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Gupta AC, Cazoulat G, Al Taie M, Yedururi S, Rigaud B, Castelo A, Wood J, Yu C, O'Connor C, Salem U, Silva JAM, Jones AK, McCulloch M, Odisio BC, Koay EJ, Brock KK. Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images. Sci Rep 2024; 14:4678. [PMID: 38409252 DOI: 10.1038/s41598-024-53997-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 02/07/2024] [Indexed: 02/28/2024] Open
Abstract
Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (CCH) and 20 (CPVE) CECT of LC patients. [Formula: see text] outperformed [Formula: see text] across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03-0.05 (p < 0.05). [Formula: see text], and [Formula: see text] were not statistically different (p > 0.05), however, both were slightly better than [Formula: see text] by DSC up to 0.02. The final model, [Formula: see text], showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5-8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score [Formula: see text] 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.
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Affiliation(s)
- Aashish C Gupta
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mais Al Taie
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sireesha Yedururi
- Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Austin Castelo
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Wood
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cenji Yu
- 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
| | - Caleb O'Connor
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Usama Salem
- Abdominal Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Aaron Kyle Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Molly McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bruno C Odisio
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- 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.
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Jeon SK, Joo I, Park J, Kim JM, Park SJ, Yoon SH. Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images. Sci Rep 2024; 14:4378. [PMID: 38388824 PMCID: PMC10883917 DOI: 10.1038/s41598-024-55137-y] [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: 08/25/2023] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open
Abstract
A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous phase (PVP) and spatiotemporally-matched virtual non-contrast images, and tested using a single-energy (SE) CT dataset comprising PVP and true non-contrast (TNC) images. The algorithm showed robust accuracy in segmenting the liver, spleen, right kidney (RK), and left kidney (LK), with mean dice similarity coefficients (DSCs) exceeding 0.94 for each organ, regardless of contrast enhancement. However, pancreas segmentation demonstrated slightly lower performance with mean DSCs of around 0.8. In organ volume estimation, the algorithm demonstrated excellent agreement with ground-truth measurements for the liver, spleen, RK, and LK (intraclass correlation coefficients [ICCs] > 0.95); while the pancreas showed good agreements (ICC = 0.792 in SE-PVP, 0.840 in TNC). Accurate volume estimation within a 10% deviation from ground-truth was achieved in over 90% of cases involving the liver, spleen, RK, and LK. These findings indicate the efficacy of our 3D nnU-Net-based algorithm, developed using DECT images, which provides precise segmentation of the liver, spleen, and RK and LK in both non-contrast and post-contrast CT images, enabling reliable organ volumetry, albeit with relatively reduced performance for the pancreas.
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Affiliation(s)
- Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center Seoul National University Hospital, Seoul, Korea.
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | | | | | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- MEDICALIP. Co. Ltd., Seoul, Korea
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Lin YM, Paolucci I, Albuquerque Marques Silva J, O'Connor CS, Hong J, Shah KY, Abdelsalam ME, Habibollahi P, Jones KA, Brock KK, Odisio BC. Ablative margin quantification using deformable versus rigid image registration in colorectal liver metastasis thermal ablation: a retrospective single-center study. Eur Radiol 2024:10.1007/s00330-024-10632-8. [PMID: 38334762 DOI: 10.1007/s00330-024-10632-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 01/11/2024] [Accepted: 01/19/2024] [Indexed: 02/10/2024]
Abstract
PURPOSE To investigate the correlation of minimal ablative margin (MAM) quantification using biomechanical deformable (DIR) versus intensity-based rigid image registration (RIR) with local outcomes following colorectal liver metastasis (CLM) thermal ablation. METHODS This retrospective single-institution study included consecutive patients undergoing thermal ablation between May 2016 and October 2021. Patients who did not have intraprocedural pre- and post-ablation contrast-enhanced CT images for MAM quantification or follow-up period less than 1 year without residual tumor or local tumor progression (LTP) were excluded. DIR and RIR methods were used to quantify the MAM. The registration accuracy was compared using Dice similarity coefficient (DSC). Area under the receiver operating characteristic curve (AUC) was used to test MAM in predicting local tumor outcomes. RESULTS A total of 72 patients (mean age 57; 44 men) with 139 tumors (mean diameter 1.5 cm ± 0.8 (SD)) were included. During a median follow-up of 29.4 months, there was one residual unablated tumor and the LTP rate was 17% (24/138). The ranges of DSC were 0.96-0.98 and 0.67-0.98 for DIR and RIR, respectively (p < 0.001). When using DIR, 27 (19%) tumors were partially or totally registered outside the liver, compared to 46 (33%) with RIR. Using DIR versus RIR, the corresponding median MAM was 4.7 mm versus 4.0 mm, respectively (p = 0.5). The AUC in predicting residual tumor and 1-year LTP for DIR versus RIR was 0.89 versus 0.72, respectively (p < 0.001). CONCLUSION Ablative margin quantified on intra-procedural CT imaging using DIR method outperformed RIR for predicting local outcomes of CLM thermal ablation. CLINICAL RELEVANCE STATEMENT The study supports the role of biomechanical deformable image registration as the preferred image registration method over rigid image registration for quantifying minimal ablative margins using intraprocedural contrast-enhanced CT images. KEY POINTS • Accurate and reproducible image registration is a prerequisite for clinical application of image-based ablation confirmation methods. • When compared to intensity-based rigid image registration, biomechanical deformable image registration for minimal ablative margin quantification was more accurate for liver registration using intraprocedural contrast-enhanced CT images. • Biomechanical deformable image registration outperformed intensity-based rigid image registration for predicting local tumor outcomes following colorectal liver metastasis thermal ablation.
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Affiliation(s)
- Yuan-Mao Lin
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Iwan Paolucci
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jessica Albuquerque Marques Silva
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Caleb S O'Connor
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jun Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Ketan Y Shah
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Mohamed E Abdelsalam
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Peiman Habibollahi
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Kyle A Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Bruno C Odisio
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
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Paolucci I, Albuquerque Marques Silva J, Lin YM, Fellman BM, Jones KA, Tatsui CE, Weinberg JS, Ruiz J, Tan J, Brock KK, Bale R, Odisio BC. Study Protocol STEREOLAB: Stereotactic Liver Ablation Assisted with Intra-Arterial CT Hepatic Arteriography and Ablation Confirmation Software Assessment. Cardiovasc Intervent Radiol 2023; 46:1748-1754. [PMID: 37563313 DOI: 10.1007/s00270-023-03524-9] [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: 04/27/2023] [Accepted: 07/22/2023] [Indexed: 08/12/2023]
Abstract
PURPOSE This study aims to evaluate the technical efficacy and local tumor progression-free survival (LTPFS) of a standardized workflow for thermal ablation of colorectal liver metastases (CRLM) consisting of CT during hepatic arteriography (CTHA)-based imaging analysis, stereotactic thermal ablation, and computer-based software assessment of ablation margins. MATERIALS AND METHODS This investigator initiated, single-center, single-arm prospective trial will enroll up to 50 patients (≤ 5 CRLM, Measuring ≤ 5 cm). Procedures will be performed in an angio-CT suite under general anesthesia. The primary objective is to estimate LTPFS with a follow-up of up to 2 years and secondary objectives are analysis of the impact of minimal ablative margins on LTPFS, adverse events, contrast media utilization and radiation exposure, overall oncological outcomes, and anesthesia/procedural time. Adverse events (AE) will be recorded by CTCAE (Common Toxicity Criteria for Adverse Events), and Bayesian optimal phase-2 design will be applied for major intraprocedural AE stop boundaries. The institutional CRLM ablation registry will be used as benchmark for comparative analysis with the historical cohort. DISCUSSION The STEREOLAB trial will introduce a high-precision and standardized thermal ablation workflow for CRLM consisting of CT during hepatic arteriography imaging, stereotactic guidance, and ablation confirmation. Trial Registration ClinicalTrials.gov identifier: (NCT05361551).
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Affiliation(s)
- Iwan Paolucci
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1471, Houston, TX, USA
| | - Jessica Albuquerque Marques Silva
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1471, Houston, TX, USA
| | - Yuan-Mao Lin
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1471, Houston, TX, USA
| | - Bryan M Fellman
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kyle A Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Claudio E Tatsui
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jeffrey S Weinberg
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joseph Ruiz
- Department of Anesthesiology and Perioperative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jens Tan
- Department of Anesthesiology and Perioperative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Reto Bale
- Interventionelle Onkologie-Mikroinvasive Therapie (SIP), Medizinische Universität Innsbruck, Innsbruck, Austria
| | - Bruno C Odisio
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1471, Houston, TX, USA.
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Lin YM, Paolucci I, O’Connor CS, Anderson BM, Rigaud B, Fellman BM, Jones KA, Brock KK, Odisio BC. Ablative Margins of Colorectal Liver Metastases Using Deformable CT Image Registration and Autosegmentation. Radiology 2023; 307:e221373. [PMID: 36719291 PMCID: PMC10102669 DOI: 10.1148/radiol.221373] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/10/2022] [Accepted: 11/18/2022] [Indexed: 02/01/2023]
Abstract
Background Confirming ablation completeness with sufficient ablative margin is critical for local tumor control following colorectal liver metastasis (CLM) ablation. An image-based confirmation method considering patient- and ablation-related biomechanical deformation is an unmet need. Purpose To evaluate a biomechanical deformable image registration (DIR) method for three-dimensional (3D) minimal ablative margin (MAM) quantification and the association with local disease progression following CT-guided CLM ablation. Materials and Methods This single-institution retrospective study included patients with CLM treated with CT-guided microwave or radiofrequency ablation from October 2015 to March 2020. A biomechanical DIR method with AI-based autosegmentation of liver, tumors, and ablation zones on CT images was applied for MAM quantification retrospectively. The per-tumor incidence of local disease progression was defined as residual tumor or local tumor progression. Factors associated with local disease progression were evaluated using the multivariable Fine-Gray subdistribution hazard model. Local disease progression sites were spatially localized with the tissue at risk for tumor progression (<5 mm) using a 3D ray-tracing method. Results Overall, 213 ablated CLMs (mean diameter, 1.4 cm) in 124 consecutive patients (mean age, 57 years ± 12 [SD]; 69 women) were evaluated, with a median follow-up interval of 25.8 months. In ablated CLMs, an MAM of 0 mm was depicted in 14.6% (31 of 213), from greater than 0 to less than 5 mm in 40.4% (86 of 213), and greater than or equal to 5 mm in 45.1% (96 of 213). The 2-year cumulative incidence of local disease progression was 72% for 0 mm and 12% for greater than 0 to less than 5 mm. No local disease progression was observed for an MAM greater than or equal to 5 mm. Among 117 tumors with an MAM less than 5 mm, 36 had local disease progression and 30 were spatially localized within the tissue at risk for tumor progression. On multivariable analysis, an MAM of 0 mm (subdistribution hazard ratio, 23.3; 95% CI: 10.8, 50.5; P < .001) was independently associated with local disease progression. Conclusion Biomechanical deformable image registration and autosegmentation on CT images enabled identification and spatial localization of colorectal liver metastases at risk for local disease progression following ablation, with a minimal ablative margin greater than or equal to 5 mm as the optimal end point. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sofocleous in this issue.
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Affiliation(s)
- Yuan-Mao Lin
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
| | - Iwan Paolucci
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
| | - Caleb S. O’Connor
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
| | - Brian M. Anderson
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
| | - Bastien Rigaud
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
| | - Bryan M. Fellman
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
| | - Kyle A. Jones
- From the Departments of Interventional Radiology (Y.M.L., I.P.,
B.C.O.), Imaging Physics (C.S.O., B.M.A., B.R., K.A.J., K.K.B.), and
Biostatistics (B.M.F.), The University of Texas MD Anderson Cancer Center, 1515
Holcombe Blvd, Houston, TX 77030
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Paolucci I, Lin YM, Jones AK, Brock KK, Odisio BC. Use of Contrast Media During CT-guided Thermal Ablation of Colorectal Liver Metastasis for Procedure Planning is Associated with Improved Immediate Outcomes. Cardiovasc Intervent Radiol 2023; 46:327-336. [PMID: 36609863 PMCID: PMC10446157 DOI: 10.1007/s00270-022-03333-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/29/2022] [Indexed: 01/09/2023]
Abstract
PURPOSE The aim of this study was to analyze the impact of using intra-procedural pre-ablation contrast-enhanced CT prior to percutaneous thermal ablation (pre-ablation CECT) of colorectal liver metastases (CLM) on local outcomes. MATERIALS AND METHODS This retrospective analysis of a prospectively collected liver ablation registry included 144 consecutive patients (median age 57 years IQR [49, 65], 60% men) who underwent 173 CT-guided ablation sessions for 250 CLM between October 2015 and March 2020. In addition to oncologic outcomes, technical success was retrospectively evaluated using a biomechanical deformable image registration software for 3D-minimal ablative margin (3D-MAM) quantification. Bayesian regression was used to estimate effects of pre-ablation CECT on residual unablated tumor, 3D-MAM, and local tumor progression-free survival (LTPFS). RESULTS Pre-ablation CECT was acquired in 71/173 (41%) sessions. Residual unablated tumor was present in one (0.9%) versus nine tumors (6.6%) ablated with versus without using pre-ablation CECT, respectively (p = 0.024). Pre-ablation CECT use decreased the odds of residual disease on first follow-up by 78% (CI95% [5, 86]) and incomplete ablation (3D-MAM ≤ 0 mm) by 58% (CI95% [13, 122]). The odds ratio for residual unablated tumor for larger CLM was lower when pre-ablation CECT was used (odds ratio 1.0 with pre-ablation CECT vs. 2.52 without). Pre-ablation CECT use was not associated with improvements on LTPFS. CONCLUSIONS Pre-ablation CECT is associated with improved immediate outcomes by significantly reducing the incidence of residual unablated tumor and by mitigating the risk of incomplete ablation for larger CLM. We recommend performing baseline intra-procedural pre-ablation CECT as a standard imaging protocol. LEVEL OF EVIDENCE Level 3 (retrospective cohort study).
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Affiliation(s)
- Iwan Paolucci
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA
| | - Yuan-Mao Lin
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA
| | - A Kyle Jones
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kristy K Brock
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bruno C Odisio
- Department of Interventional Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA.
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Baroudi H, Brock KK, Cao W, Chen X, Chung C, Court LE, El Basha MD, Farhat M, Gay S, Gronberg MP, Gupta AC, Hernandez S, Huang K, Jaffray DA, Lim R, Marquez B, Nealon K, Netherton TJ, Nguyen CM, Reber B, Rhee DJ, Salazar RM, Shanker MD, Sjogreen C, Woodland M, Yang J, Yu C, Zhao Y. Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'? Diagnostics (Basel) 2023; 13:diagnostics13040667. [PMID: 36832155 PMCID: PMC9955359 DOI: 10.3390/diagnostics13040667] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/21/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
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Affiliation(s)
- Hana Baroudi
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kristy K. Brock
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenhua Cao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xinru Chen
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
| | - Mohammad D. El Basha
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Maguy Farhat
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Skylar Gay
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Mary P. Gronberg
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Aashish Chandra Gupta
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soleil Hernandez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kai Huang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - David A. Jaffray
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rebecca Lim
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Barbara Marquez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kelly Nealon
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Callistus M. Nguyen
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Brandon Reber
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ramon M. Salazar
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mihir D. Shanker
- The University of Queensland, Saint Lucia 4072, Australia
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carlos Sjogreen
- Department of Physics, University of Houston, Houston, TX 77004, USA
| | - McKell Woodland
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Jinzhong Yang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Cenji Yu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Zhao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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8
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Targeted exome-based predictors of patterns of progression of colorectal liver metastasis after percutaneous thermal ablation. Br J Cancer 2023; 128:130-136. [PMID: 36319850 PMCID: PMC9814547 DOI: 10.1038/s41416-022-02030-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Percutaneous thermal ablation is a curative-intent locoregional therapy (LRT) for selected patients with unresectable colorectal liver metastasis (CLM). Several factors have been identified that contribute to local tumour control after ablation. However, factors contributing to disease progression outside the ablation zone after ablation are poorly understood. METHODS In this retrospective study, using next-generation sequencing, we identified genetic biomarkers associated with different patterns of progression following thermal ablation of CLM. RESULTS A total of 191 ablation naïve patients between January 2011 and March 2020 were included in the analysis, and 101 had genomic profiling available. Alterations in the TGFβ pathway were associated with increased risk of development of new intrahepatic tumours (hazard ratio [HR], 2.75, 95% confidence interval [95% CI] 1.39-5.45, P = 0.004); and alterations in the Wnt pathway were associated with increased probability of receiving salvage LRT for any intrahepatic progression (HR, 5.8, 95% CI 1.94-19.5, P = 0.003). CONCLUSIONS Our findings indicate that genomic alterations in cancer-related signalling pathways can predict different progression patterns and the likelihood of receiving salvage LRT following percutaneous thermal ablation of CLM.
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McCulloch MM, Cazoulat G, Svensson S, Gryshkevych S, Rigaud B, Anderson BM, Kirimli E, De B, Mathew RT, Zaid M, Elganainy D, Peterson CB, Balter P, Koay EJ, Brock KK. Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures. Front Oncol 2022; 12:1015608. [PMID: 36408172 PMCID: PMC9666494 DOI: 10.3389/fonc.2022.1015608] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/10/2022] [Indexed: 12/29/2023] Open
Abstract
PURPOSE Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms. MATERIALS AND METHODS Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses. RESULTS Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach. CONCLUSION This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.
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Affiliation(s)
- Molly M. McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | | | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brian M. Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ezgi Kirimli
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brian De
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ryan T. Mathew
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mohamed Zaid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dalia Elganainy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Christine B. Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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10
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Anderson BM, Rigaud B, Lin YM, Jones AK, Kang HC, Odisio BC, Brock KK. Automated segmentation of colorectal liver metastasis and liver ablation on contrast-enhanced CT images. Front Oncol 2022; 12:886517. [PMID: 36033508 PMCID: PMC9403767 DOI: 10.3389/fonc.2022.886517] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/01/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives Colorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones. Methods Four FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from the liver tumor segmentation (LiTS) challenge. For the evaluation, we acquired 15 patients from the 3D-IRCADb database, 18 patients from our institution (CRLM = 24, ablation-zone = 19), and those submitted to the LiTS challenge (n = 70). Qualitative evaluations of our institutional data were performed by two board-certified radiologists (interventional and diagnostic) and a radiology-trained physician fellow, using a Likert scale of 1–5. Results The most accurate model was the Hybrid-WNet. On a patient-by-patient basis in the 3D-IRCADb dataset, the median (min–max) Dice similarity coefficient (DSC) was 0.73 (0.41–0.88), the median surface distance was 1.75 mm (0.57–7.63 mm), and the number of false positives was 1 (0–4). In the LiTS challenge (n = 70), the global DSC was 0.810. The model sensitivity was 98% (47/48) for sites ≥15 mm in diameter. Qualitatively, 100% (24/24; minority vote) of the CRLM and 84% (16/19; majority vote) of the ablation zones had Likert scores ≥4. Conclusion The Hybrid-WNet model provided fast (<30 s) and accurate segmentations of CRLM and ablation zones on contrast-enhanced CT scans, with positive physician reviews.
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Affiliation(s)
- Brian M. Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Brian Anderson,
| | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Yuan-Mao Lin
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - A. Kyle Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - HynSeon Christine Kang
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Bruno C. Odisio
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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11
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Anderson BM, Lin YM, Lin EY, Cazoulat G, Gupta S, Kyle Jones A, Odisio BC, Brock KK. A novel use of biomechanical model-based deformable image registration (DIR) for assessing colorectal liver metastases ablation outcomes. Med Phys 2021; 48:6226-6236. [PMID: 34342018 PMCID: PMC9380122 DOI: 10.1002/mp.15147] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/04/2021] [Accepted: 07/20/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose: Colorectal cancer is the third most common form of cancer in the United States, and up to 60% of these patients develop liver metastasis. While hepatic resection is the curative treatment of choice, only 20% of patients are candidates at the time of diagnosis. While percutaneous thermal ablation (PTA) has demonstrated 24%–51% overall 5-year survival rates, assurance of sufficient ablation margin delivery (5 mm) can be challenging, with current methods of 2D distance measurement not ensuring 3D minimum margin. We hypothesized that biomechanical model-based deformable image registration (DIR) can reduce spatial uncertainties and differentiate local tumor progression (LTP) patients from LTP-free patients. Methods: We retrospectively acquired 30 patients (16 LTP and 14 LTP-free) at our institution who had undergone PTA and had a contrast-enhanced pre-treatment and post-ablation CT scan. Liver, disease, and ablation zone were manually segmented. Biomechanical model-based DIR between the pre-treatment and post-ablation CT mapped the gross tumor volume onto the ablation zone and measured 3D minimum delivered margin (MDM). An in-house cone-tracing algorithm determined if progression qualitatively collocated with insufficient 5 mm margin achieved. Results: Mann–Whitney U test showed a significant difference (p < 0.01) in MDM from the LTP and LTP-free groups. A total of 93% (13/14) of patients with LTP had a correlation between progression and missing 5 mm of margin volume. Conclusions: Biomechanical DIR is able to reduce spatial uncertainty and allow measurement of delivered 3D MDM. This minimum margin can help ensure sufficient ablation delivery, and our workflow can provide valuable information in a clinically useful timeframe.
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Affiliation(s)
- Brian M Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, USA
| | - Yuan-Mao Lin
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ethan Y Lin
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sanjay Gupta
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - A Kyle Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bruno C Odisio
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Anderson BM, Wahid KA, Brock KK. Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays. Pract Radiat Oncol 2021; 11:226-229. [PMID: 33607331 PMCID: PMC8102371 DOI: 10.1016/j.prro.2021.02.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 10/22/2022]
Abstract
Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data types exists, no purpose-built Python module for converting NumPy arrays into RT structures exists. The 2 most popular deep learning libraries, Tensorflow and PyTorch, are both implemented within Python, and we believe a set of tools built in Python for manipulating DICOM images and RT structures would be useful and could save medical researchers large amounts of time and effort during the preprocessing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-structure files by identifying unique region of interest (ROI) names and ROI structure locations and allowing multiple ROI names to represent the same structure. It is also capable of converting DICOM images and RT structures into NumPy arrays and SimpleITK Images, the most commonly used formats for image analysis and inputs into deep learning architectures and radiomic feature calculations. Furthermore, the tool provides a simple method for creating a DICOM RT-structure from predicted NumPy arrays, which are commonly the output of semantic segmentation deep learning models. Accessing DicomRTTool via the public Github project invites open collaboration, and the deployment of our module in PyPi ensures painless distribution and installation. We believe our tool will be increasingly useful as deep learning in medicine progresses.
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Affiliation(s)
- Brian M Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Kareem A Wahid
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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