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Gupta AC, Cazoulat G, Al Taie M, Yedururi S, Rigaud B, Castelo A, Wood J, Yu C, O'Connor C, Salem U, Silva JAM, Jones AK, McCulloch M, Odisio BC, Koay EJ, Brock KK. Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images. Sci Rep 2024; 14:4678. [PMID: 38409252 DOI: 10.1038/s41598-024-53997-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 02/07/2024] [Indexed: 02/28/2024] Open
Abstract
Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (CCH) and 20 (CPVE) CECT of LC patients. [Formula: see text] outperformed [Formula: see text] across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03-0.05 (p < 0.05). [Formula: see text], and [Formula: see text] were not statistically different (p > 0.05), however, both were slightly better than [Formula: see text] by DSC up to 0.02. The final model, [Formula: see text], showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5-8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score [Formula: see text] 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.
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Affiliation(s)
- Aashish C Gupta
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mais Al Taie
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sireesha Yedururi
- Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Austin Castelo
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Wood
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cenji Yu
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caleb O'Connor
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Usama Salem
- Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Aaron Kyle Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Molly McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bruno C Odisio
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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He Y, Cazoulat G, Wu C, Svensson S, Almodovar-Abreu L, Rigaud B, McCollum E, Peterson C, Wooten Z, Rhee DJ, Balter P, Pollard-Larkin J, Cardenas C, Court L, Liao Z, Mohan R, Brock K. Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy. Int J Radiat Oncol Biol Phys 2024; 118:231-241. [PMID: 37552151 PMCID: PMC11379060 DOI: 10.1016/j.ijrobp.2023.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/04/2023] [Accepted: 07/14/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.
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Affiliation(s)
- Yulun He
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, Texas; Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Guillaume Cazoulat
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carol Wu
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | | | - Bastien Rigaud
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Emma McCollum
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine Peterson
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zachary Wooten
- Department of Statistics, Rice University, Houston, Texas
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Peter Balter
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Julianne Pollard-Larkin
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Laurence Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zhongxing Liao
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Radhe Mohan
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy Brock
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
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Xiao H, Xue X, Zhu M, Jiang X, Xia Q, Chen K, Li H, Long L, Peng K. Deep learning-based lung image registration: A review. Comput Biol Med 2023; 165:107434. [PMID: 37696177 DOI: 10.1016/j.compbiomed.2023.107434] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/13/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.
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Affiliation(s)
- Hanguang Xiao
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Xufeng Xue
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Mi Zhu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
| | - Xin Jiang
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Qingling Xia
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Kai Chen
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Huanqi Li
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Li Long
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Ke Peng
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
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McCulloch MM, Cazoulat G, Svensson S, Gryshkevych S, Rigaud B, Anderson BM, Kirimli E, De B, Mathew RT, Zaid M, Elganainy D, Peterson CB, Balter P, Koay EJ, Brock KK. Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures. Front Oncol 2022; 12:1015608. [PMID: 36408172 PMCID: PMC9666494 DOI: 10.3389/fonc.2022.1015608] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/10/2022] [Indexed: 12/29/2023] Open
Abstract
PURPOSE Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms. MATERIALS AND METHODS Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses. RESULTS Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach. CONCLUSION This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.
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Affiliation(s)
- Molly M. McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | | | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brian M. Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ezgi Kirimli
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brian De
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ryan T. Mathew
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mohamed Zaid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dalia Elganainy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Christine B. Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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