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Temple SWP, Rowbottom CG. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients. J Appl Clin Med Phys 2024:e14273. [PMID: 38263866 DOI: 10.1002/acm2.14273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024] Open
Abstract
PURPOSE Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-observer variability. There has been little research investigating gross failure rates and failure modes of such systems. METHOD 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI-generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid). An AI-based commercial system was applied to 500 H&N patients. AI-generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI-based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub-types (setup position, anatomy, image artefacts and unknown). RESULTS There were 24 true failures of the AI-based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid). CONCLUSION True failures of the AI-based system were predominantly associated with a non-standard element within the CT scan. It is likely that these non-standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI-based system in the H&N region for the OARs investigated was low (∼1%).
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Affiliation(s)
- Simon W P Temple
- Medical Physics Department, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK
| | - Carl G Rowbottom
- Medical Physics Department, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK
- Department of Physics, University of Liverpool, Liverpool, UK
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Peng Y, Liu Y, Shen G, Chen Z, Chen M, Miao J, Zhao C, Deng J, Qi Z, Deng X. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning. Oral Oncol 2023; 136:106261. [PMID: 36446186 DOI: 10.1016/j.oraloncology.2022.106261] [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: 08/16/2022] [Revised: 11/13/2022] [Accepted: 11/19/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE We examined a modified encoder-decoder architecture-based fully convolutional neural network, OrganNet, for simultaneous auto-segmentation of 24 organs at risk (OARs) in the head and neck, followed by validation tests and evaluation of clinical application. MATERIALS AND METHODS Computed tomography (CT) images from 310 radiotherapy plans were used as the experimental data set, of which 260 and 50 were used as the training and test sets, respectively. An improved U-Net architecture was established by introducing a batch normalization layer, residual squeeze-and-excitation layer, and unique organ-specific loss function for deep learning training. The performance of the trained network model was evaluated by comparing the manual-delineation and the STAPLE contour of 10 physicians from different centers. RESULTS Our model achieved good segmentation in all 24 OARs in nasopharyngeal cancer radiotherapy plan CT images, with an average Dice similarity coefficient of 83.75%. Specifically, the mean Dice coefficients in large-volume organs (brainstem, spinal cord, left/right parotid glands, left/right temporal lobes, and left/right mandibles) were 84.97% - 95.00%, and in small-volume organs (pituitary, lens, optic nerve, and optic chiasma) were 55.46% - 91.56%. respectively. Using the STAPLE contours as standard contour, the OrganNet achieved comparable or better DICE in organ segmentation then that of the manual-delineation as well. CONCLUSION The established OrganNet enables simultaneous automatic segmentation of multiple targets on CT images of the head and neck radiotherapy plans, effectively improves the accuracy of U-Net based segmentation for OARs, especially for small-volume organs.
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Affiliation(s)
- Yinglin Peng
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yimei Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Guanzhu Shen
- Department of Radiation Oncology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zijie Chen
- Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China
| | - Meining Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jingjing Miao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chong Zhao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jincheng Deng
- Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China
| | - Zhenyu Qi
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
| | - Xiaowu Deng
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
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Dossun C, Niederst C, Noel G, Meyer P. Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies. Phys Med 2022; 101:137-157. [PMID: 36007403 DOI: 10.1016/j.ejmp.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/21/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The performance of deformable medical image registration (DIR) algorithms has become a major concern. METHODS We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines. RESULTS One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed. CONCLUSIONS This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.
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Affiliation(s)
- C Dossun
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - C Niederst
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - G Noel
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - P Meyer
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France; ICUBE, CNRS UMR 7357, Team IMAGES, Strasbourg, France.
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Wang B, Hu W, Shan G, Xu X. Estimating the accumulative dose uncertainty for intracavitary and interstitial brachytherapy. Biomed Eng Online 2021; 20:106. [PMID: 34663336 PMCID: PMC8524953 DOI: 10.1186/s12938-021-00942-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/03/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Image-guided adaptive brachytherapy shows the ability to deliver high doses to tumors while sparing normal tissues. However, interfraction dose delivery introduces uncertainties to high dose estimation, which relates to normal tissue toxicity. The purpose of this study was to investigate the high-dose regions of two applicator approaches in brachytherapy. METHOD For 32 cervical cancer patients, the CT images from each fraction were wrapped to a reference image, and the displacement vector field (DVF) was calculated with a hybrid intensity-based deformable registration algorithm. The fractional dose was then accumulated to calculate the position and the overlap of high dose (D2cc) during multiple fractions. RESULT The overall Dice similarity coefficient (DSC) of the deformation algorithm for the bladder and the rectum was (0.97 and 0.91). No significant difference was observed between the two applicators. However, the location of the intracavitary brachytherapy (ICBT) high-dose region was relatively concentrated. The overlap volume of bladder and rectum D2cc was 0.42 and 0.71, respectively, which was higher than that of interstitial brachytherapy (ISBT) (0.26 and 0.31). The cumulative dose was overestimated in ISBT cases when using the GEC-recommended method. The ratio of bladder and rectum D2cc to the GEC method was 0.99 and 1, respectively, which was higher than that of the ISBT method (0.96 and 0.94). CONCLUSION High-dose regions for brachytherapy based on different applicator types were different. The 3D-printed ICBT has better high-dose region consistency than freehand ISBT and hence is more predictable.
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Affiliation(s)
- Binbing Wang
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, No. 1. East Banshan Road, Gongshu District, Hangzhou, 310022 Zhejiang China
| | - Weibiao Hu
- Taizhou Hospital of Zhejiang Province, Taizhou, 318000 Zhejiang China
| | - Guoping Shan
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, No. 1. East Banshan Road, Gongshu District, Hangzhou, 310022 Zhejiang China
| | - Xiaoxian Xu
- Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, No. 1. East Banshan Road, Gongshu District, Hangzhou, 310022 Zhejiang China
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Is VLSM a valid tool for determining the functional anatomy of the brain? Usefulness of additional Bayesian network analysis. Neuropsychologia 2018; 121:69-78. [DOI: 10.1016/j.neuropsychologia.2018.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 08/16/2018] [Accepted: 10/01/2018] [Indexed: 12/21/2022]
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Interobserver variations in the delineation of target volumes and organs at risk and their impact on dose distribution in intensity-modulated radiation therapy for nasopharyngeal carcinoma. Oral Oncol 2018; 82:1-7. [DOI: 10.1016/j.oraloncology.2018.04.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 04/10/2018] [Accepted: 04/30/2018] [Indexed: 12/13/2022]
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Siciarz P, Mccurdy B, Alshafa F, Greer P, Hatton J, Wright P. Evaluation of CT to CBCT non-linear dense anatomical block matching registration for prostate patients. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aacada] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Li X, Zhang Y, Shi Y, Wu S, Xiao Y, Gu X, Zhen X, Zhou L. Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy. PLoS One 2017; 12:e0175906. [PMID: 28414799 PMCID: PMC5393623 DOI: 10.1371/journal.pone.0175906] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 04/02/2017] [Indexed: 01/16/2023] Open
Abstract
Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) for propagating contours between planning computerized tomography (CT) images and treatment CT/cone-beam CT (CBCT) images to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contour mapping, ten intensity-based DIR strategies, which were classified into four categories—optical flow-based, demons-based, level-set-based and spline-based—were tested on planning CT and fractional CBCT images acquired from twenty-one head & neck (H&N) cancer patients who underwent 6~7-week intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e., the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), were employed to measure the agreement between the propagated contours and the physician-delineated ground truths of four OARs, including the vertebra (VTB), the vertebral foramen (VF), the parotid gland (PG) and the submandibular gland (SMG). It was found that the evaluated DIRs in this work did not necessarily outperform rigid registration. DIR performed better for bony structures than soft-tissue organs, and the DIR performance tended to vary for different ROIs with different degrees of deformation as the treatment proceeded. Generally, the optical flow-based DIR performed best, while the demons-based DIR usually ranked last except for a modified demons-based DISC used for CT-CBCT DIR. These experimental results suggest that the choice of a specific DIR algorithm depends on the image modality, anatomic site, magnitude of deformation and application. Therefore, careful examinations and modifications are required before accepting the auto-propagated contours, especially for automatic re-planning ART systems.
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Affiliation(s)
- Xin Li
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuyu Zhang
- Department of Radiotherapy Oncology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yinghua Shi
- Department of Radiotherapy Oncology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Shuyu Wu
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yang Xiao
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Xuejun Gu
- Department of Radiotherapy Oncology, The University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Xin Zhen
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- * E-mail: (XZ); (LZ)
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- * E-mail: (XZ); (LZ)
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Mohamed ASR, Rosenthal DI, Awan MJ, Garden AS, Kocak-Uzel E, Belal AM, El-Gowily AG, Phan J, Beadle BM, Gunn GB, Fuller CD. Methodology for analysis and reporting patterns of failure in the Era of IMRT: head and neck cancer applications. Radiat Oncol 2016; 11:95. [PMID: 27460585 PMCID: PMC4962405 DOI: 10.1186/s13014-016-0678-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Accepted: 07/22/2016] [Indexed: 12/04/2022] Open
Abstract
Background The aim of this study is to develop a methodology to standardize the analysis and reporting of the patterns of loco-regional failure after IMRT of head and neck cancer. Material and Methods Twenty-one patients with evidence of local and/or regional failure following IMRT for head-and-neck cancer were retrospectively reviewed under approved IRB protocol. Manually delineated recurrent gross disease (rGTV) on the diagnostic CT documenting recurrence (rCT) was co-registered with the original planning CT (pCT) using both deformable (DIR) and rigid (RIR) image registration software. Subsequently, mapped rGTVs were compared relative to original planning target volumes (TVs) and dose using a centroid-based approaches. Failures were then classified into five types based on combined spatial and dosimetric criteria; A (central high dose), B (peripheral high dose), C (central elective dose), D (peripheral elective dose), and E (extraneous dose). Results A total of 26 recurrences were identified. Using DIR, recurrences were assigned to more central TVs compared to RIR as detected using the spatial centroid-based method (p = 0.0002). rGTVs mapped using DIR had statistically significant higher mean doses when compared to rGTVs mapped rigidly (mean dose 70 vs. 69 Gy, p = 0.03). According to the proposed classification 22 out of 26 failures were of type A (central high dose) as assessed by DIR method compared to 18 out of 26 for the RIR because of the tendencey of RIR to assign failures more peripherally. Conclusions RIR tends to assigns failures more peripherally. DIR-based methods showed that the vast majority of failures originated in the high dose target volumes and received full prescribed doses suggesting biological rather than technology-related causes of failure. Validated DIR-based registration is recommended for accurate failure characterization and a novel typology-indicative taxonomy is recommended for failure reporting in the IMRT era. Electronic supplementary material The online version of this article (doi:10.1186/s13014-016-0678-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Abdallah S R Mohamed
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA. .,Department of Clinical Oncology and nuclear medicine, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
| | - David I Rosenthal
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Musaddiq J Awan
- Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH, USA
| | - Adam S Garden
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Esengul Kocak-Uzel
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA.,Department of Radiation Oncology, Beykent University, Istanbul, Turkey
| | - Abdelaziz M Belal
- Department of Clinical Oncology and nuclear medicine, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Ahmed G El-Gowily
- Department of Clinical Oncology and nuclear medicine, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Jack Phan
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Beth M Beadle
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - G Brandon Gunn
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Clifton D Fuller
- Head and Neck Section, Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX, 77030, USA. .,Graduate School of Biomedical Science, University of Texas Health Science Center, Houston, TX, USA.
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Maffei N, Guidi G, Vecchi C, Ciarmatori A, Gottardi G, Meduri B, D'Angelo E, Bruni A, Mazzeo E, Pratissoli S, Giacobazzi P, Baldazzi G, Lohr F, Costi T. SIS epidemiological model for adaptive RT: Forecasting the parotid glands shrinkage during tomotherapy treatment. Med Phys 2016; 43:4294. [DOI: 10.1118/1.4954004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Beasley WJ, McWilliam A, Aitkenhead A, Mackay RI, Rowbottom CG. The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy. J Appl Clin Med Phys 2016; 17:41-49. [PMID: 27074471 PMCID: PMC5875550 DOI: 10.1120/jacmp.v17i2.5889] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 12/11/2015] [Accepted: 12/09/2015] [Indexed: 11/23/2022] Open
Abstract
Contouring structures in the head and neck is time‐consuming, and automatic segmentation is an important part of an adaptive radiotherapy workflow. Geometric accuracy of automatic segmentation algorithms has been widely reported, but there is no consensus as to which metrics provide clinically meaningful results. This study investigated whether geometric accuracy (as quantified by several commonly used metrics) was associated with dosimetric differences for the parotid and larynx, comparing automatically generated contours against manually drawn ground truth contours. This enabled the suitability of different commonly used metrics to be assessed for measuring automatic segmentation accuracy of the parotid and larynx. Parotid and larynx structures for 10 head and neck patients were outlined by five clinicians to create ground truth structures. An automatic segmentation algorithm was used to create automatically generated normal structures, which were then used to create volumetric‐modulated arc therapy plans. The mean doses to the automatically generated structures were compared with those of the corresponding ground truth structures, and the relative difference in mean dose was calculated for each structure. It was found that this difference did not correlate with the geometric accuracy provided by several metrics, notably the Dice similarity coefficient, which is a commonly used measure of spatial overlap. Surface‐based metrics provided stronger correlation and are, therefore, more suitable for assessing automatic segmentation of the parotid and larynx. PACS number(s): 87.57.nm, 87.55.D, 87.55.Qr
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Guidi G, Maffei N, Vecchi C, Ciarmatori A, Mistretta GM, Gottardi G, Meduri B, Baldazzi G, Bertoni F, Costi T. A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities. Phys Med 2015; 31:442-51. [PMID: 25958225 DOI: 10.1016/j.ejmp.2015.04.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 04/09/2015] [Accepted: 04/15/2015] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning. METHODS 1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(®) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments. RESULTS Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area. CONCLUSIONS Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints.
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Affiliation(s)
- Gabriele Guidi
- Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Physics Department, University of Bologna, Italy.
| | - Nicola Maffei
- Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Physics Department, University of Bologna, Italy
| | | | - Alberto Ciarmatori
- Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Post-graduate School in Medical Physics, University of Bologna, Italy
| | | | - Giovanni Gottardi
- Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy
| | - Bruno Meduri
- Radiation Oncology Department, Az. Ospedaliero-Universitaria di Modena, Italy
| | | | - Filippo Bertoni
- Radiation Oncology Department, Az. Ospedaliero-Universitaria di Modena, Italy
| | - Tiziana Costi
- Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy
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Validation of a deformable image registration produced by a commercial treatment planning system in head and neck. Phys Med 2015; 31:219-23. [DOI: 10.1016/j.ejmp.2015.01.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Revised: 01/14/2015] [Accepted: 01/16/2015] [Indexed: 11/19/2022] Open
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Zhen X, Chen H, Yan H, Zhou L, Mell LK, Yashar CM, Jiang S, Jia X, Gu X, Cervino L. A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images. Phys Med Biol 2015; 60:2981-3002. [DOI: 10.1088/0031-9155/60/7/2981] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Mohamed ASR, Ruangskul MN, Awan MJ, Baron CA, Kalpathy-Cramer J, Castillo R, Castillo E, Guerrero TM, Kocak-Uzel E, Yang J, Court LE, Kantor ME, Gunn GB, Colen RR, Frank SJ, Garden AS, Rosenthal DI, Fuller CD. Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms. Radiology 2014; 274:752-63. [PMID: 25380454 DOI: 10.1148/radiol.14132871] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation computed tomography (CT) with diagnostic CT coregistration. MATERIALS AND METHODS Radiation therapy-simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison. RESULTS A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness. CONCLUSION Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation.
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Affiliation(s)
- Abdallah S R Mohamed
- From the Departments of Radiation Oncology (A.S.R.M., M.N.R., M.J.A., C.A.B., R.C., E.C., T.M.G., E.K.U., J.Y., L.C., M.E.K., G.B.G., S.J.F., A.S.G., D.I.R., C.D.F.) and Radiology (R.R.C.), University of Texas MD Anderson Cancer Center, Box 0097, 1515 Holcombe Blvd, Houston, TX 77030; Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (J.K.C.); Department of Computational and Applied Mathematics, Rice University, Houston, Tex (R.C., E.C., T.M.G.); and Graduate School of Biomedical Science, University of Texas Health Science Center, Houston, Tex (E.C., T.M.G., L.C., C.D.F.)
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