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Zhang H, Fu C, Fan M, Lu L, Chen Y, Liu C, Sun H, Zhao Q, Han D, Li B, Huang W. Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis. Front Oncol 2022; 12:841771. [PMID: 35992838 PMCID: PMC9381816 DOI: 10.3389/fonc.2022.841771] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis. Method A retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All positioning images were transferred to Eclipse treatment planning systems (TPSs). Six MRI sequences (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) were registered with positioning CT. Five radiation oncologists delineated the tumor boundary to obtain the gross tumor volume (GTV). Conformity index (CI) and dice coefficient (DC) were used to measure differences among observers. Results The differences in delineation mean volumes, CI, and DC among CT and MRIs were significant. Multiple comparisons were made between MRI sequences and CT. Among them, DWI, T2WI, and T1WI+C 10 minutes sequences were statistically significant with CT in mean volumes, DC, and CI. The mean volume of DWI, T2WI, and T1WI+C 10 minutes sequence in the target area is significantly smaller than that on the CT sequence, but the consistency is higher than that of CT sequences. Conclusions The recognition of atelectasis by MRI was better than that by CT, which could reduce interobserver variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis.
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Affiliation(s)
- Hongjiao Zhang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chengrui Fu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Min Fan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Liyong Lu
- West China School of Public Health, Sichuan University, Chengdu, China
| | - Yiru Chen
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chengxin Liu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hongfu Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qian Zhao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Dan Han
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Wei Huang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- *Correspondence: Wei Huang,
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Kumar S, Holloway L, Boxer M, Ling Yap M, Chlap P, Moses D, Vinod S. Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for Lung radiotherapy. Radiother Oncol 2021; 167:292-299. [PMID: 34896156 DOI: 10.1016/j.radonc.2021.11.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE To compare gross tumour volume (GTV) delineation of lung cancer on magnetic resonance imaging (MRI) and positron emission tomography (PET) versus computed tomography (CT) and PET. METHODS Three experienced thoracic radiation oncologists delineated GTVs on twenty-six patients with lung cancer, based on CT registered to PET, T2-weighted MRI registered to PET and T1-weighted MRI registered with PET. All observers underwent education on reviewing T1 and T2 images along with guidance on window and level setup. Interobserver and intermodality variation was performed based ondice similarity coefficient (DSC), Hausdorff distance (HD), and average Hausdorff distance (AvgHD) metrics. To compute interobserver variability (IOV) a simultaneous truth and performance level estimation (STAPLE) volume for each image modality was used as reference volume. For intermodality analysis, each observers CT based primary and nodal GTV was used as reference volume. RESULTS A mean DSC of 0.9 across all observers for primary GTV (GTVp) and a DSC of > 0.7 for nodal GTV (GTVn) was demonstrated for IOV. Mean T2 and T1 GTVp and GTVn were smaller than CT GTVp and GTVn but the difference in volume between modalities was not statistically significant. Significant difference (p<0.01) for GTVp and GTVn was found between T2 and T1 GTVp and GTVn compared to CT GTVp and GTVn based on DSC metrics. Large variation in volume similarity was noted based on HD of up-to 5.4cm for observer volumes compared to STAPLE volume. CONCLUSION Interobserver variability in GTV delineation was similar for MRI and PET versus CT and PET. The significant difference between MRI compared to CT delineated volumes needs to be further explored.
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Affiliation(s)
- Shivani Kumar
- South West Sydney Cancer Services, Liverpool, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Australia, Sydney, NSW, Australia
| | - Lois Holloway
- South West Sydney Cancer Services, Liverpool, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Australia, Sydney, NSW, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW Australia; School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW Australia
| | - Miriam Boxer
- South West Sydney Cancer Services, Liverpool, NSW, Australia; ICON Cancer Centre, Concord, NSW, Australia
| | - Mei Ling Yap
- South West Sydney Cancer Services, Liverpool, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Australia, Sydney, NSW, Australia; School of Medicine, Western Sydney University, Campbelltown, NSW, Australia; Sydney Medical School, Public Health, University of Sydney, Sydney, NSW, Australia
| | - Phillip Chlap
- South West Sydney Cancer Services, Liverpool, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Australia, Sydney, NSW, Australia
| | - Daniel Moses
- Prince of Wales Hospital, Sydney, NSW, Australia; School of Medical Sciences, Faculty of Medicine, The University of New South Wales, Sydney, NSW, Australia
| | - Shalini Vinod
- South West Sydney Cancer Services, Liverpool, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Australia, Sydney, NSW, Australia
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Dubec M, Brown S, Chuter R, Hales R, Whiteside L, Rodgers J, Parker J, Eccles CL, van Herk M, Faivre-Finn C, Cobben D. MRI and CBCT for lymph node identification and registration in patients with NSCLC undergoing radical radiotherapy. Radiother Oncol 2021; 159:112-118. [PMID: 33775713 DOI: 10.1016/j.radonc.2021.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE This study compared MRI to CBCT for the identification and registration of lymph nodes (LN) in patients with locally advanced (LA)-NSCLC, to assess the suitability of targeting LNs in future MR-image guided radiotherapy (MRgRT) workflows. METHOD Radiotherapy radiographers carried out Visual Grading Analysis (VGA) assessment of image quality, LN registration and graded their confidence in registration for each of the 24 LNs on CBCT and two MR sequences, MR1 (T2w Turbo Spin Echo) and MR2 (T1w DIXON water only image). RESULTS Pre-registration image quality assessment revealed MR1 and MR2 as significantly superior to CBCT in terms of image quality (p ≤ 0.01). No significant differences were noted in interobserver variability for LN registration between CBCT, MR1 and MR2. Observers were more confident in their MR registrations compared to their CBCT based LN registrations (p ≤ 0.02). SUMMARY Interobserver setup correction variability was not found to be significantly different between CBCT and MR. Image quality and registration confidence were found to be superior for MRI sequences. This is a promising step towards MR-guided radiotherapy for the treatment of LA-NSCLC.
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Affiliation(s)
- Michael Dubec
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK.
| | - Sean Brown
- Gloucestershire Oncology Centre, Cheltenham General Hospital, Cheltenham, UK
| | - Robert Chuter
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Rosie Hales
- Department of Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - Lee Whiteside
- Department of Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - John Rodgers
- Department of Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - Jacqui Parker
- Department of Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - Cynthia L Eccles
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK; Department of Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
| | - Marcel van Herk
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - David Cobben
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
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Freedman JN, Gurney-Champion OJ, Nill S, Shiarli AM, Bainbridge HE, Mandeville HC, Koh DM, McDonald F, Kachelrieß M, Oelfke U, Wetscherek A. Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula. Radiother Oncol 2021; 159:209-217. [PMID: 33812914 PMCID: PMC8216429 DOI: 10.1016/j.radonc.2021.03.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/07/2021] [Accepted: 03/26/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE 4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times. METHODS Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study. RESULTS For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images. CONCLUSION Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.
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Affiliation(s)
- Joshua N Freedman
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, The Netherlands.
| | - Simeon Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Anna-Maria Shiarli
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Hannah E Bainbridge
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; Department of Radiotherapy, Portsmouth Hospitals University NHS Trust, Queen Alexandra Hospital, United Kingdom.
| | - Henry C Mandeville
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Dow-Mu Koh
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Fiona McDonald
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Marc Kachelrieß
- Division of X-Ray Imaging and CT, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
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Feng H, Shi G, Liu H, Du Y, Zhang N, Wang Y. The Value of PETRA in Pulmonary Nodules of <3 cm Among Patients With Lung Cancer. Front Oncol 2021; 11:649625. [PMID: 34084745 PMCID: PMC8167054 DOI: 10.3389/fonc.2021.649625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/15/2021] [Indexed: 11/25/2022] Open
Abstract
Objective This study aimed to evaluate the visibility of different subgroups of lung nodules of <3 cm using the pointwise encoding time reduction with radial acquisition (PETRA) sequence on 3T magnetic resonance imaging (MRI) in comparison with that obtained using low-dose computed tomography (LDCT). Methods The appropriate detection rate was calculated for each of the different subgroups of lung nodules of <3 cm. The mean diameter of each detected nodule was determined. The detection rates and diameters of the lung nodules detected by MRI with the PETRA sequence were compared with those detected by computed tomography (CT). The sensitivity of detection for the different subgroups of pulmonary nodules was determined based on the location, size, type of nodules and morphologic characteristics. Agreement of nodule characteristics between CT and MRI were assessed by intraclass correlation coefficient (ICC) and Kappa test. Results The CT scans detected 256 lung nodules, comprising 99 solid nodules (SNs) and 157 subsolid nodules with a mean nodule diameter of 8.3 mm. For the SNs, the MRI detected 30/47 nodules of <6 mm in diameter and 52/52 nodules of ≥6 mm in diameter. For the subsolid nodules, the MRI detected 30/51 nodules of <6 mm in diameter and 102/106 nodules of ≥6 mm in diameter. The PETRA sequence returned a high detection rate (84%). The detection rates of SN, ground glass nodules, and PSN were 82%, 72%, and 94%, respectively. For nodules with a diameter of >6 mm, the sensitivity of the PETRA sequence reached 97%, with a higher rate for nodules located in the upper lung fields than those in the middle and lower lung fields. Strong agreement was found between the CT and PETRA results (correlation coefficients = 0.97). Conclusion The PETRA technique had high sensitivity for different type of nodule detection and enabled accurate assessment of their diameter and morphologic characteristics. It may be an effective alternative to CT as a tool for screening and follow up pulmonary nodules.
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Affiliation(s)
- Hui Feng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hui Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yu Du
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ning Zhang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yaning Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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