Guided Proton Therapy: A Comprehensive Review.Cancers · 2023Review
- Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.Medical physics · 2022Article
- A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images.Radiation oncology (London, England) · 2022Article
- Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAdaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.
methodsA dataset of 33 thoracic cancer patients, containing CBCTs, same-day repeat CTs (rCT), planning-CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT-based correction method. Mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU-accuracy of sCTs in terms of range errors.
resultsOn average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%).
conclusionCBCT-based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.
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