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Radici L, Piva C, Casanova Borca V, Cante D, Ferrario S, Paolini M, Cabras L, Petrucci E, Franco P, La Porta MR, Pasquino M. Clinical evaluation of a deep learning CBCT auto-segmentation software for prostate adaptive radiation therapy. Clin Transl Radiat Oncol 2024; 47:100796. [PMID: 38884004 PMCID: PMC11176659 DOI: 10.1016/j.ctro.2024.100796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 05/09/2024] [Accepted: 05/16/2024] [Indexed: 06/18/2024] Open
Abstract
Purpose Aim of the present study is to characterize a deep learning-based auto-segmentation software (DL) for prostate cone beam computed tomography (CBCT) images and to evaluate its applicability in clinical adaptive radiation therapy routine. Materials and methods Ten patients, who received exclusive radiation therapy with definitive intent on the prostate gland and seminal vesicles, were selected. Femoral heads, bladder, rectum, prostate, and seminal vesicles were retrospectively contoured by four different expert radiation oncologists on patients CBCT, acquired during treatment. Consensus contours (CC) were generated starting from these data and compared with those created by DL with different algorithms, trained on CBCT (DL-CBCT) or computed tomography (DL-CT). Dice similarity coefficient (DSC), centre of mass (COM) shift and volume relative variation (VRV) were chosen as comparison metrics. Since no tolerance limit can be defined, results were also compared with the inter-operator variability (IOV), using the same metrics. Results The best agreement between DL and CC was observed for femoral heads (DSC of 0.96 for both DL-CBCT and DL-CT). Performance worsened for low-contrast soft tissue organs: the worst results were found for seminal vesicles (DSC of 0.70 and 0.59 for DL-CBCT and DL-CT, respectively). The analysis shows that it is appropriate to use algorithms trained on the specific imaging modality. Furthermore, the statistical analysis showed that, for almost all considered structures, there is no significant difference between DL-CBCT and human operator in terms of IOV. Conclusions The accuracy of DL-CBCT is in accordance with CC; its use in clinical practice is justified by the comparison with the inter-operator variability.
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Affiliation(s)
| | | | | | | | | | | | - Laura Cabras
- Medical Physics Department, ASL TO4 Ivrea, Italy
| | | | - Pierfrancesco Franco
- Department of Translational Sciences (DIMET), University of Eastern Piedmont, Novara, Italy
- Department of Radiation Oncology, 'Maggiore della Carità' University Hospital, Novara, Italy
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Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
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Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
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Hizam DA, Tan LK, Saad M, Muaadz A, Ung NM. Comparison of commercial atlas-based automatic segmentation software for prostate radiotherapy treatment planning. Phys Eng Sci Med 2024:10.1007/s13246-024-01411-2. [PMID: 38647633 DOI: 10.1007/s13246-024-01411-2] [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: 09/26/2023] [Accepted: 02/20/2024] [Indexed: 04/25/2024]
Abstract
This study aims to assess the accuracy of automatic atlas-based contours for various key anatomical structures in prostate radiotherapy treatment planning. The evaluated structures include the bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. CT images from 20 patients who underwent intensity-modulated radiotherapy were randomly chosen to create an atlas library. Atlas contours of the seven anatomical structures were generated using four software packages: ABAS, Eclipse, MIM, and RayStation. These contours were then compared to manual delineations performed by oncologists, which served as the ground truth. Evaluation metrics such as dice similarity coefficient (DSC), mean distance to agreement (MDA), and volume ratio (VR) were calculated to assess the accuracy of the contours. Additionally, the time taken by each software to generate the atlas contour was recorded. The mean DSC values for the bladder exhibited strong agreement (>0.8) with manual delineations for all software except for Eclipse and RayStation. Similarly, the femoral heads showed significant similarity between the atlas contours and ground truth across all software, with mean DSC values exceeding 0.9 and MDA values close to zero. On the other hand, the penile bulb displayed only moderate agreement with the ground truth, with mean DSC values ranging from 0.5 to 0.7 for all software. A similar trend was observed in the prostate atlas contours, except for MIM, which achieved a mean DSC of over 0.8. For the rectum, both ABAS and MIM atlases demonstrated strong agreement with the ground truth, resulting in mean DSC values of more than 0.8. Overall, MIM and ABAS outperformed Eclipse and RayStation in both DSC and MDA. These results indicate that the atlas-based segmentation employed in this study produces acceptable contours for the anatomical structures of interest in prostate radiotherapy treatment planning.
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Affiliation(s)
- Diyana Afrina Hizam
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Li Kuo Tan
- Department of Biomedical Imaging, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Marniza Saad
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Asyraf Muaadz
- Department of Clinical Oncology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Ngie Min Ung
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
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Vagni M, Tran HE, Romano A, Chiloiro G, Boldrini L, Zormpas-Petridis K, Kawula M, Landry G, Kurz C, Corradini S, Belka C, Indovina L, Gambacorta MA, Placidi L, Cusumano D. Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models. Phys Med 2024; 119:103297. [PMID: 38310680 DOI: 10.1016/j.ejmp.2024.103297] [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: 07/03/2023] [Revised: 12/04/2023] [Accepted: 01/23/2024] [Indexed: 02/06/2024] Open
Abstract
PURPOSE Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients. RESULTS In the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient. CONCLUSIONS The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.
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Affiliation(s)
- Marica Vagni
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Huong Elena Tran
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | | | - Maria Kawula
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Department of Radiation Oncology, Munich, Germany
| | - Luca Indovina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia, SS, Italy
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Brooks J, Tryggestad E, Anand A, Beltran C, Foote R, Lucido JJ, Laack NN, Routman D, Patel SH, Seetamsetty S, Moseley D. Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy. Front Oncol 2024; 14:1295251. [PMID: 38487718 PMCID: PMC10937434 DOI: 10.3389/fonc.2024.1295251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/05/2024] [Indexed: 03/17/2024] Open
Abstract
Introduction Manual review of organ at risk (OAR) contours is crucial for creating safe radiotherapy plans but can be time-consuming and error prone. Statistical and deep learning models show the potential to automatically detect improper contours by identifying outliers using large sets of acceptable data (knowledge-based outlier detection) and may be able to assist human reviewers during review of OAR contours. Methods This study developed an automated knowledge-based outlier detection method and assessed its ability to detect erroneous contours for all common head and neck (HN) OAR types used clinically at our institution. We utilized 490 accurate CT-based HN structure sets from unique patients, each with forty-two HN OAR contours when anatomically present. The structure sets were distributed as 80% for training, 10% for validation, and 10% for testing. In addition, 190 and 37 simulated contours containing errors were added to the validation and test sets, respectively. Single-contour features, including location, shape, orientation, volume, and CT number, were used to train three single-contour feature models (z-score, Mahalanobis distance [MD], and autoencoder [AE]). Additionally, a novel contour-to-contour relationship (CCR) model was trained using the minimum distance and volumetric overlap between pairs of OAR contours to quantify overlap and separation. Inferences from single-contour feature models were combined with the CCR model inferences and inferences evaluating the number of disconnected parts in a single contour and then compared. Results In the test dataset, before combination with the CCR model, the area under the curve values were 0.922/0.939/0.939 for the z-score, MD, and AE models respectively for all contours. After combination with CCR model inferences, the z-score, MD, and AE had sensitivities of 0.838/0.892/0.865, specificities of 0.922/0.907/0.887, and balanced accuracies (BA) of 0.880/0.900/0.876 respectively. In the validation dataset, with similar overall performance and no signs of overfitting, model performance for individual OAR types was assessed. The combined AE model demonstrated minimum, median, and maximum BAs of 0.729, 0.908, and 0.980 across OAR types. Discussion Our novel knowledge-based method combines models utilizing single-contour and CCR features to effectively detect erroneous OAR contours across a comprehensive set of 42 clinically used OAR types for HN radiotherapy.
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Affiliation(s)
- Jamison Brooks
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - Erik Tryggestad
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - Aman Anand
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Chris Beltran
- Department of Radiation Oncology, Mayo Clinic Florida, Jacksonville, FL, United States
| | - Robert Foote
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - J. John Lucido
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - Nadia N. Laack
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - David Routman
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - Samir H. Patel
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Srinivas Seetamsetty
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
| | - Douglas Moseley
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN, United States
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RLJ, Liu T, Wang T, Yang X. Deep learning in MRI-guided radiation therapy: A systematic review. J Appl Clin Med Phys 2024; 25:e14155. [PMID: 37712893 PMCID: PMC10860468 DOI: 10.1002/acm2.14155] [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: 03/21/2023] [Revised: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x-ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi-modal, visual transformer, and diffusion models.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
- School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Richard L. J. Qiu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation OncologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Tonghe Wang
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
- School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
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Li Y, Fu Y, Gayo IJMB, Yang Q, Min Z, Saeed SU, Yan W, Wang Y, Noble JA, Emberton M, Clarkson MJ, Huisman H, Barratt DC, Prisacariu VA, Hu Y. Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration. Med Image Anal 2023; 90:102935. [PMID: 37716198 DOI: 10.1016/j.media.2023.102935] [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: 09/07/2022] [Revised: 06/08/2023] [Accepted: 08/16/2023] [Indexed: 09/18/2023]
Abstract
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment, support mask conditioning module is proposed to further utilise the annotation available from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the support mask conditioning, all of which made positive contributions independently or collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether the support data come from the same or different institutes.
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Affiliation(s)
- Yiwen Li
- Active Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Yunguan Fu
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; InstaDeep Ltd., London, UK
| | - Iani J M B Gayo
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Qianye Yang
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Zhe Min
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Shaheer U Saeed
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Wen Yan
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yipei Wang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - J Alison Noble
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Matthew J Clarkson
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Henkjan Huisman
- Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Dean C Barratt
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Victor A Prisacariu
- Active Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Yipeng Hu
- Department of Medical Physics and Biomedical Engineering, UCL Centre for Medical Image Computing, and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
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Palazzo G, Mangili P, Deantoni C, Fodor A, Broggi S, Castriconi R, Ubeira Gabellini MG, del Vecchio A, Di Muzio NG, Fiorino C. Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning. Phys Imaging Radiat Oncol 2023; 28:100501. [PMID: 37920450 PMCID: PMC10618761 DOI: 10.1016/j.phro.2023.100501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/04/2023] Open
Abstract
Background and purpose Artificial Intelligence (AI)-based auto-contouring for treatment planning in radiotherapy needs extensive clinical validation, including the impact of editing after automatic segmentation. The aims of this study were to assess the performance of a commercial system for Clinical Target Volumes (CTVs) (prostate/seminal vesicles) and selected Organs at Risk (OARs) (rectum/bladder/femoral heads + femurs), evaluating also inter-observer variability (manual vs automatic + editing) and the reduction of contouring time. Materials and methods Two expert observers contoured CTVs/OARs of 20 patients in our Treatment Planning System (TPS). Computed Tomography (CT) images were sent to the automatic contouring workstation: automatic contours were generated and sent back to TPS, where observers could edit them if necessary. Inter- and intra-observer consistency was estimated using Dice Similarity Coefficients (DSC). Radiation oncologists were also asked to score the quality of automatic contours, ranging from 1 (complete re-contouring) to 5 (no editing). Contouring times (manual vs automatic + edit) were compared. Results DSCs (manual vs automatic only) were consistent with inter-observer variability (between 0.65 for seminal vesicles and 0.94 for bladder); editing further improved performances (range: 0.76-0.94). The median clinical score was 4 (little editing) and it was <4 in 3/2 patients for the two observers respectively. Inter-observer variability of automatic + editing contours improved significantly, being lower than manual contouring (e.g.: seminal vesicles: 0.83vs0.73; prostate: 0.86vs0.83; rectum: 0.96vs0.81). Oncologist contouring time reduced from 17 to 24 min of manual contouring time to 3-7 min of editing time for the two observers (p < 0.01). Conclusion Automatic contouring with a commercial AI-based system followed by editing can replace manual contouring, resulting in significantly reduced time for segmentation and better consistency between operators.
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Affiliation(s)
- Gabriele Palazzo
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Paola Mangili
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Radiotherapy, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Andrei Fodor
- Radiotherapy, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | | | | | | | - Nadia G. Di Muzio
- Radiotherapy, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Italy
| | - Claudio Fiorino
- Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
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Ribeiro MF, Marschner S, Kawula M, Rabe M, Corradini S, Belka C, Riboldi M, Landry G, Kurz C. Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors. Radiat Oncol 2023; 18:135. [PMID: 37574549 PMCID: PMC10424424 DOI: 10.1186/s13014-023-02330-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/03/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of organs-at-risk (OARs), which is also prone to inter- and intra-observer variability. Therefore, deep learning autosegmentation (DLAS) is becoming increasingly attractive. No investigation of its application to OARs in thoracic magnetic resonance images (MRIs) from MRgRT has been done so far. This study aimed to fill this gap. MATERIALS AND METHODS 122 planning MRIs from patients treated at a 0.35 T MR-Linac were retrospectively collected. Using an 80/19/23 (training/validation/test) split, individual 3D U-Nets for segmentation of the left lung, right lung, heart, aorta, spinal canal and esophagus were trained. These were compared to the clinically used contours based on Dice similarity coefficient (DSC) and Hausdorff distance (HD). They were also graded on their clinical usability by a radiation oncologist. RESULTS Median DSC was 0.96, 0.96, 0.94, 0.90, 0.88 and 0.78 for left lung, right lung, heart, aorta, spinal canal and esophagus, respectively. Median 95th percentile values of the HD were 3.9, 5.3, 5.8, 3.0, 2.6 and 3.5 mm, respectively. The physician preferred the network generated contours over the clinical contours, deeming 85 out of 129 to not require any correction, 25 immediately usable for treatment planning, 15 requiring minor and 4 requiring major corrections. CONCLUSIONS We trained 3D U-Nets on clinical MRI planning data which produced accurate delineations in the thoracic region. DLAS contours were preferred over the clinical contours.
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Affiliation(s)
- Marvin F Ribeiro
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Sebastian Marschner
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maria Kawula
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
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10
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Liu X, Shi J, Li Z, Huang Y, Zhang Z, Zhang C. The Present and Future of Artificial Intelligence in Urological Cancer. J Clin Med 2023; 12:4995. [PMID: 37568397 PMCID: PMC10419644 DOI: 10.3390/jcm12154995] [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: 05/05/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.
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Affiliation(s)
| | | | | | | | - Zhihong Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
| | - Changwen Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
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11
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Kawula M, Hadi I, Nierer L, Vagni M, Cusumano D, Boldrini L, Placidi L, Corradini S, Belka C, Landry G, Kurz C. Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation. Med Phys 2023; 50:1573-1585. [PMID: 36259384 DOI: 10.1002/mp.16056] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustments allow to capture anatomical changes, improve target volume coverage, and reduce the risk of side effects. The introduction of automatic segmentation techniques could help to further improve the online adaptive workflow by shortening the re-contouring time and reducing intra- and inter-observer variability. In fractionated RT, prior knowledge, such as planning images and manual expert contours, is usually available before irradiation, but not used by current artificial intelligence-based autocontouring approaches. PURPOSE The goal of this study was to train convolutional neural networks (CNNs) for automatic segmentation of bladder, rectum (organs at risk, OARs), and clinical target volume (CTV) for prostate cancer patients treated at 0.35 T MR-Linacs. Furthermore, we tested the CNNs generalization on data from independent facilities and compared them with the MR-Linac treatment planning system (TPS) propagated structures currently used in clinics. Finally, expert planning delineations were utilized for patient- (PS) and facility-specific (FS) transfer learning to improve auto-segmentation of CTV and OARs on fraction images. METHODS In this study, data from fractionated treatments at 0.35 T MR-Linacs were leveraged to develop a 3D U-Net-based automatic segmentation. Cohort C1 had 73 planning images and cohort C2 had 19 planning and 240 fraction images. The baseline models (BMs) were trained solely on C1 planning data using 53 MRIs for training and 10 for validation. To assess their accuracy, the models were tested on three data subsets: (i) 10 C1 planning images not used for training, (ii) 19 C2 planning, and (iii) 240 C2 fraction images. BMs also served as a starting point for FS and PS transfer learning, where the planning images from C2 were used for network parameter fine tuning. The segmentation output of the different trained models was compared against expert ground truth by means of geometric metrics. Moreover, a trained physician graded the network segmentations as well as the segmentations propagated by the clinical TPS. RESULTS The BMs showed dice similarity coefficients (DSC) of 0.88(4) and 0.93(3) for the rectum and the bladder, respectively, independent of the facility. CTV segmentation with the BM was the best for intermediate- and high-risk cancer patients from C1 with DSC=0.84(5) and worst for C2 with DSC=0.74(7). The PS transfer learning brought a significant improvement in the CTV segmentation, yielding DSC=0.72(4) for post-prostatectomy and low-risk patients and DSC=0.88(5) for intermediate- and high-risk patients. The FS training did not improve the segmentation accuracy considerably. The physician's assessment of the TPS-propagated versus network-generated structures showed a clear advantage of the latter. CONCLUSIONS The obtained results showed that the presented segmentation technique has potential to improve automatic segmentation for MR-guided RT.
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Affiliation(s)
- Maria Kawula
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Indrawati Hadi
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Lukas Nierer
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Marica Vagni
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
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12
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Sminia P, Guipaud O, Viktorsson K, Ahire V, Baatout S, Boterberg T, Cizkova J, Dostál M, Fernandez-Palomo C, Filipova A, François A, Geiger M, Hunter A, Jassim H, Edin NFJ, Jordan K, Koniarová I, Selvaraj VK, Meade AD, Milliat F, Montoro A, Politis C, Savu D, Sémont A, Tichy A, Válek V, Vogin G. Clinical Radiobiology for Radiation Oncology. RADIOBIOLOGY TEXTBOOK 2023:237-309. [DOI: 10.1007/978-3-031-18810-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
AbstractThis chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.
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13
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Chang J, Porter IR, Forman MA, Shcherban N, Basran PS. Intra- and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images. Vet Radiol Ultrasound 2023; 64:131-139. [PMID: 36049073 PMCID: PMC10087235 DOI: 10.1111/vru.13148] [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: 11/19/2021] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 01/25/2023] Open
Abstract
Measurements of intestinal wall thicknesses from ultrasound imaging (US) are routinely used to support diagnoses of intestinal disorders in cats, however published studies describing observer agreement are currently lacking. The aim of this retrospective, observer agreement study was to quantify inter- and intraobserver repeatability and agreement in the measurement of intestinal wall layer thicknesses and the segmentation of transverse sections of small intestines in US images of 20 cats. Intestinal wall layer thickness measurements of the mucosa, submucosa, muscularis, serosa layer, and total thickness of these layers were performed on five cats with small cell epitheliotropic lymphoma, five with inflammatory bowel disease, and 10 with other conditions. Thickness measurements and the segmentation encompassing the serosa layer were obtained from five observers four times non-sequentially. The average standard deviation in thickness measurements (95% confidence interval) in the mucosa, submucosa, muscularis, serosa, and total thickness were 0.35 (0.07-0.95), 0.24 (0.07-0.52), 0.22 (0.06-0.49), 0.20 (0.05-0.49), and 0.57 (0.11-1.60) mm, respectively. The average intraclass correlation coefficients, which estimates the degree of consistency in thickness measurements and segmentation areas for each observer, ranged from 0.355 to 0.870 and 0.850 to 0.993, respectively. The interclass correlation coefficient, which estimates the degree of consistency when measuring a thickness or segmentation area over all observers ranged from 0.115 to 0.753, and 0.811 to 0.902, respectively. The overall average Dice Coefficient, which estimates the extent of overlap of the segmentations for all observers was 0.957 (0.933 to 0.972). Our results suggest segmentations of small intestines have a higher interobserver agreement than measurements of intestinal wall thicknesses.
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Affiliation(s)
- Jasmine Chang
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Ian R Porter
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Marnin A Forman
- Cornell University Veterinary Specialists, Stamford, Connecticut, USA.,Visiting Associate Clinical Professor of Medicine, Cornell University College of Veterinary Medicine, Ithaca, New York, USA
| | - Natalya Shcherban
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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14
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Henke LE, Fischer-Valuck BW, Rudra S, Wan L, Samson PS, Srivastava A, Gabani P, Roach MC, Zoberi I, Laugeman E, Mutic S, Robinson CG, Hugo GD, Cai B, Kim H. Prospective imaging comparison of anatomic delineation with rapid kV cone beam CT on a novel ring gantry radiotherapy device. Radiother Oncol 2023; 178:109428. [PMID: 36455686 DOI: 10.1016/j.radonc.2022.11.017] [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: 04/30/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022]
Abstract
INTRODUCTION A kV imager coupled to a novel, ring-gantry radiotherapy system offers improved on-board kV-cone-beam computed tomography (CBCT) acquisition time (17-40 seconds) and image quality, which may improve CT radiotherapy image-guidance and enable online adaptive radiotherapy. We evaluated whether inter-observer contour variability over various anatomic structures was non-inferior using a novel ring gantry kV-CBCT (RG-CBCT) imager as compared to diagnostic-quality simulation CT (simCT). MATERIALS/METHODS Seven patients undergoing radiotherapy were imaged with the RG-CBCT system at breath hold (BH) and/or free breathing (FB) for various disease sites on a prospective imaging study. Anatomy was independently contoured by seven radiation oncologists on: 1. SimCT 2. Standard C-arm kV-CBCT (CA-CBCT), and 3. Novel RG-CBCT at FB and BH. Inter-observer contour variability was evaluated by computing simultaneous truth and performance level estimation (STAPLE) consensus contours, then computing average symmetric surface distance (ASSD) and Dice similarity coefficient (DSC) between individual raters and consensus contours for comparison across image types. RESULTS Across 7 patients, 18 organs-at-risk (OARs) were evaluated on 27 image sets. Both BH and FB RG-CBCT were non-inferior to simCT for inter-observer delineation variability across all OARs and patients by ASSD analysis (p < 0.001), whereas CA-CBCT was not (p = 0.923). RG-CBCT (FB and BH) also remained non-inferior for abdomen and breast subsites compared to simCT on ASSD analysis (p < 0.025). On DSC comparison, neither RG-CBCT nor CA-CBCT were non-inferior to simCT for all sites (p > 0.025). CONCLUSIONS Inter-observer ability to delineate OARs using novel RG-CBCT images was non-inferior to simCT by the ASSD criterion but not DSC criterion.
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Affiliation(s)
- Lauren E Henke
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Benjamin W Fischer-Valuck
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Soumon Rudra
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States
| | - Leping Wan
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Pamela S Samson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Amar Srivastava
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Prashant Gabani
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | | | - Imran Zoberi
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric Laugeman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States; Varian Medical Systems, Palo Alto, California, USA
| | - Clifford G Robinson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Geoffrey D Hugo
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States
| | - Bin Cai
- Department of Radiation Oncology, University of Texas Southwestern School of Medicine, Dallas, TX, United States
| | - Hyun Kim
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, United States.
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15
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Li X, Bagher-Ebadian H, Gardner S, Kim J, Elshaikh M, Movsas B, Zhu D, Chetty IJ. An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning. Med Phys 2023; 50:311-322. [PMID: 36112996 DOI: 10.1002/mp.15982] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 01/25/2023] Open
Abstract
PURPOSE Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprising a U-Net and a variational autoencoder (VAE) for automatic contouring of the prostate gland incorporating interobserver variation for radiotherapy treatment planning. The U-Net/VAE generates an ensemble set of segmentations for each image CT slice. A novel outlier mitigation (OM) technique was implemented to enhance the model segmentation accuracy. METHODS The primary source dataset (source_prim) consisted of 19 200 CT slices (from 300 patient planning CT image datasets) with manually contoured prostate glands. A smaller secondary source dataset (source_sec) comprised 640 CT slices (from 10 patient CT datasets), where prostate glands were segmented by 5 independent physicians on each dataset to account for interobserver variability. Data augmentation via random rotation (<5 degrees), cropping, and horizontal flipping was applied to each dataset to increase sample size by a factor of 100. A probabilistic hierarchical U-Net with VAE was implemented and pretrained using the augmented source_prim dataset for 30 epochs. Model parameters of the U-Net/VAE were fine-tuned using the augmented source_sec dataset for 100 epochs. After the first round of training, outlier contours in the training dataset were automatically detected and replaced by the most accurate contours (based on Dice similarity coefficient, DSC) generated by the model. The U-Net/OM-VAE was retrained using the revised training dataset. Metrics for comparison included DSC, Hausdorff distance (HD, mm), normalized cross-correlation (NCC) coefficient, and center-of-mass (COM) distance (mm). RESULTS Results for U-Net/OM-VAE with outliers replaced in the training dataset versus U-Net/VAE without OM were as follows: DSC = 0.82 ± 0.01 versus 0.80 ± 0.02 (p = 0.019), HD = 9.18 ± 1.22 versus 10.18 ± 1.35 mm (p = 0.043), NCC = 0.59 ± 0.07 versus 0.62 ± 0.06, and COM = 3.36 ± 0.81 versus 4.77 ± 0.96 mm over the average of 15 contours. For the average of 15 highest accuracy contours, values were as follows: DSC = 0.90 ± 0.02 versus 0.85 ± 0.02, HD = 5.47 ± 0.02 versus 7.54 ± 1.36 mm, and COM = 1.03 ± 0.58 versus 1.46 ± 0.68 mm (p < 0.03 for all metrics). Results for the U-Net/OM-VAE with outliers removed were as follows: DSC = 0.78 ± 0.01, HD = 10.65 ± 1.95 mm, NCC = 0.46 ± 0.10, COM = 4.17 ± 0.79 mm for the average of 15 contours, and DSC = 0.88 ± 0.02, HD = 7.00 ± 1.17 mm, COM = 1.58 ± 0.63 mm for the average of 15 highest accuracy contours. All metrics for U-Net/VAE trained on the source_prim and source_sec datasets via pretraining, followed by fine-tuning, show statistically significant improvement over that trained on the source_sec dataset only. Finally, all metrics for U-Net/VAE with or without OM showed statistically significant improvement over those for the standard U-Net. CONCLUSIONS A VAE combined with a hierarchical U-Net and an OM strategy (U-Net/OM-VAE) demonstrates promise toward capturing interobserver variability and produces accurate prostate auto-contours for radiotherapy planning. The availability of multiple contours for each CT slice enables clinicians to determine trade-offs in selecting the "best fitting" contour on each CT slice. Mitigation of outlier contours in the training dataset improves prediction accuracy, but one must be wary of reduction in variability in the training dataset.
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Affiliation(s)
- Xin Li
- Department of Computer Science, Wayne State University, Detroit, Michigan, USA
| | - Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, USA
| | - Stephen Gardner
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, USA
| | - Joshua Kim
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, USA
| | - Mohamed Elshaikh
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, USA
| | - Dongxiao Zhu
- Department of Computer Science, Wayne State University, Detroit, Michigan, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan, USA
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16
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Yan C, Guo B, Tendulkar R, Xia P. Contour similarity and its implication on inverse prostate SBRT treatment planning. J Appl Clin Med Phys 2022; 24:e13809. [PMID: 36300837 PMCID: PMC9924104 DOI: 10.1002/acm2.13809] [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: 12/13/2020] [Revised: 08/01/2022] [Accepted: 09/13/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Success of auto-segmentation is measured by the similarity between auto and manual contours that is often quantified by Dice coefficient (DC). The dosimetric impact of contour variability on inverse planning has been rarely reported. The main aim of this study is to investigate whether automatically generated organs-at-risk (OARs) could be used in inverse prostate stereotactic body radiation therapy (SBRT) planning and whether the dosimetric parameters are still clinically acceptable after radiation oncologists modify the OARs. METHODS AND MATERIALS Planning computed tomography images from 10 patients treated with SBRT for prostate cancer were selected and automatically segmented by commercially available atlas-based software. The automatically generated OAR contours were compared with the manually drawn contours. Two volumetric modulated arc therapy (VMAT) plans, autoRec-VMAT (where only automatically generated rectums were used in optimization) and autoAll-VMAT (where automatically generated OARs were used in inverse optimization) were generated. Dosimetric parameters based on the manually drawn PTV and OARs were compared with the clinically approved plans. RESULTS The DCs for the rectum contours varied from 0.55 to 0.74 with a mean value of 0.665. Differences of D95 of the PTV between autoRec-VMAT and manu-VMAT plans varied from 0.03% to -2.85% with a mean value of -0.64%. Differences of D0.03cc of manual rectum between the two plans varied from -0.86% to 9.94% with a mean value of 2.71%. D95 of PTV between autoAll-VMAT and manu-VMAT plans varied from 0.28% to -2.9% with a mean value -0.83%. Differences of D0.03cc of manual rectum between the two plans varied from -0.76% to 6.72% with a mean value of 2.62%. CONCLUSION Our study implies that it is possible to use unedited automatically generated OARs to perform initial inverse prostate SBRT planning. After radiation oncologists modify/approve the OARs, the plan qualities based on the manually drawn OARs are still clinically acceptable, and a re-optimization may not be needed.
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Affiliation(s)
- Chenyu Yan
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
| | - Bingqi Guo
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
| | - Rahul Tendulkar
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
| | - Ping Xia
- Department of Radiation OncologyCleveland Clinic FoundationClevelandOhioUSA
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Emergence of MR-Linac in Radiation Oncology: Successes and Challenges of Riding on the MRgRT Bandwagon. J Clin Med 2022; 11:jcm11175136. [PMID: 36079065 PMCID: PMC9456673 DOI: 10.3390/jcm11175136] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 12/05/2022] Open
Abstract
The special issue of JCM on “Advances of MRI in Radiation Oncology” provides a unique forum for scientific literature related to MR imaging in radiation oncology. This issue covered many aspects, such as MR technology, motion management, economics, soft-tissue–air interface issues, and disease sites such as the pancreas, spine, sarcoma, prostate, head and neck, and rectum from both camps—the Unity and MRIdian systems. This paper provides additional information on the success and challenges of the two systems. A challenging aspect of this technology is low throughput and the monumental task of education and training that hinders its use for the majority of therapy centers. Additionally, the cost of this technology is too high for most institutions, and hence widespread use is still limited. This article highlights some of the difficulties and how to resolve them.
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Mancosu P, Lambri N, Castiglioni I, Dei D, Iori M, Loiacono D, Russo S, Talamonti C, Villaggi E, Scorsetti M, Avanzo M. Applications of artificial intelligence in stereotactic body radiation therapy. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7e18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/04/2022] [Indexed: 11/12/2022]
Abstract
Abstract
This topical review focuses on the applications of artificial intelligence (AI) tools to stereotactic body radiation therapy (SBRT). The high dose per fraction and the limited number of fractions in SBRT require stricter accuracy than standard radiation therapy. The intent of this review is to describe the development and evaluate the possible benefit of AI tools integration into the radiation oncology workflow for SBRT automation. The selected papers were subdivided into four sections, representative of the whole radiotherapy process: ‘AI in SBRT target and organs at risk contouring’, ‘AI in SBRT planning’, ‘AI during the SBRT delivery’, and ‘AI for outcome prediction after SBRT’. Each section summarises the challenges, as well as limits and needs for improvement to achieve better integration of AI tools in the clinical workflow.
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Kendrick J, Francis RJ, Hassan GM, Rowshanfarzad P, Ong JSL, Ebert MA. Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [ 68Ga]Ga-PSMA-11 PET/CT images. Eur J Nucl Med Mol Imaging 2022; 50:67-79. [PMID: 35976392 DOI: 10.1007/s00259-022-05927-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/01/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [68Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. METHODS Three hundred thirty-seven [68Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLVauto) and total lesional uptake (TLUauto) were calculated from the automated segmentations, and Kaplan-Meier analysis was used to assess biomarker relationship with patient overall survival. RESULTS At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLVauto and TLUauto showed they were significantly associated with patient overall survival (both p < 0.005). CONCLUSION The fully automated assessment of whole-body [68Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival. TRIAL REGISTRATION This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia.
| | - Roslyn J Francis
- Medical School, University of Western Australia, Crawley, WA, Australia.,Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Jeremy S L Ong
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Martin A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia.,Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia.,5D Clinics, Claremont, WA, Australia
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Lempart M, Nilsson MP, Scherman J, Gustafsson CJ, Nilsson M, Alkner S, Engleson J, Adrian G, Munck Af Rosenschöld P, Olsson LE. Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network. Radiat Oncol 2022; 17:114. [PMID: 35765038 PMCID: PMC9238000 DOI: 10.1186/s13014-022-02088-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/20/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. METHODS A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1-4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. RESULTS Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. CONCLUSIONS Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.
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Affiliation(s)
- Michael Lempart
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden. .,Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden.
| | - Martin P Nilsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Jonas Scherman
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Mikael Nilsson
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Sara Alkner
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Jens Engleson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Gabriel Adrian
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Per Munck Af Rosenschöld
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Lars E Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
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21
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Amjad A, Xu J, Thill D, Lawton C, Hall W, Awan MJ, Shukla M, Erickson BA, Li XA. General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis. Med Phys 2022; 49:1686-1700. [PMID: 35094390 PMCID: PMC8917093 DOI: 10.1002/mp.15507] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-established deep convolutional neural network (DCNN). METHODS Five CT-based autosegmentation models for 42 organs at risk (OARs) in head and neck (HN), abdomen (ABD), and male pelvis (MP) were developed using a full three-dimensional (3D) DCNN architecture. Two types of deep learning (DL) models were separately trained using either general diversified multi-institutional datasets or custom well-controlled single-institution datasets. To improve segmentation accuracy, an adaptive spatial resolution approach for small and/or narrow OARs and a pseudo scan extension approach, when CT scan length is too short to cover entire organs, were implemented. The performance of the obtained models was evaluated based on accuracy and clinical applicability of the autosegmented contours using qualitative visual inspection and quantitative calculation of dice similarity coefficient (DSC), mean distance to agreement (MDA), and time efficiency. RESULTS The five DL autosegmentation models developed for the three anatomical sites were found to have high accuracy (DSC ranging from 0.8 to 0.98) for 74% OARs and marginally acceptable for 26% OARs. The custom models performed slightly better than the general models, even with smaller custom datasets used for the custom model training. The organ-based approaches improved autosegmentation accuracy for small or complex organs (e.g., eye lens, optic nerves, inner ears, and bowels). Compared with traditional manual contouring times, the autosegmentation times, including subsequent manual editing, if necessary, were substantially reduced by 88% for MP, 80% for HN, and 65% for ABD models. CONCLUSIONS The obtained autosegmentation models, incorporating organ-based approaches, were found to be effective and accurate for most OARs in the male pelvis, head and neck, and abdomen. We have demonstrated that our multianatomical DL autosegmentation models are clinically useful for radiation treatment planning.
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Affiliation(s)
- Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | | | | | - Colleen Lawton
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - William Hall
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Musaddiq J. Awan
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Monica Shukla
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Beth A. Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
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22
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Nourzadeh H, Hui C, Ahmad M, Sadeghzadehyazdi N, Watkins WT, Dutta SW, Alonso CE, Trifiletti DM, Siebers JV. Knowledge-based quality control of organ delineations in radiation therapy. Med Phys 2022; 49:1368-1381. [PMID: 35028948 DOI: 10.1002/mp.15458] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 10/17/2021] [Accepted: 12/17/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge-based quality control (KBQC) system, which discriminates between valid and anomalous delineations is developed. METHOD AND MATERIALS The KBQC is comprised of a group-wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient computational tomographies (CTs). The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follow those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on nonrelational features with a 10-fold cross-validation scheme. The anomaly detection module is a bank of ANN autoencoders, each corresponding with an organ, trained on nonrelational features. A heuristic rule detects anomalous organs that exceed predefined organ-specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer-generated, or propagated based on a transformation obtained by imperfect registrations. Both peer-review-based scoring system and shape similarity coefficient (DSC) were used to label regions of interest (ROIs) as normal or anomalous in two independent test cohorts. RESULTS The accuracy of the classifiers was ≥ $\ge$ 99.8%, and the minimum per-class F1-scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN, respectively. The group-wise inference system reduced the miss-classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected > $>$ 79% of the anomalous ROIs. For 1320 auto-segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations, which also had low Dice similarity coefficient values with respect to manually delineated organs in the training data set. CONCLUSION The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities.
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Affiliation(s)
- Hamidreza Nourzadeh
- Sidney Kimmel Cancer Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Radiation Oncology Department, University of Virginia, Charlottesville, Virginia, USA
| | | | - Mahmoud Ahmad
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | - Sunil W Dutta
- Radiation Oncology Department, Emory University, Georgia, USA
| | | | | | - Jeffrey V Siebers
- Radiation Oncology Department, University of Virginia, Charlottesville, Virginia, USA
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23
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Almeida G, Figueira AR, Lencart J, Tavares JMRS. Segmentation of male pelvic organs on computed tomography with a deep neural network fine-tuned by a level-set method. Comput Biol Med 2022; 140:105107. [PMID: 34872011 DOI: 10.1016/j.compbiomed.2021.105107] [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: 09/27/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022]
Abstract
Computed Tomography (CT) imaging is used in Radiation Therapy planning, where the treatment is carefully tailored to each patient in order to maximize radiation dose to the target while decreasing adverse effects to nearby healthy tissues. A crucial step in this process is manual organ contouring, which if performed automatically could considerably decrease the time to starting treatment and improve outcomes. Computerized segmentation of male pelvic organs has been studied for decades and deep learning models have brought considerable advances to the field, but improvements are still demanded. A two-step framework for automatic segmentation of the prostate, bladder and rectum is presented: a convolutional neural network enhanced with attention gates performs an initial segmentation, followed by a region-based active contour model to fine-tune the segmentations to each patient's specific anatomy. The framework was evaluated on a large collection of planning CTs of patients who had Radiation Therapy for prostate cancer. The Surface Dice Coefficient improved from 79.41 to 81.00% on segmentation of the prostate, 94.03-95.36% on the bladder and 82.17-83.68% on the rectum, comparing the proposed framework with the baseline convolutional neural network. This study shows that traditional image segmentation algorithms can help improve the immense gains that deep learning models have brought to the medical imaging segmentation field.
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Affiliation(s)
- Gonçalo Almeida
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.
| | - Ana Rita Figueira
- Serviço de Radioterapia, Centro Hospitalar Universitário de São João, Porto, Portugal.
| | - Joana Lencart
- Serviço de Física Médica e Grupo de Física Médica Radiobiologia e Protecção Radiológica do Centro de Investigação, Instituto Português de Oncologia do Porto (CI-IPOP), Porto, Portugal.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.
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24
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Saini S, Patnaikuni S, Chandola R, Chandrakar P, Chaudhary V. Normal tissue risk estimation using biological knowledge-based fuzzy logic in volumetric modulated Arc therapy of prostate cancer: Rectum. J Med Phys 2022; 47:126-135. [PMID: 36212203 PMCID: PMC9543004 DOI: 10.4103/jmp.jmp_91_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 01/20/2022] [Accepted: 01/22/2022] [Indexed: 11/16/2022] Open
Abstract
Objective: Most radiotherapy patients with prostate cancer are treated with volumetric modulated arc therapy (VMAT). Advantages of VMAT may be limited by daily treatment uncertainties such as setup errors, internal organ motion, and deformation. The position and shape of prostate target as well as normal organ, i.e., rectum volume around the target, may change during the course of treatment. The aim of the present work is to estimate rectal toxicity estimation using a novel two-level biological knowledge-based fuzzy logic method. Both prostate and rectal internal motions as well as setup uncertainties are considered without compromising target dose distribution in the present study. Materials and Methods: The Mamdani-type fuzzy logic framework was considered in two levels. The prostate target volume changes from minimum to maximum during the course of treatment. In the first level, the fuzzy logic was applied for determining biological acceptable target margin using tumor control probability and normal tissue complication probability (NTCP) parameters based on prostate target motion limits, and then, fuzzy margin was derived. The output margin of first-level fuzzy logic was compared to currently used margins. In second-level fuzzy, rectal volume variation with weekly analysis of cone-beam computed tomography (CBCT) was considered. The biological parameter (NTCP) was calculated corresponding to rectal subvolume variation with weekly CBCT image analysis. Using irradiated volume versus organ risk relationship from treatment planning, the overlapped risk volumes were estimated. Fuzzy rules and membership function were used based on setup errors, asymmetrical nature of organ motion, and limitations of normal tissue toxicity in Mamdani-type Fuzzy Inference System. Results: For total displacement, standard errors of prostate ranging from 0 to 5 mm range were considered in the present study. In the first level, fuzzy planning target volume (PTV) margin was found to be similar or up to 0.5 mm bigger than the conventional margin, but taking the modeling uncertainty into account resulted in a good match between the calculated fuzzy PTV margin and conventional margin formulations under error 0–5 mm standard deviation (SD) range. With application of fuzzy margin obtained from first-level fuzzy, overlapped rectal volumes and corresponding NTCP values were fuzzified in second-level fuzzy using rectal volume variations. The final risk factor (RF) of rectum was qualitatively assessed and found clinically acceptable for each fractional volume of irradiated to total volume and relevant NTCP values. The reason may be at 5 mm SD displacement error range, NTCP values would be within acceptable limit without compromising the tumor dose distribution though the confounding factors such as organ motion, deformation of rectum, and in-house image matching protocols exist. Conclusion: A new approach of two-level fuzzy logic may be suitable to estimate possible organ-at-risk (OAR) toxicity biologically without compromising tumor volume that includes both prostate target and OAR rectum deformation even at displacement standard errors of prostate ranging from 0 to 5 mm range which was considered in the present study. Using proposed simple and fast method, there is an interplay between volume-risk relationship and NTCP of OARs to judge real-time normal organ risk level or alter the treatment margins, particularly concern to individual factors such as comorbidities, genetic predisposition, and other lifestyle choices even at high displacement errors >5 mm SD range.
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25
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Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021; 11:771787. [PMID: 34790581 PMCID: PMC8591174 DOI: 10.3389/fonc.2021.771787] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Roslyn Francis
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Collin Kasisi
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Branimir Rusanov
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia
- 5D Clinics, Claremont, WA, Australia
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26
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Lim Joon D, Chao M, Piccolo A, Schneider M, Anderson N, Handley M, Benci M, Ong WL, Daly K, Morrell R, Wan K, Lawrentschuk N, Foroudi F, Jenkins T, Angus D, Wada M, Sengupta S, Khoo V. Proximal seminal vesicle displacement and margins for prostate cancer radiotherapy. J Med Radiat Sci 2021; 68:289-297. [PMID: 33432719 PMCID: PMC8424309 DOI: 10.1002/jmrs.457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 12/14/2020] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION Guidelines recommend that the proximal seminal vesicles (PrSV) should be included in the clinical target volume for locally advanced prostate cancer patients undergoing radiotherapy. Verification and margins for the prostate may not necessarily account for PrSV displacement. The purpose was to determine the inter-fraction displacement of the PrSV relative to the prostate during radiotherapy. METHODS Fiducials were inserted into the prostate, and right and left PrSV (RSV and LSV) in 30 prostate cancer patients. Correctional shifts for the prostate, right and left PrSV and pelvic bones were determined from each patient's 39 daily orthogonal portal images relative to reference digitally reconstructed radiographs. RESULTS There was a significant displacement of the RSV relative to the prostate in all directions: on average 0.38 mm (95% confidence interval (CI) 0.26 to 0.50) to the left, 0.80-0.81 mm (CI 0.68 to 0.93) superiorly and 1.51 mm (CI 1.36 to 1.65) posteriorly. The LSV was significantly displaced superiorly to the prostate 1.09-1.13 mm (CI 0.97 to 1.25) and posteriorly 1.81 mm (CI 1.67 to 1.96), but not laterally (mean 0.06, CI -0.06 to 0.18). The calculated PTV margins (left-right, superior-inferior, posterior-anterior) were 4.9, 5.3-5.6 and 4.8 mm for the prostate, 5.2, 7.1-8.0 and 9.7 mm for the RSV, and 7.2, 7.5-7.6 and 8.6 mm for the LSV. CONCLUSION There is a significant displacement of the PrSV relative to the prostate during radiotherapy. Greater margins are recommended for the PrSV compared to the prostate.
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Affiliation(s)
- Daryl Lim Joon
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Michael Chao
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Angelina Piccolo
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | | | - Nigel Anderson
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Monica Handley
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Margaret Benci
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Wee Loon Ong
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Karen Daly
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Rebecca Morrell
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Kenneth Wan
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | | | - Farshad Foroudi
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - Trish Jenkins
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | - David Angus
- Department of UrologyAustin HealthMelbourneVic.Australia
| | - Morikatsu Wada
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
| | | | - Vincent Khoo
- Department of Radiation OncologyOlivia Newton‐John Cancer Wellness and Research CentreAustin HealthMelbourneVic.Australia
- Monash UniversityMelbourneVic.Australia
- Royal Marsden NHS Foundation TrustLondonUK
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Tsubouchi T, Hamatani N, Takashina M, Wakisaka Y, Ogawa A, Yagi M, Terasawa A, Shimazaki K, Chatani M, Mizoe J, Kanai T. Carbon ion radiotherapy using fiducial markers for prostate cancer in Osaka HIMAK: Treatment planning. J Appl Clin Med Phys 2021; 22:242-251. [PMID: 34339590 PMCID: PMC8425940 DOI: 10.1002/acm2.13376] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/06/2021] [Accepted: 07/18/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Carbon ion radiotherapy for prostate cancer was performed using two fine needle Gold Anchor (GA) markers for patient position verification in Osaka Heavy Ion Medical Accelerator in Kansai (Osaka HIMAK). The present study examined treatment plans for prostate cases using beam-specific planning target volume (bsPTV) based on the effect of the markers on dose distribution and analysis of target movements. MATERIALS AND METHODS Gafchromic EBT3 film was used to measure dose perturbations caused by markers. First, the relationships between the irradiated film density and absolute dose with different linear energy transfer distributions within a spread-out Bragg peak (SOBP) were confirmed. Then, to derive the effect of markers, two types of markers, including GA, were placed at the proximal, center, and distal depths within the same SOBP, and dose distributions behind the markers were measured using the films. The amount of internal motion of prostate was derived from irradiation results and analyzed to determine the margins of the bsPTV. RESULTS The linearity of the film densities against absolute doses was constant within the SOBP and the amount of dose perturbations caused by the markers was quantitatively estimated from the film densities. The dose perturbation close behind the markers was smallest (<10% among depths within the SOBP regardless of types of markers) and increased with depth. The effect of two types of GAs on dose distributions was small and could be ignored in the treatment planning. Based on the analysis results of internal motions of prostate, required margins of the bsPTV were found to be 8, 7, and 7 mm in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. CONCLUSION We evaluated the dose reductions caused by markers and determined the margins of the bsPTV, which was applied to the treatment using fiducial markers, using the analysis results of prostate movements.
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Affiliation(s)
| | | | | | | | | | - Masashi Yagi
- Department of Carbon Ion RadiotherapyOsaka University Graduate School of MedicineSuita CityOsakaJapan
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Pilot study comparing dominant intraprostatic lesion volume using Ga-68 prostate-specific membrane antigen PET-computed tomography and multiparametric MRI. Nucl Med Commun 2021; 41:1291-1298. [PMID: 32941400 DOI: 10.1097/mnm.0000000000001283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The standard imaging used for delineation of dominant intraprostatic lesion (DIL) is multiparametric MRI (mpMRI). The use of biologic imaging such as Ga-68 prostate-specific membrane antigen (PSMA) PET-computed tomography (PET-CT) for this purpose is being explored in view of increased sensitivity of this modality and the associated ease of delineation. MATERIALS AND METHODS The primary objective of the study was to compare the autogenerated volumes of the DIL in Ga-68 PSMA PET-CT with the standard volume delineated in mpMRI. Twenty patients with biopsy-proven untreated prostatic adenocarcinoma were included. Multiple percentages of the maximum standardized uptake value (%SUVmax) were used to autogenerate DIL volumes in Ga-68 PSMA PET-CT and these volumes were numerically matched with the consensus DIL volume in mpMRI. PSMA tumor volume (PSMA-TV) and total lesion PSMA (TL-PSMA) were also calculated for each lesion. RESULTS Median volume of DIL in mpMRI was 4 cm (interquartile range, IQR = 2.5-7.6 cm). The IQR for interobserver variability was 0.5-2.5 cm. Median SUVmax of the DIL was 14.1 (IQR = 10.2-22.3). Median %SUVmax corresponding to mpMRI volume was 41% of SUVmax (IQR = 34-55%). There was a strong negative correlation between MRI volume and %SUVmax (r = -0.829, P < 0.001). There was a significant correlation between TL-PSMA and prostate-specific antigen (r = 0.609, P = 0.004). CONCLUSIONS The median DIL volume was 4 cm and median %SUVmax corresponding to MR volume of DIL was 41%. A strong inverse relationship is found between mpMRI-defined DIL volume and the %SUVmax which generates similar volume in Ga-68 PSMA PET-CT. TL-PSMA could be a quantitative biomarker for tumor load and prognosis.
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Sadeghi S, Siavashpour Z, Vafaei Sadr A, Farzin M, Sharp R, Gholami S. A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy. Biomed Phys Eng Express 2021; 7. [PMID: 34265746 DOI: 10.1088/2057-1976/ac14d0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/15/2021] [Indexed: 11/11/2022]
Abstract
Background and purpose.Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment.Methods and materials.An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). 'Organ at risk', 'target', 'delineation', 'uncertainties', 'radiotherapy' and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities.Results.A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate.Conclusion.This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.
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Affiliation(s)
- Sogand Sadeghi
- Department of Nuclear Physics, Faculty of Sciences, University of Mazandaran, Babolsar, Iran
| | - Zahra Siavashpour
- Department of Radiation Oncology, Shohada-e Tajrish Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Vafaei Sadr
- Département de Physique Théorique and Center for Astroparticle Physics, Université de Genève, Geneva, Switzerland
| | - Mostafa Farzin
- Radiation Oncology Research Center (RORC), Tehran University of Medical Science, Tehran, Iran.,Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ryan Sharp
- Department of Health Physics and Diagnostic Sciences, University of Nevada, Las Vegas, NV, United States of America
| | - Somayeh Gholami
- Radiotherapy Oncology Department, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
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30
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Rong Y, Rosu-Bubulac M, Benedict SH, Cui Y, Ruo R, Connell T, Kashani R, Latifi K, Chen Q, Geng H, Sohn J, Xiao Y. Rigid and Deformable Image Registration for Radiation Therapy: A Self-Study Evaluation Guide for NRG Oncology Clinical Trial Participation. Pract Radiat Oncol 2021; 11:282-298. [PMID: 33662576 PMCID: PMC8406084 DOI: 10.1016/j.prro.2021.02.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/05/2021] [Accepted: 02/16/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE The registration of multiple imaging studies to radiation therapy computed tomography simulation, including magnetic resonance imaging, positron emission tomography-computed tomography, etc. is a widely used strategy in radiation oncology treatment planning, and these registrations have valuable roles in image guidance, dose composition/accumulation, and treatment delivery adaptation. The NRG Oncology Medical Physics subcommittee formed a working group to investigate feasible workflows for a self-study credentialing process of image registration commissioning. METHODS AND MATERIALS The American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) report on the use of image registration and fusion algorithms in radiation therapy provides basic guidelines for quality assurance and quality control of the image registration algorithms and the overall clinical process. The report recommends a series of tests and the corresponding metrics that should be evaluated and reported during commissioning and routine quality assurance, as well as a set of recommendations for vendors. The NRG Oncology medical physics subcommittee working group found incompatibility of some digital phantoms with commercial systems. Thus, there is still a need to provide further recommendations in terms of compatible digital phantoms, clinical feasible workflow, and achievable thresholds, especially for future clinical trials involving deformable image registration algorithms. Nine institutions participated and evaluated 4 commonly used commercial imaging registration software and various versions in the field of radiation oncology. RESULTS AND CONCLUSIONS The NRG Oncology Working Group on image registration commissioning herein provides recommendations on the use of digital phantom/data sets and analytical software access for institutions and clinics to perform their own self-study evaluation of commercial imaging systems that might be employed for coregistration in radiation therapy treatment planning and image guidance procedures. Evaluation metrics and their corresponding values were given as guidelines to establish practical tolerances. Vendor compliance for image registration commissioning was evaluated, and recommendations were given for future development.
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Affiliation(s)
- Yi Rong
- Department of Radiation Oncology, University of California Davis Cancer Center, Sacramento, California; Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
| | - Mihaela Rosu-Bubulac
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Cancer Center, Sacramento, California
| | - Yunfeng Cui
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina
| | - Russell Ruo
- Department of Medical Physics, McGill University Health Center, Montreal, QC, Canada
| | - Tanner Connell
- Department of Medical Physics, McGill University Health Center, Montreal, QC, Canada
| | - Rojano Kashani
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky
| | - Huaizhi Geng
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jason Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
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31
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Cha E, Elguindi S, Onochie I, Gorovets D, Deasy JO, Zelefsky M, Gillespie EF. Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy. Radiother Oncol 2021; 159:1-7. [DOI: 10.1016/j.radonc.2021.02.040] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/29/2021] [Accepted: 02/24/2021] [Indexed: 12/17/2022]
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32
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Jenkins A, Mullen TS, Johnson-Hart C, Green A, McWilliam A, Aznar M, van Herk M, Vasquez Osorio E. Novel methodology to assess the effect of contouring variation on treatment outcome. Med Phys 2021; 48:3234-3242. [PMID: 33772803 DOI: 10.1002/mp.14865] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Contouring variation is one of the largest systematic uncertainties in radiotherapy, yet its effect on clinical outcome has never been analyzed quantitatively. We propose a novel, robust methodology to locally quantify target contour variation in a large patient cohort and find where this variation correlates with treatment outcome. We demonstrate its use on biochemical recurrence for prostate cancer patients. METHOD We propose to compare each patient's target contours to a consistent and unbiased reference. This reference was created by auto-contouring each patient's target using an externally trained deep learning algorithm. Local contour deviation measured from the reference to the manual contour was projected to a common frame of reference, creating contour deviation maps for each patient. By stacking the contour deviation maps, time to event was modeled pixel-wise using a multivariate Cox proportional hazards model (CPHM). Hazard ratio (HR) maps for each covariate were created, and regions of significance found using cluster-based permutation testing on the z-statistics. This methodology was applied to clinical target volume (CTV) contours, containing only the prostate gland, from 232 intermediate- and high-risk prostate cancer patients. The reference contours were created using ADMIRE® v3.4 (Elekta AB, Sweden). Local contour deviations were computed in a spherical coordinate frame, where differences between reference and clinical contours were projected in a 2D map corresponding to sampling across the coronal and transverse angles every 3°. Time to biochemical recurrence was modeled using the pixel-wise CPHM analysis accounting for contour deviation, patient age, Gleason score, and treated CTV volume. RESULTS We successfully applied the proposed methodology to a large patient cohort containing data from 232 patients. In this patient cohort, our analysis highlighted regions where the contour variation was related to biochemical recurrence, producing expected and unexpected results: (a) the interface between prostate-bladder and prostate-seminal vesicle interfaces where increase in the manual contour relative to the reference was related to a reduction of risk of biochemical recurrence by 4-8% per mm and (b) the prostate's right, anterior and posterior regions where an increase in the manual contour relative to the reference contours was related to an increase in risk of biochemical recurrence by 8-24% per mm. CONCLUSION We proposed and successfully applied a novel methodology to explore the correlation between contour variation and treatment outcome. We analyzed the effect of contour deviation of the prostate CTV on biochemical recurrence for a cohort of more than 200 prostate cancer patients while taking basic clinical variables into account. Applying this methodology to a larger dataset including additional clinically important covariates and externally validating it can more robustly identify regions where contour variation directly relates to treatment outcome. For example, in the prostate case we use to demonstrate our novel methodology, external validation will help confirm or reject the counter-intuitive results (larger contours resulting in higher risk). Ultimately, the results of this methodology could inform contouring protocols based on actual patient outcomes.
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Affiliation(s)
- Alexander Jenkins
- School of Physics & Astronomy - Faculty of Science and Engineering, University of Manchester, Manchester, M13 9PL, UK.,Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK
| | - Thomas Soares Mullen
- School of Physics & Astronomy - Faculty of Science and Engineering, University of Manchester, Manchester, M13 9PL, UK.,Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa, 1400-038, Portugal
| | - Corinne Johnson-Hart
- Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK.,Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, M20 4BX, UK
| | - Andrew Green
- Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK
| | - Alan McWilliam
- Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK
| | - Marianne Aznar
- Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK
| | - Marcel van Herk
- Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK
| | - Eliana Vasquez Osorio
- Division of Cancer Studies - School of Medical Sciences - Faculty of Biology- Medicine and Health, University of Manchester, Manchester, M20 4BX, UK
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Lei Y, Wang T, Tian S, Fu Y, Patel P, Jani AB, Curran WJ, Liu T, Yang X. Male pelvic CT multi-organ segmentation using synthetic MRI-aided dual pyramid networks. Phys Med Biol 2021; 66. [PMID: 33780918 DOI: 10.1088/1361-6560/abf2f9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 03/29/2021] [Indexed: 12/17/2022]
Abstract
The delineation of the prostate and organs-at-risk (OARs) is fundamental to prostate radiation treatment planning, but is currently labor-intensive and observer-dependent. We aimed to develop an automated computed tomography (CT)-based multi-organ (bladder, prostate, rectum, left and right femoral heads (RFHs)) segmentation method for prostate radiation therapy treatment planning. The proposed method uses synthetic MRIs (sMRIs) to offer superior soft-tissue information for male pelvic CT images. Cycle-consistent adversarial networks (CycleGAN) were used to generate CT-based sMRIs. Dual pyramid networks (DPNs) extracted features from both CTs and sMRIs. A deep attention strategy was integrated into the DPNs to select the most relevant features from both CTs and sMRIs to identify organ boundaries. The CT-based sMRI generated from our previously trained CycleGAN and its corresponding CT images were inputted to the proposed DPNs to provide complementary information for pelvic multi-organ segmentation. The proposed method was trained and evaluated using datasets from 140 patients with prostate cancer, and were then compared against state-of-art methods. The Dice similarity coefficients and mean surface distances between our results and ground truth were 0.95 ± 0.05, 1.16 ± 0.70 mm; 0.88 ± 0.08, 1.64 ± 1.26 mm; 0.90 ± 0.04, 1.27 ± 0.48 mm; 0.95 ± 0.04, 1.08 ± 1.29 mm; and 0.95 ± 0.04, 1.11 ± 1.49 mm for bladder, prostate, rectum, left and RFHs, respectively. Mean center of mass distances was within 3 mm for all organs. Our results performed significantly better than those of competing methods in most evaluation metrics. We demonstrated the feasibility of sMRI-aided DPNs for multi-organ segmentation on pelvic CT images, and its superiority over other networks. The proposed method could be used in routine prostate cancer radiotherapy treatment planning to rapidly segment the prostate and standard OARs.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Sibo Tian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Ashesh B Jani
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
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Huang K, Rhee DJ, Ger R, Layman R, Yang J, Cardenas CE, Court LE. Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms. J Appl Clin Med Phys 2021; 22:168-174. [PMID: 33779037 PMCID: PMC8130223 DOI: 10.1002/acm2.13207] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/12/2020] [Accepted: 01/30/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose To investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms. Methods Eleven scans from patients with head‐and‐neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low‐dose simulation software. The impact of these imaging parameters on two in‐house auto‐contouring algorithms, one convolutional neural network (CNN)‐based and one multiatlas‐based system (MACS) was investigated for 183 reconstructed scans. For each algorithm, auto‐contours for organs‐at‐risk were compared with auto‐contours from scans with 3 mm slice thickness, 0.977 mm pixel size, and 100% CT dose using Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD). Results Increasing the slice thickness from baseline value of 3 mm gave a progressive reduction in DSC and an increase in HD and MSD on average for all structures. Reducing the CT dose only had a relatively minimal effect on DSC and HD. The rate of change with respect to dose for both auto‐contouring methods is approximately 0. Changes in pixel size had a small effect on DSC and HD for CNN‐based auto‐contouring with differences in DSC being within 0.07. Small structures had larger deviations from the baseline values than large structures for DSC. The relative differences in HD and MSD between the large and small structures were small. Conclusions Auto‐contours can deviate substantially with changes in CT acquisition and reconstruction parameters, especially slice thickness and pixel size. The CNN was less sensitive to changes in pixel size, and dose levels than the MACS. The results contraindicated more restrictive values for the parameters should be used than a typical imaging protocol for head‐and‐neck.
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Affiliation(s)
- Kai Huang
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dong Joo Rhee
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rachel Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rick Layman
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Patrick HM, Souhami L, Kildea J. Reduction of inter-observer contouring variability in daily clinical practice through a retrospective, evidence-based intervention. Acta Oncol 2021; 60:229-236. [PMID: 32988249 DOI: 10.1080/0284186x.2020.1825801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND Inter-observer variations (IOVs) arising during contouring can potentially impact plan quality and patient outcomes. Regular assessment of contouring IOV is not commonly performed in clinical practice due to the large time commitment required of clinicians from conventional methods. This work uses retrospective information from past treatment plans to facilitate a time-efficient, evidence-based intervention to reduce contouring IOV. METHODS The contours of 492 prostate cancer treatment plans created by four radiation oncologists were analyzed in this study. Structure volumes, lengths, and DVHs were extracted from the treatment planning system and stratified based on primary oncologist and inclusion of a pelvic lymph node (PLN) target. Inter-observer variations and their dosimetric consequences were assessed using Student's t-tests. Results of this analysis were presented at an intervention meeting, where new consensus contour definitions were agreed upon. The impact of the intervention was assessed one-year later by repeating the analysis on 152 new plans. RESULTS Significant IOV in prostate and PLN target delineation existed pre-intervention between oncologists, impacting dose to nearby OARs. IOV was also present for rectum and penile-bulb structures. Post-intervention, IOV decreased for all previously discordant structures. Dosimetric variations were also reduced. Although target contouring concordance increased significantly, some variations still persisted for PLN structures, highlighting remaining areas for improvement. CONCLUSION We detected significant contouring IOV in routine practice using easily accessible retrospective data and successfully decreased IOV in our clinic through a reflective intervention. Continued application of this approach may aid improvements in practice standardization and enhance quality of care.
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Affiliation(s)
- H. M. Patrick
- Medical Physics Unit, McGill University, Montreal, Canada
| | - L. Souhami
- Department of Oncology, McGill University Health Centre, Montreal, Canada
| | - J. Kildea
- Medical Physics Unit, McGill University, Montreal, Canada
- Department of Oncology, McGill University Health Centre, Montreal, Canada
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Men K, Chen X, Zhu J, Yang B, Zhang Y, Yi J, Jianrong Dai A. Continual improvement of nasopharyngeal carcinoma segmentation with less labeling effort. Phys Med 2020; 80:347-351. [PMID: 33271391 DOI: 10.1016/j.ejmp.2020.11.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/22/2020] [Accepted: 11/02/2020] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Convolutional neural networks (CNNs) offer a promising approach to automated segmentation. However, labeling contours on a large scale is laborious. Here we propose a method to improve segmentation continually with less labeling effort. METHODS The cohort included 600 patients with nasopharyngeal carcinoma. The proposed method was comprised of four steps. First, an initial CNN model was trained from scratch to perform segmentation of the clinical target volume. Second, a binary classifier was trained using a secondary CNN to identify samples for which the initial model gave a dice similarity coefficient (DSC) < 0.85. Third, the classifier was used to select such samples from the new coming data. Forth, the final model was fine-tuned from the initial model, using only selected samples. RESULTS The classifier can detect poor segmentation of the model with an accuracy of 92%. The proposed segmentation method improved the DSC from 0.82 to 0.86 while reducing the labeling effort by 45%. CONCLUSIONS The proposed method reduces the amount of labeled training data and improves segmentation by continually acquiring, fine-tuning, and transferring knowledge over long time spans.
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Affiliation(s)
- 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
| | - Xinyuan 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
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ye Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - And 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.
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Casati M, Piffer S, Calusi S, Marrazzo L, Simontacchi G, Di Cataldo V, Greto D, Desideri I, Vernaleone M, Francolini G, Livi L, Pallotta S. Methodological approach to create an atlas using a commercial auto-contouring software. J Appl Clin Med Phys 2020; 21:219-230. [PMID: 33236827 PMCID: PMC7769405 DOI: 10.1002/acm2.13093] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/12/2020] [Accepted: 10/16/2020] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The aim of this work was to establish a methodological approach for creation and optimization of an atlas for auto-contouring, using the commercial software MIM MAESTRO (MIM Software Inc. Cleveland OH). METHODS A computed tomography (CT) male pelvis atlas was created and optimized to evaluate how different tools and options impact on the accuracy of automatic segmentation. Pelvic lymph nodes (PLN), rectum, bladder, and femurs of 55 subjects were reviewed for consistency by a senior consultant radiation oncologist with 15 yr of experience. Several atlas and workflow options were tuned to optimize the accuracy of auto-contours. The deformable image registration (DIR), the finalization method, the k number of atlas best matching subjects, and several post-processing options were studied. To test our atlas performances, automatic and reference manual contours of 20 test subjects were statistically compared based on dice similarity coefficient (DSC) and mean distance to agreement (MDA) indices. The effect of field of view (FOV) reduction on auto-contouring time was also investigated. RESULTS With the optimized atlas and workflow, DSC and MDA median values of bladder, rectum, PLN, and femurs were 0.91 and 1.6 mm, 0.85 and 1.6 mm, 0.85 and 1.8 mm, and 0.96 and 0.5 mm, respectively. Auto-contouring time was more than halved by strictly cropping the FOV of the subject to be contoured to the pelvic region. CONCLUSION A statistically significant improvement of auto-contours accuracy was obtained using our atlas and optimized workflow instead of the MIM Software pelvic atlas.
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Affiliation(s)
- Marta Casati
- Department of Medical Physics, Careggi University Hospital, Florence, Italy
| | - Stefano Piffer
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy.,National Institute of Nuclear Physics (INFN), Florence, Italy
| | - Silvia Calusi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Livia Marrazzo
- Department of Medical Physics, Careggi University Hospital, Florence, Italy
| | | | | | - Daniela Greto
- Department of Radiation Oncology, Careggi University Hospital, Florence, Italy
| | - Isacco Desideri
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Marco Vernaleone
- Department of Radiation Oncology, Careggi University Hospital, Florence, Italy
| | - Giulio Francolini
- Department of Radiation Oncology, Careggi University Hospital, Florence, Italy
| | - Lorenzo Livi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Stefania Pallotta
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
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Kiljunen T, Akram S, Niemelä J, Löyttyniemi E, Seppälä J, Heikkilä J, Vuolukka K, Kääriäinen OS, Heikkilä VP, Lehtiö K, Nikkinen J, Gershkevitsh E, Borkvel A, Adamson M, Zolotuhhin D, Kolk K, Pang EPP, Tuan JKL, Master Z, Chua MLK, Joensuu T, Kononen J, Myllykangas M, Riener M, Mokka M, Keyriläinen J. A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics (Basel) 2020; 10:E959. [PMID: 33212793 PMCID: PMC7697786 DOI: 10.3390/diagnostics10110959] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/06/2020] [Accepted: 11/13/2020] [Indexed: 12/24/2022] Open
Abstract
A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency.
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Affiliation(s)
- Timo Kiljunen
- Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland; (T.J.); (J.K.); (M.M.); (M.R.)
| | - Saad Akram
- MVision Ai, c/o Terkko Health hub, Haartmaninkatu 4, FI-00290 Helsinki, Finland; (S.A.); (J.N.)
| | - Jarkko Niemelä
- MVision Ai, c/o Terkko Health hub, Haartmaninkatu 4, FI-00290 Helsinki, Finland; (S.A.); (J.N.)
| | - Eliisa Löyttyniemi
- Department of Biostatistics, University of Turku, Kiinamyllynkatu 10, FI-20014 Turku, Finland;
| | - Jan Seppälä
- Kuopio University Hospital, Center of Oncology, Kelkkailijantie 7, FI-70210 Kuopio, Finland; (J.S.); (J.H.); (K.V.); (O.-S.K.)
| | - Janne Heikkilä
- Kuopio University Hospital, Center of Oncology, Kelkkailijantie 7, FI-70210 Kuopio, Finland; (J.S.); (J.H.); (K.V.); (O.-S.K.)
| | - Kristiina Vuolukka
- Kuopio University Hospital, Center of Oncology, Kelkkailijantie 7, FI-70210 Kuopio, Finland; (J.S.); (J.H.); (K.V.); (O.-S.K.)
| | - Okko-Sakari Kääriäinen
- Kuopio University Hospital, Center of Oncology, Kelkkailijantie 7, FI-70210 Kuopio, Finland; (J.S.); (J.H.); (K.V.); (O.-S.K.)
| | - Vesa-Pekka Heikkilä
- Oulu University Hospital, Department of Oncology and Radiotherapy, Kajaanintie 50, FI-90220 Oulu, Finland; (V.-P.H.); (K.L.); (J.N.)
- University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Aapistie 5 A, FI-90220 Oulu, Finland
| | - Kaisa Lehtiö
- Oulu University Hospital, Department of Oncology and Radiotherapy, Kajaanintie 50, FI-90220 Oulu, Finland; (V.-P.H.); (K.L.); (J.N.)
| | - Juha Nikkinen
- Oulu University Hospital, Department of Oncology and Radiotherapy, Kajaanintie 50, FI-90220 Oulu, Finland; (V.-P.H.); (K.L.); (J.N.)
- University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Aapistie 5 A, FI-90220 Oulu, Finland
| | - Eduard Gershkevitsh
- North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia; (E.G.); (A.B.); (M.A.); (D.Z.); (K.K.)
| | - Anni Borkvel
- North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia; (E.G.); (A.B.); (M.A.); (D.Z.); (K.K.)
| | - Merve Adamson
- North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia; (E.G.); (A.B.); (M.A.); (D.Z.); (K.K.)
| | - Daniil Zolotuhhin
- North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia; (E.G.); (A.B.); (M.A.); (D.Z.); (K.K.)
| | - Kati Kolk
- North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia; (E.G.); (A.B.); (M.A.); (D.Z.); (K.K.)
| | - Eric Pei Ping Pang
- National Cancer Centre Singapore, Division of Radiation Oncology, 11 Hospital Crescent, Singapore 169610, Singapore; (E.P.P.P); (J.K.L.T); (Z.M.); (M.L.K.C)
| | - Jeffrey Kit Loong Tuan
- National Cancer Centre Singapore, Division of Radiation Oncology, 11 Hospital Crescent, Singapore 169610, Singapore; (E.P.P.P); (J.K.L.T); (Z.M.); (M.L.K.C)
- Oncology Academic Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Zubin Master
- National Cancer Centre Singapore, Division of Radiation Oncology, 11 Hospital Crescent, Singapore 169610, Singapore; (E.P.P.P); (J.K.L.T); (Z.M.); (M.L.K.C)
| | - Melvin Lee Kiang Chua
- National Cancer Centre Singapore, Division of Radiation Oncology, 11 Hospital Crescent, Singapore 169610, Singapore; (E.P.P.P); (J.K.L.T); (Z.M.); (M.L.K.C)
- Oncology Academic Programme, Duke-NUS Medical School, Singapore 169857, Singapore
- National Cancer Centre Singapore, Division of Medical Sciences, Singapore 169610, Singapore
| | - Timo Joensuu
- Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland; (T.J.); (J.K.); (M.M.); (M.R.)
| | - Juha Kononen
- Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland; (T.J.); (J.K.); (M.M.); (M.R.)
| | - Mikko Myllykangas
- Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland; (T.J.); (J.K.); (M.M.); (M.R.)
| | - Maigo Riener
- Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland; (T.J.); (J.K.); (M.M.); (M.R.)
| | - Miia Mokka
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland; (M.M.); (J.K.)
| | - Jani Keyriläinen
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland; (M.M.); (J.K.)
- Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
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Harris AA, Wu M, Deirmenjian JM, Shea SM, Kang H, Patel R, Fielder D, Mysz ML, Harkenrider MM, Solanki AA. Computed tomography versus magnetic resonance imaging in high-dose-rate prostate brachytherapy planning: The impact on patient-reported health-related quality of life. Brachytherapy 2020; 20:66-74. [PMID: 33160849 DOI: 10.1016/j.brachy.2020.09.002] [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: 07/13/2020] [Revised: 09/03/2020] [Accepted: 09/04/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE High-dose-rate (HDR) prostate brachytherapy uses volumetric imaging for treatment planning. Our institution transitioned from computed tomography (CT)-based planning to MRI-based planning with the hypothesis that improved visualization could reduce treatment-related toxicity. This study aimed to compare the patient-reported health-related quality of life (hrQOL) and physician-graded toxicity outcomes of CT-based and MRI-based HDR prostate brachytherapy. METHODS From 2016 to 2019, 122 patients with low- or intermediate-risk prostate cancer were treated with HDR brachytherapy as monotherapy. Patients underwent CT only or CT and MRI imaging for treatment planning and were grouped per treatment planning imaging modality. Patient-reported hrQOL in the genitourinary (GU), gastrointestinal (GI), and sexual domains was assessed using International Prostate Symptom Score and Expanded Prostate Cancer Index Composite Short Form-26 questionnaires. Baseline characteristics, changes in hrQOL scores, and physician-graded toxicities were compared between groups. RESULTS The median follow-up was 18 months. Patient-reported GU, GI, and sexual scores worsened after treatment but returned toward baseline over time. The CT cohort had a lower baseline mean International Prostate Symptom Score (5.8 vs. 7.8, p = 0.03). The other patient-reported GU and GI scores did not differ between groups. Overall, sexual scores were similar between the CT and MRI cohorts (p = 0.08) but favored the MRI cohort at later follow-up with a smaller decrease in Expanded Prostate Cancer Index Composite Short Form-26 sexual score from baseline at 18 months (4.9 vs. 19.8, p = 0.05). Maximum physician-graded GU, GI, and sexual toxicity rates of grade ≥2 were 68%, 3%, and 53%, respectively, with no difference between the cohorts (p = 0.31). CONCLUSION Our study shows that CT- and MRI-based HDR brachytherapy results in similar rates of GU and GI toxicity. MRI-based planning may result in improved erectile function recovery compared with CT-based planning.
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Affiliation(s)
- Alexander A Harris
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Megan Wu
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Jacqueline M Deirmenjian
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Steven M Shea
- Department of Radiology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Hyejoo Kang
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Rakesh Patel
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Derek Fielder
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Michael L Mysz
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Matthew M Harkenrider
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL
| | - Abhishek A Solanki
- Department of Radiation Oncology, Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Maywood, IL.
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Oktay O, Nanavati J, Schwaighofer A, Carter D, Bristow M, Tanno R, Jena R, Barnett G, Noble D, Rimmer Y, Glocker B, O’Hara K, Bishop C, Alvarez-Valle J, Nori A. Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Netw Open 2020; 3:e2027426. [PMID: 33252691 PMCID: PMC7705593 DOI: 10.1001/jamanetworkopen.2020.27426] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/01/2020] [Indexed: 12/17/2022] Open
Abstract
Importance Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. Objective To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. Design, Setting, and Participants This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. Main Outcomes and Measures The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. Results A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time. Conclusions and Relevance In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.
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Affiliation(s)
- Ozan Oktay
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Jay Nanavati
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | | | - David Carter
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Melissa Bristow
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Ryutaro Tanno
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Rajesh Jena
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Gill Barnett
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - David Noble
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, United Kingdom
- now with Edinburgh Cancer Centre, Western General Hospital, Edinburgh, United Kingdom
| | - Yvonne Rimmer
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, United Kingdom
| | - Ben Glocker
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | - Kenton O’Hara
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
| | | | | | - Aditya Nori
- Health Intelligence, Microsoft Research, Cambridge, United Kingdom
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Sultana S, Robinson A, Song DY, Lee J. Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network. JOURNAL OF MEDICAL IMAGING (BELLINGHAM, WASH.) 2020; 7:055001. [PMID: 33102622 DOI: 10.1117/1.jmi.7.5.055001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 09/28/2020] [Indexed: 01/17/2023]
Abstract
Purpose: Accurate segmentation of treatment planning computed tomography (CT) images is important for radiation therapy (RT) planning. However, low soft tissue contrast in CT makes the segmentation task challenging. We propose a two-step hierarchical convolutional neural network (CNN) segmentation strategy to automatically segment multiple organs from CT. Approach: The first step generates a coarse segmentation from which organ-specific regions of interest (ROIs) are produced. The second step produces detailed segmentation of each organ. The ROIs are generated using UNet, which automatically identifies the area of each organ and improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we combined UNet with a generative adversarial network. The generator is designed as a UNet that is trained to segment organ structures and the discriminator is a fully convolutional network, which distinguishes whether the segmentation is real or generator-predicted, thus improving the segmentation accuracy. We validated the proposed method on male pelvic and head and neck (H&N) CTs used for RT planning of prostate and H&N cancer, respectively. For the pelvic structure segmentation, the network was trained to segment the prostate, bladder, and rectum. For H&N, the network was trained to segment the parotid glands (PG) and submandibular glands (SMG). Results: The trained segmentation networks were tested on 15 pelvic and 20 H&N independent datasets. The H&N segmentation network was also tested on a public domain dataset ( N = 38 ) and showed similar performance. The average dice similarity coefficients ( mean ± SD ) of pelvic structures are 0.91 ± 0.05 (prostate), 0.95 ± 0.06 (bladder), 0.90 ± 0.09 (rectum), and H&N structures are 0.87 ± 0.04 (PG) and 0.86 ± 0.05 (SMG). The segmentation for each CT takes < 10 s on average. Conclusions: Experimental results demonstrate that the proposed method can produce fast, accurate, and reproducible segmentation of multiple organs of different sizes and shapes and show its potential to be applicable to different disease sites.
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Affiliation(s)
- Sharmin Sultana
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
| | - Adam Robinson
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
| | - Daniel Y Song
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
| | - Junghoon Lee
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
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Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review. J Med Syst 2020; 44:179. [DOI: 10.1007/s10916-020-01641-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 08/05/2020] [Indexed: 12/11/2022]
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Alfano R, Bauman GS, Liu W, Thiessen JD, Rachinsky I, Pavlosky W, Butler J, Gaed M, Moussa M, Gomez JA, Chin JL, Pautler S, Ward AD. Histologic validation of auto-contoured dominant intraprostatic lesions on [ 18F] DCFPyL PSMA-PET imaging. Radiother Oncol 2020; 152:34-41. [PMID: 32827589 DOI: 10.1016/j.radonc.2020.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/22/2020] [Accepted: 08/14/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND PSMA-PET1 has shown good concordance with histology, but there is a need to investigate the ability of PSMA-PET to delineate DIL2 boundaries for guided biopsy and focal therapy planning. OBJECTIVE To determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity. DESIGN, SETTING AND PARTICIPANTS We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.4 mm intervals from 12 patients to pre-surgical PSMA-PET/MRI by mapping histology to ex-vivo imaging to in-vivo imaging. We generated PET-derived tumor volumes using boundaries defined by thresholded PET volumes from 1-100% of SUV3max in 1% intervals. At each interval, we applied margins of 0-30 voxels in one voxel increments, giving 3000 volumes/patient. OUTCOME MEASUREMENTS Mean and standard deviation of sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient. RESULTS AND LIMITATIONS A threshold of 67% SUV max with an 8.4 mm margin achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%. CONCLUSIONS Preliminary evidence of thresholding and margin expansion of PSMA-PET images targeted at DILs validated with histopathology demonstrated excellent mean sensitivity and specificity in the setting of focal therapy/boosting and guided biopsy. These parameters can be used in a larger validation study supporting clinical translation.
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Affiliation(s)
- Ryan Alfano
- Baines Imaging Research Laboratory, London, Canada; London Health Sciences Centre, London, Canada; Western University Department of Medical Biophysics, London, Canada.
| | - Glenn S Bauman
- London Health Sciences Centre, London, Canada; Western University Department of Medical Biophysics, London, Canada; Western University Department of Oncology, London, Canada.
| | - Wei Liu
- London Health Sciences Centre, London, Canada; Western University Department of Oncology, London, Canada.
| | - Jonathan D Thiessen
- Western University Department of Medical Biophysics, London, Canada; St. Joseph's Health Centre, London, Canada; Western University Department of Medical Imaging, London, Canada.
| | - Irina Rachinsky
- London Health Sciences Centre, London, Canada; Western University Department of Medical Imaging, London, Canada.
| | - William Pavlosky
- Western University Department of Medical Imaging, London, Canada.
| | | | - Mena Gaed
- Western University Department of Pathology and Laboratory Medicine, London, Canada.
| | - Madeleine Moussa
- London Health Sciences Centre, London, Canada; Western University Department of Pathology and Laboratory Medicine, London, Canada.
| | - Jose A Gomez
- London Health Sciences Centre, London, Canada; Western University Department of Pathology and Laboratory Medicine, London, Canada.
| | - Joseph L Chin
- London Health Sciences Centre, London, Canada; Western University Department of Surgery, London, Canada; Western University Department of Oncology, London, Canada.
| | - Stephen Pautler
- St. Joseph's Health Centre, London, Canada; Western University Department of Oncology, London, Canada.
| | - Aaron D Ward
- Baines Imaging Research Laboratory, London, Canada; London Health Sciences Centre, London, Canada; Western University Department of Medical Biophysics, London, Canada; Western University Department of Oncology, London, Canada.
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Chen W, Li Y, Dyer BA, Feng X, Rao S, Benedict SH, Chen Q, Rong Y. Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images. Radiat Oncol 2020; 15:176. [PMID: 32690103 PMCID: PMC7372849 DOI: 10.1186/s13014-020-01617-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 07/13/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images. MATERIAL AND METHODS Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods. RESULTS DLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83 ± 0.03 to 0.89 ± 0.02, the ABAS mean DSC ranged from 0.79 ± 0.05 to 0.85 ± 0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P > 0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS. CONCLUSIONS DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.
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Affiliation(s)
- Wen Chen
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, China.,Department of Radiation Oncology, University of California Davis Medical Center, 4501 X Street, Suite 0152, Sacramento, California, 95817, USA
| | - Yimin Li
- Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Brandon A Dyer
- Department of Radiation Oncology, University of California Davis Medical Center, 4501 X Street, Suite 0152, Sacramento, California, 95817, USA.,Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Xue Feng
- Carina Medical LLC, 145 Graham Ave, A168, Lexington, KY, 40536, USA
| | - Shyam Rao
- Department of Radiation Oncology, University of California Davis Medical Center, 4501 X Street, Suite 0152, Sacramento, California, 95817, USA
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Medical Center, 4501 X Street, Suite 0152, Sacramento, California, 95817, USA
| | - Quan Chen
- Carina Medical LLC, 145 Graham Ave, A168, Lexington, KY, 40536, USA. .,Department of Radiation Oncology, Markey Cancer Center, University of Kentucky, RM CC063, 800 Rose St, Lexington, KY, 40536, USA.
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, 4501 X Street, Suite 0152, Sacramento, California, 95817, USA.
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Comparing Setup Errors Using Portal Imaging in Patients With Gynecologic Cancers by Two Methods of Alignment. J Med Imaging Radiat Sci 2020; 51:394-403. [PMID: 32444331 DOI: 10.1016/j.jmir.2020.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/29/2020] [Accepted: 04/02/2020] [Indexed: 12/25/2022]
Abstract
AIMS Alignment tattoos on a lax abdomen contribute to misalignment of patients undergoing abdomino-pelvic radiotherapy (RT). The present study was undertaken to assess setup reproducibility in gynecologic cancer patients positioned identically but aligned for treatment to machine isocenter by two different ways. MATERIALS AND METHODS A prospective study in 35 women treated with radical RT for gynecologic malignancy was undertaken. A RT planning contrast-enhanced computed tomography scan in the supine position using an foot and ankle positioning device was done, and three reference points tattooed on the reference plane, anteriorly at the mons pubis and one on each side laterally at a fixed table top-to-vertical height of 10 cm, whereas a fourth point was tattooed at the xiphoid in the anterior midline. Patients were aligned using either a field center, that is, conventional method (Arm I, n = 18) or by a new setup isocenter (Arm II, n = 17) defined by a cranial offset of 4 cm to the reference plane for daily treatment. Anterior and right lateral digitally reconstructed radiograph setup fields were created at the treatment isocenters and compared with orthogonal megavoltage portal images (PI) taken during initial 3 days of RT and subsequently twice weekly. Setup deviations-rotations and translations were analysed in mediolateral (ML), craniocaudal, and anteroposterior direction. No online and offline corrections were performed. Population systematic error and random error were calculated and planning target volume margins required were estimated using van Herk's formula. RESULTS Arm I had 209 PI while Arm II had 188 PI. Patients in arm II had a lesser systematic error in the ML direction. Patients with large pelvic girth (>95 cm) were susceptible for greater movements during treatment, more so in Arm I, major shifts (>5 mm) with respect to Arm II in the ML direction (37% vs. 22%, P = .001). A larger planning target volume expansion was required in Arm I (1.6 cm) compared with Arm II (0.9 cm). The margin expansion required from clinical target volume in anteroposterior direction was about 0.6 cm and about a cm in the craniocaudal direction in both the arm. CONCLUSIONS Alignment of patient with anterior tattoo at the relatively immobile portion of lower abdomen (mons pubis) Arm II (setup) is superior to a more cranial location over the flabby abdomen during radiation treatment.
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Shanker MD, Kim AN, Brown A, Tan AH. Anatomical and dosimetric assessment of the prostate apex: A pilot comparison of image-guided transperineal ultrasound to conventional computed tomography simulation. J Med Imaging Radiat Oncol 2020; 64:839-844. [PMID: 32383303 DOI: 10.1111/1754-9485.13045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Inaccuracies in prostate apex contour delineation based on simulation computed tomography (CT) imaging can impact treatment outcomes and toxicity profiles for prostate cancer radiotherapy. Transperineal ultrasound (TPUS) is a non-invasive imaging modality that can improve delineation of prostate volumes. We performed a pilot analysis to assess for differences in anatomical position between conventional CT and a TPUS delineated prostate apex and determined whether these translated into a clinically significant difference in apical point dose. METHODS A 2D 5 MHz TPUS autoscan image guidance system was utilised during definitive intensity-modulated radiotherapy (IMRT) for prostate cancer. Distances were measured from a fixed reference point to prostate apex on both US and CT in the mid-sagittal plane. Differences between groups were assessed using the Wilcoxon sign rank test with a two-tailed significance of α = 0.05. RESULTS Fifty-nine consecutive patients were independently assessed. There was strong evidence of a difference between CT and TPUS delineated apex position (P = 0.0075). Median apex position was 3.6 mm caudal on TPUS vs. CT imaging (95% CI: 2.5-4.8 mm). There was strong evidence of a difference in point dose between CT and TPUS delineated apex (P = 0.0029). Median point dose at the TPUS contoured apex was 1.9 Gy lower than CT (95% CI: 0.7-3.1 Gy) corresponding to 98% of prescribed dose. CONCLUSIONS This study demonstrates a difference in anatomical delineation of prostate apex position between CT imaging compared to TPUS, corresponding to a statistically significant difference in apex point dose. Further analysis will determine whether this translates to a clinically significant difference in outcomes.
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Affiliation(s)
- Mihir D Shanker
- Queensland Health, Brisbane, Queensland, Australia.,Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Anna Nh Kim
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.,ICON Cancer Centre, Brisbane, Queensland, Australia
| | - Amy Brown
- Queensland Health, Brisbane, Queensland, Australia.,Townsville Cancer Centre, Townsville Hospital and Health Service, Queensland, Australia.,James Cook University, Townsville, Queensland, Australia
| | - Alex Hm Tan
- Queensland Health, Brisbane, Queensland, Australia.,Townsville Cancer Centre, Townsville Hospital and Health Service, Queensland, Australia.,James Cook University, Townsville, Queensland, Australia
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Patnaikuni SK, Saini SM, Chandola RM, Chandrakar P, Chaudhary V. Study of Asymmetric Margins in Prostate Cancer Radiation Therapy Using Fuzzy Logic. J Med Phys 2020; 45:88-97. [PMID: 32831491 PMCID: PMC7416865 DOI: 10.4103/jmp.jmp_110_19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/18/2020] [Accepted: 04/23/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE The purpose of present study is to estimate asymmetric margins of prostate target volume based on biological limitations with help of knowledge based fuzzy logic considering the effect of organ motion and setup errors. MATERIALS AND METHODS A novel application of fuzzy logic modelling technique considering radiotherapy uncertainties including setup, delineation and organ motion was used in this study to derive margins. The new margin was applied in prostate cancer treatment planning and the results compared very well to current techniques Here volumetric modulated arc therapy treatment plans using stepped increments of asymmetric margins of planning target volume (PTV) were performed to calculate the changes in prostate radiobiological indices and results were used to formulate the rule based and membership function for Mamdani-type fuzzy inference system. The optimum fuzzy rules derived from input data, the clinical goals and knowledge-based conditions imposed on the margin limits. The PTV margin obtained using the fuzzy model was compared to the commonly used margin recipe. RESULTS For total displacement standard errors ranging from 0 to 5 mm the fuzzy PTV margin was found to be up to 0.5 mm bigger than the vanHerk derived margin, however taking the modelling uncertainty into account results in a good match between the PTV margin calculated using our model and the one based on van Herk et al. formulation for equivalent errors of up to 5 mm standard deviation (s. d.) at this range. When the total displacement standard errors exceed 5 mm s. d., the fuzzy margin remained smaller than the van Herk margin. CONCLUSION The advantage of using knowledge based fuzzy logic is that a practical limitation on the margin size is included in the model for limiting the dose received by the critical organs. It uses both physical and radiobiological data to optimize the required margin as per clinical requirement in real time or adaptive planning, which is an improvement on most margin models which mainly rely on physical data only.
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Affiliation(s)
- Santosh Kumar Patnaikuni
- Department of Physics, National Institute of Technology, Raipur, Chhattisgarh, India
- Department of Radiotherapy, Pt. JNM Medical College, Raipur, Chhattisgarh, India
| | - Sapan Mohan Saini
- Department of Physics, National Institute of Technology, Raipur, Chhattisgarh, India
| | | | - Pradeep Chandrakar
- Department of Radiotherapy, Pt. JNM Medical College, Raipur, Chhattisgarh, India
| | - Vivek Chaudhary
- Department of Radiotherapy, Pt. JNM Medical College, Raipur, Chhattisgarh, India
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Sultana S, Robinson A, Song DY, Lee J. CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11315. [PMID: 32341620 DOI: 10.1117/12.2549979] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Accurate segmentation of organs-at-risk is important inprostate cancer radiation therapy planning. However, poor soft tissue contrast in CT makes the segmentation task very challenging. We propose a deep convolutional neural network approach to automatically segment the prostate, bladder, and rectum from pelvic CT. A hierarchical coarse-to-fine segmentation strategy is used where the first step generates a coarse segmentation from which an organ-specific region of interest (ROI) localization map is produced. The second step produces detailed and accurate segmentation of the organs. The ROI localization map is generated using a 3D U-net. The localization map helps adjusting the ROI of each organ that needs to be segmented and hence improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we designed a fully convolutional network (FCN) by combining a generative adversarial network (GAN) with a U-net. Specifically, the generator is a 3D U-net that is trained to predict individual pelvic structures, and the discriminator is an FCN which fine-tunes the generator predicted segmentation map by comparing it with the ground truth. The network was trained using 100 CT datasets and tested on 15 datasets to segment the prostate, bladder and rectum. The average Dice similarity (mean±SD) of the prostate, bladder and rectum are 0.90±0.05, 0.96±0.06 and 0.91±0.09, respectively, and Hausdorff distances of these three structures are 5.21±1.17, 4.37±0.56 and 6.11±1.47(mm), respectively. The proposed method produces accurate and reproducible segmentation of pelvic structures, which can be potentially valuable for prostate cancer radiotherapy treatment planning.
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Affiliation(s)
- Sharmin Sultana
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Adam Robinson
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Daniel Y Song
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA
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Arculeo S, Miglietta E, Nava F, Morra A, Leonardi MC, Comi S, Ciardo D, Fiore MS, Gerardi MA, Pepa M, Gugliandolo SG, Livi L, Orecchia R, Jereczek-Fossa BA, Dicuonzo S. The emerging role of radiation therapists in the contouring of organs at risk in radiotherapy: analysis of inter-observer variability with radiation oncologists for the chest and upper abdomen. Ecancermedicalscience 2020; 14:996. [PMID: 32153651 PMCID: PMC7032938 DOI: 10.3332/ecancer.2020.996] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Indexed: 12/25/2022] Open
Abstract
Aims To compare the contouring of organs at risk (OAR) between a clinical specialist radiation therapist (CSRT) and radiation oncologists (ROs) with different levels of expertise (senior–SRO, junior–JRO, fellow–FRO). Methods On ten planning computed tomography (CT) image sets of patients undergoing breast radiotherapy (RT), the observers independently contoured the contralateral breast, heart, left anterior descending artery (LAD), oesophagus, kidney, liver, spinal cord, stomach and trachea. The CSRT was instructed by the JRO e SRO. The inter-observer variability of contoured volumes was measured using the Dice similarity coefficient (DSC) (threshold of ≥ 0.7 for good concordance) and the centre of mass distance (CMD). The analysis of variance (ANOVA) was performed and a p-value < 0.01 was considered statistically significant. Results Good overlaps (DSC > 0.7) were obtained for all OARs, except for LAD (DSC = 0.34 ± 0.17, mean ± standard deviation) and oesophagus (DSC = 0.66 ± 0.06, mean ± SD). The mean CMD < 1 cm was achieved for all the OARs, but spinal cord (CMD = 1.22 cm). By pairing the observers, mean DSC > 0.7 and mean CMD < 1 cm were achieved in all cases. The best overlaps were seen for the pairs JRO-CSRT(DSC = 0.82; CMD = 0.49 cm) and SRO-JRO (DSC = 0.80; CMD = 0.51 cm). Conclusions Overall, good concordance was found for all the observers. Despite the short training in contouring, CSRT obtained good concordance with his tutor (JRO). Great variability was seen in contouring the LAD, due to its difficult visualization and identification of CT scans without contrast.
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Affiliation(s)
- Simona Arculeo
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy.,Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Eleonora Miglietta
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Fabrizio Nava
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Anna Morra
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Maria Cristina Leonardi
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Stefania Comi
- Unit of Medical Physics, European Institute of Oncology IRCCS (IEO), 20141 Milan, Italy
| | - Delia Ciardo
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Massimo Sarra Fiore
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Marianna Alessandra Gerardi
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Simone Giovanni Gugliandolo
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
| | - Lorenzo Livi
- Radiation Oncology Unit, Oncology Department, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Largo Piero Palagi 1, 50139 Florence, Italy
| | - Roberto Orecchia
- Scientific Directorate, European Institute of Oncology IRCCS (IEO), 20141 Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy.,Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Samantha Dicuonzo
- Division of Radiation Oncology, European Institute of Oncology IRCCS (IEO), Via Ripamonti 435, 20141 Milan, Italy
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Cacicedo J, Navarro-Martin A, Gonzalez-Larragan S, De Bari B, Salem A, Dahele M. Systematic review of educational interventions to improve contouring in radiotherapy. Radiother Oncol 2019; 144:86-92. [PMID: 31786422 DOI: 10.1016/j.radonc.2019.11.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/31/2019] [Accepted: 11/04/2019] [Indexed: 01/28/2023]
Abstract
BACKGROUND AND PURPOSE Contouring is a critical step in the radiotherapy process, but there is limited research on how to teach it and no consensus about the best method. We summarize the current evidence regarding improvement of contouring skills. METHODS AND MATERIALS Comprehensive literature search of the Pubmed-MEDLINE database, EMBASE database and Cochrane Library to identify relevant studies (independently examined by two investigators) that included baseline contouring followed by a re-contouring assessment after an educational intervention. RESULTS 598 papers were identified. 16 studies met the inclusion criteria representing 370 participants (average number of participants per study of 23; range (4-141). Regarding the teaching methodology, 5/16 used onsite courses, 8/16 online courses, and 2/16 used blended learning. Study quality was heterogenous. There were only 3 randomized studies and only 3 analyzed the dosimetric impact of improving contouring homogeneity. Dice similarity coefficient was the most common evaluation metric (7/16), and in all these studies at least some contours improved significantly post-intervention. The time frame for evaluating the learning effect of the teaching intervention was almost exclusively short-time, with only one study evaluating the long-term utility of the educational program beyond 6 months. CONCLUSION The literature on educational interventions designed to improve contouring performance is limited and heterogenous. Onsite, online and blended learning courses have all been shown to be helpful, however, sample sizes are small and impact assessment is almost exclusively short-term and typically does not take into account the effect on treatment planning. The most effective teaching methodology/format is unknown and impact on daily clinical practice is uncertain.
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Affiliation(s)
- Jon Cacicedo
- Radiation Oncology Department, Cruces University Hospital, Osakidetza/Biocruces Health Research Institute/Department of Surgery, Radiology and Physical Medicine of the University of the Basque Country (UPV/EHU), Barakaldo, Spain.
| | - Arturo Navarro-Martin
- Radiation Oncology Department, Hospital Duran i Reynals (ICO) Avda, Gran VIa de ĹHospitalet, Barcelona, Spain.
| | | | - Berardino De Bari
- Radiation Oncology Department, Centre Hospitalier Régional Universitaire Jean Minjoz, INSERM U1098 EFS/BFC, Besançon, France.
| | - Ahmed Salem
- Division of Cancer Sciences, University of Manchester, United Kingdom; Department of Clinical Oncology, The Christie Hospital NHS Trust, Manchester, United Kingdom.
| | - Max Dahele
- Department of Radiation Oncology, Cancer Center Amsterdam, Amsterdam UMC (VUmc location), the Netherlands.
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