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Daenen LHBA, van de Worp WRPH, Rezaeifar B, de Bruijn J, Qiu P, Webster JM, Peeters S, De Ruysscher D, Langen RCJ, Wolfs CJA, Verhaegen F. Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography. Phys Med Biol 2024; 69:205005. [PMID: 39299273 DOI: 10.1088/1361-6560/ad7d5b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 09/19/2024] [Indexed: 09/22/2024]
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.
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Affiliation(s)
- Lars H B A Daenen
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Medical Image Analysis group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter R P H van de Worp
- Department of Respiratory Medicine, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Behzad Rezaeifar
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Joël de Bruijn
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Peiyu Qiu
- Department of Respiratory Medicine, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Justine M Webster
- Department of Respiratory Medicine, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Stéphanie Peeters
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ramon C J Langen
- Department of Respiratory Medicine, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Cecile J A Wolfs
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- SmART Scientific Solutions BV, Maastricht, The Netherlands
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Viar-Hernandez D, Molina-Maza JM, Vera-Sánchez JA, Perez-Moreno JM, Mazal A, Rodriguez-Vila B, Malpica N, Torrado-Carvajal A. Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers. Med Phys 2024; 51:4922-4935. [PMID: 38569141 DOI: 10.1002/mp.17057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 03/01/2024] [Accepted: 03/17/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCT images. PURPOSE To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinical applications. METHODS The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testing purposes. RESULTS The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results of CBCT. CONCLUSIONS We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.
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Affiliation(s)
- David Viar-Hernandez
- Universidad Rey Juan Carlos, Medical Image Analysis and Biometry Laboratory, Madrid, Spain
| | | | | | | | - Alejandro Mazal
- Centro de Protonterapia Quironsalud, Servicio de física médica, Madrid, Spain
| | - Borja Rodriguez-Vila
- Universidad Rey Juan Carlos, Medical Image Analysis and Biometry Laboratory, Madrid, Spain
| | - Norberto Malpica
- Universidad Rey Juan Carlos, Medical Image Analysis and Biometry Laboratory, Madrid, Spain
| | - Angel Torrado-Carvajal
- Universidad Rey Juan Carlos, Medical Image Analysis and Biometry Laboratory, Madrid, Spain
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Aouadi S, Yoganathan SA, Torfeh T, Paloor S, Caparrotti P, Hammoud R, Al-Hammadi N. Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients. Biomed Phys Eng Express 2023; 9:055020. [PMID: 37489854 DOI: 10.1088/2057-1976/acea27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/25/2023] [Indexed: 07/26/2023]
Abstract
Purpose.To create a synthetic CT (sCT) from daily CBCT using either deep residual U-Net (DRUnet), or conditional generative adversarial network (cGAN) for adaptive radiotherapy planning (ART).Methods.First fraction CBCT and planning CT (pCT) were collected from 93 Head and Neck patients who underwent external beam radiotherapy. The dataset was divided into training, validation, and test sets of 58, 10 and 25 patients respectively. Three methods were used to generate sCT, 1. Nonlocal means patch based method was modified to include multiscale patches defining the multiscale patch based method (MPBM), 2. An encoder decoder 2D Unet with imbricated deep residual units was implemented, 3. DRUnet was integrated to the generator part of cGAN whereas a convolutional PatchGAN classifier was used as the discriminator. The accuracy of sCT was evaluated geometrically using Mean Absolute Error (MAE). Clinical Volumetric Modulated Arc Therapy (VMAT) plans were copied from pCT to registered CBCT and sCT and dosimetric analysis was performed by comparing Dose Volume Histogram (DVH) parameters of planning target volumes (PTVs) and organs at risk (OARs). Furthermore, 3D Gamma analysis (2%/2mm, global) between the dose on the sCT or CBCT and that on the pCT was performed.Results. The average MAE calculated between pCT and CBCT was 180.82 ± 27.37HU. Overall, all approaches significantly reduced the uncertainties in CBCT. Deep learning approaches outperformed patch-based methods with MAE = 67.88 ± 8.39HU (DRUnet) and MAE = 72.52 ± 8.43HU (cGAN) compared to MAE = 90.69 ± 14.3HU (MPBM). The percentages of DVH metric deviations were below 0.55% for PTVs and 1.17% for OARs using DRUnet. The average Gamma pass rate was 99.45 ± 1.86% for sCT generated using DRUnet.Conclusion.DL approaches outperformed MPBM. Specifically, DRUnet could be used for the generation of sCT with accurate intensities and realistic description of patient anatomy. This could be beneficial for CBCT based ART.
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Affiliation(s)
- Souha Aouadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - S A Yoganathan
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Tarraf Torfeh
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Satheesh Paloor
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Palmira Caparrotti
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Rabih Hammoud
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050 Doha, Qatar
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Hamming VC, Andersson S, Maduro JH, Langendijk JA, Both S, Sijtsema NM. Daily dose evaluation based on corrected CBCTs for breast cancer patients: accuracy of dose and complication risk assessment. Radiat Oncol 2022; 17:205. [PMID: 36510254 PMCID: PMC9746176 DOI: 10.1186/s13014-022-02174-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES The goal of this study is to validate different CBCT correction methods to select the superior method that can be used for dose evaluation in breast cancer patients with large anatomical changes treated with photon irradiation. MATERIALS AND METHOD Seventy-six breast cancer patients treated with a partial VMAT photon technique (70% conformal, 30% VMAT) were included in this study. All patients showed at least a 5 mm variation (swelling or shrinkage) of the breast on the CBCT compared to the planning-CT (pCT) and had a repeat-CT (rCT) for dose evaluation acquired within 3 days of this CBCT. The original CBCT was corrected using four methods: (1) HU-override correction (CBCTHU), (2) analytical correction and conversion (CBCTCC), (3) deep learning (DL) correction (CTDL) and (4) virtual correction (CTV). Image quality evaluation consisted of calculating the mean absolute error (MAE) and mean error (ME) within the whole breast clinical target volume (CTV) and the field of view of the CBCT minus 2 cm (CBCT-ROI) with respect to the rCT. The dose was calculated on all image sets using the clinical treatment plan for dose and gamma passing rate analysis. RESULTS The MAE of the CBCT-ROI was below 66 HU for all corrected CBCTs, except for the CBCTHU with a MAE of 142 HU. No significant dose differences were observed in the CTV regions in the CBCTCC, CTDL and CTv. Only the CBCTHU deviated significantly (p < 0.01) resulting in 1.7% (± 1.1%) average dose deviation. Gamma passing rates were > 95% for 2%/2 mm for all corrected CBCTs. CONCLUSION The analytical correction and conversion, deep learning correction and virtual correction methods can be applied for an accurate CBCT correction that can be used for dose evaluation during the course of photon radiotherapy of breast cancer patients.
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Affiliation(s)
- Vincent C. Hamming
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | | | - John H. Maduro
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Johannes A. Langendijk
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Stefan Both
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Nanna M. Sijtsema
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Sun H, Xi Q, Sun J, Fan R, Xie K, Ni X, Yang J. Research on new treatment mode of radiotherapy based on pseudo-medical images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106932. [PMID: 35671601 DOI: 10.1016/j.cmpb.2022.106932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 04/20/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Multi-modal medical images with multiple feature information are beneficial for radiotherapy. A new radiotherapy treatment mode based on triangle generative adversarial network (TGAN) model was proposed to synthesize pseudo-medical images between multi-modal datasets. METHODS CBCT, MRI and CT images of 80 patients with nasopharyngeal carcinoma were selected. The TGAN model based on multi-scale discriminant network was used for data training between different image domains. The generator of the TGAN model refers to cGAN and CycleGAN, and only one generation network can establish the non-linear mapping relationship between multiple image domains. The discriminator used multi-scale discrimination network to guide the generator to synthesize pseudo-medical images that are similar to real images from both shallow and deep aspects. The accuracy of pseudo-medical images was verified in anatomy and dosimetry. RESULTS In the three synthetic directions, namely, CBCT → CT, CBCT → MRI, and MRI → CT, significant differences (p < 0.05) in the three-fold-cross validation results on PSNR and SSIM metrics between the pseudo-medical images obtained based on TGAN and the real images. In the testing stage, for TGAN, the MAE metric results in the three synthesis directions (CBCT → CT, CBCT → MRI, and MRI → CT) were presented as mean (standard deviation), which were 68.67 (5.83), 83.14 (8.48), and 79.96 (7.59), and the NMI metric results were 0.8643 (0.0253), 0.8051 (0.0268), and 0.8146 (0.0267) respectively. In terms of dose verification, the differences in dose distribution between the pseudo-CT obtained by TGAN and the real CT were minimal. The H values of the measurement results of dose uncertainty in PGTV, PGTVnd, PTV1, and PTV2 were 42.510, 43.121, 17.054, and 7.795, respectively (P < 0.05). The differences were statistically significant. The gamma pass rate (2%/2 mm) of pseudo-CT obtained by the new model was 94.94% (0.73%), and the numerical results were better than those of the three other comparison models. CONCLUSIONS The pseudo-medical images acquired based on TGAN were close to the real images in anatomy and dosimetry. The pseudo-medical images synthesized by the TGAN model have good application prospects in clinical adaptive radiotherapy.
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Affiliation(s)
- Hongfei Sun
- School of Automation, Northwestern Polytechnical University, Xi'an, 710129, People's Republic of China.
| | - Qianyi Xi
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, 213003, People's Republic of China; Center of Medical Physics, Nanjing Medical University, Changzhou, 213003,People's Republic of China.
| | - Jiawei Sun
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, 213003, People's Republic of China; Center of Medical Physics, Nanjing Medical University, Changzhou, 213003,People's Republic of China.
| | - Rongbo Fan
- School of Automation, Northwestern Polytechnical University, Xi'an, 710129, People's Republic of China.
| | - Kai Xie
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, 213003, People's Republic of China; Center of Medical Physics, Nanjing Medical University, Changzhou, 213003,People's Republic of China.
| | - Xinye Ni
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, 213003, People's Republic of China; Center of Medical Physics, Nanjing Medical University, Changzhou, 213003,People's Republic of China.
| | - Jianhua Yang
- School of Automation, Northwestern Polytechnical University, Xi'an, 710129, People's Republic of China.
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