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Takagi H, Takeda K, Kadoya N, Inoue K, Endo S, Takahashi N, Yamamoto T, Umezawa R, Jingu K. Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy. Radiol Phys Technol 2024:10.1007/s12194-024-00832-8. [PMID: 39143386 DOI: 10.1007/s12194-024-00832-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024]
Abstract
Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate's position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.
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Affiliation(s)
- Hisamichi Takagi
- Course of Radiological Technology, Health Sciences, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Ken Takeda
- Course of Radiological Technology, Health Sciences, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Koki Inoue
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
- Elith Inc., Shibuya, Tokyo, Japan
| | - Shiki Endo
- Course of Radiological Technology, Health Sciences, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Kadhim M, Haraldsson A, Kügele M, Enocson H, Bäck S, Ceberg S. Surface guided ring gantry radiotherapy in deep inspiration breath hold for breast cancer patients. J Appl Clin Med Phys 2024:e14463. [PMID: 39138877 DOI: 10.1002/acm2.14463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/22/2024] [Accepted: 06/24/2024] [Indexed: 08/15/2024] Open
Abstract
PURPOSE This study investigated the use of surface guided radiotherapy (SGRT) in combination with a tomotherapy treatment mode using discrete delivery angles for deep inspiration breath hold (DIBH) treatments of breast cancer (bc). We aimed to assess the feasibility and dosimetric advantages of this approach. MATERIALS AND METHODS We evaluated camera occlusion in the Radixact treatment system bore and the stability of DIBH signals during couch movement. The SGRT system's ability to maintain signal and surface image accuracy was analyzed at different depths within the bore. Dosimetric parameters were compared and measured for 20 left-sided bc patients receiving TomoDirect (TD) tangential radiotherapy in both DIBH and free breathing (FB). RESULTS The SGRT system maintained surface coverage and precise DIBH-signal at depths up to 40 cm beyond the treatment center. Camera occlusion occurred in the clavicular and neck regions due to the patient's morphology and gantry geometry. Nonetheless, the system accurately detected respiratory motion for all measurements. The DIBH plans significantly (p < 0.001) reduced mean heart and left anterior descending artery (LAD) radiation doses by up to 40%, with a 50% reduction in near-maximum heart and LAD doses, respectively. No significant dosimetric differences between DIBH and FB were observed in other investigated parameters and volumes. CONCLUSIONS Camera occlusion and couch movement minimally impacted the real-time surface image accuracy needed for DIBH treatments of bc. DIBH reduced heart and LAD radiation doses significantly compared to FB, indicating the feasibility and dosimetric benefits of combining these modalities.
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Affiliation(s)
- Mustafa Kadhim
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - André Haraldsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Malin Kügele
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Hedda Enocson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Sven Bäck
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Sofie Ceberg
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
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Jiao S, Zhao X, Zhou P, Geng M. Technical note: MR image-based synthesis CT for CyberKnife robotic stereotactic radiosurgery. Biomed Phys Eng Express 2024; 10:057002. [PMID: 39094608 DOI: 10.1088/2057-1976/ad6a62] [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: 01/16/2024] [Accepted: 08/02/2024] [Indexed: 08/04/2024]
Abstract
The purpose of this study is to investigate whether deep learning-based sCT images enable accurate dose calculation in CK robotic stereotactic radiosurgery. A U-net convolutional neural network was trained using 2446 MR-CT pairs and used it to translate 551 MR images to sCT images for testing. The sCT of CK patient was encapsulated into a quality assurance (QA) validation phantom for dose verification. The CT value difference between CT and sCT was evaluated using mean absolute error (MAE) and the statistical significance of dose differences between CT and sCT was tested using the Wilcoxon signed rank test. For all CK patients, the MAE value of the whole brain region did not exceed 25 HU. The percentage dose difference between CT and sCT was less than ±0.4% on GTV (D2(Gy), -0.29%, D95(Gy), -0.09%), PTV (D2(Gy), -0.25%, D95(Gy), -0.10%), and brainstem (max dose(Gy), 0.31%). The percentage dose difference between CT and sCT for most regions of interest (ROIs) was no more than ±0.04%. This study extended MR-based sCT prediction to CK robotic stereotactic radiosurgery, expanding the application scenarios of MR-only radiation therapy. The results demonstrated the remarkable accuracy of dose calculation on sCT for patients treated with CK robotic stereotactic radiosurgery.
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Affiliation(s)
- Shengxiu Jiao
- Department of Nuclear Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China
| | - Xiaoqian Zhao
- Department of Nuclear Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China
| | - Peng Zhou
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing People's Republic of China
| | - Mingying Geng
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing People's Republic of China
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Rabe M, Kurz C, Thummerer A, Landry G. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlenther Onkol 2024:10.1007/s00066-024-02277-9. [PMID: 39138806 DOI: 10.1007/s00066-024-02277-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 07/07/2024] [Indexed: 08/15/2024]
Abstract
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Adrian Thummerer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- German Cancer Consortium (DKTK), partner site Munich, a partnership between the DKFZ and the LMU University Hospital Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- Bavarian Cancer Research Center (BZKF), Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
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105
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Malone C, Ryan S, Nicholson J, McArdle O, Brennan S, McCavana P, McClean B, Duane F. Intrafraction Motion in Surface-Guided Breast Radiation Therapy and its Implications on a Single Planning Target Volume Margin Strategy. Pract Radiat Oncol 2024:S1879-8500(24)00196-6. [PMID: 39142389 DOI: 10.1016/j.prro.2024.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/05/2024] [Accepted: 06/25/2024] [Indexed: 08/16/2024]
Abstract
PURPOSE This study quantifies intrafraction motion in surface-guided radiation therapy (SGRT) for breast cancer and considers the need for individualized intrafraction motion measures when calculating planning target volume (PTV) margins. METHODS AND MATERIALS SGRT was used to assess intrafraction motion in consecutive patients according to (1) site irradiated (whole-breast/chest wall vs whole-breast/chest wall + regional lymph nodes) and (2) the use of deep inspiration breath hold versus free breathing. Intrafraction motion variation was evaluated throughout the treatment course for all cases. Associations between intrafraction motion and patient-specific characteristics were explored. The usefulness of individualized intrafraction motion measures for PTV margin determination was considered. RESULTS One hundred two patients undergoing 1360 fractions were included. On a population level, average intrafraction motion was less than 0.4 mm and 0.2 degrees for translational and rotational directions, respectively, with 95th percentiles <1.2 mm and 0.6 degrees, respectively. No clinically meaningful differences in intrafraction motion were observed according to the site irradiated or the use of deep inspiration breath hold. Consistency in intrafraction motion was noted for all patients throughout the treatment course. No clinically meaningful associations were found between intrafraction motion and patient-specific characteristics such as age, seroma volume, PTV volume, and mean body volume. CONCLUSIONS Intrafractional deviations with SGRT, using manufacturer-recommended regions of interest, are minimal, do not vary substantially for different treatment techniques or patient-specific characteristics, and remain constant throughout the treatment course. A universal intrafraction motion measure may be sufficient for calculating PTV margins. Further validation studies are needed to evaluate the impact of region of interest size and coverage.
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Affiliation(s)
- Ciaran Malone
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland.
| | - Samantha Ryan
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland
| | - Jill Nicholson
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland; Discipline of Radiation Therapy, Trinity St James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Orla McArdle
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland
| | - Sinead Brennan
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland; Discipline of Radiation Therapy, Trinity St James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - Pat McCavana
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland
| | - Brendan McClean
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland; UCD School of Physics, University College Dublin, Belfield, Dublin, Ireland
| | - Frances Duane
- St. Luke's Radiation Oncology Network, St. Luke's Hospital, Dublin, Ireland; Discipline of Radiation Therapy, Trinity St James's Cancer Institute, Trinity College Dublin, Dublin, Ireland
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Bertholet J, Guyer G, Mueller S, Loebner HA, Volken W, Aebersold DM, Manser P, Fix MK. Robust optimization and assessment of dynamic trajectory and mixed-beam arc radiotherapy: a preliminary study. Phys Med Biol 2024; 69:165032. [PMID: 39079553 DOI: 10.1088/1361-6560/ad6950] [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: 04/02/2024] [Accepted: 07/30/2024] [Indexed: 08/13/2024]
Abstract
Objective.Dynamic trajectory radiotherapy (DTRT) and dynamic mixed-beam arc therapy (DYMBARC) exploit non-coplanarity and, for DYMBARC, simultaneously optimized photon and electron beams. Margin concepts to account for set-up uncertainties during delivery are ill-defined for electron fields. We develop robust optimization for DTRT&DYMBARC and compare dosimetric plan quality and robustness for both techniques and both optimization strategies for four cases.Approach.Cases for different treatment sites and clinical target volume (CTV) to planning target volume (PTV) margins,m, were investigated. Dynamic gantry-table-collimator photon paths were optimized to minimize PTV/organ-at-risk (OAR) overlap in beam's-eye-view and minimize potential photon multileaf collimator (MLC) travel. For DYMBARC plans, non-isocentric partial electron arcs or static fields with shortened source-to-surface distance (80 cm) were added. Direct aperture optimization (DAO) was used to simultaneously optimize MLC-based intensity modulation for both photon and electron beams yielding deliverable PTV-based DTRT&DYMBARC plans. Robust-optimized plans used the same paths/arcs/fields. DAO with stochastic programming was used for set-up uncertainties with equal weights in all translational directions and magnitudeδsuch thatm= 0.7δ. Robust analysis considered random errors in all directions with or without an additional systematic error in the worst 3D direction for the adjacent OARs.Main results.Electron contribution was 7%-41% of target dose depending on the case and optimization strategy for DYMBARC. All techniques achieved similar CTV coverage in the nominal (no error) scenario. OAR sparing was overall better in the DYMBARC plans than in the DTRT plans and DYMBARC plans were generally more robust to the considered uncertainties. OAR sparing was better in the PTV-based than in robust-optimized plans for OARs abutting or overlapping with the target volume, but more affected by uncertainties.Significance.Better plan robustness can be achieved with robust optimization than with margins. Combining electron arcs/fields with non-coplanar photon trajectories further improves robustness and OAR sparing.
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Affiliation(s)
- Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Gian Guyer
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Silvan Mueller
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Hannes A Loebner
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Werner Volken
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Daniel M Aebersold
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Peter Manser
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
| | - Michael K Fix
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, 3010 Bern, Switzerland
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Iarkin V, de Jong EEC, Hendrix R, Verhaegen F, Wolfs CJA. Turning the attention to time-resolved EPID-images: treatment error classification with transformer multiple instance learning. Phys Med Biol 2024; 69:165030. [PMID: 39084643 DOI: 10.1088/1361-6560/ad69f6] [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: 04/15/2024] [Accepted: 07/31/2024] [Indexed: 08/02/2024]
Abstract
Objective.The aim of this work was to develop a novel artificial intelligence-assistedin vivodosimetry method using time-resolved (TR) dose verification data to improve quality of external beam radiotherapy.Approach. Although threshold classification methods are commonly used in error classification, they may lead to missing errors due to the loss of information resulting from the compression of multi-dimensional electronic portal imaging device (EPID) data into one or a few numbers. Recent research has investigated the classification of errors on time-integrated (TI)in vivoEPID images, with convolutional neural networks showing promise. However, it has been observed previously that TI approaches may cancel out the error presence onγ-maps during dynamic treatments. To address this limitation, simulated TRγ-maps for each volumetric modulated arc radiotherapy angle were used to detect treatment errors caused by complex patient geometries and beam arrangements. Typically, such images can be interpreted as a set of segments where only set class labels are provided. Inspired by recent weakly supervised approaches on histopathology images, we implemented a transformer based multiple instance learning approach and utilized transfer learning from TI to TRγ-maps.Main results. The proposed algorithm performed well on classification of error type and error magnitude. The accuracy in the test set was up to 0.94 and 0.81 for 11 (error type) and 22 (error magnitude) classes of treatment errors, respectively.Significance. TR dose distributions can enhance treatment delivery decision-making, however manual data analysis is nearly impossible due to the complexity and quantity of this data. Our proposed model efficiently handles data complexity, substantially improving treatment error classification compared to models that leverage TI data.
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Affiliation(s)
- Viacheslav Iarkin
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evelyn E C de Jong
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Rutger Hendrix
- Medical Image Analysis group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, 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
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Mukwada G, Chamunyonga C, Rowshanfarzad P, Gill S, Ebert MA. Insights into the dosimetric and geometric characteristics of stereotactic radiosurgery for multiple brain metastases: A systematic review. PLoS One 2024; 19:e0307088. [PMID: 39121064 PMCID: PMC11315342 DOI: 10.1371/journal.pone.0307088] [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: 04/21/2024] [Accepted: 06/30/2024] [Indexed: 08/11/2024] Open
Abstract
BACKGROUND GammaKnife (GK) and CyberKnife (CK) have been the mainstay stereotactic radiosurgery (SRS) solution for multiple brain metastases (MBM) for several years. Recent technological advancement has seen an increase in single-isocentre C-arm linac-based SRS. This systematic review focuses on dosimetric and geometric insights into contemporary MBM SRS and thereby establish if linac-based SRS has matured to match the mainstay SRS delivery systems. METHODS The PubMed, Web of Science and Scopus databases were interrogated which yielded 891 relevant articles that narrowed to 20 articles after removing duplicates and applying the inclusion and exclusion criteria. Primary studies which reported the use of SRS for treatment of MBM SRS and reported the technical aspects including dosimetry were included. The review was limited to English language publications from January 2015 to August 2023. Only full-length papers were included in the final analysis. Opinion papers, commentary pieces, letters to the editor, abstracts, conference proceedings and editorials were excluded. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The reporting of conformity indices (CI) and gradient indices, V12Gy, monitor units and the impact of translational and rotational shifts were extracted and analysed. RESULTS The single-isocentre technique for MBM dominated recent SRS studies and the most studied delivery platforms were Varian. The C-arm linac-based SRS plan quality and normal brain tissue sparing was comparable to GK and CK and in some cases better. The most used nominal beam energy was 6FFF, and optimised couch and collimator angles could reduce mean normal brain dose by 11.3%. Reduction in volume of the healthy brain receiving a certain dose was dependent on the number and size of the metastases and the relative geometric location. GK and CK required 4.5-8.4 times treatment time compared with linac-based SRS. Rotational shifts caused larger changes in CI in C-arm linac-based single-isocentre SRS. CONCLUSION C-arm linac-based SRS produced comparable MBM plan quality and the delivery is notably shorter compared to GK and CK SRS.
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Affiliation(s)
- Godfrey Mukwada
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands, Western Australia, Australia
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
| | - Crispen Chamunyonga
- School of Clinical Sciences, Discipline of Radiation Therapy, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, Western Australia, Australia
| | - Suki Gill
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands, Western Australia, Australia
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
| | - Martin A. Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands, Western Australia, Australia
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, Western Australia, Australia
- School of Medicine and Population Health, University of Wisconsin, Madison, Wisconsin, United States of America
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Huang Y, Gomaa A, Höfler D, Schubert P, Gaipl U, Frey B, Fietkau R, Bert C, Putz F. Principles of artificial intelligence in radiooncology. Strahlenther Onkol 2024:10.1007/s00066-024-02272-0. [PMID: 39105746 DOI: 10.1007/s00066-024-02272-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/17/2024] [Indexed: 08/07/2024]
Abstract
PURPOSE In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology. METHODS In light of the extensive variety of AI methodologies, this review selectively concentrates on the specific domain of deep learning. It emphasizes the principal categories of deep learning models and delineates the methodologies for training these models effectively. RESULTS This review initially delineates the distinctions between AI and deep learning as well as between supervised and unsupervised learning. Subsequently, it elucidates the fundamental principles of major deep learning models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), diffusion-based generative models, and reinforcement learning. For each category, it presents representative networks alongside their specific applications in radiation oncology. Moreover, the review outlines critical factors essential for training deep learning models, such as data preprocessing, loss functions, optimizers, and other pivotal training parameters including learning rate and batch size. CONCLUSION This review provides a comprehensive overview of deep learning principles tailored toward radiation oncology. It aims to enhance the understanding of AI-based research and software applications, thereby bridging the gap between complex technological concepts and clinical practice in radiation oncology.
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Affiliation(s)
- Yixing Huang
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany.
| | - Ahmed Gomaa
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Daniel Höfler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Philipp Schubert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Udo Gaipl
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
- Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Benjamin Frey
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
- Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054, Erlangen, Germany
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Fransson S, Strand R, Tilly D. Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy. Med Phys 2024. [PMID: 39106418 DOI: 10.1002/mp.17312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 07/05/2024] [Accepted: 07/08/2024] [Indexed: 08/09/2024] Open
Abstract
BACKGROUND Daily adaptive radiotherapy, as performed with the Elekta Unity MR-Linac, requires choosing between different adaptation methods, namely ATP (Adapt to Position) and ATS (Adapt to Shape), where the latter requires daily re-contouring to obtain a dose plan tailored to the daily anatomy. These steps are inherently resource-intensive, and quickly predicting the dose distribution and the dosimetric evaluation criteria while the patient is on the table could facilitate a fast selection of adaptation method and decrease the treatment times. PURPOSE In this work, we aimed to develop a deep-learning-based dose-prediction pipeline for prostate MR-Linac treatments. METHODS Two hundred twelve MR-images, structure sets, and dose distributions from 35 prostate patients treated with 6.1 Gy for 7 or 6 fractions at our MR-Linac were included, split into train/test partitions of 152/60 images, respectively. A deep-learning segmentation network was trained to segment the CTV (prostate), bladder, and rectum. A second network was trained to predict the dose distribution based on manually delineated structures. At inference, the predicted segmentations acted as input to the dose prediction network, and the predicted dose was compared to the true (optimized in the treatment planning system) dose distribution. RESULTS Median DSC values from the segmentation network were 0.90/0.94/0.87 for CTV/bladder/rectum. Predicted segmentations as input to the dose prediction resulted in mean differences between predicted and true doses of 0.7%/0.7%/1.7% (relative to the prescription dose) for D98%/D95%/D2% for the CTV. For the bladder, the difference was 0.7%/0.3% for Dmean/D2% and for the rectum 0.1/0.2/0.2 pp (percentage points) for V33Gy/V38Gy/V41Gy. In comparison, true segmentations as input resulted in differences of 1.1%/0.9%/1.6% for CTV, 0.5%/0.4% for bladder, and 0.7/0.4/0.3 pp for the rectum. Only D2% for CTV and Dmean/D2% for bladder were found to be statistically significantly better when using true structures instead of predicted structures as input to the dose prediction. CONCLUSIONS Small differences in the fulfillment of clinical dose-volume constraints are seen between utilizing deep-learning predicted structures as input to a dose prediction network and manual structures. Overall mean differences <2% indicate that the dose-prediction pipeline is useful as a decision support tool where differences are >2%.
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Affiliation(s)
- Samuel Fransson
- Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Robin Strand
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - David Tilly
- Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
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Ren J, Teuwen J, Nijkamp J, Rasmussen M, Gouw Z, Grau Eriksen J, Sonke JJ, Korreman S. Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images. Phys Med Biol 2024; 69:165018. [PMID: 39059432 DOI: 10.1088/1361-6560/ad682d] [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: 03/15/2024] [Accepted: 07/26/2024] [Indexed: 07/28/2024]
Abstract
Objective.Deep learning shows promise in autosegmentation of head and neck cancer (HNC) primary tumours (GTV-T) and nodal metastases (GTV-N). However, errors such as including non-tumour regions or missing nodal metastases still occur. Conventional methods often make overconfident predictions, compromising reliability. Incorporating uncertainty estimation, which provides calibrated confidence intervals can address this issue. Our aim was to investigate the efficacy of various uncertainty estimation methods in improving segmentation reliability. We evaluated their confidence levels in voxel predictions and ability to reveal potential segmentation errors.Approach.We retrospectively collected data from 567 HNC patients with diverse cancer sites and multi-modality images (CT, PET, T1-, and T2-weighted MRI) along with their clinical GTV-T/N delineations. Using the nnUNet 3D segmentation pipeline, we compared seven uncertainty estimation methods, evaluating them based on segmentation accuracy (Dice similarity coefficient, DSC), confidence calibration (Expected Calibration Error, ECE), and their ability to reveal segmentation errors (Uncertainty-Error overlap using DSC, UE-DSC).Main results.Evaluated on the hold-out test dataset (n= 97), the median DSC scores for GTV-T and GTV-N segmentation across all uncertainty estimation methods had a narrow range, from 0.73 to 0.76 and 0.78 to 0.80, respectively. In contrast, the median ECE exhibited a wider range, from 0.30 to 0.12 for GTV-T and 0.25 to 0.09 for GTV-N. Similarly, the median UE-DSC also ranged broadly, from 0.21 to 0.38 for GTV-T and 0.22 to 0.36 for GTV-N. A probabilistic network-PhiSeg method consistently demonstrated the best performance in terms of ECE and UE-DSC.Significance.Our study highlights the importance of uncertainty estimation in enhancing the reliability of deep learning for autosegmentation of HNC GTV. The results show that while segmentation accuracy can be similar across methods, their reliability, measured by calibration error and uncertainty-error overlap, varies significantly. Used with visualisation maps, these methods may effectively pinpoint uncertainties and potential errors at the voxel level.
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Affiliation(s)
- Jintao Ren
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Jonas Teuwen
- Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jasper Nijkamp
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Mathis Rasmussen
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Zeno Gouw
- Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jesper Grau Eriksen
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Stine Korreman
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
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Ding X, Jiang X, Zheng H, Shi H, Wang B, Chan S. MARes-Net: multi-scale attention residual network for jaw cyst image segmentation. Front Bioeng Biotechnol 2024; 12:1454728. [PMID: 39161348 PMCID: PMC11330813 DOI: 10.3389/fbioe.2024.1454728] [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: 06/25/2024] [Accepted: 07/25/2024] [Indexed: 08/21/2024] Open
Abstract
Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network's perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People's Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.
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Affiliation(s)
- Xiaokang Ding
- College of Mechanical Engineering, Quzhou University, Quzhou, China
| | - Xiaoliang Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou, China
| | - Huixia Zheng
- Department of Stomatology, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Hualuo Shi
- College of Mechanical Engineering, Quzhou University, Quzhou, China
- School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Ban Wang
- School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Sixian Chan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China
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Tsui T, Podgorsak A, Roeske JC, Small W, Refaat T, Kang H. Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures. J Appl Clin Med Phys 2024:e14461. [PMID: 39092893 DOI: 10.1002/acm2.14461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/09/2024] [Accepted: 06/23/2024] [Indexed: 08/04/2024] Open
Abstract
The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p = 0.17) and SCV95% (p = 0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p = 0.02). For post-mastectomy plans, AxNV95% (p = 0.35) and SCV95% (p = 0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation <5% when DSC > 0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p = 0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.
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Affiliation(s)
- Tiffany Tsui
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - Alexander Podgorsak
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York, USA
| | - John C Roeske
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - William Small
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - Tamer Refaat
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - Hyejoo Kang
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
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Du S, Gong G, Chen M, Liu R, Meng K, Yin Y. The effect of time-delayed contrast-enhanced scanning in determining the gross tumor target volume of large-volume brain metastases. Radiother Oncol 2024; 197:110330. [PMID: 38768715 DOI: 10.1016/j.radonc.2024.110330] [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: 12/19/2023] [Revised: 04/07/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND AND PURPOSE To assess the variation of large-volume brain metastases (BMs) boundaries and shapes using enhanced magnetic resonance (MR) scanning with different delay times and to provide a basis for determining the gross tumor target volume (GTV) for radiotherapy of BMs. MATERIALS AND METHODS We prospectively enrolled 155 patients initially diagnosed with BMs (561 lesions > 1 cm). Contrast-enhanced (CE) T1-weighted imaging scans were performed 1, 3, 5, 10, 18, and 20 min after gadolinium-based contrast agent injection and GTVs were determined as GTV-1min, GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min, respectively, which were subsequently fused in different phases. Fusion of the six GTVs was defined as GTV-total, which was set as the reference GTV. The volume, shape, and signal intensity of the GTVs and brain white matter (BWM) were compared at different delay times. RESULTS GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min volumes increased by 2.2 %, 3.8 %, 6.5 %, 9.5 %, and 10.6 %, respectively (P < 0.05) compared with GTV-1min. Compared with GTV-total, GTV-1min, GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min volumes reduced by 25.4 %, 22.1 %, 18.7 %, 15.0 %, 11.2 %, and 10.3 %, respectively (P < 0.05). Compared with GTV-total, 29 (51.8 %) fused GTVs had a volume reduction rate < 5 %, 45 (80.4 %) had a Dice similarity coefficient > 0.95, and all contained GTV-10min, GTV-18min or GTV-20min. The signal intensity ratio between the GTV and BWM peaked at 5 min (0.351 ± 0.24). CONCLUSION Enhanced MR scans with different delay times show significant differences in the boundaries and shapes of large-volume BMs, and time-delayed multi-phase CE scanning should be used in GTV determination, with time phases ≥ 10 min being mandatory.
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Affiliation(s)
- Shanshan Du
- Department of Oncology, Afliated Hospital of Southwest Medical University, No.25 Taiping Street, Jiangyang District, Luzhou 646000, Sichuan, China; Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji Yan Road No.440, 250117 Jinan, China
| | - Guanzhong Gong
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji Yan Road No.440, 250117 Jinan, China
| | - Mingming Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Rui Liu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji Yan Road No.440, 250117 Jinan, China
| | - Kangning Meng
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji Yan Road No.440, 250117 Jinan, China
| | - Yong Yin
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji Yan Road No.440, 250117 Jinan, China.
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Fu W, Zhang Y, Mehta K, Chen A, Musunuru HB, Pucci P, Kubis J, Huq MS. Evaluating intra-fractional tumor motion in lung stereotactic radiotherapy with deep inspiration breath-hold. J Appl Clin Med Phys 2024; 25:e14414. [PMID: 38803045 PMCID: PMC11302799 DOI: 10.1002/acm2.14414] [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: 02/27/2024] [Revised: 04/19/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024] Open
Abstract
PURPOSE To evaluate the intra-fractional tumor motion in lung stereotactic body radiotherapy (SBRT) with deep inspiration breath-hold (DIBH), and to investigate the adequacy of the current planning target volume (PTV) margins. METHODS Twenty-eight lung SBRT patients with DIBH were selected in this study. Among the lesions, twenty-three were at right or left lower lobe, two at right middle lobe, and three at right or left upper lobe. Post-treatment gated cone-beam computed tomography (CBCT) was acquired to quantify the intra-fractional tumor shift at each treatment. These obtained shifts were then used to calculate the required PTV margin, which was compared with the current applied margin of 5 mm margin in anterior-posterior (AP) and right-left (RL) directions and 8 mm in superior-inferior (SI) direction. The beam delivery time was prolonged with DIBH. The actual beam delivery time with DIBH (Tbeam_DIBH) was compared with the beam delivery time without DIBH (Tbeam_wo_DIBH) for the corresponding SBRT plan. RESULTS A total of 113 treatments were analyzed. At six treatments (5.3%), the shifts exceeded the tolerance defined by the current PTV margin. The average shifts were 0.0 ± 1.9 mm, 0.1±1.5 mm, and -0.5 ± 3.7 mm in AP, RL, and SI directions, respectively. The required PTV margins were determined to be 4.5, 3.9, and 7.4 mm in AP, RL, and SI directions, respectively. The average Tbeam_wo_DIBH and Tbeam_DIBH were 2.4 ± 0.4 min and 3.6 ± 1.5 min, respectively. The average treatment slot for lung SBRT with DIBH was 25.3 ± 7.9 min. CONCLUSION Intra-fractional tumor motion is the predominant source of treatment uncertainties in CBCT-guided lung SBRT with DIBH. The required PTV margin should be determined based on data specific to each institute, considering different techniques and populations. Our data indicate that our current applied PTV margin is adequate, and it is possible to reduce further in the RL direction. The time increase of Tbeam_DIBH, relative to the treatment slot, is not clinically significant.
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Affiliation(s)
- Weihua Fu
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - Yongqian Zhang
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - Kiran Mehta
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - Alex Chen
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - Hima Bindu Musunuru
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - Pietro Pucci
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - Jason Kubis
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
| | - M. Saiful Huq
- Department of Radiation OncologyUniversity of Pittsburgh School of Medicine and UPMC Hillman Cancer CenterPittsburghPennsylvaniaUSA
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Wang N, Fan J, Xu Y, Yan L, Chen D, Wang W, Men K, Dai J, Liu Z. Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer. Phys Med 2024; 124:104492. [PMID: 39094213 DOI: 10.1016/j.ejmp.2024.104492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 07/12/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
PURPOSE The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer. METHODS A DL model was developed for predicting patient-specific doses, trained and validated on a dataset of 235 patients with diverse target volumes and prescriptions. The model was integrated into clinical workflow with DL-predicted objective functions. The automatic plans were retrospectively designed for additional 50 treated manual volumetric modulated arc therapy (VMAT) plans. A comparison was made between automatic and manual plans in terms of dosimetric indexes, monitor units (MUs) and planning time. Plan quality metric (PQM) encompassing these indexes was evaluated, with higher PQM values indicating superior plan quality. Qualitative evaluations of two plans were conducted by four reviewers. RESULTS The PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans (P = 0.75). Compared to manual plans, the targets coverage and homogeneity of automatic plans demonstrated no significant difference. Manual plans exhibited better sparing for lung in V5 (difference: 1.8 ± 4.2 %, P = 0.02), whereas automatic plans showed enhanced sparing for heart in V30 (difference: 1.4 ± 4.7 %, P = 0.02) and for spinal cord in Dmax (difference: 0.7 ± 4.7 Gy, P = 0.04). The planning time and MUs of automatic plans were significantly reduced by 70.5 ± 20.0 min and 97.4 ± 82.1. Automatic plans were deemed acceptable in 88 % of the reviews (176/200). CONCLUSIONS The DL-assisted approach for lung cancer notably decreased planning time and MUs, while demonstrating comparable or superior quality relative to manual plans. It has the potential to provide benefit to lung cancer patients.
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Affiliation(s)
- Ningyu Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Shanghai Clinical Research Center for Radiation Oncology, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China.
| | - Yingjie Xu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Lingling Yan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wenqing Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Zhiqiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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van Grinsven EE, Cialdella F, Gmelich Meijling Y, Verhoeff JJC, Philippens MEP, van Zandvoort MJE. Individualized trajectories in postradiotherapy neurocognitive functioning of patients with brain metastases. Neurooncol Pract 2024; 11:441-451. [PMID: 39006520 PMCID: PMC11241367 DOI: 10.1093/nop/npae024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024] Open
Abstract
Background The increasing incidence of brain metastases (BMs) and improved survival rates underscore the necessity to investigate the effects of treatments on individuals. The aim of this study was to evaluate the individual trajectories of subjective and objective cognitive performance after radiotherapy in patients with BMs. Methods The study population consisted of adult patients with BMs referred for radiotherapy. A semi-structured interview and comprehensive neurocognitive assessment (NCA) were used to assess both subjective and objective cognitive performance before, 3 months and ≥ 11 months after radiotherapy. Reliable change indices were used to identify individual, clinically meaningful changes. Results Thirty-six patients completed the 3-month follow-up, and 14 patients completed the ≥ 11-months follow-up. Depending on the domain, subjective cognitive decline was reported by 11-22% of patients. In total, 50% of patients reported subjective decline in at least one cognitive domain. Intracranial progression 3 months postradiotherapy was a risk-factor for self-reported deterioration (P = .031). Objective changes were observed across all domains, with a particular vulnerability for decline in memory at 3 months postradiotherapy. The majority of patients (81%) experienced both a deterioration as well as improvement (eg, mixed response) in objective cognitive functioning. Results were similar for the long-term follow-up (3 to ≥11 months). No risk factors for objective cognitive change 3 months postradiotherapy were identified. Conclusions Our study revealed that the majority of patients with BMs will show a mixed cognitive response following radiotherapy, reflecting the complex impact. This underscores the importance of patient-tailored NCAs 3 months postradiotherapy to guide optimal rehabilitation strategies.
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Affiliation(s)
- Eva E van Grinsven
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Fia Cialdella
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yoniet Gmelich Meijling
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marielle E P Philippens
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martine J E van Zandvoort
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
- Department of Experimental Psychology and Helmholtz Institute, Utrecht University, The Netherlands
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Balaji S, Wiley N, Poorman ME, Kolind SH. Low-field MRI for use in neurological diseases. Curr Opin Neurol 2024; 37:381-391. [PMID: 38813835 DOI: 10.1097/wco.0000000000001282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
PURPOSE OF REVIEW To review recent clinical uses of low-field magnetic resonance imaging (MRI) to guide incorporation into neurological practice. RECENT FINDINGS Use of low-field MRI has been demonstrated in applications including tumours, vascular pathologies, multiple sclerosis, brain injury, and paediatrics. Safety, workflow, and image quality have also been evaluated. SUMMARY Low-field MRI has the potential to increase access to critical brain imaging for patients who otherwise may not obtain imaging in a timely manner. This includes areas such as the intensive care unit and emergency room, where patients could be imaged at the point of care rather than be transported to the MRI scanner. Such systems are often more affordable than conventional systems, allowing them to be more easily deployed in resource constrained settings. A variety of systems are available on the market or in a research setting and are currently being used to determine clinical uses for these devices. The utility of such devices must be fully evaluated in clinical scenarios before adoption into standard practice can be achieved. This review summarizes recent clinical uses of low-field MR as well as safety, workflows, and image quality to aid practitioners in assessing this new technology.
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Affiliation(s)
- Sharada Balaji
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Neale Wiley
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Shannon H Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medicine (Neurology)
- Department of Radiology
- International Collaboration on Repair Discoveries, Blusson Spinal Cord Centre, University of British Columbia, Vancouver, British Columbia, Canada
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Hurkmans C, Bibault JE, Brock KK, van Elmpt W, Feng M, David Fuller C, Jereczek-Fossa BA, Korreman S, Landry G, Madesta F, Mayo C, McWilliam A, Moura F, Muren LP, El Naqa I, Seuntjens J, Valentini V, Velec M. A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy. Radiother Oncol 2024; 197:110345. [PMID: 38838989 DOI: 10.1016/j.radonc.2024.110345] [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: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND AND PURPOSE Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.
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Affiliation(s)
- Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands; Department of Electrical Engineering, Technical University Eindhoven, Eindhoven, the Netherlands.
| | | | - Kristy K Brock
- Departments of Imaging Physics and Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Mary Feng
- University of California San Francisco, San Francisco, CA, USA
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX
| | - Barbara A Jereczek-Fossa
- Dept. of Oncology and Hemato-oncology, University of Milan, Milan, Italy; Dept. of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stine Korreman
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | - Frederic Madesta
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Chuck Mayo
- Institute for Healthcare Policy and Innovation, University of Michigan, USA
| | - Alan McWilliam
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Filipe Moura
- CrossI&D Lisbon Research Center, Portuguese Red Cross Higher Health School Lisbon, Portugal
| | - Ludvig P Muren
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Jan Seuntjens
- Princess Margaret Cancer Centre, Radiation Medicine Program, University Health Network & Departments of Radiation Oncology and Medical Biophysics, University of Toronto, Toronto, Canada
| | - Vincenzo Valentini
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Michael Velec
- Radiation Medicine Program, Princess Margaret Cancer Centre and Department of Radiation Oncology, University of Toronto, Toronto, Canada
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120
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Tarigan VN, Rahmawati DL, Octavius GS. Transrectal Ultrasound in Cervical Cancer: A Systematic Review of its Current Application. J Obstet Gynaecol India 2024; 74:303-310. [PMID: 39280200 PMCID: PMC11399511 DOI: 10.1007/s13224-024-02047-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 07/30/2024] [Indexed: 09/18/2024] Open
Abstract
Introduction The use of transrectal ultrasound (TRUS) is established in prostate cancer but remains limited in cervical cancer. This systematic review aims to aggregate and describe the current use and advancements of TRUS in cervical cancer to identify gaps in the literature. Methods This study follows a protocol registered in the PROSPERO database (CRD42024520099). It includes cervical cancer patients confirmed by histopathological analysis, where TRUS was used for diagnosis or as an adjunct to therapeutic procedures. Cross-sectional, case-control, cohort, or randomized controlled trials published in any language were included. The risk of bias was assessed using the Newcastle Ottawa Scale (NOS). Results From an initial pool of 3380 articles, 50 duplicates were removed, leaving 3330 unique articles. After screening titles and abstracts, 2932 articles were excluded, resulting in 31 studies included in the review. These studies involved 1635 women with cervical cancers, with a mean age of 52.9 years. Histopathologically, 81.2% were squamous cell carcinoma (SCC), and 39.6% were at FIGO stage IIB. Nineteen studies were prospective, five retrospective, and fourteen used consecutive sampling. Only 10 articles had a fair rating, while the rest received poor ratings. Complications from post-TRUS included pain (N = 106), haemorrhage (N = 59), and perforations (N = 10). TRUS was used in seven areas, including cancer extension and pre-operative assessment. It showed a strong correlation with MRI but had lower sensitivity. TRUS was useful in staging, diagnosis, and guiding brachytherapy, demonstrating comparable accuracy to MRI in several instances. Conclusion The recommended use of TRUS in cervical cancer is still limited in formal guidelines, and clinical research remains insufficient. Supplementary Information The online version contains supplementary material available at 10.1007/s13224-024-02047-8.
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Affiliation(s)
- Vera Nevyta Tarigan
- Breast and Female Reproductive Radiology, Department of Radiology, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia
- Department of Radiology, Siloam Hospital Kebon Jeruk, Jl. Perjuangan No.8, RT.14/RW.10, Kb. Jeruk, Kec. Kb. Jeruk, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, 11530 Indonesia
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Moore LC, Nematollahi F, Li L, Meyers SM, Kisling K. Improving 3D dose prediction for breast radiotherapy using novel glowing masks and gradient-weighted loss functions. Med Phys 2024. [PMID: 39088756 DOI: 10.1002/mp.17326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/17/2024] [Accepted: 07/08/2024] [Indexed: 08/03/2024] Open
Abstract
BACKGROUND The quality of treatment plans for breast cancer can vary greatly. This variation could be reduced by using dose prediction to automate treatment planning. Our work investigates novel methods for training deep-learning models that are capable of producing high-quality dose predictions for breast cancer treatment planning. PURPOSE The goal of this work was to compare the performance impact of two novel techniques for deep learning dose prediction models for tangent field treatments for breast cancer. The first technique, a "glowing" mask algorithm, encodes the distance from a contour into each voxel in a mask. The second, a gradient-weighted mean squared error (MSE) loss function, emphasizes the error in high-dose gradient regions in the predicted image. METHODS Four 3D U-Net deep learning models were trained using the planning CT and contours of the heart, lung, and tumor bed as inputs. The dataset consisted of 305 treatment plans split into 213/46/46 training/validation/test sets using a 70/15/15% split. We compared the impact of novel "glowing" anatomical mask inputs and a novel gradient-weighted MSE loss function to their standard counterparts, binary anatomical masks, and MSE loss, using an ablation study methodology. To assess performance, we examined the mean error and mean absolute error (ME/MAE) in dose across all within-body voxels, the error in mean dose to heart, ipsilateral lung, and tumor bed, dice similarity coefficient (DSC) across isodose volumes defined by 0%-100% prescribed dose thresholds, and gamma analysis (3%/3 mm). RESULTS The combination of novel glowing masks and gradient weighted loss function yielded the best-performing model in this study. This model resulted in a mean ME of 0.40%, MAE of 2.70%, an error in mean dose to heart and lung of -0.10 and 0.01 Gy, and an error in mean dose to the tumor bed of -0.01%. The median DSC at 50/95/100% isodose levels were 0.91/0.87/0.82. The mean 3D gamma pass rate (3%/3 mm) was 93%. CONCLUSIONS This study found the combination of novel anatomical mask inputs and loss function for dose prediction resulted in superior performance to their standard counterparts. These results have important implications for the field of radiotherapy dose prediction, as the methods used here can be easily incorporated into many other dose prediction models for other treatment sites. Additionally, this dose prediction model for breast radiotherapy has sufficient performance to be used in an automated planning pipeline for tangent field radiotherapy and has the major benefit of not requiring a PTV for accurate dose prediction.
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Affiliation(s)
- Lance C Moore
- Radiation Medicine and Applied Sciences, University of California, La Jolla, San Diego, California, USA
| | - Fatemeh Nematollahi
- Radiation Medicine and Applied Sciences, University of California, La Jolla, San Diego, California, USA
| | - Lingyi Li
- Radiation Medicine and Applied Sciences, University of California, La Jolla, San Diego, California, USA
| | - Sandra M Meyers
- Radiation Medicine and Applied Sciences, University of California, La Jolla, San Diego, California, USA
| | - Kelly Kisling
- Radiation Medicine and Applied Sciences, University of California, La Jolla, San Diego, California, USA
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Sterpin E, Widesott L, Poels K, Hoogeman M, Korevaar EW, Lowe M, Molinelli S, Fracchiolla F. Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review. Radiother Oncol 2024; 197:110365. [PMID: 38830538 DOI: 10.1016/j.radonc.2024.110365] [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: 08/09/2023] [Revised: 04/30/2024] [Accepted: 05/29/2024] [Indexed: 06/05/2024]
Abstract
Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation. We propose to overcome this deficiency by following the modified Delphi consensus method. This method first requires a systematic review of the literature. We performed this review using the PubMed and Web Of Science databases, via two different experts. Potential conflicts were resolved by a third expert. We then explored the different methods before focusing on clinical studies that evaluate robustness on a significant number of patients. Many robustness assessment methods are proposed in the literature. Some are more successful than others and their implementation varies between centers. Moreover, they are not all statistically or mathematically equivalent. The most sophisticated and rigorous methods have seen more limited application due to the difficulty of their implementation and their lack of widespread availability.
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Affiliation(s)
- E Sterpin
- KU Leuven - Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; UCLouvain - Institution de Recherche Expérimentale et Clinique, Center of Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium; Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium.
| | - L Widesott
- Proton Therapy Center - UO Fisica Sanitaria, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - K Poels
- Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium; UZ Leuven, Department of Radiation Oncology, Leuven, Belgium
| | - M Hoogeman
- Erasmus Medical Center, Cancer Institute, Department of Radiotherapy, Rotterdam, the Netherlands; HollandPTC, Delft, the Netherlands
| | - E W Korevaar
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - M Lowe
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - S Molinelli
- Fondazione CNAO - Medical Physics Unit, Pavia, Italy
| | - F Fracchiolla
- Proton Therapy Center - UO Fisica Sanitaria, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
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123
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Kocak B, Akinci D'Antonoli T, Ates Kus E, Keles A, Kala A, Kose F, Kadioglu M, Solak S, Sunman S, Temiz ZH. Self-reported checklists and quality scoring tools in radiomics: a meta-research. Eur Radiol 2024; 34:5028-5040. [PMID: 38180530 DOI: 10.1007/s00330-023-10487-5] [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: 09/24/2023] [Revised: 11/11/2023] [Accepted: 11/24/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. METHODS Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers. RESULTS The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of - 7.2 (standard deviation, 6.8). CONCLUSION Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable. CLINICAL RELEVANCE STATEMENT Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward. KEY POINTS • In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey.
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Ece Ates Kus
- Department of Neuroradiology, Klinikum Lippe, Lemgo, Germany
| | - Ali Keles
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Ahmet Kala
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Fadime Kose
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Mehmet Kadioglu
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Sila Solak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Seyma Sunman
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Zisan Hayriye Temiz
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
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Tham BZ, Aleman DM, Nordström H, Nygren N, Coolens C. Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery. Adv Radiat Oncol 2024; 9:101534. [PMID: 39104874 PMCID: PMC11298584 DOI: 10.1016/j.adro.2024.101534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/16/2024] [Indexed: 08/07/2024] Open
Abstract
Purpose Dose painting radiation therapy delivers a nonuniform dose to tumors to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife machines making large-volume brain tumor treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on stereotactic radiosurgery (SRS) plan quality has not yet been studied. This research investigates the plan quality achievable for Gamma Knife SRS dose painting treatments when using optimization techniques and automated isocenter placement in treatment planning. Methods and Materials Dose painting prescription functions with varying parameters were applied to convert voxel image intensities to prescriptions for 10 sample cases. To study achievable plan quality and optimization, clinically placed isocenters were used with each dose painting prescription and optimized using a semi-infinite linear programming formulation. To study automated isocenter placement, a grassfire sphere-packing algorithm and a clinically available Leksell gamma plan isocenter fill algorithm were used. Plan quality for each optimized treatment plan was measured with dose painting SRS metrics. Results Optimization can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show more achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocenter placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocenters improves plan quality. The computational solve time for optimization is within 5 minutes in most cases, which is suitable for clinical planning. Conclusions The impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimization and automated isocenter placement are shown as possible treatment planning methods to obtain high quality plans.
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Affiliation(s)
- Benjamin Z. Tham
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Dionne M. Aleman
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Catherine Coolens
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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Walls GM, Hill N, McMahon M, Kearney BÓ, McCann C, McKavanagh P, Giacometti V, Cole AJ, Jain S, McGarry CK, Butterworth K, McAleese J, Harbinson M, Hanna GG. Baseline Cardiac Parameters as Biomarkers of Radiation Cardiotoxicity in Lung Cancer: An NI-HEART Analysis. JACC CardioOncol 2024; 6:529-540. [PMID: 39239328 PMCID: PMC11372030 DOI: 10.1016/j.jaccao.2024.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 09/07/2024] Open
Abstract
Background Radiation-induced cardiotoxicity poses a significant challenge in lung cancer management because of the close anatomical proximity of the heart to the lungs, compounded by a high prevalence of cardiovascular risk factors among patients. Objectives The aim of this study was to assess the predictive value of routinely available clinical and imaging-based cardiac parameters in identifying "high risk" patients for major adverse cardiac events (MACE) and mortality following radiation therapy (RT). Methods The medical records of patients who underwent definitive RT for non-small cell lung cancer using modern planning techniques at a single center between 2015 and 2020 were retrospectively reviewed. Cardiac events were verified by cardiologists, and mortality data were confirmed with the national registry. Cardiac substructures were autosegmented on RT planning scans for retrospective structure and dose analysis, and their correlation with clinical factors was examined. Fine-Gray models were used to analyze relationships while considering the competing risk for death. Results Among 478 patients included in the study, 77 (16%) developed 88 MACE, with a median time to event of 16.3 months. A higher burden of pre-existing cardiac diseases was associated with an increased cumulative incidence of MACE (55% [95% CI: 12%-20%] vs 16% [95% CI: 35%-71%]; P < 0.001). Left atrial and left ventricular enlargement on RT planning scans was associated with cumulative incidence of atrial arrhythmia (14% [95% CI: 9%-20%] vs 4% [95% CI: 2%-8%]; P = 0.001) and heart failure (13% [95% CI: 8%-18%] vs 6% [95% CI: 3%-10%]; P = 0.007) at 5 years, respectively. However, myocardial infarction was not associated with the presence of coronary calcium (4.2% [95% CI: 2%-7%] vs 0% [95% CI: 0%-0%]; P = 0.094). No cardiac imaging metrics were found to be both clinically and statistically associated with survival. Conclusions The present findings suggest that cardiac history and RT planning scan parameters may offer potential utility in prospectively evaluating cardiotoxicity risk following RT for patients with lung cancer.
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Affiliation(s)
- Gerard M Walls
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Nicola Hill
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
| | - Michael McMahon
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
| | | | - Conor McCann
- Department of Cardiology, Belfast Health & Social Care Trust, Belfast, United Kingdom
| | - Peter McKavanagh
- Department of Cardiology, South Eastern Health & Social Care Trust, Dundonald, United Kingdom
| | - Valentina Giacometti
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Aidan J Cole
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Suneil Jain
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Conor K McGarry
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Karl Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Jonathan McAleese
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Mark Harbinson
- Department of Cardiology, South Eastern Health & Social Care Trust, Dundonald, United Kingdom
- School of Medicine, Dentistry & Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Gerard G Hanna
- Cancer Centre Belfast City Hospital, Belfast, United Kingdom
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
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Keenan KE, Jordanova KV, Ogier SE, Tamada D, Bruhwiler N, Starekova J, Riek J, McCracken PJ, Hernando D. Phantoms for Quantitative Body MRI: a review and discussion of the phantom value. MAGMA (NEW YORK, N.Y.) 2024; 37:535-549. [PMID: 38896407 PMCID: PMC11417080 DOI: 10.1007/s10334-024-01181-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/18/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.
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Affiliation(s)
- Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA.
| | - Kalina V Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
| | - Stephen E Ogier
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
- Department of Physics, University of Colorado Boulder, Boulder, CO, USA
| | | | - Natalie Bruhwiler
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
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Jia PF, Li YR, Wang LY, Lu XR, Guo X. Radiomics in esophagogastric junction cancer: A scoping review of current status and advances. Eur J Radiol 2024; 177:111577. [PMID: 38905802 DOI: 10.1016/j.ejrad.2024.111577] [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: 08/01/2023] [Revised: 06/03/2024] [Accepted: 06/14/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer. METHODS We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively. RESULTS A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%). CONCLUSION Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.
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Affiliation(s)
- Ping-Fan Jia
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yu-Ru Li
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Lu-Yao Wang
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Xiao-Rui Lu
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Xing Guo
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
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Behzadipour M, Palta J, Ma T, Yuan L, Kim S, Kirby S, Torkelson L, Baker J, Koenig T, Khalifa MA, Hawranko R, Richeson D, Fields E, Weiss E, Song WY. Optimization of treatment workflow for 0.35T MR-Linac system. J Appl Clin Med Phys 2024; 25:e14393. [PMID: 38742819 PMCID: PMC11302807 DOI: 10.1002/acm2.14393] [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: 12/06/2023] [Revised: 03/15/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
PURPOSE This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.
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Affiliation(s)
- Mojtaba Behzadipour
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tianjun Ma
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Lulin Yuan
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Siyong Kim
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Suzanne Kirby
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Laurel Torkelson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - James Baker
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tammy Koenig
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Mateb Al Khalifa
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert Hawranko
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Dylan Richeson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Emma Fields
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Elisabeth Weiss
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William Y. Song
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
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Semeniuk O, Shessel A, Velec M, Fodor T, Rocca CC, Barry A, Lukovic J, Yan M, Mesci A, Kim J, Wong R, Dawson LA, Hosni A, Stanescu T. Development and validation of an MR-driven dose-of-the-day procedure for online adaptive radiotherapy in upper gastrointestinal cancer patients. Phys Med Biol 2024; 69:165009. [PMID: 39048106 DOI: 10.1088/1361-6560/ad6745] [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: 01/22/2024] [Accepted: 07/23/2024] [Indexed: 07/27/2024]
Abstract
Objective.To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system.Approach.DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline's input included the adapt-to-shape (ATS) plan-i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution-and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients).Main results.The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum.Significance.The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.
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Affiliation(s)
- Oleksii Semeniuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Andrea Shessel
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Michael Velec
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Tudor Fodor
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Cathy-Carpino Rocca
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Aisling Barry
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Jelena Lukovic
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Michael Yan
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Aruz Mesci
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - John Kim
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Rebecca Wong
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Laura A Dawson
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Ali Hosni
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Teo Stanescu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
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Bahmanpour A, Ghoreishian SM, Sepahvandi A. Electromagnetic Modulation of Cell Behavior: Unraveling the Positive Impacts in a Comprehensive Review. Ann Biomed Eng 2024; 52:1941-1954. [PMID: 38652384 DOI: 10.1007/s10439-024-03519-8] [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: 02/07/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
There are numerous effective procedures for cell signaling, in which humans directly transmit detectable signals to cells to govern their essential behaviors. From a biomedical perspective, the cellular response to the combined influence of electrical and magnetic fields holds significant promise in various domains, such as cancer treatment, targeted drug delivery, gene therapy, and wound healing. Among these modern cell signaling methods, electromagnetic fields (EMFs) play a pivotal role; however, there remains a paucity of knowledge concerning the effects of EMFs across all wavelengths. It's worth noting that most wavelengths are incompatible with human cells, and as such, this study excludes them from consideration. In this review, we aim to comprehensively explore the most effective and current EMFs, along with their therapeutic impacts on various cell types. Specifically, we delve into the influence of alternating electromagnetic fields (AEMFs) on diverse cell behaviors, encompassing proliferation, differentiation, biomineralization, cell death, and cell migration. Our findings underscore the substantial potential of these pivotal cellular behaviors in advancing the treatment of numerous diseases. Moreover, AEMFs wield a significant role in the realms of biomaterials and tissue engineering, given their capacity to decisively influence biomaterials, facilitate non-invasive procedures, ensure biocompatibility, and exhibit substantial efficacy. It is worth mentioning that AEMFs often serve as a last-resort treatment option for various diseases. Much about electromagnetic fields remains a mystery to the scientific community, and we have yet to unravel the precise mechanisms through which wavelengths control cellular fate. Consequently, our understanding and knowledge in this domain predominantly stem from repeated experiments yielding similar effects. In the ensuing sections of this article, we delve deeper into our extended experiments and research.
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Kang KH, Price AT, Reynoso FJ, Laugeman E, Morris ED, Samson PP, Huang J, Badiyan SN, Kim H, Brenneman RJ, Abraham CD, Knutson NC, Henke LE. A Pilot Study of Simulation-Free Hippocampal-Avoidance Whole Brain Radiation Therapy Using Diagnostic MRI-Based and Online Adaptive Planning. Int J Radiat Oncol Biol Phys 2024; 119:1422-1428. [PMID: 38580083 DOI: 10.1016/j.ijrobp.2024.03.039] [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: 07/30/2023] [Revised: 03/03/2024] [Accepted: 03/24/2024] [Indexed: 04/07/2024]
Abstract
PURPOSE We aimed to demonstrate the clinical feasibility and safety of simulation-free hippocampal avoidance whole brain radiation therapy (HA-WBRT) in a pilot study (National Clinical Trial 05096286). METHODS AND MATERIALS Ten HA-WBRT candidates were enrolled for treatment on a commercially available computed tomography (CT)-guided linear accelerator with online adaptive capabilities. Planning structures were contoured on patient-specific diagnostic magnetic resonance imaging (MRI), which were registered to a CT of similar head shape, obtained from an atlas-based database (AB-CT). These patient-specific diagnostic MRI and AB-CT data sets were used for preplan calculation, using NRG-CC001 constraints. At first fraction, AB-CTs were used as primary data sets and deformed to patient-specific cone beam CTs (CBCT) to give patient-matched density information. Brain, ventricle, and brain stem contours were matched through rigid translation and rotation to the corresponding anatomy on CBCT. Lens, optic nerve, and brain contours were manually edited based on CBCT visualization. Preplans were then reoptimized through online adaptation to create final, simulation-free plans, which were used if they met all objectives. Workflow tasks were timed. In addition, patients underwent CT-simulation to create immobilization devices and for prospective dosimetric comparison of simulation-free and simulation-based plans. RESULTS Median time from MRI importation to completion of "preplan" was 1 weekday (range, 1-4). Median on-table workflow duration was 41 minutes (range, 34-70). NRG-CC001 constraints were achieved by 90% of the simulation-free plans. One patient's simulation-free plan failed a planning target volume coverage objective (89% instead of 90% coverage); this was deemed acceptable for first-fraction delivery, with an offline replan used for subsequent fractions. Both simulation-free and simulation CT-based plans otherwise met constraints, without clinically meaningful differences. CONCLUSIONS Simulation-free HA-WBRT using online adaptive radiation therapy is feasible, safe, and results in dosimetrically comparable treatment plans to simulation CT-based workflows while providing convenience and time savings for patients.
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Affiliation(s)
- Kylie H Kang
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Alex T Price
- University Hospitals, Department of Radiation Oncology, Case Western Reserve University, Cleveland, Ohio
| | - Francisco J Reynoso
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Eric Laugeman
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Eric D Morris
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Pamela P Samson
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Jiayi Huang
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Shahed N Badiyan
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, Dallas, Texas
| | - Hyun Kim
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Randall J Brenneman
- Banner MD Anderson Cancer Center at Banner North Colorado Medical Center, Greeley, Colorado
| | - Christopher D Abraham
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Nels C Knutson
- Washington University School of Medicine in St Louis, Department of Radiation Oncology, St Louis, Missouri
| | - Lauren E Henke
- University Hospitals, Department of Radiation Oncology, Case Western Reserve University, Cleveland, Ohio.
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Kornek D, Bert C. Process failure mode and effects analysis for external beam radiotherapy: Introducing a literature-based template and a novel action priority. Z Med Phys 2024; 34:358-370. [PMID: 38429170 PMCID: PMC11384953 DOI: 10.1016/j.zemedi.2024.02.002] [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: 11/10/2023] [Revised: 01/21/2024] [Accepted: 02/07/2024] [Indexed: 03/03/2024]
Abstract
PURPOSE The first aim of the study was to create a general template for analyzing potential failures in external beam radiotherapy, EBRT, using the process failure mode and effects analysis (PFMEA). The second aim was to modify the action priority (AP), a novel prioritization method originally introduced by the Automotive Industry Action Group (AIAG), to work with different severity, occurrence, and detection rating systems used in radiation oncology. METHODS AND MATERIALS The AIAG PFMEA approach was employed in combination with an extensive literature survey to develop the EBRT-PFMEA template. Subsets of high-risk failure modes found through the literature survey were added to the template where applicable. Our modified AP for radiation oncology (RO AP) was defined using a weighted sum of severity, occurrence, and detectability. Then, Monte Carlo simulations were conducted to compare the original AIAG AP, the RO AP, and the risk priority number (RPN). The results of the simulations were used to determine the number of additional corrective actions per failure mode and to parametrize the RO AP to our department's rating system. RESULTS An EBRT-PFMEA template comprising 75 high-risk failure modes could be compiled. The AIAG AP required 1.7 additional corrective actions per failure mode, while the RO AP ranged from 1.3 to 3.5, and the RPN required 3.6. The RO AP could be parametrized so that it suited our rating system and evaluated severity, occurrence, and detection ratings equally to the AIAG AP. CONCLUSIONS An adjustable EBRT-PFMEA template is provided which can be used as a practical starting point for creating institution-specific templates. Moreover, the RO AP introduces transparent action levels that can be adapted to any rating system.
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Affiliation(s)
- Dominik Kornek
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany.
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Zhang S, Li K, Sun Y, Wan Y, Ao Y, Zhong Y, Liang M, Wang L, Chen X, Pei X, Hu Y, Chen D, Li M, Shan H. Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study. Int J Radiat Oncol Biol Phys 2024; 119:1590-1600. [PMID: 38432286 DOI: 10.1016/j.ijrobp.2024.02.035] [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: 11/02/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.
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Affiliation(s)
- Shuaitong Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yuchen Sun
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yun Wan
- Department of Radiology, Xinyi City People's Hospital, Xinyi, Guangdong, China
| | - Yong Ao
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yinghua Zhong
- Department of Radiology, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong, China
| | - Mingzhu Liang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Lizhu Wang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Xiaofeng Pei
- Department of Radiation Oncology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yi Hu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Man Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
| | - Hong Shan
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Department of Interventional Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
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Decker SM, Bruza P, Zhang R, Williams BB, Jarvis LA, Pogue BW, Gladstone DJ. Technical note: Visual, rapid, scintillation point dosimetry for in vivo MV photon beam radiotherapy treatments. Med Phys 2024; 51:5754-5763. [PMID: 38598093 PMCID: PMC11321933 DOI: 10.1002/mp.17071] [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: 10/26/2023] [Revised: 03/07/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND While careful planning and pre-treatment checks are performed to ensure patient safety during external beam radiation therapy (EBRT), inevitable daily variations mean that in vivo dosimetry (IVD) is the only way to attain the true delivered dose. Several countries outside the US require daily IVD for quality assurance. However, elsewhere, the manual labor and time considerations of traditional in vivo dosimeters may be preventing frequent use of IVD in the clinic. PURPOSE This study expands upon previous research using plastic scintillator discs for optical dosimetry for electron therapy treatments. We present the characterization of scintillator discs for in vivo x-ray dosimetry and describe additional considerations due to geometric complexities. METHODS Plastic scintillator discs were coated with reflective white paint on all sides but the front surface. An anti-reflective, matte coating was applied to the transparent face to minimize specular reflection. A time-gated iCMOS camera imaged the discs under various irradiation conditions. In post-processing, background-subtracted images of the scintillators were fit with Gaussian-convolved ellipses to extract several parameters, including integral output, and observation angle. RESULTS Dose linearity and x-ray energy independence were observed, consistent with ideal characteristics for a dosimeter. Dose measurements exhibited less than 5% variation for incident beam angles between 0° and 75° at the anterior surface and 0-60∘ $^\circ $ at the posterior surface for exit beam dosimetry. Varying the angle between the disc surface and the camera lens did not impact the integral output for the same dose up to 55°. Past this point, up to 75°, there is a sharp falloff in response; however, a correction can be used based on the detected width of the disc. The reproducibility of the integral output for a single disc is 2%, and combined with variations from the gantry angle, we report the accuracy of the proposed scintillator disc dosimeters as ±5.4%. CONCLUSIONS Plastic scintillator discs have characteristics that are well-suited for in vivo optical dosimetry for x-ray radiotherapy treatments. Unlike typical point dosimeters, there is no inherent readout time delay, and an optical recording of the measurement is saved after treatment for future reference. While several factors influence the integral output for the same dose, they have been quantified here and may be corrected in post-processing.
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Affiliation(s)
| | - Petr Bruza
- Thayer School of Engineering, Dartmouth College, Hanover NH 03755
| | - Rongxiao Zhang
- Geisel School of Medicine, Dartmouth College, Hanover NH 03755
| | | | - Lesley A. Jarvis
- Geisel School of Medicine, Dartmouth College, Hanover NH 03755
- Dartmouth Cancer Center, Dartmouth Health, Lebanon, NH, 03765
| | - Brian W. Pogue
- Thayer School of Engineering, Dartmouth College, Hanover NH 03755
| | - David J. Gladstone
- Thayer School of Engineering, Dartmouth College, Hanover NH 03755
- Geisel School of Medicine, Dartmouth College, Hanover NH 03755
- Dartmouth Cancer Center, Dartmouth Health, Lebanon, NH, 03765
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Voon NS, Manan HA, Yahya N. Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates. J Cancer Surviv 2024; 18:1297-1308. [PMID: 37010777 PMCID: PMC10069366 DOI: 10.1007/s11764-023-01371-8] [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: 02/09/2023] [Accepted: 03/22/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE Irradiation of the brain regions from nasopharyngeal carcinoma (NPC) radiotherapy (RT) is frequently unavoidable, which may result in radiation-induced cognitive deficit. Using deep learning (DL), the study aims to develop prediction models in predicting compromised cognition in patients following NPC RT using remote assessments and determine their relation to the quality of life (QoL) and MRI changes. METHODS Seventy patients (20-76 aged) with MRI imaging (pre- and post-RT (6 months-1 year)) and complete cognitive assessments were recruited. Hippocampus, temporal lobes (TLs), and cerebellum were delineated and dosimetry parameters were extracted. Assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE), and QLQ-H&N 43). Regression and deep neural network (DNN) models were used to predict post-RT cognition using anatomical and treatment dose features. RESULTS Remote cognitive assessments were inter-correlated (r > 0.9). TLs showed significance in pre- and post-RT volume differences and cognitive deficits, that are correlated with RT-associated volume atrophy and dose distribution. Good classification accuracy based on DNN area under receiver operating curve (AUROC) for cognitive prediction (T-MoCA AUROC = 0.878, TICS AUROC = 0.89, Tele-MACE AUROC = 0.919). CONCLUSION DL-based prediction models assessed using remote assessments can assist in predicting cognitive deficit following NPC RT. Comparable results of remote assessments in assessing cognition suggest its possibility in replacing standard assessments. IMPLICATIONS FOR CANCER SURVIVORS Application of prediction models in individual patient enables tailored interventions to be provided in managing cognitive changes following NPC RT.
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Affiliation(s)
- Noor Shatirah Voon
- Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia
- National Cancer Institute, Ministry of Health, Jalan P7, Presint 7, 62250, Putrajaya, Malaysia
| | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, 56000, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia.
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Hu F, Lucas A, Chen AA, Coleman K, Horng H, Ng RWS, Tustison NJ, Davis KA, Shou H, Li M, Shinohara RT. DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data. Hum Brain Mapp 2024; 45:e26708. [PMID: 39056477 PMCID: PMC11273293 DOI: 10.1002/hbm.26708] [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: 02/26/2024] [Revised: 04/19/2024] [Accepted: 04/25/2024] [Indexed: 07/28/2024] Open
Abstract
Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, Department of EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew A. Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kyle Coleman
- Statistical Center for Single‐Cell and Spatial GenomicsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Raymond W. S. Ng
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Nicholas J. Tustison
- Department of Radiology and Medical ImagingUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, Department of EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Mingyao Li
- Statistical Center for Single‐Cell and Spatial GenomicsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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Wegener S, Exner F, Weick S, Stark S, Hutzel H, Lutyj P, Tamihardja J, Razinskas G. Prospective risk analysis of the online-adaptive artificial intelligence-driven workflow using the Ethos treatment system. Z Med Phys 2024; 34:384-396. [PMID: 36504142 PMCID: PMC11384068 DOI: 10.1016/j.zemedi.2022.11.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/20/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE The recently introduced Varian Ethos system allows adjusting radiotherapy treatment plans to anatomical changes on a daily basis. The system uses artificial intelligence to speed up the process of creating adapted plans, comes with its own software solutions and requires a substantially different workflow. A detailed analysis of possible risks of the associated workflow is presented. METHODS A prospective risk analysis of the adaptive workflow with the Ethos system was performed using Failure Modes and Effects Analysis (FMEA). An interprofessional team collected possible adverse events and evaluated their severity as well as their chance of occurrence and detectability. Measures to reduce the risks were discussed. RESULTS A total of 122 events were identified, and scored. Within the 20 events with the highest-ranked risks, the following were identified: Challenges due to the stand-alone software solution with very limited connectivity to the existing record and verify software and digital patient file, unfamiliarity with the new software and its limitations and the adaption process relying on results obtained by artificial intelligence. The risk analysis led to the implementation of additional quality assurance measures in the workflow. CONCLUSIONS The thorough analysis of the risks associated with the new treatment technique was the basis for designing details of the workflow. The analysis also revealed challenges to be addressed by both, the vendor and customers. On the vendor side, this includes improving communication between their different software solutions. On the customer side, this especially includes establishing validation strategies to monitor the results of the black box adaption process making use of artificial intelligence.
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Affiliation(s)
- Sonja Wegener
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Florian Exner
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Stefan Weick
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Silke Stark
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Heike Hutzel
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Paul Lutyj
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Jörg Tamihardja
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
| | - Gary Razinskas
- University of Wuerzburg, Department of Radiation Oncology, Wuerzburg, Germany.
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138
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Kim LH, Juneja BR, Viscariello NN. A method for empirically validating FMEA RPN scores in a radiation oncology clinic using physics QC data. J Appl Clin Med Phys 2024; 25:e14391. [PMID: 38988053 PMCID: PMC11302802 DOI: 10.1002/acm2.14391] [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: 01/10/2024] [Revised: 04/02/2024] [Accepted: 04/22/2024] [Indexed: 07/12/2024] Open
Abstract
In failure modes and effects analysis (FMEA), the components of the risk priority number (RPN) for a failure mode (FM) are often chosen by consensus. We describe an empirical method for estimating the occurrence (O) and detectability (D) components of a RPN. The method requires for a given FM that its associated quality control measure be performed twice as is the case when a FM is checked for in an initial physics check and again during a weekly physics check. If instances of the FM caught by these checks are recorded, O and D can be computed. Incorporation of the remaining RPN component, Severity, is discussed. This method can be used as part of quality management design ahead of an anticipated FMEA or afterwards to validate consensus values.
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Affiliation(s)
- Leonard H. Kim
- Department of Radiation OncologyMD Anderson Cancer Center at CooperCamdenNew JerseyUSA
| | - Badal R. Juneja
- Department of Radiation OncologyMD Anderson Cancer Center at CooperCamdenNew JerseyUSA
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Hayes C, King RB, Hounsell AR, Agnew CE. Impact of target degradation on the 6FFF output of a Varian TrueBeam Linac. Phys Med 2024; 124:103424. [PMID: 39002424 DOI: 10.1016/j.ejmp.2024.103424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/07/2024] [Accepted: 06/29/2024] [Indexed: 07/15/2024] Open
Abstract
The dosimetric output of a 6FFF beam, produced from a Varian TrueBeam linac exhibited an unexpected downward trend over time that was contrary to well-established expectations. To elucidate the cause of this uncharacteristic trend, a review of the linac's quality control results over its lifetime was performed, including, constancy checks of the dosimetric output, beam energy, flatness and symmetry, and percentage depth dose characteristics. These results were supplemented with a comprehensive series of measurements including flatness and symmetry measurements with a 1D-diode array, high-resolution measurements of the photon beam's build-up region with a parallel-plate chamber and measurement of the beam's output as a function of the x-ray target position. The review of the linac's QC results and supplemental tests identified no deviations in the linac's performance from its commissioning and baseline measurements. However, the 6FFF beam output exhibited a significant dependence on the target location relative to its default position, increasing by 5.43 % with a 0.5 mm target translation, indicating that target degradation was the cause of the atypical output trend. The change in output behaviour was believed to be the result of primary electrons escaping the degraded target and interacting with the linac's monitor chamber. Replacement of the x-ray target caused the 6FFF output to realign with expected trends. Target degradation was uncovered due to a robust quality control trending database and awareness of typical output behaviour. These results demonstrate the importance of data trending to identify component failure and provide centres with knowledge to recognise this potential fault.
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Affiliation(s)
- Chris Hayes
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland, United Kingdom.
| | - Raymond B King
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland, United Kingdom
| | - Alan R Hounsell
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland, United Kingdom; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland, United Kingdom
| | - Christina E Agnew
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland, United Kingdom
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Lee BM, Kim JS, Chang Y, Choi SH, Park JW, Byun HK, Kim YB, Lee IJ, Chang JS. Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases. Int J Radiat Oncol Biol Phys 2024; 119:1579-1589. [PMID: 38431232 DOI: 10.1016/j.ijrobp.2024.02.041] [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: 04/03/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments. METHODS AND MATERIALS The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95). RESULTS We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection. CONCLUSIONS In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.
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Affiliation(s)
- Byung Min Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | | | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Won Park
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hwa Kyung Byun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ik Jae Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Rasmussen ME, Akbarov K, Titovich E, Nijkamp JA, Van Elmpt W, Primdahl H, Lassen P, Cacicedo J, Cordero-Mendez L, Uddin AFMK, Mohamed A, Prajogi B, Brohet KE, Nyongesa C, Lomidze D, Prasiko G, Ferraris G, Mahmood H, Stojkovski I, Isayev I, Mohamad I, Shirley L, Kochbati L, Eftodiev L, Piatkevich M, Bonilla Jara MM, Spahiu O, Aralbayev R, Zakirova R, Subramaniam S, Kibudde S, Tsegmed U, Korreman SS, Eriksen JG. Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study. JCO Glob Oncol 2024; 10:e2400173. [PMID: 39236283 PMCID: PMC11404336 DOI: 10.1200/go.24.00173] [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: 04/22/2024] [Revised: 06/19/2024] [Accepted: 07/10/2024] [Indexed: 09/07/2024] Open
Abstract
PURPOSE Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs). MATERIALS AND METHODS Ninety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs. RESULTS AI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (-1.41 [-2.44; -0.25]), mandible (-6.60 [-8.09; -3.35]), optic nerve (-0.19 [-0.47; -0.02]), parotid (-1.80 [-2.66; -0.32]), and thyroid (-1.03 [-2.18; -0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [-0.51; 5.14]), oral cavity (1.42 [-0.08; 4.14]), and thyroid (1.60 [-0.04; 2.22]). CONCLUSION The study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.
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Affiliation(s)
| | | | | | | | - Wouter Van Elmpt
- MAASTRO clinic, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Hanne Primdahl
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Pernille Lassen
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jon Cacicedo
- Department of Radiation Oncology, Cruces University Hospital, Bilbao, Spain
| | | | - A F M Kamal Uddin
- Labaid Cancer Hospital and Super Speciality Centre, Dhaka, Bangladesh
| | - Ahmed Mohamed
- National Cancer Institute, University of Gezira, Wad Madani, Sudan
| | - Ben Prajogi
- Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | | | | | - Darejan Lomidze
- Tbilisi State Medical University and Ingorokva High Medical Technology University Clinic, Tbilisi, Georgia
| | | | | | | | - Igor Stojkovski
- University Clinic of Radiotherapy and Oncology, Skopje, Macedonia
| | - Isa Isayev
- National Center of Oncology, Baku, Azerbaijan
| | | | - Leivon Shirley
- Christian Institute of Health Science and Research, Dimapur, India
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Cheng SH, Lee SY, Lee HH. Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance. Cancers (Basel) 2024; 16:2710. [PMID: 39123438 PMCID: PMC11311467 DOI: 10.3390/cancers16152710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 07/22/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.
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Affiliation(s)
- Sarah Hsin Cheng
- Department of Clinical Education and Training, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Shao-Yun Lee
- Department of Medical Education, Taichung Veterans General Hospital, Taichung 407, Taiwan;
| | - Hsin-Hua Lee
- Department of Radiation Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Ph.D. Program in Environmental and Occupational Medicine, Kaohsiung Medical University and National Health Research Institutes, Kaohsiung 807, Taiwan
- Department of Radiation Oncology, Faculty of Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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Marquez B, Wooten ZT, Salazar RM, Peterson CB, Fuentes DT, Whitaker TJ, Jhingran A, Pollard-Larkin J, Prajapati S, Beadle B, Cardenas CE, Netherton TJ, Court LE. Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues. Diagnostics (Basel) 2024; 14:1632. [PMID: 39125508 PMCID: PMC11311423 DOI: 10.3390/diagnostics14151632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.
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Affiliation(s)
- Barbara Marquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | | | - Ramon M. Salazar
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Christine B. Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David T. Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - T. J. Whitaker
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Julianne Pollard-Larkin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Surendra Prajapati
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Beth Beadle
- Department of Radiation Oncology–Radiation Therapy, Stanford University, Stanford, CA 94305, USA;
| | - Carlos E. Cardenas
- Department of Radiation Oncology, The University of Alabama, Birmingham, AL 35294, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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Sjogreen C, Netherton TJ, Lee A, Soliman M, Gay SS, Nguyen C, Mumme R, Vazquez I, Rhee DJ, Cardenas CE, Martel MK, Beadle BM, Court LE. Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer. J Appl Clin Med Phys 2024:e14474. [PMID: 39074490 DOI: 10.1002/acm2.14474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 07/01/2024] [Accepted: 07/09/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease. PURPOSE The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines. METHODS The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists. RESULTS The auto-contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto-contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94 ± 0.02 and MSD of 0.4 ± 0.4 mm for CTV-Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm for CTV-Overall. Upon independent review, two H&N physicians found the auto-contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto-contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases. CONCLUSIONS The study demonstrates the ability of an auto-contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases.
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Affiliation(s)
- Carlos Sjogreen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tucker J Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anna Lee
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Moaaz Soliman
- Department of Radiation Oncology, Karmanos Cancer Institute, Detroit, Michigan, USA
| | - Skylar S Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Callistus Nguyen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ivan Vazquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos E Cardenas
- Department of Radiation Oncology, University of Alabama Birmingham, Birmingham, Alabama, USA
| | - Mary K Martel
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, California, USA
| | - Laurence Edward Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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145
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Huang Z, Tian L, Janssens G, Smeets J, Xie Y, Kevin Teo BK, Nilsson R, Traneus E, Parodi K, Pinto M. An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system. Phys Med Biol 2024; 69:155025. [PMID: 38981589 DOI: 10.1088/1361-6560/ad6116] [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: 01/24/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective.Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for PG range verification compares the detected PG profile with a predicted one. Recently, a novel analytical PG prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. This work validates the performance of the filtering PG prediction approach.Approach.The said algorithm is validated against experimental data and benchmarked with another well-established PG prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation.Main results.The mean shifts between the experimental data and the predicted PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be1.5±2.1mm and-0.6±2.2mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach.Significance.The novel filtering approach is successfully validated against experimental data and another widely used PG prediction algorithm. The workflow designed in this work selects spots with high-quality PGD shift calculation results, and performs sensitivity and specificity analyses to assist clinical decisions.
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Affiliation(s)
- Ze Huang
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Liheng Tian
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
| | | | | | - Yunhe Xie
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
| | - Boon-Keng Kevin Teo
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | | | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marco Pinto
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
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Garbarino GM, Polici M, Caruso D, Laghi A, Mercantini P, Pilozzi E, van Berge Henegouwen MI, Gisbertz SS, van Grieken NCT, Berardi E, Costa G. Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience. Cancers (Basel) 2024; 16:2664. [PMID: 39123392 PMCID: PMC11311587 DOI: 10.3390/cancers16152664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/20/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. METHODS The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024. RESULTS Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. CONCLUSIONS Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.
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Affiliation(s)
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Paolo Mercantini
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Emanuela Pilozzi
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Mark I. van Berge Henegouwen
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, 1081 HV Amsterdam, The Netherlands
| | - Suzanne S. Gisbertz
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, 1081 HV Amsterdam, The Netherlands
| | - Nicole C. T. van Grieken
- Department of Pathology, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Biology and Immunology, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Eva Berardi
- Department of Radiology, San Camillo Hospital, ASL RM 1, 00152 Rome, Italy
| | - Gianluca Costa
- Department of Life Science, Health and Health Professions, Link Campus University, 00165 Rome, Italy
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Yu L, Yan R, Yang D, Xia C, Zhang Z. Comparative efficacy of radical prostatectomy and radiotherapy in the treatment of high-risk prostate cancer. Technol Health Care 2024:THC240910. [PMID: 39093097 DOI: 10.3233/thc-240910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
BACKGROUND Both radical prostatectomy and radiation therapy are effective in controlling the condition of patients with hormone-resistant prostate cancer (HRPCa). However, there is limited research on the prognosis and quality of life of HRPCa patients after different treatment modalities. OBJECTIVE To explore the efficacy of radical prostatectomy (RP) and radiotherapy (RT), when treating high-risk prostate cancer (HRPCa). METHODS Overall 103 HRPCa patients were included and were divided into RP group and RT group according to different treatment methods. The propensity score matching method (PSM) was used to balance the baseline data of the two groups and match 34 patients in each group. The prognosis, quality of life, and basic efficacy of patients were compared. RESULTS After intervention, the disease-free survival rate of the RT group was higher than that of the RP group (79.41% vs. 55.88%, p= 0.038). Quality of life scores between the two treatment methods had no difference before intervention (p> 0.05), but higher in RT group than that of the RP group after intervention (p< 0.05). After treatment, there was no statistically significant difference in total effective rate of treatment between two groups (44.12% vs. 58.82%, p> 0.05), but the disease control rate was significantly higher in RT group (94.12% vs. 76.47%, p= 0.040). CONCLUSION Radical radiotherapy is effective in the clinical treatment of HRPCa patients, with a higher disease-free survival rate and improved quality of life after treatment, and is worth promoting.
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148
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Zhu L, Dong H, Sun J, Wang L, Xing Y, Hu Y, Lu J, Yang J, Chu J, Yan C, Yuan F, Zhong J. Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study. Eur Radiol 2024:10.1007/s00330-024-10976-1. [PMID: 39048741 DOI: 10.1007/s00330-024-10976-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 06/18/2024] [Accepted: 07/05/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVES To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. METHODS A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. CONCLUSION The radiomics features were non-reproducible with significant variability in values among different CT techniques. CLINICAL RELEVANCE STATEMENT Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. KEY POINTS CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.
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Affiliation(s)
- Lan Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Haipeng Dong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jing Sun
- Department of General Surgery, Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Jiarui Yang
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Jingshen Chu
- Department of Science and Technology Development, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chao Yan
- Department of Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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149
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Knill C, Halford R, Sandhu R, Loughery B, Shamim S, Junn F, Lee K, Almahariq M, Seymour Z. Evaluating stereotactic accuracy with patient-specific MRI distortion corrections for frame-based radiosurgery. J Appl Clin Med Phys 2024:e14472. [PMID: 39042450 DOI: 10.1002/acm2.14472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 04/15/2024] [Accepted: 06/15/2024] [Indexed: 07/24/2024] Open
Abstract
PURPOSE This study examines how MRI distortions affect frame-based SRS treatments and assesses the need for clinical distortion corrections. METHODS The study included 18 patients with 80 total brain targets treated using frame-based radiosurgery. Distortion within patients' MRIs were corrected using Cranial Distortion Correction (CDC) software, which utilizes the patient's CT to alter planning MRIs to reduce inherent intra-cranial distortion. Distortion was evaluated by comparing the original planning target volumes (PTVORIG) to targets contoured on corrected MRIs (PTVCORR). To provide an internal control, targets were also re-contoured on uncorrected (PTVRECON) MRIs. Additional analysis was done to assess if 1 mm expansions to PTVORIG targets would compensate for patient-specific distortions. Changes in target volumes, DICE and JACCARD similarity coefficients, minimum PTV dose (Dmin), dose to 95% of the PTV (D95%), and normal tissue receiving 12 Gy (V12Gy), 10 Gy (V10Gy), and 5 Gy (V5Gy) were calculated and evaluated. Student's t-tests were used to determine if changes in PTVCORR were significantly different than intra-contouring variability quantified by PTVRECON. RESULTS PTVRECON and PTVCORR relative changes in volume were 6.19% ± 10.95% and 1.48% ± 32.92%. PTVRECON and PTVCORR similarity coefficients were 0.90 ± 0.08 and 0.73 ± 0.16 for DICE and 0.82 ± 0.12 and 0.60 ± 0.18 for JACCARD. PTVRECON and PTVCORR changes in Dmin were -0.88% ± 8.77% and -12.9 ± 17.3%. PTVRECON and PTVCORR changes in D95% were -0.34% ± 5.89 and -8.68% ± 13.21%. The 1 mm expanded PTVORIG targets did not entirely cover 14 of the 80 PTVCORR targets. Normal tissue changes (V12Gy, V10Gy, V5Gy) calculated with PTVRECON were (-0.09% ± 7.39%, -0.38% ± 5.67%, -0.08% ± 2.04%) and PTVCORR were (-2.14% ± 7.34%, -1.42% ± 5.45%, -0.61% ± 1.93%). Except for V10Gy, all PTVCORR changes were significantly different (p < 0.05) than PTVRECON. CONCLUSION MRIs used for SRS target delineation exhibit notable geometric distortions that may compromise optimal dosimetric accuracy. A uniform 1 mm expansion may result in geometric misses; however, the CDC algorithm provides a feasible solution for rectifying distortions, thereby enhancing treatment precision.
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Affiliation(s)
- Cory Knill
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Robert Halford
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Raminder Sandhu
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Brian Loughery
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Sharjil Shamim
- William Beaumont School of Medicine, Oakland University, Rochester, Michigan, USA
| | - Fred Junn
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Kuei Lee
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Muayad Almahariq
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Zachary Seymour
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
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150
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Nikou P, Thompson A, Nisbet A, Gulliford S, McClelland J. Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment. Phys Med Biol 2024; 69:155017. [PMID: 38981595 DOI: 10.1088/1361-6560/ad611b] [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: 03/06/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective.Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes.Approach.Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models.Main results.Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons.Significance.In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.
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Affiliation(s)
- Poppy Nikou
- University College London, London, WC1E 6AE, United Kingdom
| | - Anna Thompson
- University College London Hospital, London, NW1 2BU, United Kingdom
| | - Andrew Nisbet
- University College London, London, WC1E 6AE, United Kingdom
| | - Sarah Gulliford
- University College London, London, WC1E 6AE, United Kingdom
- University College London Hospital, London, NW1 2BU, United Kingdom
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