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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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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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van Timmeren JE, Bussink J, Koopmans P, Smeenk RJ, Monshouwer R. Longitudinal Image Data for Outcome Modeling. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00277-2. [PMID: 39003124 DOI: 10.1016/j.clon.2024.06.053] [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: 10/23/2023] [Revised: 04/15/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
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Affiliation(s)
- J E van Timmeren
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - J Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - P Koopmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R J Smeenk
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
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Panettieri V, Gagliardi G. Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready. J Med Radiat Sci 2024; 71:174-176. [PMID: 38641984 PMCID: PMC11177026 DOI: 10.1002/jmrs.791] [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: 02/13/2024] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.
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Affiliation(s)
- Vanessa Panettieri
- Department of Physical SciencesPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyThe University of MelbourneMelbourneVictoriaAustralia
- Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
- Department of Medical Imaging and Radiation SciencesMonash UniversityClaytonVictoriaAustralia
| | - Giovanna Gagliardi
- Medical Radiation Physics DepartmentKarolinska University HospitalStockholmSweden
- Department of Oncology‐PathologyKarolinska InstitutetStockholmSweden
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Liu W, Shen N, Zhang L, Wang X, Chen B, Liu Z, Yang C. Research in the application of artificial intelligence to lung cancer diagnosis. Front Med (Lausanne) 2024; 11:1343485. [PMID: 38352145 PMCID: PMC10861801 DOI: 10.3389/fmed.2024.1343485] [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: 11/23/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024] Open
Abstract
The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.
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Affiliation(s)
- Wenjuan Liu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Nan Shen
- Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Limin Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiaoxi Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bainan Chen
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhuo Liu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chao Yang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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Gianoli C, De Bernardi E, Parodi K. "Under the hood": artificial intelligence in personalized radiotherapy. BJR Open 2024; 6:tzae017. [PMID: 39104573 PMCID: PMC11299549 DOI: 10.1093/bjro/tzae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 05/10/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks involved in the radiotherapy workflow. The radiotherapy framework is presented on 2 different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy. The second level is referred to as biology-driven workflow, explored in the research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A 2-fold role for AI is defined according to these 2 different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers that were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or multiomics, when complemented by clinical and biological parameters (ie, biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI's growing role in personalized radiotherapy.
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Affiliation(s)
- Chiara Gianoli
- Department of Experimental Physics – Medical Physics, Faculty for Physics of the Ludwig-Maximilians-Universität München (LMU Munich), Geschwister-Scholl-Platz 1, München, 80539, Germany
| | - Elisabetta De Bernardi
- School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, Milano, 20126, Italy
| | - Katia Parodi
- Department of Experimental Physics – Medical Physics, Faculty for Physics of the Ludwig-Maximilians-Universität München (LMU Munich), Geschwister-Scholl-Platz 1, München, 80539, Germany
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Mentzel F, Paino J, Barnes M, Cameron M, Corde S, Engels E, Kröninger K, Lerch M, Nackenhorst O, Rosenfeld A, Tehei M, Tsoi AC, Vogel S, Weingarten J, Hagenbuchner M, Guatelli S. Accurate and Fast Deep Learning Dose Prediction for a Preclinical Microbeam Radiation Therapy Study Using Low-Statistics Monte Carlo Simulations. Cancers (Basel) 2023; 15:cancers15072137. [PMID: 37046798 PMCID: PMC10093595 DOI: 10.3390/cancers15072137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 04/09/2023] Open
Abstract
Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50μm-wide coplanar beamlets present a significant challenge for precise MC simulation of the dose deposition of an MRT irradiation treatment field in a short time frame. The long computation times inhibit the ability to perform dose optimization in treatment planning or apply online image-adaptive radiotherapy techniques to MRT. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiotherapy options such as MRT. In this work, the successful application of a fast and accurate ML dose prediction model for a preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the tumor target in rat patients. A CT imaging dataset is used to generate digital phantoms for each patient. Augmented variations of the digital phantoms are used to simulate with Geant4 the energy depositions of an MRT beam inside the phantoms with 15% (high-noise) and 2% (low-noise) statistical uncertainty. The high-noise MC simulation data are used to train the ML model to predict the energy depositions in the digital phantoms. The low-noise MC simulations data are used to test the predictive power of the ML model. The predictions of the ML model show an agreement within 3% with low-noise MC simulations for at least 77.6% of all predicted voxels (at least 95.9% of voxels containing tumor) in the case of the valley dose prediction and for at least 93.9% of all predicted voxels (100.0% of voxels containing tumor) in the case of the peak dose prediction. The successful use of high-noise MC simulations for the training, which are much faster to produce, accelerates the production of the training data of the ML model and encourages transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.
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Affiliation(s)
- Florian Mentzel
- Department of Physics, TU Dortmund University, D-44227 Dortmund, Germany
| | - Jason Paino
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Micah Barnes
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Imaging and Medical Beamline, Australian Synchrotron, ANSTO, Clayton, VIC 3168, Australia
- Peter MacCallum Cancer Center, Physical Sciences, Melbourne, VIC 3000, Australia
| | - Matthew Cameron
- Imaging and Medical Beamline, Australian Synchrotron, ANSTO, Clayton, VIC 3168, Australia
| | - Stéphanie Corde
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
- Prince of Wales Hospital, Randwick, NSW 2031, Australia
| | - Elette Engels
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Imaging and Medical Beamline, Australian Synchrotron, ANSTO, Clayton, VIC 3168, Australia
- Peter MacCallum Cancer Center, Physical Sciences, Melbourne, VIC 3000, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Kevin Kröninger
- Department of Physics, TU Dortmund University, D-44227 Dortmund, Germany
| | - Michael Lerch
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Olaf Nackenhorst
- Department of Physics, TU Dortmund University, D-44227 Dortmund, Germany
| | - Anatoly Rosenfeld
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Moeava Tehei
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Ah Chung Tsoi
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Sarah Vogel
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Jens Weingarten
- Department of Physics, TU Dortmund University, D-44227 Dortmund, Germany
| | - Markus Hagenbuchner
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2500, Australia
| | - Susanna Guatelli
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2500, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW 2500, Australia
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Lee D, Alam S, Jiang J, Cervino L, Hu YC, Zhang P. Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy. Med Phys 2023; 50:970-979. [PMID: 36303270 PMCID: PMC10388694 DOI: 10.1002/mp.16026] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/25/2022] [Accepted: 10/02/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART). METHODS To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration. The major upgrades are (1) expansion of inputs to all weekly cone-beam computed tomography (CBCTs) acquired for monitoring treatment responses throughout a radiotherapy course, for registration to their planning CT; (2) incorporation of 3D convolutional long short-term memory between the encoder and decoder of VoxelMorph, to parse the temporal patterns of anatomical changes; and (3) addition of bidirectional pathways to calculate and minimize inverse consistency errors (ICEs). Longitudinal image sets from 50 patients, including a planning CT and 6 weekly CBCTs per patient, were utilized for network training and cross-validation. The outputs were deformation vector fields for all the registration pairs. The loss function was composed of a normalized cross-correlation for image intensity similarity, a DICE for contour similarity, an ICE, and a deformation regularization term. For performance evaluation, DICE and Hausdorff distance (HD) for the manual versus predicted contours of tumor and esophagus on weekly basis were quantified and further compared with other state-of-the-art algorithms, including conventional VoxelMorph and large deformation diffeomorphic metric mapping (LDDMM). RESULTS Visualization of the hidden states of Seq2Morph revealed distinct spatiotemporal anatomy change patterns. Quantitatively, Seq2Morph performed similarly to LDDMM, but significantly outperformed VoxelMorph as measured by GTV DICE: (0.799±0.078, 0.798±0.081, and 0.773±0.078), and 50% HD (mm): (0.80±0.57, 0.88±0.66, and 0.95±0.60). The per-patient inference of Seq2Morph took 22 s, much less than LDDMM (∼30 min). CONCLUSIONS Seq2Morph can provide accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns. It closely matches the clinical workflow and has the potential to serve both online and offline ART.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
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