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Hsu DG, Ballangrud Å, Prezelski K, Swinburne NC, Young R, Beal K, Deasy JO, Cerviño L, Aristophanous M. Automatically tracking brain metastases after stereotactic radiosurgery. Phys Imaging Radiat Oncol 2023; 27:100452. [PMID: 37720463 PMCID: PMC10500025 DOI: 10.1016/j.phro.2023.100452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/12/2023] [Accepted: 05/26/2023] [Indexed: 09/19/2023] Open
Abstract
Background and purpose Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy. Methods The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists' assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change. Results A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3-9 months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p < 0.001); the mean residual error was -8 ± 17%. The mean registration error was 1.5 ± 0.2 mm. Conclusions Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy.
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Affiliation(s)
- Dylan G. Hsu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Åse Ballangrud
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Kayla Prezelski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Nathaniel C. Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Robert Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Kathryn Beal
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY 10065, United States
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Laura Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Michalis Aristophanous
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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Yang Z, Liu H, Liu Y, Stojadinovic S, Timmerman R, Nedzi L, Dan T, Wardak Z, Lu W, Gu X. A web-based brain metastases segmentation and labeling platform for stereotactic radiosurgery. Med Phys 2020; 47:3263-3276. [PMID: 32333797 DOI: 10.1002/mp.14201] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time-consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. METHOD This platform was developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning-based BMs segmentation; and affine registration-based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false-positive contours; (d) exporting contours as DICOM RTStruct files; etc. RESULTS: We evaluated this platform on 10 clinical cases with BMs number varied from 12-81 per case. The overall operation took about 4-5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface-to-surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case-specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively. CONCLUSION The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.
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Affiliation(s)
- Zi Yang
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.,Biomedical Engineering Graduate Program, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Hui Liu
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Yan Liu
- College of Electrical Engineering, Sichuan University, Chengdu, 610065, China
| | - Strahinja Stojadinovic
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Robert Timmerman
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lucien Nedzi
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Tu Dan
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zabi Wardak
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Weiguo Lu
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xuejun Gu
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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Shearkhani O, Khademi A, Eilaghi A, Hojjat SP, Symons SP, Heyn C, Machnowska M, Chan A, Sahgal A, Maralani PJ. Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field. AJNR Am J Neuroradiol 2017; 38:2059-2066. [PMID: 28882862 DOI: 10.3174/ajnr.a5352] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 06/15/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Accurate follow-up of metastatic brain tumors has important implications for patient prognosis and management. The aim of this study was to develop and evaluate the accuracy of a semiautomated algorithm in detecting growing or shrinking metastatic brain tumors on longitudinal brain MRIs. MATERIALS AND METHODS We used 50 pairs of successive MR imaging datasets, 30 on 1.5T and 20 on 3T, containing contrast-enhanced 3D T1-weighted sequences. These yielded 150 growing or shrinking metastatic brain tumors. To detect them, we completed 2 major steps: 1) spatial normalization and calculation of the Jacobian operator field to quantify changes between scans, and 2) metastatic brain tumor candidate segmentation and detection of volume-changing metastatic brain tumors with the Jacobian operator field. Receiver operating characteristic analysis was used to assess the detection accuracy of the algorithm, and it was verified with jackknife resampling. The reference standard was based on detections by a neuroradiologist. RESULTS The areas under the receiver operating characteristic curves were 0.925 for 1.5T and 0.965 for 3T. Furthermore, at its optimal performance, the algorithm achieved a sensitivity of 85.1% and 92.1% and specificity of 86.7% and 91.3% for 1.5T and 3T, respectively. Vessels were responsible for most false-positives. Newly developed or resolved metastatic brain tumors were a major source of false-negatives. CONCLUSIONS The proposed algorithm could detect volume-changing metastatic brain tumors on longitudinal brain MRIs with statistically high accuracy, demonstrating its potential as a computer-aided change-detection tool for complementing the performance of radiologists, decreasing inter- and intraobserver variability, and improving efficacy.
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Affiliation(s)
- O Shearkhani
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - A Khademi
- Department of Biomedical Engineering (A.K.), Ryerson University, Toronto, Ontario, Canada
| | - A Eilaghi
- Mechanical Engineering Department (A.E.), Australian College of Kuwait, Kuwait City, Kuwait
| | - S-P Hojjat
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - S P Symons
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - C Heyn
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - M Machnowska
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - A Chan
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - A Sahgal
- Radiation Oncology (A.S.), University of Toronto, Toronto, Ontario, Canada
| | - P J Maralani
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
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Rivaz H, Chen SJS, Collins DL. Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:366-380. [PMID: 25248177 DOI: 10.1109/tmi.2014.2354352] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this work, we present a novel algorithm for registration of 3-D volumetric ultrasound (US) and MR using Robust PaTch-based cOrrelation Ratio (RaPTOR). RaPTOR computes local correlation ratio (CR) values on small patches and adds the CR values to form a global cost function. It is therefore invariant to large amounts of spatial intensity inhomogeneity. We also propose a novel outlier suppression technique based on the orientations of the RaPTOR gradients. Our deformation is modeled with free-form cubic B-splines. We analytically derive the derivatives of RaPTOR with respect to the transformation, i.e., the displacement of the B-spline nodes, and optimize RaPTOR using a stochastic gradient descent approach. RaPTOR is validated on MR and tracked US images of neurosurgery. Deformable registration of the US and MR images acquired, respectively, preoperation and postresection is of significant clinical significance, but challenging due to, among others, the large amount of missing correspondences between the two images. This work is also novel in that it performs automatic registration of this challenging dataset. To validate the results, we manually locate corresponding anatomical landmarks in the US and MR images of tumor resection in brain surgery. Compared to rigid registration based on the tracking system alone, RaPTOR reduces the mean initial mTRE over 13 patients from 5.9 to 2.9 mm, and the maximum initial TRE from 17.0 to 5.9 mm. Each volumetric registration using RaPTOR takes about 30 sec on a single CPU core. An important challenge in the field of medical image analysis is the shortage of publicly available dataset, which can both facilitate the advancement of new algorithms to clinical settings and provide a benchmark for comparison. To address this problem, we will make our manually located landmarks available online.
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Weizman L, Sira LB, Joskowicz L, Rubin DL, Yeom KW, Constantini S, Shofty B, Bashat DB. Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 2014; 41:052303. [PMID: 24784396 PMCID: PMC4000396 DOI: 10.1118/1.4871040] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 02/19/2014] [Accepted: 03/26/2014] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans. METHODS The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a "gold standard" was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution. RESULTS Compared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard. CONCLUSIONS The authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.
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Affiliation(s)
- Lior Weizman
- School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Liat Ben Sira
- Department of Radiology, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 64239, Israel
| | - Leo Joskowicz
- School of Engineering and Computer Science and The Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Daniel L Rubin
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Kristen W Yeom
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Shlomi Constantini
- Tel Aviv Medical Center, Dana Children's Hospital, Tel Aviv University, Tel Aviv 64239, Israel
| | - Ben Shofty
- Tel Aviv Medical Center, Dana Children's Hospital, Tel Aviv University, Tel Aviv 64239, Israel
| | - Dafna Ben Bashat
- Tel Aviv Medical Center, Functional Brain Center, Tel Aviv University, Tel Aviv 64239, Israel
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