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Kaufman AC, Fu F, Martinez MC, Fischbein N, Popelka GR, Butts Pauly K, Blevins NH. Use of Ultra-Short Echo Time MRI to Improve Temporal Bone Imaging. Laryngoscope 2024. [PMID: 39166732 DOI: 10.1002/lary.31707] [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: 03/11/2024] [Revised: 07/29/2024] [Accepted: 07/31/2024] [Indexed: 08/23/2024]
Abstract
OBJECTIVE The short T2 nature of cortical bone causes it to appear similar to air on MR, forcing clinicians to rely on computed tomography imaging, with its attendant ionizing radiation exposure, to define temporal bone structures. Through the use of novel MR sequences with ultra-short echo times (UTE), short T2 structures are now able to be visualized, allowing for improved understanding of anatomical relationships. METHODS Eight patients (50% female) undergoing MR imaging of the skull base for diagnostic purposes (62.5% for vestibular schwannoma surveillance) at a tertiary care center were enrolled to evaluate the safety and efficacy of UTE imaging. CT scans were completed in 37.5% of the patients as part of their workup and used for comparison purposes. The repetition time, short echo time, and long echo time for the UTE sequence were 11, 0.032, and 2.2 msec, respectively. RESULTS The protocol added 6 min to the total scanning time, and all patients tolerated the sequence without issue. The ossicles, mastoid air cells, antrum, and epitympanum were able to be seen and had a high Dice similarity coefficient when compared to CT (>0.5). UTE allowed for clear delineation of all segments of the facial nerve with a signal-to-noise ratio of 35 (although the BRAVO sequences had a superior ratio of 140). Vestibular schwannomas were able to be distinguished from normal brain parenchyma. CONCLUSIONS UTE is safe and effective for visualizing anatomic structures not normally seen on traditional MRI, potentially allowing for improved surgical planning in patients. LEVEL OF EVIDENCE III Laryngoscope, 2024.
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Affiliation(s)
- A C Kaufman
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland, Baltimore, Maryland, U.S.A
| | - F Fu
- Department of Otorhinolaryngology-Head and Neck Surgery, Stanford University, Palo Alto, California, U.S.A
| | - M C Martinez
- School of Medicine, Stanford University, Palo Alto, California, U.S.A
| | - N Fischbein
- Department of Radiology, Stanford University, Palo Alto, California, U.S.A
| | - G R Popelka
- Department of Radiology, Stanford University, Palo Alto, California, U.S.A
| | - K Butts Pauly
- Department of Radiology, Stanford University, Palo Alto, California, U.S.A
| | - N H Blevins
- Department of Otorhinolaryngology-Head and Neck Surgery, Stanford University, Palo Alto, California, U.S.A
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Adam DP, Grudzinski JJ, Marsh IR, Hill PM, Cho SY, Bradshaw TJ, Longcor J, Burr A, Bruce JY, Harari PM, Bednarz BP. Voxel-Level Dosimetry for Combined Iodine 131 Radiopharmaceutical Therapy and External Beam Radiation Therapy Treatment Paradigms for Head and Neck Cancer. Int J Radiat Oncol Biol Phys 2024; 119:1275-1284. [PMID: 38367914 DOI: 10.1016/j.ijrobp.2024.02.005] [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: 08/15/2023] [Revised: 12/20/2023] [Accepted: 02/08/2024] [Indexed: 02/19/2024]
Abstract
PURPOSE Targeted radiopharmaceutical therapy (RPT) in combination with external beam radiation therapy (EBRT) shows promise as a method to increase tumor control and mitigate potential high-grade toxicities associated with re-treatment for patients with recurrent head and neck cancer. This work establishes a patient-specific dosimetry framework that combines Monte Carlo-based dosimetry from the 2 radiation modalities at the voxel level using deformable image registration (DIR) and radiobiological constructs for patients enrolled in a phase 1 clinical trial combining EBRT and RPT. METHODS AND MATERIALS Serial single-photon emission computed tomography (SPECT)/computed tomography (CT) patient scans were performed at approximately 24, 48, 72, and 168 hours postinjection of 577.2 MBq/m2 (15.6 mCi/m2) CLR 131, an iodine 131-containing RPT agent. Using RayStation, clinical EBRT treatment plans were created with a treatment planning CT (TPCT). SPECT/CT images were deformably registered to the TPCT using the Elastix DIR module in 3D Slicer software and assessed by measuring mean activity concentrations and absorbed doses. Monte Carlo EBRT dosimetry was computed using EGSnrc. RPT dosimetry was conducted using RAPID, a GEANT4-based RPT dosimetry platform. Radiobiological metrics (biologically effective dose and equivalent dose in 2-Gy fractions) were used to combine the 2 radiation modalities. RESULTS The DIR method provided good agreement for the activity concentrations and calculated absorbed dose in the tumor volumes for the SPECT/CT and TPCT images, with a maximum mean absorbed dose difference of -11.2%. Based on the RPT absorbed dose calculations, 2 to 4 EBRT fractions were removed from patient EBRT treatments. For the combined treatment, the absorbed dose to target volumes ranged from 57.14 to 75.02 Gy. When partial volume corrections were included, the mean equivalent dose in 2-Gy fractions to the planning target volume from EBRT + RPT differed -3.11% to 1.40% compared with EBRT alone. CONCLUSIONS This work demonstrates the clinical feasibility of performing combined EBRT + RPT dosimetry on TPCT scans. Dosimetry guides treatment decisions for EBRT, and this work provides a bridge for the same paradigm to be implemented within the rapidly emerging clinical RPT space.
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Affiliation(s)
- David P Adam
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joseph J Grudzinski
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Ian R Marsh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick M Hill
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Steve Y Cho
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
| | - Tyler J Bradshaw
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Adam Burr
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
| | - Justine Y Bruce
- University of Wisconsin Carbone Cancer Center, Madison, Wisconsin; Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Paul M Harari
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
| | - Bryan P Bednarz
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.
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Moore R, Yeung R, Chen W, Zeng Q, Prisman E, Salcudean S. Enabling extracorporeal ultrasound imaging with the da Vinci robot for transoral robotic surgery: a feasibility study. Int J Comput Assist Radiol Surg 2024; 19:1251-1258. [PMID: 38789882 DOI: 10.1007/s11548-024-03160-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024]
Abstract
PURPOSE Transoral robotic surgery (TORS) is a challenging procedure due to its small workspace and complex anatomy. Ultrasound (US) image guidance has the potential to improve surgical outcomes, but an appropriate method for US probe manipulation has not been defined. This study evaluates using an additional robotic (4th) arm on the da Vinci Surgical System to perform extracorporeal US scanning for image guidance in TORS. METHODS A stereoscopic imaging system and da Vinci-compatible US probe attachment were developed to enable control of the extracorporeal US probe from the surgeon console. The prototype was compared to freehand US by nine operators in three tasks on a healthy volunteer: (1) identification of the common carotid artery, (2) carotid artery scanning, and (3) identification of the submandibular gland. Operator workload and user experience were evaluated using a questionnaire. RESULTS The robotic US tasks took longer than freehand US tasks (2.09x longer; p = 0.001 ) and had higher operator workload (2.12x higher; p = 0.004 ). However, operator-rated performance was closer (avg robotic/avg freehand = 0.66; p = 0.017 ), and scanning performance measured by MRI-US average Hausdorff distance provided no statistically significant difference. CONCLUSION Extracorporeal US scanning for intraoperative US image guidance is a convenient approach for providing the surgeon direct control over the US image plane during TORS, with little modification to the existing operating room workflow. Although more time-consuming and higher operator workload, several methods have been identified to address these limitations.
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Affiliation(s)
- Randy Moore
- Department of Electrical and Computer Engineering, The University of British Columbia, 5500-2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Ryan Yeung
- School of Biomedical Engineering, The University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Wanwen Chen
- Department of Electrical and Computer Engineering, The University of British Columbia, 5500-2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Qi Zeng
- Department of Electrical and Computer Engineering, The University of British Columbia, 5500-2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Eitan Prisman
- Department of Otolaryngology, Vancouver General Hospital, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
| | - Septimiu Salcudean
- Department of Electrical and Computer Engineering, The University of British Columbia, 5500-2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada
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dos Santos PV, Scoczynski Ribeiro Martins M, Amorim Nogueira S, Gonçalves C, Maffei Loureiro R, Pacheco Calixto W. Unsupervised model for structure segmentation applied to brain computed tomography. PLoS One 2024; 19:e0304017. [PMID: 38870119 PMCID: PMC11175403 DOI: 10.1371/journal.pone.0304017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/03/2024] [Indexed: 06/15/2024] Open
Abstract
This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings.
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Affiliation(s)
- Paulo Victor dos Santos
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Sao Paulo, Brazil
- Technology Research and Development Center (GCITE), Federal Institute of Goias, Goiania, Brazil
| | - Marcella Scoczynski Ribeiro Martins
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
- Federal University of Technology - Parana, Ponta Grossa, Parana, Brazil
| | - Solange Amorim Nogueira
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Sao Paulo, Brazil
| | | | - Rafael Maffei Loureiro
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Sao Paulo, Brazil
| | - Wesley Pacheco Calixto
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
- Technology Research and Development Center (GCITE), Federal Institute of Goias, Goiania, Brazil
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Haslam P, Brown E, Burbery J, Hargrave C, Lee YY. To degas or not to degas? The effectiveness of pharmaceuticals in reducing the impact of bowel volume variations during liver SBRT treatment. J Med Radiat Sci 2024; 71:156-162. [PMID: 37584089 PMCID: PMC10920934 DOI: 10.1002/jmrs.714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/28/2023] [Indexed: 08/17/2023] Open
Abstract
The indications for liver stereotactic body radiation therapy (SBRT) continue to expand in the management of liver cancer due to the improved rates of local control with acceptable normal tissue toxicity. Changes in internal anatomy, such as the bowel, may negatively impact the precision of treatment delivery of SBRT liver treatment by influencing daily image matching. Institutions have developed various approaches to promoting bowel volume consistency. One such strategy is the administration of pharmaceuticals. The administration of pharmaceuticals, such as Simethicone, has been adopted by the Princess Alexandra Hospital Radiation Oncology Department (ROPAIR) as a method to promote consistency in the amount of bowel gas observed in liver cancer patients. This case series examines a group of patients treated at ROPAIR with liver SBRT to determine whether current practices effectively reduce the impact of bowel volume variations for liver cancer patients. Initial observations from this hypothesis generating research suggest potential improved consistency of the small bowel's anatomical position for liver SBRT patients who were administered Simethicone (Bowel bag dice similarity coefficient - Simethicone group = 0.79-0.92, Standard group = 0.24-0.93). However, it appeared that this strategy alone may not be entirely effective achieving consistency in the amount of bowel gas present throughout the duration of treatment. Further investigation into the refinement of liver SBRT pre-treatment preparation is therefore recommended.
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Affiliation(s)
- Phoebe Haslam
- Queensland University of TechnologyFaculty of HealthSchool of Clinical SciencesBrisbaneQueenslandAustralia
- Radiation Oncology Princess Alexandra HospitalBrisbaneQueenslandAustralia
| | - Elizabeth Brown
- Queensland University of TechnologyFaculty of HealthSchool of Clinical SciencesBrisbaneQueenslandAustralia
- Radiation Oncology Princess Alexandra HospitalBrisbaneQueenslandAustralia
| | - Julie Burbery
- Queensland University of TechnologyFaculty of HealthSchool of Clinical SciencesBrisbaneQueenslandAustralia
| | - Catriona Hargrave
- Queensland University of TechnologyFaculty of HealthSchool of Clinical SciencesBrisbaneQueenslandAustralia
- Radiation Oncology Princess Alexandra HospitalRaymond TerraceBrisbaneQueenslandAustralia
| | - Yoo Young Lee
- Radiation Oncology Princess Alexandra HospitalBrisbaneQueenslandAustralia
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Oar B, Brown A, Newman G, Boles A, Rumley CN, Doyle R, Baines J, Tan A. Improvement in male pelvis magnetic resonance image contouring following radiologist-delivered training. J Med Radiat Sci 2024; 71:114-122. [PMID: 37740640 PMCID: PMC10920942 DOI: 10.1002/jmrs.727] [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: 12/06/2022] [Accepted: 09/07/2023] [Indexed: 09/24/2023] Open
Abstract
INTRODUCTION The magnetic resonance linear accelerator (MRL) combines both magnetic resonance imaging and a linear accelerator, allowing for daily treatment adaptation. This study aimed to assess the impact of radiologist-delivered training in magnetic resonance (MR) contouring of relevant structures within the male pelvis. METHODS Two radiation oncologists, two radiation oncology registrars and seven radiation therapists completed contouring on 10 male pelvis MR datasets both pre- and post-training. A 2-hour MR anatomy training session was delivered by a radiologist, who also provided the 'gold standard' contours. The pre- and post-training contours were compared against the gold standard with Dice similarity coefficient (DSC) and Hausdorff distances calculated; and the pre- and post-confidence scores and timing were compared. RESULTS The improvement in DSC were significant in prostate, rectum and seminal vesicles, with a post-training median DSC of 0.87 ± 0.06, 0.92 ± 0.04 and 0.80 ± 0.14, respectively. The median Hausdorff improved with a median of 1.46 ± 0.78 mm, 0.52 ± 0.32 mm and 1.11 ± 0.86 mm for prostate, rectum and seminal vesicles, respectively. Bladder concordance was high both pre- and post-training. Urethra contours improved post-training, however, remained difficult to contour with a median post-DSC of 0.51 ± 0.24. Overall, confidence scoring improved (P < 0.001) and timing decreased by an average of 4.4 ± 16.4 min post-training. CONCLUSION Radiologist-delivered training improved concordance of male pelvis contouring on MR datasets. Further work is required in the identification of urethra on MRs. These findings are of importance in the MRL adaptive workflow.
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Affiliation(s)
- Bronwyn Oar
- Townsville University HospitalTownsvilleQueenslandAustralia
| | - Amy Brown
- Townsville University HospitalTownsvilleQueenslandAustralia
- Queensland University of TechnologyBrisbaneQueenslandAustralia
- James Cook UniversityTownsvilleQueenslandAustralia
| | - Glen Newman
- Townsville University HospitalTownsvilleQueenslandAustralia
| | - Alan Boles
- Queensland XRayTownsvilleQueenslandAustralia
| | - Christopher N. Rumley
- Townsville University HospitalTownsvilleQueenslandAustralia
- James Cook UniversityTownsvilleQueenslandAustralia
| | - Rachel Doyle
- Townsville University HospitalTownsvilleQueenslandAustralia
| | - John Baines
- Townsville University HospitalTownsvilleQueenslandAustralia
- James Cook UniversityTownsvilleQueenslandAustralia
| | - Alex Tan
- Townsville University HospitalTownsvilleQueenslandAustralia
- James Cook UniversityTownsvilleQueenslandAustralia
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7
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Lee PY, Huang BS, Lee SH, Chan TY, Yen E, Lee TF, Cho IC. An investigation into the impact of volumetric rescanning and fractionation treatment on dose homogeneity in liver cancer proton therapy. JOURNAL OF RADIATION RESEARCH 2024; 65:100-108. [PMID: 38037473 PMCID: PMC10803156 DOI: 10.1093/jrr/rrad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/16/2023] [Indexed: 12/02/2023]
Abstract
The Pencil Beam Scanning (PBS) technique in modern particle therapy offers a highly conformal dose distribution but poses challenges due to the interplay effect, an interaction between respiration-induced organ movement and PBS. This study evaluates the effectiveness of different volumetric rescanning strategies in mitigating this effect in liver cancer proton therapy. We used a Geant4-based Monte Carlo simulation toolkit, 'TOPAS,' and an image registration toolbox, 'Elastix,' to calculate 4D dose distributions from 5 patients' four-dimensional computed tomography (4DCT). We analyzed the homogeneity index (HI) value of the Clinical Tumor Volume (CTV) at different rescan numbers and treatment times. Our results indicate that dose homogeneity stabilizes at a low point after a week of treatment, implying that both rescanning and fractionation treatments help mitigate the interplay effect. Notably, an increase in the number of rescans doesn't significantly reduce the mean dose to normal tissue but effectively prevents high localized doses to tissue adjacent to the CTV. Rescanning techniques, based on statistical averaging, require no extra equipment or patient cooperation, making them widely accessible. However, the number of rescans, tumor location, diaphragm movement, and treatment fractionation significantly influence their effectiveness. Therefore, deciding the number of rescans should involve considering the number of beams, treatment fraction size, and total delivery time to avoid unnecessary treatment extension without significant clinical benefits. The results showed that 2-3 rescans are more clinically suitable for liver cancer patients undergoing proton therapy.
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Affiliation(s)
- Pei-Yi Lee
- Proton and Radiation Therapy Center, Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital, No. 129, Dapi Rd., Niaosong Dist., Kaohsiung City, 833401, Taiwan
| | - Bing-Shen Huang
- Proton and Radiation Therapy Center, Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital, No. 129, Dapi Rd., Niaosong Dist., Kaohsiung City, 833401, Taiwan
- Proton and Radiation Therapy Center, Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital, No. 15, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 333011, Taiwan
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 333323, Taiwan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 333323, Taiwan
| | - Shen-Hao Lee
- Proton and Radiation Therapy Center, Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital, No. 15, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 333011, Taiwan
| | - Tsz-Yui Chan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 333323, Taiwan
| | - Eric Yen
- Institute of Physics, Academia Sinica, No. 128, Sec. 2, Academia Rd., Nangang Dist., Taipei City, 115201, Taiwan
| | - Tsair-Fwu Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist., Kaohsiung City, 807618, Taiwan
| | - I-Chun Cho
- Research Center for Radiation Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 333323, Taiwan
- Institute for Radiological Research, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taiwan Taoyuan City, 333323 Taiwan
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Kossmann MRP, Ehret F, Roohani S, Winter SF, Ghadjar P, Acker G, Senger C, Schmid S, Zips D, Kaul D. Histopathologically confirmed radiation-induced damage of the brain - an in-depth analysis of radiation parameters and spatio-temporal occurrence. Radiat Oncol 2023; 18:198. [PMID: 38087368 PMCID: PMC10717523 DOI: 10.1186/s13014-023-02385-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Radiation-induced damage (RID) after radiotherapy (RT) of primary brain tumors and metastases can be challenging to clinico-radiographically distinguish from tumor progression. RID includes pseudoprogression and radiation necrosis; the latter being irreversible and often associated with severe symptoms. While histopathology constitutes the diagnostic gold standard, biopsy-controlled clinical studies investigating RID remain limited. Whether certain brain areas are potentially more vulnerable to RID remains an area of active investigation. Here, we analyze histopathologically confirmed cases of RID in relation to the temporal and spatial dose distribution. METHODS Histopathologically confirmed cases of RID after photon-based RT for primary or secondary central nervous system malignancies were included. Demographic, clinical, and dosimetric data were collected from patient records and treatment planning systems. We calculated the equivalent dose in 2 Gy fractions (EQD22) and the biologically effective dose (BED2) for normal brain tissue (α/β ratio of 2 Gy) and analyzed the spatial and temporal distribution using frequency maps. RESULTS Thirty-three patients were identified. High-grade glioma patients (n = 18) mostly received one normofractionated RT series (median cumulative EQD22 60 Gy) to a large planning target volume (PTV) (median 203.9 ccm) before diagnosis of RID. Despite the low EQD22 and BED2, three patients with an accelerated hyperfractionated RT developed RID. In contrast, brain metastases patients (n = 15; 16 RID lesions) were often treated with two or more RT courses and with radiosurgery or fractionated stereotactic RT, resulting in a higher cumulative EQD22 (median 162.4 Gy), to a small PTV (median 6.7 ccm). All (n = 34) RID lesions occurred within the PTV of at least one of the preceding RT courses. RID in the high-grade glioma group showed a frontotemporal distribution pattern, whereas, in metastatic patients, RID was observed throughout the brain with highest density in the parietal lobe. The cumulative EQD22 was significantly lower in RID lesions that involved the subventricular zone (SVZ) than in lesions without SVZ involvement (median 60 Gy vs. 141 Gy, p = 0.01). CONCLUSIONS Accelerated hyperfractionated RT can lead to RID despite computationally low EQD22 and BED2 in high-grade glioma patients. The anatomical location of RID corresponded to the general tumor distribution of gliomas and metastases. The SVZ might be a particularly vulnerable area.
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Affiliation(s)
- Mario R P Kossmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Department of Radiotherapy and Radiation Oncology, Pius-Hospital Oldenburg, Georgstr. 12, 26121, Oldenburg, Germany
| | - Felix Ehret
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Siyer Roohani
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Sebastian F Winter
- Division of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Pirus Ghadjar
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Güliz Acker
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurosurgery, Charitéplatz 1, 10117, Berlin, Germany
| | - Carolin Senger
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Simone Schmid
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, 10117, Berlin, Germany
| | - Daniel Zips
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Kaul
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Tjelta J, Fjæra LF, Ytre-Hauge KS, Boer CG, Stokkevåg CH. A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose. Phys Med Biol 2023; 68:225010. [PMID: 37820690 DOI: 10.1088/1361-6560/ad0281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023]
Abstract
Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS.Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%-20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models.Main results.The PBPs ranges were within ±0.3mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient.Significance. Aprocedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.
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Affiliation(s)
- Johannes Tjelta
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Department of Physics and Technology, University of Bergen, Bergen, Norway
| | - Lars Fredrik Fjæra
- Department of Physics and Technology, University of Bergen, Bergen, Norway
- Department of Oncology and Medical Physics, Oslo University Hospital, Oslo, Norway
| | | | | | - Camilla Hanquist Stokkevåg
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Department of Physics and Technology, University of Bergen, Bergen, Norway
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Wang H, Barbhaiya CR, Yuan Y, Barbee D, Chen T, Axel L, Chinitz LA, Evans AJ, Byun DJ. A Tool to Integrate Electrophysiological Mapping for Cardiac Radioablation of Ventricular Tachycardia. Adv Radiat Oncol 2023; 8:101272. [PMID: 37415904 PMCID: PMC10320498 DOI: 10.1016/j.adro.2023.101272] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/08/2023] [Indexed: 07/08/2023] Open
Abstract
Purpose Cardiac radioablation is an emerging therapy for recurrent ventricular tachycardia. Electrophysiology (EP) data, including electroanatomic maps (EAM) and electrocardiographic imaging (ECGI), provide crucial information for defining the arrhythmogenic target volume. The absence of standardized workflows and software tools to integrate the EP maps into a radiation planning system limits their use. This study developed a comprehensive software tool to enable efficient utilization of the mapping for cardiac radioablation treatment planning. Methods and Materials The tool, HeaRTmap, is a Python-scripted plug-in module on the open-source 3D Slicer software platform. HeaRTmap is able to import EAM and ECGI data and visualize the maps in 3D Slicer. The EAM is translated into a 3D space by registration with cardiac magnetic resonance images (MRI) or computed tomography (CT). After the scar area is outlined on the mapping surface, the tool extracts and extends the annotated patch into a closed surface and converts it into a structure set associated with the anatomic images. The tool then exports the structure set and the images as The Digital Imaging and Communications in Medicine Standard in Radiotherapy for a radiation treatment planning system to import. Overlapping the scar structure on simulation CT, a transmural target volume is delineated for treatment planning. Results The tool has been used to transfer Ensite NavX EAM data into the Varian Eclipse treatment planning system in radioablation on 2 patients with ventricular tachycardia. The ECGI data from CardioInsight was retrospectively evaluated using the tool to derive the target volume for a patient with left ventricular assist device, showing volumetric matching with the clinically used target with a Dice coefficient of 0.71. Conclusions HeaRTmap smoothly fuses EP information from different mapping systems with simulation CT for accurate definition of radiation target volume. The efficient integration of EP data into treatment planning potentially facilitates the study and adoption of the technique.
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Affiliation(s)
- Hesheng Wang
- Department of Radiation Oncology, New York University Grossman School of Medicine, New York, New York
| | - Chirag R. Barbhaiya
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Ye Yuan
- Atropos Health, Palo Alto, California
| | - David Barbee
- Department of Radiation Oncology, New York University Grossman School of Medicine, New York, New York
| | - Ting Chen
- Department of Radiation Oncology, New York University Grossman School of Medicine, New York, New York
| | - Leon Axel
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
- Department of Radiology, New York University Grossman School of Medicine, New York, New York
| | - Larry A. Chinitz
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Andrew J. Evans
- Department of Radiation Oncology, New York University Grossman School of Medicine, New York, New York
| | - David J. Byun
- Department of Radiation Oncology, New York University Grossman School of Medicine, New York, New York
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11
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Xie X, Song Y, Ye F, Wang S, Yan H, Zhao X, Dai J. Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy. Radiat Oncol 2023; 18:170. [PMID: 37840132 PMCID: PMC10577969 DOI: 10.1186/s13014-023-02355-9] [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: 04/21/2023] [Accepted: 09/25/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model. METHODS To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance. RESULTS The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05). CONCLUSIONS The introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.
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Affiliation(s)
- Xin Xie
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No 420, Fuma Road, Jinan District, Fuzhou, 350011, China
| | - Yuchun Song
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Feng Ye
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shulian Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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12
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Lemus OMD, Tanny S, Cummings M, Webster M, Wancura J, Jung H, Zhou Y, Yoon J, Pacella M, Zheng D. Influence of air mapping errors on the dosimetric accuracy of prostate CBCT-guided online adaptive radiation therapy. J Appl Clin Med Phys 2023; 24:e14057. [PMID: 37276082 PMCID: PMC10562036 DOI: 10.1002/acm2.14057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/27/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023] Open
Abstract
PURPOSE CBCT-guided online adaptive radiotherapy (oART) plans presently utilize daily synthetic CTs (sCT) that are automatically generated using deformable registration algorithms. These algorithms may have poor performance at reproducing variable volumes of gas present during treatment. Therefore, we have analyzed the air mapping error between the daily CBCTs and the corresponding sCT and explored its dosimetric effect on oART plan calculation. METHODS Abdominopelvic air volume was contoured on both the daily CBCT images and the corresponding synthetic images for 207 online adaptive pelvic treatments. Air mapping errors were tracked over all fractions. For two case studies representing worst case scenarios, dosimetric effects of air mapping errors were corrected in the sCT images using the daily CBCT air contours, then recalculating dose. Dose volume histogram statistics and 3D gamma passing rates were used to compare the original and air-corrected sCT-based dose calculations. RESULTS All analyzed patients showed observable air pocket contour differences between the sCT and the CBCT images. The largest air volume difference observed in daily CBCT images for a given patient was 276.3 cc, a difference of more than 386% compared to the sCT. For the two case studies, the largest observed change in DVH metrics was a 2.6% reduction in minimum PTV dose, with all other metrics varying by less than 1.5%. 3D gamma passing rates using 1%/1 mm criteria were above 90% when comparing the uncorrected and corrected dose distributions. CONCLUSION Current CBCT-based oART workflow can lead to inaccuracies in the mapping of abdominopelvic air pockets from daily CBCT to the sCT images used for the optimization and calculation of the adaptive plan. Despite the large observed mapping errors, the dosimetric effects of such differences on the accuracy of the adapted plan dose calculation are unlikely to cause differences greater than 3% for prostate treatments.
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Affiliation(s)
- Olga M. Dona Lemus
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Sean Tanny
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Michael Cummings
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Matthew Webster
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Joshua Wancura
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Hyunuk Jung
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Yuwei Zhou
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Jihyung Yoon
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Matthew Pacella
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
| | - Dandan Zheng
- Department of Radiation OncologyUniversity of Rochester Medical CenterNew YorkNew YorkUSA
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13
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Jung S, Kim B, Lee SY, Chang WI, Son J, Park JM, Choi CH, Lee JH, Wu HG, Kim JI, Kim JH. Novel tongue-positioning device to reduce tongue motions during radiation therapy for head and neck cancer: Geometric and dosimetric evaluation. PLoS One 2023; 18:e0291712. [PMID: 37733674 PMCID: PMC10513285 DOI: 10.1371/journal.pone.0291712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 09/04/2023] [Indexed: 09/23/2023] Open
Abstract
This study aimed to assess the performance of a tongue-positioning device in interfractional tongue position reproducibility by cone-beam computed tomography (CBCT). Fifty-two patients treated with radiation therapy (RT) while using a tongue positioning device were included in the study. All patients were treated with 28 or 30 fractions using the volumetric modulated arc therapy technique. CBCT images were acquired at the 1st, 7th, 11th, 15th, 19th, 23th, and 27th fractions. Tongues on planning computed tomography (pCT) and CBCT images were contoured in the treatment planning system. Geometric differences in the tongue between pCT and CBCT were assessed by the Dice similarity coefficient (DSC) and averaged Hausdorff distance (AHD). Two-dimensional in vivo measurements using radiochromic films were performed in 13 patients once a week during sessions. The planned dose distributions were compared with the measured dose distributions using gamma analysis with criteria of 3%/3 mm. In all patients, the mean DSC at the 1st fraction (pCT versus 1st CBCT) was 0.80 while the mean DSC at the 27th fraction (pCT versus 27th CBCT) was 0.77 with statistical significance (p-value = 0.015). There was no statistically significant difference in DSC between the 1st fraction and any other fraction, except for the 27th fraction. There was statistically significant difference in AHD between the 1st fraction and the 19th, 23th, and 27th fractions (p-value < 0.05). In vivo measurements showed an average gamma passing rate of 90.54%. There was no significant difference between measurements at the 1st week and those at other weeks. The tongue geometry during RT was compared between pCT and CBCT. In conclusion, the novel tongue-positioning device was found to minimize interfractional variations in position and shape of the tongue.
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Affiliation(s)
- Seongmoon Jung
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Bitbyeol Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung Young Lee
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Won Ick Chang
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jaeman Son
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jong Min Park
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chang Heon Choi
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Joo Ho Lee
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hong-Gyun Wu
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jung-in Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jin Ho Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Basran PS, Shcherban N, Forman M, Chang J, Nelissen S, Recchia BK, Porter IR. Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning. Vet Radiol Ultrasound 2023; 64:890-903. [PMID: 37394240 DOI: 10.1111/vru.13250] [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: 01/06/2023] [Revised: 04/06/2023] [Accepted: 04/15/2023] [Indexed: 07/04/2023] Open
Abstract
This retrospective analytical observational cohort study aimed to model and predict the classification of feline intestinal diseases from segmentations of a transverse section from small intestine ultrasound (US) image, complete blood count (CBC), and serum biochemical profile data using a variety of machine-learning approaches. In 149 cats from three institutions, images were obtained from cats with biopsy-confirmed small cell epitheliotropic lymphoma (lymphoma), inflammatory bowel disease (IBD), no pathology ("healthy"), and other conditions (warrant a biopsy for further diagnosis). CBC, blood serum chemistry, small intestinal ultrasound, and small intestinal biopsy were obtained within a 2-week interval. CBC and serum biomarkers and radiomic features were combined for modeling. Four classification schemes were investigated: (1) normal versus abnormal; (2) warranting or not warranting a biopsy; (3) lymphoma, IBD, healthy, or other conditions; and (4) lymphoma, IBD, or other conditions. Two feature selection methods were used to identify the top 3, 5, 10, and 20 features, and six machine learning models were trained. The average (95% CI) performance of models for all combinations of features, numbers of features, and types of classifiers was 0.886 (0.871-0.912) for Model 1 (normal vs. abnormal), 0.751 (0.735-0.818) for Model 2 (biopsy vs. no biopsy), 0.504 (0.450-0.556) for Model 3 (lymphoma, IBD, healthy, or other), and 0.531 (0.426-0.589), for Model 4 (lymphoma, IBD, or other). Our findings suggest model accuracies above 0.85 can be achieved in Model 1 and 2, and that including CBC and biochemistry data with US radiomics data did not significantly improve accuracy in our models.
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Affiliation(s)
- Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
- Department of Biological Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Natalya Shcherban
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Marnin Forman
- Cornell University Veterinary Specialists, Stamford, Connecticut, USA
| | - Jasmine Chang
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Sophie Nelissen
- Department of Biomedical Sciences, Section of Anatomic Pathology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | | | - Ian R Porter
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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15
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Yoon E, Kim JI, Park JM, Choi CH, Jung S. Extension of matRad with a modified microdosimetric kinetic model for carbon ion treatment planning: Comparison with Monte Carlo calculation. Med Phys 2023; 50:5884-5896. [PMID: 37162309 DOI: 10.1002/mp.16449] [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: 08/16/2022] [Revised: 04/09/2023] [Accepted: 04/24/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Treatment planning is essential for in silico particle therapy studies. matRad is an open-source research treatment planning system (TPS) based on the local effect model, which is a type of relative biological effectiveness (RBE) model. PURPOSE This study aims to implement a microdosimetric kinetic model (MKM) in matRad and develop an automation algorithm for Monte Carlo (MC) dose recalculation using the TOPAS code. In addition, we provide the developed MKM extension as open-source tool for users. METHODS Carbon beam data were generated using TOPAS MC pencil beam irradiation. We parameterized the TOPAS MC beam data with a double-Gaussian fit and modeled the integral depth doses and lateral spot profiles in the range of 100-430 MeV/u. To implement the MKM, the specific energy data table for Z = 1-6 and integrated depth-specific energy data were acquired based on the Kiefer-Chatterjee track structure and TOPAS MC simulation, respectively. Generic data were integrated into matRad, and treatment planning was performed based on these data. The optimized plan parameters were automatically converted into MC simulation input. Finally, the matRad TPS and TOPAS MC simulations were compared using the RBE-weighted dose calculation results. A comparison was made for three geometries: homogeneous water phantom, inhomogeneous phantom, and patient. RESULTS The RBE-weighted dose (DRBE ) distribution agreed with TOPAS MC within 1.8% for all target sizes for the homogeneous phantom. For the inhomogeneous phantom, the relative difference in the range of 80% of the prescription dose in the distal fall-off region (R80) between the matRad TPS and TOPAS MC was 0.6% (1.1 mm). DRBE between the TPS and the MC was within 4.0%. In the patient case, the difference in the dose-volume histogram parameters for the target volume between the TPS and the MC was less than 2.7%. The relative difference in R80 was 0.7% (1.2 mm). CONCLUSIONS The MKM was successfully implemented in matRad TPS, and the RBE-weighted dose was comparable to that of TOPAS MC. The MKM-implemented matRad was released as an open-source tool. Further investigations with MC simulations can be conducted using this tool, providing a good option for carbon ion research.
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Affiliation(s)
- Euntaek Yoon
- Interdisciplinary program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jung-In Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jong Min Park
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Chang Heon Choi
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seongmoon Jung
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
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16
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Beaulieu L, Ballester F, Granero D, Tedgren ÅC, Haworth A, Lowenstein JR, Ma Y, Mourtada F, Papagiannis P, Rivard MJ, Siebert FA, Sloboda RS, Smith RL, Thomson RM, Verhaegen F, Fonseca G, Vijande J. AAPM WGDCAB Report 372: A joint AAPM, ESTRO, ABG, and ABS report on commissioning of model-based dose calculation algorithms in brachytherapy. Med Phys 2023; 50:e946-e960. [PMID: 37427750 DOI: 10.1002/mp.16571] [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: 10/03/2022] [Revised: 02/16/2023] [Accepted: 04/24/2023] [Indexed: 07/11/2023] Open
Abstract
The introduction of model-based dose calculation algorithms (MBDCAs) in brachytherapy provides an opportunity for a more accurate dose calculation and opens the possibility for novel, innovative treatment modalities. The joint AAPM, ESTRO, and ABG Task Group 186 (TG-186) report provided guidance to early adopters. However, the commissioning aspect of these algorithms was described only in general terms with no quantitative goals. This report, from the Working Group on Model-Based Dose Calculation Algorithms in Brachytherapy, introduced a field-tested approach to MBDCA commissioning. It is based on a set of well-characterized test cases for which reference Monte Carlo (MC) and vendor-specific MBDCA dose distributions are available in a Digital Imaging and Communications in Medicine-Radiotherapy (DICOM-RT) format to the clinical users. The key elements of the TG-186 commissioning workflow are now described in detail, and quantitative goals are provided. This approach leverages the well-known Brachytherapy Source Registry jointly managed by the AAPM and the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center (with associated links at ESTRO) to provide open access to test cases as well as step-by-step user guides. While the current report is limited to the two most widely commercially available MBDCAs and only for 192 Ir-based afterloading brachytherapy at this time, this report establishes a general framework that can easily be extended to other brachytherapy MBDCAs and brachytherapy sources. The AAPM, ESTRO, ABG, and ABS recommend that clinical medical physicists implement the workflow presented in this report to validate both the basic and the advanced dose calculation features of their commercial MBDCAs. Recommendations are also given to vendors to integrate advanced analysis tools into their brachytherapy treatment planning system to facilitate extensive dose comparisons. The use of the test cases for research and educational purposes is further encouraged.
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Affiliation(s)
- Luc Beaulieu
- Service de Physique Médicale et Radioprotection et Axe Oncologie du Centre de Recherche du CHU de Québec, CHU de Québec-Université Laval, Québec, Québec, Canada
- Département de Physique, de Génie Physique et d'Optique et Centre de Recherche sur le Cancer, Université Laval, Québec, Québec, Canada
| | - Facundo Ballester
- Departamento de Física Atómica, Molecular y Nuclear, IRIMED, IIS-La Fe-Universitat de Valencia, Burjassot, Spain
| | - Domingo Granero
- Departamento de Física Atómica, Molecular y Nuclear, IRIMED, IIS-La Fe-Universitat de Valencia, Burjassot, Spain
| | - Åsa Carlsson Tedgren
- Department of Health, Medicine and Caring Sciences (HMV), Radiation Physics, Linköping University, Linköping, Sweden
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology Pathology, Karolinska Institute, Stockholm, Sweden
| | | | - Jessica R Lowenstein
- Department of Radiation Physics, UT MD Anderson Cancer Center, Houston, Texas, USA
| | - Yunzhi Ma
- Service de Physique Médicale et Radioprotection et Axe Oncologie du Centre de Recherche du CHU de Québec, CHU de Québec-Université Laval, Québec, Québec, Canada
- Département de Physique, de Génie Physique et d'Optique et Centre de Recherche sur le Cancer, Université Laval, Québec, Québec, Canada
| | - Firas Mourtada
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Panagiotis Papagiannis
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Mark J Rivard
- Department of Radiation Oncology, Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Frank-André Siebert
- Clinic of Radiotherapy, University Hospital of Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Ron S Sloboda
- Department of Medical Physics, Cross Cancer Institute, Edmonton, Alberta, Canada
- Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
| | - Ryan L Smith
- Alfred Health Radiation Oncology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Rowan M Thomson
- Carleton Laboratory for Radiotherapy Physics, Department of Physics, Carleton University, Ottawa, Ontario, Canada
| | - Frank Verhaegen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Gabriel Fonseca
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Javier Vijande
- Departamento de Física Atómica, Molecular y Nuclear, IRIMED, IIS-La Fe-Universitat de Valencia, Burjassot, Spain
- Instituto de Física Corpuscular, IFIC (UV-CSIC), Burjassot, Spain
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17
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Liu Y, Shang X, Zhao W, Li N, Qu B, Zou Y, Le X, Zhang G, Xu S. Commissioning dose computation model for proton source in pencil beam scanning therapy by convolution neural networks. Phys Med Biol 2023; 68:155010. [PMID: 37406635 DOI: 10.1088/1361-6560/ace49b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/05/2023] [Indexed: 07/07/2023]
Abstract
Objective. Proton source model commissioning (PSMC) is critical for ensuring accurate dose calculation in pencil beam scanning (PBS) proton therapy using Monte Carlo (MC) simulations. PSMC aims to match the calculated dose to the delivered dose. However, commissioning the 'nominal energy' and 'energy spread' parameters in PSMC can be challenging, as these parameters cannot be directly obtained from solving equations. To efficiently and accurately commission the nominal energy and energy spread in a proton source model, we developed a convolution neural network (CNN) named 'PSMC-Net.'Methods. The PSMC-Net was trained separately for 33 energies (E, 70-225 MeV with a step of 5 MeV plus 226.09 MeV). For eachE, a dataset was generated consisting of 150 source model parameters (15 nominal energies ∈ [E,E+ 1.5 MeV], ten spreads ∈ [0, 1]) and the corresponding 150 MC integrated depth doses (IDDs). Of these 150 data pairs, 130 were used for training the network, 10 for validation, and 10 for testing.Results. The source model, built by 33 measured IDDs and 33 PSMC-Nets (cost 0.01 s), was used to compute the MC IDDs. The gamma passing rate (GPRs, 1 mm/1%) between MC and measured IDDs was 99.91 ± 0.12%. However, when no commissioning was made, the corresponding GPR was reduced to 54.11 ± 22.36%, highlighting the tremendous significance of our CNN commissioning method. Furthermore, the MC doses of a spread-out Bragg peak and 20 patient PBS plans were also calculated, and average 3D GPRs (2 mm/2% with a 10% threshold) were 99.89% and 99.96 ± 0.06%, respectively.Significance. We proposed a nova commissioning method of the proton source model using CNNs, which made the PSMC process easy, efficient, and accurate.
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Affiliation(s)
- Yaoying Liu
- School of Physics, Beihang University, Beijing, 102206, People's Republic of China
- National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China
- Department of Radiation Oncology, PLA General Hospital, Beijing, 100853, People's Republic of China
| | - Xuying Shang
- School of Physics, Beihang University, Beijing, 102206, People's Republic of China
- National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China
- Department of Radiation Oncology, PLA General Hospital, Beijing, 100853, People's Republic of China
- Department of Radiation Oncology, Hebei Yizhou Tumor Hospital, Zhuozhou, 072750, People's Republic of China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, 102206, People's Republic of China
- Beihang Hangzhou Innovation Institute, Yuhang Xixi Octagon City, Hangzhou, 310030, People's Republic of China
| | - Nan Li
- Department of Radiation Oncology, Hebei Yizhou Tumor Hospital, Zhuozhou, 072750, People's Republic of China
| | - Baolin Qu
- Department of Radiation Oncology, PLA General Hospital, Beijing, 100853, People's Republic of China
| | - Yue Zou
- Department of Radiation Oncology, Hebei Yizhou Tumor Hospital, Zhuozhou, 072750, People's Republic of China
| | - Xiaoyun Le
- School of Physics, Beihang University, Beijing, 102206, People's Republic of China
| | - Gaolong Zhang
- School of Physics, Beihang University, Beijing, 102206, People's Republic of China
| | - Shouping Xu
- National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China
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18
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Adam DP, Hammer C, Malyshev JZ, Culberson WS, Bradshaw TJ, Grudzinski JJ, Harari PM, Bednarz BP. Creation of waterproof, TLD probes for dose measurements to validate image-based radiopharmaceutical therapy dosimetry workflow. Biomed Phys Eng Express 2023; 9:10.1088/2057-1976/accf22. [PMID: 37084718 PMCID: PMC11186108 DOI: 10.1088/2057-1976/accf22] [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: 01/09/2023] [Accepted: 04/21/2023] [Indexed: 04/23/2023]
Abstract
Voxel-level dosimetry based on nuclear medicine images offers patient-specific personalization of radiopharmaceutical therapy (RPT) treatments. Clinical evidence is emerging demonstrating improvements in treatment precision in patients when voxel-level dosimetry is used compared to MIRD. Voxel-level dosimetry requires absolute quantification of activity concentrations in the patient, but images from SPECT/CT scanners are not quantitative and require calibration using nuclear medicine phantoms. While phantom studies can validate a scanner's ability to recover activity concentrations, these studies provide only a surrogate for the true metric of interest: absorbed doses. Measurements using thermoluminescent dosimeters (TLDs) are a versatile and accurate method of measuring absorbed dose. In this work, a TLD probe was manufactured that can fit into currently available nuclear medicine phantoms for the measurement of absorbed dose of RPT agents. Next, 748 MBq of I-131 was administered to a 16 ml hollow source sphere placed in a 6.4 L Jaszczak phantom in addition to six TLD probes, each holding 4 TLD-100 1 × 1 × 1 mm TLD-100 (LiF:Mg,Ti) microcubes. The phantom then underwent a SPECT/CT scan in accordance with a standard SPECT/CT imaging protocol for I-131. The SPECT/CT images were then input into a Monte Carlo based RPT dosimetry platform named RAPID and a three dimensional dose distribution in the phantom was estimated. Additionally, a GEANT4 benchmarking scenario (denoted 'idealized') was created using a stylized representation of the phantom. There was good agreement for all six probes, the differences between measurement and RAPID ranged between -5.5% and 0.9%. The difference between the measured and the idealized GEANT4 scenario was calculated and ranged from -4.3% and -20.5%. This work demonstrates good agreement between TLD measurements and RAPID. In addition, it introduces a novel TLD probe that can be easily introduced into clinical nuclear medicine workflows to provide QA of image-based dosimetry for RPT treatments.
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Affiliation(s)
- David P Adam
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Clifford Hammer
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Julia Ziege Malyshev
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Wesley S Culberson
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Tyler J Bradshaw
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Joseph J Grudzinski
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Paul M Harari
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
| | - Bryan P Bednarz
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, United States of America
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Walls GM, McCann C, Ball P, Atkins KM, Mak RH, Bedair A, O'Hare J, McAleese J, Harrison C, Tumelty KA, Crockett C, Black SL, Nelson C, O'Connor J, Hounsell AR, McGarry CK, Butterworth KT, Cole AJ, Jain S, Hanna GG. IA PULMONARY VEIN ATLAS FOR RADIOTHERAPY PLANNING. Radiother Oncol 2023; 184:109680. [PMID: 37105303 DOI: 10.1016/j.radonc.2023.109680] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 04/10/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND AND PURPOSE Cardiac arrhythmia is a recognised potential complication of thoracic radiotherapy, but the responsible cardiac substructures for arrhythmogenesis have not been identified. Arrhythmogenic tissue is commonly located in the pulmonary veins (PVs) of cardiology patients with arrhythmia, however these structures are not currently considered organs-at-risk during radiotherapy planning. A standardised approach to their delineation was developed and evaluated. MATERIALS AND METHODS The gross and radiological anatomy relevant to atrial fibrillation was derived from cardiology and radiology literature by a multidisciplinary team. A region of interest and contouring instructions for radiotherapy computed tomography scans were iteratively developed and subsequently evaluated. Radiation oncologists (n=5) and radiation technologists (n=2) contoured the PVs on the four-dimensional planning datasets of five patients with locally advanced lung cancer treated with 1.8-2.75 Gy fractions. Contours were compared to reference contours agreed by the researchers using geometric and dosimetric parameters. RESULTS The mean dose to the PVs was 35% prescription dose. Geometric and dosimetric similarity of the observer contours with reference contours was fair, with an overall mean Dice of 0.80 ± 0.02. The right superior PV (mean DSC 0.83 ± 0.02) had better overlap than the left (mean DSC 0.80 ± 0.03), but the inferior PVs were equivalent (mean DSC of 0.78). The mean difference in mean dose was 0.79 Gy ± 0.71 (1.46% ± 1.25). CONCLUSION A PV atlas with multidisciplinary approval led to reproducible delineation for radiotherapy planning, supporting the utility of the atlas in future clinical radiotherapy cardiotoxicity research encompassing arrhythmia endpoints.
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Affiliation(s)
- Gerard M Walls
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Conor McCann
- Department of Cardiology, Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Peter Ball
- Department of Radiology, Royal Victoria Hospital, Belfast Health & Social Care Trust, 274 Grosvenor Rd, Belfast, Northern Ireland
| | - Katelyn M Atkins
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Raymond H Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Ahmed Bedair
- North West Cancer Centre, ltnagelvin Hospital, Glenshane Road, Derry, Northern Ireland
| | - Jolyne O'Hare
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Jonathan McAleese
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Claire Harrison
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Karen A Tumelty
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Cathryn Crockett
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Sarah-Louise Black
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Catherine Nelson
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - John O'Connor
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Alan R Hounsell
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Conor K McGarry
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Karl T Butterworth
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Aidan J Cole
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Suneil Jain
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland
| | - Gerard G Hanna
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Lisburn Road, Belfast, Northern Ireland.
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20
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Jihong C, Kerun Q, Kaiqiang C, Xiuchun Z, Yimin Z, Penggang B. CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma. Sci Rep 2023; 13:6624. [PMID: 37095147 PMCID: PMC10125979 DOI: 10.1038/s41598-023-33472-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/13/2023] [Indexed: 04/26/2023] Open
Abstract
This study aims to utilize a hybrid approach of phantom correction and deep learning for synthesized CT (sCT) images generation based on cone-beam CT (CBCT) images for nasopharyngeal carcinoma (NPC). 52 CBCT/CT paired images of NPC patients were used for model training (41), validation (11). Hounsfield Units (HU) of the CBCT images was calibrated by a commercially available CIRS phantom. Then the original CBCT and the corrected CBCT (CBCT_cor) were trained separately with the same cycle generative adversarial network (CycleGAN) to generate SCT1 and SCT2. The mean error and mean absolute error (MAE) were used to quantify the image quality. For validations, the contours and treatment plans in CT images were transferred to original CBCT, CBCT_cor, SCT1 and SCT2 for dosimetric comparison. Dose distribution, dosimetric parameters and 3D gamma passing rate were analyzed. Compared with rigidly registered CT (RCT), the MAE of CBCT, CBCT_cor, SCT1 and SCT2 were 346.11 ± 13.58 HU, 145.95 ± 17.64 HU, 105.62 ± 16.08 HU and 83.51 ± 7.71 HU, respectively. Moreover, the average dosimetric parameter differences for the CBCT_cor, SCT1 and SCT2 were 2.7% ± 1.4%, 1.2% ± 1.0% and 0.6% ± 0.6%, respectively. Using the dose distribution of RCT images as reference, the 3D gamma passing rate of the hybrid method was significantly better than the other methods. The effectiveness of CBCT-based sCT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma was confirmed. The image quality and dose accuracy of SCT2 were outperform the simple CycleGAN method. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.
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Affiliation(s)
- Chen Jihong
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Quan Kerun
- Department of Radiation Oncology, Xiangtan City Central Hospital, Xiangtan, 411100, Hunan, China
| | - Chen Kaiqiang
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Zhang Xiuchun
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Zhou Yimin
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Bai Penggang
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China.
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21
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Rüfenacht E, Kamath A, Suter Y, Poel R, Ermiş E, Scheib S, Reyes M. PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107374. [PMID: 36738608 DOI: 10.1016/j.cmpb.2023.107374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/09/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. METHODS The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. RESULTS The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. CONCLUSIONS The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.
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Affiliation(s)
- Elias Rüfenacht
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland.
| | - Amith Kamath
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland
| | - Yannick Suter
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland
| | - Robert Poel
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ekin Ermiş
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Stefan Scheib
- Varian Medical Systems Imaging Laboratory GmbH, Baden, Switzerland
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern 3008, Switzerland
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22
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Wahid KA, Lin D, Sahin O, Cislo M, Nelms BE, He R, Naser MA, Duke S, Sherer MV, Christodouleas JP, Mohamed ASR, Murphy JD, Fuller CD, Gillespie EF. Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites. Sci Data 2023; 10:161. [PMID: 36949088 PMCID: PMC10033824 DOI: 10.1038/s41597-023-02062-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/10/2023] [Indexed: 03/24/2023] Open
Abstract
Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.
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Affiliation(s)
- Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Diana Lin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Onur Sahin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael Cislo
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohammed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Simon Duke
- Department of Radiation Oncology, Cambridge University Hospitals, Cambridge, UK
| | - Michael V Sherer
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - John P Christodouleas
- Department of Radiation Oncology, The University of Pennsylvania Cancer Center, Philadelphia, PA, USA
- Elekta, Atlanta, GA, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - James D Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Erin F Gillespie
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Fred Hutchinson Cancer Center, Seattle, WA, USA.
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23
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Kraus KM, Oreshko M, Bernhardt D, Combs SE, Peeken JC. Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition. Front Oncol 2023; 13:1124592. [PMID: 37007119 PMCID: PMC10050584 DOI: 10.3389/fonc.2023.1124592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/01/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionPneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.MethodsWe investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.ResultsResults were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.ConclusionOur results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.
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Affiliation(s)
- Kim Melanie Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
- *Correspondence: Kim Melanie Kraus,
| | - Maksym Oreshko
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Medical Faculty, University hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
| | - Jan Caspar Peeken
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU) GmbH German Research Center for Environmental Health, Neuherberg, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Germany
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24
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Nousiainen K, Santurio GV, Lundahl N, Cronholm R, Siversson C, Edmund JM. Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac. J Appl Clin Med Phys 2023; 24:e13838. [PMID: 36347050 PMCID: PMC10018672 DOI: 10.1002/acm2.13838] [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: 08/04/2021] [Revised: 09/30/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE A hybrid magnetic resonance linear accelerator (MRL) can perform magnetic resonance imaging (MRI) with high soft-tissue contrast to be used for online adaptive radiotherapy (oART). To obtain electron densities needed for the oART dose calculation, a computed tomography (CT) is often deformably registered to MRI. Our aim was to evaluate an MRI-only based synthetic CT (sCT) generation as an alternative to the deformed CT (dCT)-based oART in the abdominal region. METHODS The study data consisted of 57 patients who were treated on a 0.35 T MRL system mainly for abdominal tumors. Simulation MRI-CT pairs of 43 patients were used for training and validation of a prototype convolutional neural network sCT-generation algorithm, based on HighRes3DNet, for the abdominal region. For remaining test patients, sCT images were produced from simulation MRIs and daily MRIs. The dCT-based plans were re-calculated on sCT with identical calculation parameters. The sCT and dCT were compared in terms of geometric agreement and calculated dose. RESULTS The mean and one standard deviation of the geometric agreement metrics over dCT-sCT-pairs were: mean error of 8 ± 10 HU, mean absolute error of 49 ± 10 HU, and Dice similarity coefficient of 55 ± 12%, 60 ± 5%, and 82 ± 15% for bone, fat, and lung tissues, respectively. The dose differences between the sCT and dCT-based dose for planning target volumes were 0.5 ± 0.9%, 0.6 ± 0.8%, and 0.5 ± 0.8% at D2% , D50% , and D98% in physical dose and 0.8 ± 1.4%, 0.8 ± 1.2%, and 0.6 ± 1.1% in biologically effective dose (BED). For organs-at-risk, the dose differences of all evaluated dose-volume histogram points were within [-4.5%, 7.8%] and [-1.1 Gy, 3.5 Gy] in both physical dose and BED. CONCLUSIONS The geometric agreement metrics were within typically reported values and most average relative dose differences were within 1%. Thus, an MRI-only sCT-based approach is a promising alternative to the current clinical practice of the abdominal oART on MRL.
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Affiliation(s)
- Katri Nousiainen
- Department of Physics, University of Helsinki, Helsinki, Finland.,HUS Cancer Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Grichar Valdes Santurio
- Department of Oncology, Radiotherapy Research Unit, Herlev and Gentofte Hospital, Copenhagen University, Herlev, Denmark
| | | | | | | | - Jens M Edmund
- Department of Oncology, Radiotherapy Research Unit, Herlev and Gentofte Hospital, Copenhagen University, Herlev, Denmark.,Nils Bohr Institute, Copenhagen University, Copenhagen, Denmark
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Applying Multi-Metric Deformable Image Registration for Dose Accumulation in Combined Cervical Cancer Radiotherapy. J Pers Med 2023; 13:jpm13020323. [PMID: 36836556 PMCID: PMC9963278 DOI: 10.3390/jpm13020323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/31/2023] [Accepted: 02/11/2023] [Indexed: 02/16/2023] Open
Abstract
(1) Purpose: Challenges remain in dose accumulation for cervical cancer radiotherapy combined with external beam radiotherapy (EBRT) and brachytherapy (BT) as there are many large and complex organ deformations between different treatments. This study aims to improve deformable image registration (DIR) accuracy with the introduction of multi-metric objectives for dose accumulation of EBRT and BT. (2) Materials and methods: Twenty cervical cancer patients treated with EBRT (45-50 Gy/25 fractions) and high-dose-rate BT (≥20 Gy in 4 fractions) were included for DIR. The multi-metric DIR algorithm included an intensity-based metric, three contour-based metrics, and a penalty term. Nonrigid B-spine transformation was used to transform the planning CT images from EBRT to the first BT, with a six-level resolution registration strategy. To evaluate its performance, the multi-metric DIR was compared with a hybrid DIR provided by commercial software. The DIR accuracy was measured by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) between deformed and reference organ contours. The accumulated maximum dose of 2 cc (D2cc) of the bladder and rectum was calculated and compared to simply addition of D2cc from EBRT and BT (ΔD2cc). (3) Results: The mean DSC of all organ contours for the multi-metric DIR were significantly higher than those for the hybrid DIR (p ≤ 0.011). In total, 70% of patients had DSC > 0.8 using the multi-metric DIR, while 15% of patients had DSC > 0.8 using the commercial hybrid DIR. The mean ΔD2cc of the bladder and rectum for the multi-metric DIR were 3.25 ± 2.29 and 3.54 ± 2.02 GyEQD2, respectively, whereas those for the hybrid DIR were 2.68 ± 2.56 and 2.32 ± 3.25 GyEQD2, respectively. The multi-metric DIR resulted in a much lower proportion of unrealistic D2cc than the hybrid DIR (2.5% vs. 17.5%). (4) Conclusions: Compared with the commercial hybrid DIR, the introduced multi-metric DIR significantly improved the registration accuracy and resulted in a more reasonable accumulated dose distribution.
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Zary N, Eysenbach G, Bönsch A, Gruber LJ, Ooms M, Melchior C, Motmaen I, Wilpert C, Rashad A, Kuhlen TW, Hölzle F, Puladi B. Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study. JMIR Serious Games 2023; 11:e40541. [PMID: 36656632 PMCID: PMC9947820 DOI: 10.2196/40541] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/31/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND As an integral part of computer-assisted surgery, virtual surgical planning (VSP) leads to significantly better surgery results, such as for oral and maxillofacial reconstruction with microvascular grafts of the fibula or iliac crest. It is performed on a 2D computer desktop screen (DS) based on preoperative medical imaging. However, in this environment, VSP is associated with shortcomings, such as a time-consuming planning process and the requirement of a learning process. Therefore, a virtual reality (VR)-based VSP application has great potential to reduce or even overcome these shortcomings due to the benefits of visuospatial vision, bimanual interaction, and full immersion. However, the efficacy of such a VR environment has not yet been investigated. OBJECTIVE This study aimed to demonstrate the possible advantages of a VR environment through a substep of VSP, specifically the segmentation of the fibula (calf bone) and os coxae (hip bone), by conducting a training course in both DS and VR environments and comparing the results. METHODS During the training course, 6 novices were taught how to use a software application in a DS environment (3D Slicer) and in a VR environment (Elucis) for the segmentation of the fibula and os coxae, and they were asked to carry out the maneuvers as accurately and quickly as possible. Overall, 13 fibula and 13 os coxae were segmented for each participant in both methods (VR and DS), resulting in 156 different models (78 fibula and 78 os coxae) per method (VR and DS) and 312 models in total. The individual learning processes in both environments were compared using objective criteria (time and segmentation performance) and self-reported questionnaires. The models resulting from the segmentation were compared mathematically (Hausdorff distance and Dice coefficient) and evaluated by 2 experienced radiologists in a blinded manner. RESULTS A much faster learning curve was observed for the VR environment than the DS environment (β=.86 vs β=.25). This nearly doubled the segmentation speed (cm3/min) by the end of training, leading to a shorter time (P<.001) to reach a qualitative result. However, there was no qualitative difference between the models for VR and DS (P=.99). The VR environment was perceived by participants as more intuitive and less exhausting, and was favored over the DS environment. CONCLUSIONS The more rapid learning process and the ability to work faster in the VR environment could save time and reduce the VSP workload, providing certain advantages over the DS environment.
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Affiliation(s)
| | | | - Andrea Bönsch
- Visual Computing Institute, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - Lennart Johannes Gruber
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Mark Ooms
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Claire Melchior
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Ila Motmaen
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Caroline Wilpert
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Ashkan Rashad
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Torsten Wolfgang Kuhlen
- Visual Computing Institute, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - Frank Hölzle
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Behrus Puladi
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany.,Institut of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany
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Chang J, Porter IR, Forman MA, Shcherban N, Basran PS. Intra- and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images. Vet Radiol Ultrasound 2023; 64:131-139. [PMID: 36049073 PMCID: PMC10087235 DOI: 10.1111/vru.13148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 01/25/2023] Open
Abstract
Measurements of intestinal wall thicknesses from ultrasound imaging (US) are routinely used to support diagnoses of intestinal disorders in cats, however published studies describing observer agreement are currently lacking. The aim of this retrospective, observer agreement study was to quantify inter- and intraobserver repeatability and agreement in the measurement of intestinal wall layer thicknesses and the segmentation of transverse sections of small intestines in US images of 20 cats. Intestinal wall layer thickness measurements of the mucosa, submucosa, muscularis, serosa layer, and total thickness of these layers were performed on five cats with small cell epitheliotropic lymphoma, five with inflammatory bowel disease, and 10 with other conditions. Thickness measurements and the segmentation encompassing the serosa layer were obtained from five observers four times non-sequentially. The average standard deviation in thickness measurements (95% confidence interval) in the mucosa, submucosa, muscularis, serosa, and total thickness were 0.35 (0.07-0.95), 0.24 (0.07-0.52), 0.22 (0.06-0.49), 0.20 (0.05-0.49), and 0.57 (0.11-1.60) mm, respectively. The average intraclass correlation coefficients, which estimates the degree of consistency in thickness measurements and segmentation areas for each observer, ranged from 0.355 to 0.870 and 0.850 to 0.993, respectively. The interclass correlation coefficient, which estimates the degree of consistency when measuring a thickness or segmentation area over all observers ranged from 0.115 to 0.753, and 0.811 to 0.902, respectively. The overall average Dice Coefficient, which estimates the extent of overlap of the segmentations for all observers was 0.957 (0.933 to 0.972). Our results suggest segmentations of small intestines have a higher interobserver agreement than measurements of intestinal wall thicknesses.
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Affiliation(s)
- Jasmine Chang
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Ian R Porter
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Marnin A Forman
- Cornell University Veterinary Specialists, Stamford, Connecticut, USA.,Visiting Associate Clinical Professor of Medicine, Cornell University College of Veterinary Medicine, Ithaca, New York, USA
| | - Natalya Shcherban
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
| | - Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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Dorent R, Kujawa A, Ivory M, Bakas S, Rieke N, Joutard S, Glocker B, Cardoso J, Modat M, Batmanghelich K, Belkov A, Calisto MB, Choi JW, Dawant BM, Dong H, Escalera S, Fan Y, Hansen L, Heinrich MP, Joshi S, Kashtanova V, Kim HG, Kondo S, Kruse CN, Lai-Yuen SK, Li H, Liu H, Ly B, Oguz I, Shin H, Shirokikh B, Su Z, Wang G, Wu J, Xu Y, Yao K, Zhang L, Ourselin S, Shapey J, Vercauteren T. CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation. Med Image Anal 2023; 83:102628. [PMID: 36283200 DOI: 10.1016/j.media.2022.102628] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/17/2022] [Accepted: 09/10/2022] [Indexed: 02/04/2023]
Abstract
Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to full supervision (median Dice score - VS: 92.5%; Cochleas: 87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.
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Affiliation(s)
- Reuben Dorent
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Aaron Kujawa
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Marina Ivory
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Samuel Joutard
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Ben Glocker
- Department of Computing, Imperial College London, Department of Computing, London, United Kingdom
| | - Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | | | - Arseniy Belkov
- Moscow Institute of Physics and Technology, Moscow, Russia
| | | | - Jae Won Choi
- Department of Radiology, Armed Forces Yangju Hospital, Yangju, Republic of Korea
| | | | - Hexin Dong
- Center for Data Science, Peking University, Beijing, China
| | - Sergio Escalera
- Artificial Intelligence in Medicine Lab (BCN-AIM) and Human Behavior Analysis Lab (HuPBA), Universitat de Barcelona, Barcelona, Spain
| | - Yubo Fan
- Vanderbilt University, Nashville, USA
| | - Lasse Hansen
- Institute of Medical Informatics, Universität zu Lübeck, Germany
| | | | - Smriti Joshi
- Artificial Intelligence in Medicine Lab (BCN-AIM) and Human Behavior Analysis Lab (HuPBA), Universitat de Barcelona, Barcelona, Spain
| | | | - Hyeon Gyu Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | | | | | | | - Hao Li
- Vanderbilt University, Nashville, USA
| | - Han Liu
- Vanderbilt University, Nashville, USA
| | - Buntheng Ly
- Inria, Université Côte d'Azur, Sophia Antipolis, France
| | - Ipek Oguz
- Vanderbilt University, Nashville, USA
| | - Hyungseob Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Boris Shirokikh
- Skolkovo Institute of Science and Technology, Moscow, Russia; Artificial Intelligence Research Institute (AIRI), Moscow, Russia
| | - Zixian Su
- University of Liverpool, Liverpool, United Kingdom; School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianghao Wu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanwu Xu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Kai Yao
- University of Liverpool, Liverpool, United Kingdom; School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Li Zhang
- Center for Data Science, Peking University, Beijing, China
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom; Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
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MRI-based adaptive radiotherapy has the potential to reduce dysphagia in patients with head and neck cancer. Phys Med 2023; 105:102511. [PMID: 36563523 DOI: 10.1016/j.ejmp.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 11/24/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
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3D slicer-based calculation of hematoma irregularity index for predicting hematoma expansion in intracerebral hemorrhage. BMC Neurol 2022; 22:452. [PMID: 36471307 PMCID: PMC9720921 DOI: 10.1186/s12883-022-02983-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 11/18/2022] [Indexed: 12/08/2022] Open
Abstract
BACKGROUND Irregular hematoma is considered as a risk sign of hematoma expansion. The aim of this study was to quantify hematoma irregularity with computed tomography based on 3D Slicer. METHODS Patients with spontaneous intracerebral hemorrhage who underwent an initial and subsequent non-contrast computed tomography (CT) at a single medical center between January 2019 to January 2020 were retrospectively identified. The Digital Imaging and Communication in Medicine (DICOM) standard images were loaded into the 3D Slicer, and the surface area (S) and volume (V) of hematoma were calculated. The hematoma irregularity index (HII) was defined as [Formula: see text]. Logistic regression analyses and receiver operating characteristic (ROC) curve analysis were performed to assess predictive performance of HII. RESULTS The enrolled patients were divided into those with hematoma enlargement (n = 36) and those without the enlargement (n = 57). HII in hematoma expansion group was 130.4 (125.1-140.0), and the index in non-enlarged hematoma group was 118.6 (113.5-122.3). There was significant difference in HII between the two groups (P < 0.01). Multivariate logistic regression analysis revealed that the HII was significantly associated with hematoma expansion before (odds ratio = 1.203, 95% confidence interval [CI], 1.115-1.298; P < 0.001) and after adjustment for age, hematoma volume, Glasgow Coma Scale score (odds ratio = 1.196, 95% CI, 1.102-1.298, P < 0.001). The area under the ROC curve was 0.86 (CI, 0.78-0.93, P < 0.01), and the best cutoff of HII for predicting hematoma growth was 123.8. CONCLUSION As a quantitative indicator of irregular hematoma, HII can be calculated using the 3D Slicer. And the HII was independently correlated with hematoma expansion.
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Abdul Hadi MFR, Abdullah AN, Hashikin NAA, Ying CK, Yeong CH, Yoon TL, Ng KH, Ng KH. Utilizing 3D Slicer to incorporate tomographic images into GATE Monte Carlo simulation for personalized dosimetry in yttrium-90 radioembolization. Med Phys 2022; 49:7742-7753. [PMID: 36098271 DOI: 10.1002/mp.15980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Monte Carlo (MC) simulation is an important technique that can help design advanced and challenging experimental setups. GATE (Geant4 application for tomographic emission) is a useful simulation toolkit for applications in nuclear medicine. Transarterial radioembolization is a treatment for liver cancer, where microspheres embedded with yttrium-90 (90 Y) are administered intra-arterially to the tumor. Personalized dosimetry for this treatment may provide higher dosimetry accuracy compared to the conventional partition model (PM) calculation. However, incorporation of three-dimensional tomographic input data into MC simulation is an intricate process. In this article, 3D Slicer, free and open-source software, was utilized for the incorporation of patient tomographic images into GATE to demonstrate the feasibility of personalized dosimetry in hepatic radioembolization with 90 Y. METHODS In this article, the steps involved in importing, segmenting, and registering tomographic images using 3D Slicer were thoroughly described, before importing them into GATE for MC simulation. The absorbed doses estimated using GATE were then compared with that of PM. SlicerRT, a 3D Slicer extension, was then used to visualize the isodose from the MC simulation. RESULTS A workflow diagram consisting of all the steps taken in the utilization of 3D Slicer for personalized dosimetry in 90 Y radioembolization has been presented in this article. In comparison to the MC simulation, the absorbed doses to the tumor and normal liver were overestimated by PM by 105.55% and 20.23%, respectively, whereas for lungs, the absorbed dose estimated by PM was underestimated by 25.32%. These values were supported by the isodose distribution obtained via SlicerRT, suggesting the presence of beta particles outside the volumes of interest. These findings demonstrate the importance of personalized dosimetry for a more accurate absorbed dose estimation compared to PM. CONCLUSION The methodology provided in this study can assist users (especially students or researchers who are new to MC simulation) in navigating intricate steps required in the importation of tomographic data for MC simulation. These steps can also be utilized for other radiation therapy related applications, not necessarily limited to internal dosimetry.
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Affiliation(s)
| | | | | | - Chee Keat Ying
- Oncological & Radiological Science Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Pulau Pinang, 13200, Malaysia
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Selangor, 47500, Malaysia
| | - Tiem Leong Yoon
- School of Physics, Universiti Sains Malaysia, USM, Penang, 11800, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Federal Territory of Kuala Lumpur, 50603, Malaysia.,Faculty of Medicine and Health Sciences, UCSI University, Port Dickson, Negeri Sembilan, 71010, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia.,Faculty of Medicine and Health Sciences, UCSI University. 71010 Port Dickson, Negeri Sembilan, Malaysia
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Xie X, Song Y, Ye F, Yan H, Wang S, Zhao X, Dai J. The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed. J Appl Clin Med Phys 2022; 23:e13793. [PMID: 36265074 PMCID: PMC9797164 DOI: 10.1002/acm2.13793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/20/2022] [Accepted: 09/02/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND AND PURPOSE For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non-correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial-, region-, and intensity-based) were jointly used in DIR algorithm for improved accuracy. MATERIALS AND METHODS Three types of metrics were combined to form a single-objective function in DIR algorithm. Fiducial-based metric was used to minimize the distance between the corresponding point sets of two images. Region-based metric was used to improve the overlap between the corresponding areas of two images. Intensity-based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast-conserving surgery and postoperative radiotherapy were enrolled in this study. RESULTS For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary. CONCLUSIONS Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.
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Affiliation(s)
- Xin Xie
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yuchun Song
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Feng Ye
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Shulian Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Lasso A, Herz C, Nam H, Cianciulli A, Pieper S, Drouin S, Pinter C, St-Onge S, Vigil C, Ching S, Sunderland K, Fichtinger G, Kikinis R, Jolley MA. SlicerHeart: An open-source computing platform for cardiac image analysis and modeling. Front Cardiovasc Med 2022; 9:886549. [PMID: 36148054 PMCID: PMC9485637 DOI: 10.3389/fcvm.2022.886549] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022] Open
Abstract
Cardiovascular disease is a significant cause of morbidity and mortality in the developed world. 3D imaging of the heart's structure is critical to the understanding and treatment of cardiovascular disease. However, open-source tools for image analysis of cardiac images, particularly 3D echocardiographic (3DE) data, are limited. We describe the rationale, development, implementation, and application of SlicerHeart, a cardiac-focused toolkit for image analysis built upon 3D Slicer, an open-source image computing platform. We designed and implemented multiple Python scripted modules within 3D Slicer to import, register, and view 3DE data, including new code to volume render and crop 3DE. In addition, we developed dedicated workflows for the modeling and quantitative analysis of multi-modality image-derived heart models, including heart valves. Finally, we created and integrated new functionality to facilitate the planning of cardiac interventions and surgery. We demonstrate application of SlicerHeart to a diverse range of cardiovascular modeling and simulation including volume rendering of 3DE images, mitral valve modeling, transcatheter device modeling, and planning of complex surgical intervention such as cardiac baffle creation. SlicerHeart is an evolving open-source image processing platform based on 3D Slicer initiated to support the investigation and treatment of congenital heart disease. The technology in SlicerHeart provides a robust foundation for 3D image-based investigation in cardiovascular medicine.
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Affiliation(s)
- Andras Lasso
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada
| | - Christian Herz
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Hannah Nam
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Alana Cianciulli
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | | | - Simon Drouin
- Software and Information Technology Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | | | - Samuelle St-Onge
- Software and Information Technology Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Chad Vigil
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Stephen Ching
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Kyle Sunderland
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada
| | - Gabor Fichtinger
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Matthew A. Jolley
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States,Division of Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States,*Correspondence: Matthew A. Jolley
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Marshall C, Thirion P, Mihai A, Armstrong JG, Cournane S, Hickey D, McClean B, Quinn J. Interobserver variability of Gross Tumour Volume delineation for colorectal liver metastases using CT and MRI. Adv Radiat Oncol 2022; 8:101020. [PMID: 36176355 PMCID: PMC9513217 DOI: 10.1016/j.adro.2022.101020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/28/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the interobserver variability in the contouring of the gross tumor volume (GTV) on magnetic resonance (MR) imaging and computed tomography (CT) for colorectal liver metastases in the setting of SABR. Methods and Materials Three expert radiation oncologists contoured 10 GTV volumes on 3 MR imaging sequences and on the CT image data set. Three metrics were chosen to evaluate the interobserver variability: the conformity index, the DICE coefficient, and the maximum Hausdorff distance (HDmax). Statistical analysis of the results was performed using a 1-sided permutation test. Results For all 3 metrics, the MR liver acquisition volume acquisition (MR LAVA) showed the lowest interobserver variability. Analysis showed a significant difference (P < .01) in the mean DICE, an overlap metric, for MR LAVA (0.82) and CT (0.74). The HDmax that highlights boundary errors also showed a significant difference (P = .04) with MR LAVA having a lower mean HDmax (7.2 mm) compared with CT (5.7 mm). The mean HDmax for both MR single shot fast spin echo (SSFSE) (19.3 mm) and diffusion weighted image (9.5 mm) showed large interobserver variability with MR SSFSE having a mean HDmax of 19.3 mm. A volume comparison between MR LAVA and CT showed a significantly higher volume for small GTVs (<5 cm3) when using MR LAVA for contouring in comparison to CT. Conclusions This study reported the lowest interobserver variability for the MR LAVA, thus indicating the benefit of using MR to complement CT when contouring GTV for colorectal liver metastases.
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Affiliation(s)
- Cora Marshall
- School of Physics, University College Dublin, Dublin, Ireland
- Beacon Hospital, Dublin, Ireland
- Corresponding author: Cora Marshall, MSc
| | - Pierre Thirion
- Beacon Hospital, Dublin, Ireland
- St Luke's Radiation Oncology Network, Dublin, Ireland
| | | | | | - Seán Cournane
- School of Physics, University College Dublin, Dublin, Ireland
| | | | - Brendan McClean
- School of Physics, University College Dublin, Dublin, Ireland
- St Luke's Radiation Oncology Network, Dublin, Ireland
| | - John Quinn
- School of Physics, University College Dublin, Dublin, Ireland
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Walls GM, Giacometti V, Apte A, Thor M, McCann C, Hanna GG, O'Connor J, Deasy JO, Hounsell AR, Butterworth KT, Cole AJ, Jain S, McGarry CK. Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans. Phys Imaging Radiat Oncol 2022; 23:118-126. [PMID: 35941861 PMCID: PMC9356270 DOI: 10.1016/j.phro.2022.07.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/10/2022] Open
Abstract
Cardiotoxicity is a common complication of lung cancer radiotherapy. Segmentation of cardiac substructures is time-consuming and challenging. Deep learning segmentation tools can perform this task in 3D and 4D scans. Performance is high when assessed geometrically, dosimetrically and clinically. Auto-segmentation tools may accelerate clinical workflows and enable research.
Background Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated. Materials and Methods The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015–2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool’s output. Results Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range −1.6–0.3 Gy) and maximum (median 0.4 Gy, range −2.2–0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing. Conclusions Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.
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Pneumonitis after Stereotactic Thoracic Radioimmunotherapy with Checkpoint Inhibitors: Exploration of the Dose-Volume-Effect Correlation. Cancers (Basel) 2022; 14:cancers14122948. [PMID: 35740613 PMCID: PMC9221463 DOI: 10.3390/cancers14122948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/07/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Stereotactic body radiation therapy (SBRT) is widely applied for treatment of early stage lung cancer and pulmonary metastases. Modern immune checkpoint blockade (ICB) is progressively used in cancer treatment. Pneumonitis is a relevant side effect of both thoracic SBRT and ICB. Currently, it remains unclear whether we can presume the same radiation dose–volume–effect correlations and dose constraints for safe application of SBRT + ICB. We present a dose–volume–effect correlation analysis method using pneumonitis contours and dose–volume histograms (DVH). We showed dosimetric differences for pneumonitis volumes between SBRT + ICB and SBRT alone. We found a large extent of pneumonitis, even bilateral and apart from the radiation field for combined SBRT + ICB. We noticed a shift in pneumonitis DVHs towards lower doses and a trend towards decreased areas under the curve (AUC) for SBRT + ICB. This provides a direction for re-evaluation and potential adaptation of lung dose constraints for combined SBRT and ICB. Abstract Thoracic stereotactic body radiation therapy (SBRT) is extensively used in combination with immune checkpoint blockade (ICB). While current evidence suggests that the occurrence of pneumonitis as a side effect of both treatments is not enhanced for the combination, the dose–volume correlation remains unclear. We investigate dose–volume–effect correlations for pneumonitis after combined SBRT + ICB. We analyzed patient clinical characteristics and dosimetric data for 42 data sets for thoracic SBRT with ICB treatment (13) and without (29). Dose volumes were converted into 2 Gy equivalent doses (EQD2), allowing for dosimetric comparison of different fractionation regimes. Pneumonitis volumes were delineated and corresponding DVHs were analyzed. We noticed a shift towards lower doses for combined SBRT + ICB treatment, supported by a trend of smaller areas under the curve (AUC) for SBRT+ ICB (median AUC 1337.37 vs. 5799.10, p = 0.317). We present a DVH-based dose–volume–effect correlation method and observed large pneumonitis volumes, even with bilateral extent in the SBRT + ICB group. We conclude that further studies using this method with enhanced statistical power are needed to clarify whether adjustments of the radiation dose constraints are required to better estimate risks of pneumonitis after the combination of SBRT and ICB.
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The dose accumulation and the impact of deformable image registration on dose reporting parameters in a moving patient undergoing proton radiotherapy. Radiol Oncol 2022; 56:248-258. [PMID: 35575586 PMCID: PMC9122289 DOI: 10.2478/raon-2022-0016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 02/18/2022] [Indexed: 11/20/2022] Open
Abstract
INTRODUCTION Potential changes in patient anatomy during proton radiotherapy may lead to a deviation of the delivered dose. A dose estimate can be computed through a deformable image registration (DIR) driven dose accumulation. The present study evaluates the accumulated dose uncertainties in a patient subject to an inadvertent breathing associated motion. MATERIALS AND METHODS A virtual lung tumour was inserted into a pair of single participant landmark annotated computed tomography images depicting opposite breathing phases, with the deep inspiration breath-hold the planning reference and the exhale the off-reference geometry. A novel Monte Carlo N-Particle, Version 6 (MCNP6) dose engine was developed, validated and used in treatment plan optimization. Three DIR methods were compared and used to transfer the exhale simulated dose to the reference geometry. Dose conformity and homogeneity measures from International Committee on Radioactivity Units and Measurements (ICRU) reports 78 and 83 were evaluated on simulated dose distributions registered with different DIR algorithms. RESULTS The MCNP6 dose engine handled patient-like geometries in reasonable dose calculation times. All registration methods were able to align image associated landmarks to distances, comparable to voxel sizes. A moderate deterioration of ICRU measures was encountered in comparing doses in on and off-reference anatomy. There were statistically significant DIR driven differences in ICRU measures, particularly a 10% difference in the relative D98% for planning tumour volume and in the 3 mm/3% gamma passing rate. CONCLUSIONS T he dose accumulation over two anatomies resulted in a DIR driven uncertainty, important in reporting the associated ICRU measures for quality assurance.
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Jabbarpour A, Mahdavi SR, Vafaei Sadr A, Esmaili G, Shiri I, Zaidi H. Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy. Comput Biol Med 2022; 143:105277. [PMID: 35123139 DOI: 10.1016/j.compbiomed.2022.105277] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/09/2022] [Accepted: 01/27/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE Absorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed. MATERIAL AND METHODS Overall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT. RESULTS The CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%. CONCLUSIONS A promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.
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Affiliation(s)
- Amir Jabbarpour
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Department of Theoretical Physics and Center for Astroparticle Physics, Geneva University, Geneva, Switzerland
| | | | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Spautz S, Jakobi A, Meijers A, Peters N, Löck S, Knopf AC, Troost EGC, Richter C, Stützer K. Experimental validation of 4D log file-based proton dose reconstruction for interplay assessment considering amplitude-sorted 4DCTs. Med Phys 2022; 49:3538-3549. [PMID: 35342943 DOI: 10.1002/mp.15625] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 02/01/2022] [Accepted: 03/13/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE The unpredictable interplay between dynamic proton therapy delivery and target motion in the thorax can lead to severe dose distortions. A fraction-wise four-dimensional (4D) dose reconstruction workflow allows for the assessment of the applied dose after patient treatment while considering the actual beam delivery sequence extracted from machine log files, the recorded breathing pattern and the geometric information from a 4D computed tomography scan (4DCT). Such an algorithm capable of accounting for amplitude-sorted 4DCTs was implemented and its accuracy as well as its sensitivity to input parameter variations was experimentally evaluated. METHODS An anthropomorphic thorax phantom with a movable insert containing a target surrogate and a radiochromic film was irradiated with a monoenergetic field for various 1D target motion forms (sin, sin4) and peak-to-peak amplitudes (5/10/15/20/30 mm). The measured characteristic film dose distributions were compared to the respective sections in the 4D reconstructed doses using a 2D γ-analysis (3mm, 3%); γ-pass rates were derived for different dose grid resolutions (1mm/3mm) and deformable image registrations (DIR, automatic/manual) applied during the 4D dose reconstruction process. In an additional analysis, the sensitivity of reconstructed dose distributions against potential asynchronous timing of the motion and machine log files was investigated for both a monoenergetic field and more realistic 4D robustly optimized fields by artificially introduced offsets of ± 1/5/25/50/250 ms. The resulting dose distributions with asynchronized log files were compared to those with synchronized log files by means of a 3D γ-analysis (1mm, 1%) and the evaluation of absolute dose differences. RESULTS The induced characteristic interplay patterns on the films were well reproduced by the 4D dose reconstruction with 2D γ-pass rates ≥95% for almost all cases with motion magnitudes ≤15 mm. In general, the 2D γ-pass rates showed a significant decrease for larger motion amplitudes and increase when using a finer dose grid resolution but were not affected by the choice of motion form (sin, sin4). There was also a trend, though not statistically significant, towards the manually defined DIR for better quality of the reconstructed dose distributions in the area imaged by the film. The 4D dose reconstruction results for the monoenergetic as well as the 4D robustly optimized fields were robust against small asynchronies between motion and machine log files of up to 5 ms, which is in the order of potential network latencies. CONCLUSIONS We have implemented a 4D log file-based proton dose reconstruction that accounts for amplitude-sorted 4DCTs. Its accuracy was proven to be clinically acceptable for target motion magnitudes of up to 15 mm. Particular attention should be paid to the synchronization of the log file generating systems as the reconstructed dose distribution may vary with log file asynchronies larger than those caused by realistic network delays. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Saskia Spautz
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Annika Jakobi
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nils Peters
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| | - Steffen Löck
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Antje-Christin Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department 1 of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Esther G C Troost
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Christian Richter
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristin Stützer
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
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Lemus OMD, Wang Y, Li F, Jambawalikar S, Horowitz DP, Xu Y, Wuu C. Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy. J Appl Clin Med Phys 2022; 23:e13595. [PMID: 35332646 PMCID: PMC9278692 DOI: 10.1002/acm2.13595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 02/07/2022] [Accepted: 03/01/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Dose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed tomography (CT)‐based calculation for adaptive planning in the upper abdominal region. We hypothesized that deep learning‐based synthetically generated CT images will produce comparable results to a deformed CT (CTdef) in terms of dose calculation, while displaying a more accurate representation of the daily anatomy and therefore superior dosimetric accuracy. Methods We have implemented a cycle‐consistent generative adversarial networks (CycleGANs) architecture to synthesize CT images from the daily acquired CBCT image with minimal error. CBCT and CT images from 17 liver stereotactic body radiation therapy (SBRT) patients were used to train, test, and validate the algorithm. Results The synthetically generated images showed increased signal‐to‐noise ratio, contrast resolution, and reduced root mean square error, mean absolute error, noise, and artifact severity. Superior edge matching, sharpness, and preservation of anatomical structures from the CBCT images were observed for the synthetic images when compared to the CTdef registration method. Three verification plans (CBCT, CTdef, and synthetic) were created from the original treatment plan and dose volume histogram (DVH) statistics were calculated. The synthetic‐based calculation shows comparatively similar results to the CTdef‐based calculation with a maximum mean deviation of 1.5%. Conclusions Our findings show that CycleGANs can produce reliable synthetic images for the adaptive delivery framework. Dose calculations can be performed on synthetic images with minimal error. Additionally, enhanced image quality should translate into better daily alignment, increasing treatment delivery accuracy.
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Affiliation(s)
- Olga M. Dona Lemus
- Department of Radiation OncologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Yi‐Fang Wang
- Department of Radiation OncologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Fiona Li
- Department of Radiation OncologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Sachin Jambawalikar
- Department of RadiologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - David P. Horowitz
- Department of Radiation OncologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- Herbert Irving Comprehensive Cancer CenterNew York CityNew YorkUSA
| | - Yuanguang Xu
- Department of Radiation OncologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Cheng‐Shie Wuu
- Department of Radiation OncologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
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Kayal G, Clayton N, Vergara-Gil A, Struelens L, Bardiès M. Proof-of-concept of DosiTest: A virtual multicentric clinical trial for assessing uncertainties in molecular radiotherapy dosimetry. Phys Med 2022; 97:25-35. [PMID: 35339863 DOI: 10.1016/j.ejmp.2022.03.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/19/2022] [Accepted: 03/14/2022] [Indexed: 11/16/2022] Open
Abstract
Clinical dosimetry in molecular radiotherapy (MRT) is a multi-step procedure, prone to uncertainties at every stage of the dosimetric workflow. These are difficult to assess, especially as some are complex or even impossible to measure experimentally. The DosiTest project was initiated to assess the variability associated with clinical dosimetry, by setting up a 'virtual' multicentric clinical dosimetry trial based on Monte Carlo (MC) modelling. A reference patient model with a realistic geometry and activity input for a specific tracer is considered. Reference absorbed dose rate distribution maps are generated at various time-points from MC modelling, combining precise information on density and activity distributions (voxel wise). Then, centre-specific calibration and patient SPECT/CT datasets are modelled, on which the clinical centres can perform clinical (i.e. image-based) dosimetry. The results of this dosimetric analysis can be benchmarked against the reference dosimetry to assess the variability induced by implementing different clinical dosimetry approaches. The feasibility of DosiTest is presented here for a clinical situation of therapeutic administration of 177Lu-DOTATATE (Lutathera®) peptide receptor radionuclide therapy (PRRT). From a real patient dataset composed of 5 SPECT/CT images and associated calibrations, we generated the reference absorbed dose rate images with GATE. Then, simulated SPECT/CT image generation based on GATE was performed, both for a calibration phantom and virtual patient images. Based on this simulated dataset, image-based dosimetry could be performed, and compared with reference dosimetry. The good agreement, between real and simulated images, and between reference and image-based dosimetry established the proof of concept of DosiTest.
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Affiliation(s)
- G Kayal
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France; SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, Mol 2400, Belgium.
| | - N Clayton
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | - A Vergara-Gil
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | - L Struelens
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, Mol 2400, Belgium
| | - M Bardiès
- ICM, Département de Médecine Nucléaire, Montpellier, France; IRCM, UMR 1194 INSERM, Université de Montpellier and ICM, Montpellier, France
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Maksoud Z, Schmidt MA, Huang Y, Rutzner S, Mansoorian S, Weissmann T, Bert C, Distel L, Semrau S, Lettmaier S, Eyüpoglu I, Fietkau R, Putz F. Transient Enlargement in Meningiomas Treated with Stereotactic Radiotherapy. Cancers (Basel) 2022; 14:cancers14061547. [PMID: 35326697 PMCID: PMC8946188 DOI: 10.3390/cancers14061547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Accurate assessment of treatment efficacy is a prerequisite for the improvement in therapeutic outcomes in clinical trials. However, it is very challenging to accurately track the size of meningiomas after radiotherapy, because of their complex shapes and often slow growth. Measuring the whole tumor volume as opposed to simple diameter measurements to assess treatment efficacy, therefore, is very promising but little is known on expected volumetric changes of meningiomas following radiotherapy. Therefore, in this study, we meticulously investigated volumetric changes in meningiomas following radiotherapy incorporating volumetric measurements from 468 MRI studies and evaluated newly proposed RANO volumetric criteria in the context of radiotherapy. We found that temporary tumor enlargement after radiotherapy overall was rare but occurred significantly more frequently after high than after low single doses of radiation, represented an important differential diagnosis to tumor progression and would have skewed results in a clinical trial if not accounted for. Abstract To investigate the occurrence of pseudoprogression/transient enlargement in meningiomas after stereotactic radiotherapy (RT) and to evaluate recently proposed volumetric RANO meningioma criteria for response assessment in the context of RT. Sixty-nine meningiomas (benign: 90%, atypical: 10%) received stereotactic RT from January 2005–May 2018. A total of 468 MRI studies were segmented longitudinally during a median follow-up of 42.3 months. Best response and local control were evaluated according to recently proposed volumetric RANO criteria. Transient enlargement was defined as volumetric increase ≥20% followed by a subsequent regression ≥20%. The mean best volumetric response was −23% change from baseline (range, −86% to +19%). According to RANO, the best volumetric response was SD in 81% (56/69), MR in 13% (9/69) and PR in 6% (4/69). Transient enlargement occurred in only 6% (4/69) post RT but would have represented 60% (3/5) of cases with progressive disease if not accounted for. Transient enlargement was characterized by a mean maximum volumetric increase of +181% (range, +24% to +389 %) with all cases occurring in the first year post-RT (range, 4.1–10.3 months). Transient enlargement was significantly more frequent with SRS or hypofractionation than with conventional fractionation (25% vs. 2%, p = 0.015). Five-year volumetric control was 97.8% if transient enlargement was recognized but 92.9% if not accounted for. Transient enlargement/pseudoprogression in the first year following SRS and hypofractionated RT represents an important differential diagnosis, especially because of the high volumetric control achieved with stereotactic RT. Meningioma enlargement during subsequent post-RT follow-up and after conventional fractionation should raise suspicion for tumor progression.
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Affiliation(s)
- Ziad Maksoud
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Manuel Alexander Schmidt
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Yixing Huang
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Sandra Rutzner
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Sina Mansoorian
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Thomas Weissmann
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Luitpold Distel
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Sabine Semrau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Sebastian Lettmaier
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Ilker Eyüpoglu
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
- Department of Neurosurgery, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054 Erlangen, Germany; (Z.M.); (Y.H.); (S.R.); (S.M.); (T.W.); (C.B.); (L.D.); (S.S.); (S.L.); (R.F.)
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), 91054 Erlangen, Germany; (M.A.S.); (I.E.)
- Correspondence: ; Tel.: +49-9131-853-4080
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Wu MJ, Knoll RM, Chen JX, Reinshagen K, Roychowdhury P, McKenna MJ, Kozin ED, Remenschneider AK, Jung DH. A Subset of Intracanalicular Vestibular Schwannomas Demonstrates Minimal Growth Over a 10-Year Period. Otol Neurotol 2022; 43:376-384. [PMID: 35020686 DOI: 10.1097/mao.0000000000003436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Vestibular schwannomas (VS) commonly undergo magnetic resonance imaging (MRI) surveillance, but long-term data to support the ideal frequency is limited. Herein, we aim to investigate intracanalicular VS growth predictors and long-term growth rates (GR). STUDY DESIGN Retrospective chart review. SETTING Two tertiary care centers. PATIENTS Sporadic intracanalicular VS with initial conservative management and at least two sequential MRIs. INTERVENTION Serial MRI. MAIN OUTCOME MEASURES VS were categorized by baseline internal auditory canal tertile sublocalization (fundus, midpoint, porus) and size (≤100, 100-200, >200 mm3). Throughout follow-up, volumetric GR (mm3/yr) were determined (baseline-3 yrs, 3-5 yrs, 5-10 yrs) and treatment rates were assessed. RESULTS Ninety-nine intracanalicular VS were identified (mean follow-up of 6.1 ± 4.5 yrs). Mean GR before 5-year follow-up were comparable for baseline tertile involvement and size. After 5-year follow-up, mean GR of VS involving the fundus at baseline were lower than those involving the midpoint and fundus (6.17 ± 21.16 and 119.74 ± 117.57 mm3/yr, respectively; p = 0.034). Mean GR of VS with less than or equal to 100 mm3 at baseline (-7.29 ± 25.44 mm3/yr) were lower than those with 100 to 200 mm3 (86.55 ± 103.99 mm3/yr; p = 0.011) and more than 200 mm3 (45.70 ± 35.71 mm3/yr; p = 0.031). Vestibular schwannomas involving the midpoint and fundus had greater treatment rates compared with VS involving only the fundus (p < 0.001). CONCLUSIONS Baseline tertile involvement and size may predict long-term intracanalicular VS growth where fundal tumors or those less than or equal to 100 mm3 exhibit little long-term growth. Extending surveillance after 5-year follow-up may be reasonable for fundal VS.
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Affiliation(s)
- Matthew J Wu
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
- Loyola University Chicago Stritch School of Medicine, Maywood, Illinois
| | - Renata M Knoll
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
| | - Jenny X Chen
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
| | | | - Prithwijit Roychowdhury
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, University of Massachusetts Medical Center, Worcester, Massachusetts
| | - Michael J McKenna
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
| | - Elliott D Kozin
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
| | - Aaron K Remenschneider
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, University of Massachusetts Medical Center, Worcester, Massachusetts
| | - David H Jung
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
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Kraus KM, Winter J, Zhang Y, Ahmed M, Combs SE, Wilkens JJ, Bartzsch S. Treatment Planning Study for Microbeam Radiotherapy Using Clinical Patient Data. Cancers (Basel) 2022; 14:685. [PMID: 35158953 PMCID: PMC8833598 DOI: 10.3390/cancers14030685] [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: 01/06/2022] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 11/16/2022] Open
Abstract
Microbeam radiotherapy (MRT) is a novel, still preclinical dose delivery technique. MRT has shown reduced normal tissue effects at equal tumor control rates compared to conventional radiotherapy. Treatment planning studies are required to permit clinical application. The aim of this study was to establish a dose comparison between MRT and conventional radiotherapy and to identify suitable clinical scenarios for future applications of MRT. We simulated MRT treatment scenarios for clinical patient data using an inhouse developed planning algorithm based on a hybrid Monte Carlo dose calculation and implemented the concept of equivalent uniform dose (EUD) for MRT dose evaluation. The investigated clinical scenarios comprised fractionated radiotherapy of a glioblastoma resection cavity, a lung stereotactic body radiotherapy (SBRT), palliative bone metastasis irradiation, brain metastasis radiosurgery and hypofractionated breast cancer radiotherapy. Clinically acceptable treatment plans were achieved for most analyzed parameters. Lung SBRT seemed the most challenging treatment scenario. Major limitations comprised treatment plan optimization and dose calculation considering the tissue microstructure. This study presents an important step of the development towards clinical MRT. For clinical treatment scenarios using a sophisticated dose comparison concept based on EUD and EQD2, we demonstrated the capability of MRT to achieve clinically acceptable dose distributions.
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Affiliation(s)
- Kim Melanie Kraus
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Johanna Winter
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Physics Department, Technical University of Munich (TUM), 85748 Garching, Germany
| | - Yating Zhang
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Mabroor Ahmed
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Physics Department, Technical University of Munich (TUM), 85748 Garching, Germany
| | - Stephanie Elisabeth Combs
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), 80336 Munich, Germany
| | - Jan Jakob Wilkens
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Physics Department, Technical University of Munich (TUM), 85748 Garching, Germany
| | - Stefan Bartzsch
- Department of Radiation Oncology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.W.); (Y.Z.); (M.A.); (S.E.C.); (J.J.W.); (S.B.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, 85764 Neuherberg, Germany
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Socha J, Rygielska A, Uziębło-Życzkowska B, Chałubińska-Fendler J, Jurek A, Maciorowska M, Mielniczuk M, Pawłowski P, Tyc-Szczepaniak D, Krzesiński P, Kepka L. Contouring cardiac substructures on average intensity projection 4D-CT for lung cancer radiotherapy: a proposal of a heart valve contouring atlas. Radiother Oncol 2022; 167:261-268. [PMID: 34990727 DOI: 10.1016/j.radonc.2021.12.041] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 12/21/2021] [Accepted: 12/27/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE A detailed contouring atlas of the heart valves is lacking. Existing heart contouring atlases have not been evaluated on average intensity projection four-dimensional non-contrast computed tomography (AVE-4D-CT) scans, routinely used for organ-at-risk delineation in lung cancer radiotherapy. We aimed to develop the heart valve contouring atlas and to assess inter-observer variation in delineation of the heart, its substructures, and coronary arteries on AVE-4D-CT scans, along with its impact on radiotherapy doses. MATERIALS AND METHODS A heart valve contouring atlas was developed. Five radiation oncologists and four cardiologists delineated the valves according to this atlas, and the remaining heart substructures according to the existing atlases, on AVE-4D-CT scans of ten patients who underwent radio(chemo)therapy for NSCLC. The observer contours were then compared to the collectively defined "reference" contours. Spatial variation was assessed using the Sørensen-Dice similarity coefficient (DSC), directed average Hausdorff distance (DAH), directed Hausdorff distance (HD), and the mean distance to agreement (MDA). The effect of spatial variation on radiotherapy doses was assessed using the patients' treatment plans. RESULTS Inter-observer contour overlap (mean DSC) was 0.68, 0.49, 0.45 and 0.45, and inter-observer contour separation (mean DAH) was 2.1, 3.4, 2.6 and 2.9 mm for the pulmonic (PV), aortic (AV), mitral (MV) and tricuspid valve (TV), respectively. Mean HD was higher for TV and MV (13.3 and 11.7mm) than for AV and PV (7.8 and 7mm). The highest mean MDA of 3.1mm was found for AV, and the lowest (1.9mm) for PV. Inter-observer agreement was the lowest for the coronary arteries, but statistically significant dose variation was found mainly in the left ventricular septal and anterior segments. CONCLUSION Our atlas enables reproducible delineation of the heart valves. Delineation of the heart and its substructures on AVE-4D-CT scans is feasible, with inter-observer variability similar to that reported on conventional non-contrast CT scans.
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Affiliation(s)
- Joanna Socha
- Department of Radiotherapy, Military Institute of Medicine, Warsaw, Poland; Department of Radiotherapy, Regional Oncology Centre, Czestochowa, Poland.
| | - Anna Rygielska
- Department of Radiotherapy, Medical Physics Unit, Military Institute of Medicine, Warsaw, Poland
| | | | | | - Agnieszka Jurek
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Małgorzata Maciorowska
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Marta Mielniczuk
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Paweł Pawłowski
- Department of Radiotherapy, Military Institute of Medicine, Warsaw, Poland
| | | | - Paweł Krzesiński
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Lucyna Kepka
- Department of Radiotherapy, Military Institute of Medicine, Warsaw, Poland
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Woodford K, Panettieri V, Ruben JD, Davis S, Tran Le T, Miller S, Senthi S. Oesophageal IGRT considerations for SBRT of LA-NSCLC: barium-enhanced CBCT and interfraction motion. Radiat Oncol 2021; 16:218. [PMID: 34775990 PMCID: PMC8591953 DOI: 10.1186/s13014-021-01946-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 11/03/2021] [Indexed: 12/25/2022] Open
Abstract
Background To determine the optimal volume of barium for oesophageal localisation on cone-beam CT (CBCT) for locally-advanced non-small cell lung cancers (NSCLC) and quantify the interfraction oesophageal movement relative to tumour. Methods Twenty NSCLC patients with mediastinal and/or hilar disease receiving radical radiotherapy were recruited. The first five patients received 25 ml of barium prior to their planning CT and alternate CBCTs during treatment. Subsequent five patient cohorts, received 15 ml, 10 ml and 5 ml. Six observers contoured the oesophagus on each of the 107 datasets and consensus contours were created. Overall 642 observer contours were generated and interobserver contouring reproducibility was assessed. The kappa statistic, dice coefficient and Hausdorff Distance (HD) were used to compare barium-enhanced CBCTs and non-enhanced CBCTs. Oesophageal displacement was assessed using the HD between consensus contours of barium-enhanced CBCTs and planning CTs. Results Interobserver contouring reproducibility was significantly improved in barium-enhanced CBCTs compared to non-contrast CBCTs with minimal difference between barium dose levels. Only 10 mL produced a significantly higher kappa (0.814, p = 0.008) and dice (0.895, p = 0.001). The poorer the reproducibility without barium, the greater the improvement barium provided. The median interfraction HD between consensus contours was 4 mm, with 95% of the oesophageal displacement within 15 mm. Conclusions 10 mL of barium significantly improves oesophageal localisation on CBCT with minimal image artifact. The oesophagus moves substantially and unpredictably over a course of treatment, requiring close daily monitoring in the context of hypofractionation. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01946-8.
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Affiliation(s)
- Katrina Woodford
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia. .,Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, Australia.
| | - Vanessa Panettieri
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia.,Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia
| | - Jeremy D Ruben
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia.,Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Sidney Davis
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia.,Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Trieumy Tran Le
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Stephanie Miller
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Sashendra Senthi
- Alfred Health Radiation Oncology, The Alfred, 55 Commercial Road, Melbourne, VIC, 3004, Australia.,Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, Australia
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Pakarinen T, Ojala J. Profeel-An open source dosimetry data visualization and analysis software. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106457. [PMID: 34666203 DOI: 10.1016/j.cmpb.2021.106457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES This article presents Profeel, a Matlab (MathWorks Inc., MA) based open source dosimetry data visualization and analysis software. Profeel aims to support quality assurance, dosimetry and research in the field of radiotherapy by providing an environment to visualize, process and analyse measured and simulated dosimetry data from several data sources used in radiotherapy practice and research. METHODS The processing and analysis tools are based on routinely used dosimetry analysis methods, such as gamma analysis, different data normalizations and data filtering. Additionally the Profeel performs an automatic 1 dimensional profile and percentage depth dose analysis in accordance with International Electrotechnical Commission definitions. All data can be operated by user created custom functions and lower dimensionality data can be extracted from volume doses and dose planes. RESULTS Profeel supports data import in all 3 dimensions and offers an intuitive user interface to perform data visualization, processing and analysis between simulated and measured data. Profeel and its source code are distributed free of charge under the General Public Licence (GPL). CONCLUSIONS Profeel has shown to be an agile tool for fulfilling various needs of several researchers and since Profeel is under constant development and is an open source project, community needs, issues and bug reports are taken into account in the development.
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Affiliation(s)
- Tomppa Pakarinen
- Department of Medical Physics and Department of Oncology, Tampere University Hospital, Tampere, Finland; Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Jarkko Ojala
- Department of Medical Physics and Department of Oncology, Tampere University Hospital, Tampere, Finland
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48
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Shapey J, Kujawa A, Dorent R, Wang G, Dimitriadis A, Grishchuk D, Paddick I, Kitchen N, Bradford R, Saeed SR, Bisdas S, Ourselin S, Vercauteren T. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm. Sci Data 2021; 8:286. [PMID: 34711849 PMCID: PMC8553833 DOI: 10.1038/s41597-021-01064-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models.
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Affiliation(s)
- Jonathan Shapey
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
- Department of Neurosurgery, King's College Hospital, London, United Kingdom.
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.
| | - Aaron Kujawa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reuben Dorent
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Guotai Wang
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Alexis Dimitriadis
- Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Diana Grishchuk
- Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Ian Paddick
- Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Neil Kitchen
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- The Ear Institute, University College London, London, United Kingdom
- The Royal National Throat, Nose and Ear Hospital, London, United Kingdom
| | - Sotirios Bisdas
- Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Sébastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Kha QH, Le VH, Hung TNK, Le NQK. Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas. Cancers (Basel) 2021; 13:cancers13215398. [PMID: 34771562 PMCID: PMC8582370 DOI: 10.3390/cancers13215398] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/19/2021] [Accepted: 10/26/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Low-grade gliomas (LGG) with the 1p/19q co-deletion mutation have been proven to have a better survival prognosis and response to treatment than individuals without the mutation. Identifying this mutation has a vital role in managing LGG patients; however, the current diagnostic gold standard, including the brain-tissue biopsy or the surgical resection of the tumor, remains highly invasive and time-consuming. We proposed a model based on the eXtreme Gradient Boosting (XGBoost) classifier to detect 1p/19q co-deletion mutation using non-invasive medical images. The performance of our model achieved 87% and 82.8% accuracy on the training and external test set, respectively. Significantly, the prediction was based on only seven optimal wavelet radiomics features extracted from brain Magnetic Resonance (MR) images. We believe that this model can address clinicians in the rapid diagnosis of clinical 1p/19q co-deletion mutation, thereby improving the treatment prognosis of LGG patients. Abstract The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.
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Affiliation(s)
- Quang-Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (Q.-H.K.); (V.-H.L.); (T.N.K.H.)
| | - Viet-Huan Le
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (Q.-H.K.); (V.-H.L.); (T.N.K.H.)
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang City 65000, Vietnam
| | - Truong Nguyen Khanh Hung
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (Q.-H.K.); (V.-H.L.); (T.N.K.H.)
- Department of Orthopedic and Trauma, Cho Ray Hospital, Ho Chi Minh City 70000, Vietnam
| | - Nguyen Quoc Khanh Le
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (Q.-H.K.); (V.-H.L.); (T.N.K.H.)
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-02-663-82736-1992
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50
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Xian L, Li G, Xiao Q, Li Z, Zhang X, Chen L, Hu Z, Bai S. Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations. Technol Cancer Res Treat 2021; 20:15330338211036325. [PMID: 34490802 PMCID: PMC8427914 DOI: 10.1177/15330338211036325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Purpose: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices. Methods: Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression. Results: The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R2: 0.023-0.04, P > 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and Dmean (R2: 0.689-0.988), 80% of which were P < 0.001. For the random errors, the correlations obtained by the all targets were R2 > 0.384, P < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with P < 0.05. Conclusions: Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.
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Affiliation(s)
- Lixun Xian
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Department of Oncology, Chengdu Second People's Hospital, Chengdu, Sichuan, China.,*Lixun Xian and Guangjun Li are contributed equally to this work
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,*Lixun Xian and Guangjun Li are contributed equally to this work
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhibin Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiangbin Zhang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Chen
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhenyao Hu
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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