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146 results on '"Cazoulat G"'

2. Modeling Variable Proton Relative Biological Effectiveness (RBE) Using Voxel-Level Image Density Change for Non-Small Cell Lung Cancer (NSCLC) Patients Treated with Passive Scattering Proton Therapy (PSPT) or Intensity Modulated Photon Therapy (IMRT)

Author

He, Y., primary, Adair, A.H., additional, Cazoulat, G., additional, Yepes, P., additional, Titt, U., additional, Wu, C., additional, Mirkovic, D., additional, Balter, P., additional, Pollard, J.M., additional, Cardenas, C., additional, Liao, Z., additional, Mohan, R., additional, and Brock, K.K., additional

Published
2022
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7. Improving GI Toxicity Models Through Deep Learning-Based Segmentation and Biomechanical Model-Based Dose Accumulation

Author

McCulloch, M.M., primary, Cazoulat, G., additional, Anderson, B.M., additional, Kirimli, E., additional, Rigaud, B., additional, Gryshkevych, S., additional, Svensson, S., additional, Ohrt, A.N., additional, Ohrt, J., additional, Chopra, N., additional, Mathew, R., additional, Zaid, M., additional, Elganainy, D., additional, Balter, P., additional, Koay, E.J., additional, and Brock, K.K., additional

Published
2021
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9. Optimization of Normal Liver Radiation Avoidance to Promote Liver Preservation

Author

McCulloch, M.M., primary, Cazoulat, G., additional, Anderson, B.M., additional, Rigaud, B., additional, Svensson, S., additional, Gryshkevych, S., additional, Ohrt, A.N., additional, Ohrt, J., additional, Kirimli, E., additional, Zaid, M., additional, Elganainy, D., additional, Balter, P., additional, Koay, E.J., additional, and Brock, K.K., additional

Published
2020
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11. PO-1642: CBCT Padding for Full Field of View Daily Dose Accumulation and Head and Neck Adaptive Radiotherapy

Author

Brock, K., primary, Ohrt, A., additional, Cazoulat, G., additional, McCulloch, M., additional, Balter, P., additional, Ohrt, J., additional, Svensson, S., additional, Nilsson, R., additional, Andersson, S., additional, Mohamed, A., additional, Bahig, H., additional, Ding, Y., additional, Wang, J., additional, McDonald, B., additional, Yang, J., additional, Vedam, S., additional, Elgohari, B., additional, Sen, A., additional, and Fuller, C., additional

Published
2020
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15. The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging

Author

Kipritidis, J, Tahir, BA, Cazoulat, G, Hofman, MS, Siva, S, Callahan, J, Hardcastle, N, Yamamoto, T, Christensen, GE, Reinhardt, JM, Kadoya, N, Patton, TJ, Gerard, SE, Duarte, I, Archibald-Heeren, B, Byrne, M, Sims, R, Ramsay, S, Booth, JT, Eslick, E, Hegi-Johnson, F, Woodruff, HC, Ireland, RH, Wild, JM, Cai, J, Bayouth, JE, Brock, K, Keall, PJ, Kipritidis, J, Tahir, BA, Cazoulat, G, Hofman, MS, Siva, S, Callahan, J, Hardcastle, N, Yamamoto, T, Christensen, GE, Reinhardt, JM, Kadoya, N, Patton, TJ, Gerard, SE, Duarte, I, Archibald-Heeren, B, Byrne, M, Sims, R, Ramsay, S, Booth, JT, Eslick, E, Hegi-Johnson, F, Woodruff, HC, Ireland, RH, Wild, JM, Cai, J, Bayouth, JE, Brock, K, and Keall, PJ

Abstract

PURPOSE: CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. METHODS: The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient rS , and Dice similarity coefficients evaluated for low function lung ( DSClow ) and high function lung ( DSChigh ). RESULTS: A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The rS and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings chang

Published
2019

17. Magnetic Resonance-based Response Assessment and Dose Adaptation in Human Papilloma Virus Positive Tumors of the Oropharynx treated with Radiotherapy (MR-ADAPTOR): An R-IDEAL stage 2a-2b/Bayesian phase II trial

Author

Bahig, H, Yuan, Y, Mohamed, ASR, Brock, KK, Ng, SP, Wang, J, Ding, Y, Hutcheson, K, McCulloch, M, Balter, PA, Lai, SY, Al-Mamgani, A, Sonke, J-J, van der Heide, UA, Nutting, C, Li, XA, Robbins, J, Awan, M, Karam, I, Newbold, K, Harrington, K, Oelfke, U, Bhide, S, Philippens, MEP, Terhaard, CHJ, McPartlin, AJ, Blanchard, P, Garden, AS, Rosenthal, DI, Gunn, GB, Phan, J, Cazoulat, G, Aristophanous, M, McSpadden, KK, Garcia, JA, van den Berg, CAT, Raaijmakers, CPJ, Kerkmeijer, L, Doornaert, P, Blinde, S, Frank, SJ, Fuller, CD, Bahig, H, Yuan, Y, Mohamed, ASR, Brock, KK, Ng, SP, Wang, J, Ding, Y, Hutcheson, K, McCulloch, M, Balter, PA, Lai, SY, Al-Mamgani, A, Sonke, J-J, van der Heide, UA, Nutting, C, Li, XA, Robbins, J, Awan, M, Karam, I, Newbold, K, Harrington, K, Oelfke, U, Bhide, S, Philippens, MEP, Terhaard, CHJ, McPartlin, AJ, Blanchard, P, Garden, AS, Rosenthal, DI, Gunn, GB, Phan, J, Cazoulat, G, Aristophanous, M, McSpadden, KK, Garcia, JA, van den Berg, CAT, Raaijmakers, CPJ, Kerkmeijer, L, Doornaert, P, Blinde, S, Frank, SJ, and Fuller, CD

Abstract

BACKGROUND: Current standard radiotherapy for oropharynx cancer (OPC) is associated with high rates of severe toxicities, shown to adversely impact patients' quality of life. Given excellent outcomes of human papilloma virus (HPV)-associated OPC and long-term survival of these typically young patients, treatment de-intensification aimed at improving survivorship while maintaining excellent disease control is now a central concern. The recent implementation of magnetic resonance image - guided radiotherapy (MRgRT) systems allows for individual tumor response assessment during treatment and offers possibility of personalized dose-reduction. In this 2-stage Bayesian phase II study, we propose to examine weekly radiotherapy dose-adaptation based on magnetic resonance imaging (MRI) evaluated tumor response. Individual patient's plan will be designed to optimize dose reduction to organs at risk and minimize locoregional failure probability based on serial MRI during RT. Our primary aim is to assess the non-inferiority of MRgRT dose adaptation for patients with low risk HPV-associated OPC compared to historical control, as measured by Bayesian posterior probability of locoregional control (LRC). METHODS: Patients with T1-2 N0-2b (as per AJCC 7th Edition) HPV-positive OPC, with lymph node <3 cm and <10 pack-year smoking history planned for curative radiotherapy alone to a dose of 70 Gy in 33 fractions will be eligible. All patients will undergo pre-treatment MRI and at least weekly intra-treatment MRI. Patients undergoing MRgRT will have weekly adaptation of high dose planning target volume based on gross tumor volume response. The stage 1 of this study will enroll 15 patients to MRgRT dose adaptation. If LRC at 6 months with MRgRT dose adaptation is found sufficiently safe as per the Bayesian model, stage 2 of the protocol will expand enrollment to an additional 60 patients, randomized to either MRgRT or standard IMRT. DISCUSSION: Multiple methods for safe treatment de-esc

Published
2018

49. Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.

Author

Gupta AC, Cazoulat G, Al Taie M, Yedururi S, Rigaud B, Castelo A, Wood J, Yu C, O'Connor C, Salem U, Silva JAM, Jones AK, McCulloch M, Odisio BC, Koay EJ, and Brock KK

Subjects
Humans, Spleen diagnostic imaging, Tomography, X-Ray Computed methods, Image Processing, Computer-Assisted methods, Deep Learning, Liver Neoplasms diagnostic imaging
Abstract

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (C CH ) and 20 (C PVE ) CECT of LC patients. [Formula: see text] outperformed [Formula: see text] across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03-0.05 (p < 0.05). [Formula: see text], and [Formula: see text] were not statistically different (p > 0.05), however, both were slightly better than [Formula: see text] by DSC up to 0.02. The final model, [Formula: see text], showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5-8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score [Formula: see text] 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning., (© 2024. The Author(s).)

Published
2024
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50. Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy.

Author

He Y, Cazoulat G, Wu C, Svensson S, Almodovar-Abreu L, Rigaud B, McCollum E, Peterson C, Wooten Z, Rhee DJ, Balter P, Pollard-Larkin J, Cardenas C, Court L, Liao Z, Mohan R, and Brock K

Subjects
Humans, Retrospective Studies, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted methods, Four-Dimensional Computed Tomography methods, Carcinoma, Non-Small-Cell Lung diagnostic imaging, Carcinoma, Non-Small-Cell Lung radiotherapy, Radiotherapy, Intensity-Modulated adverse effects, Radiotherapy, Intensity-Modulated methods, Lung Neoplasms diagnostic imaging, Lung Neoplasms radiotherapy
Abstract

Purpose: The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT)., Methods and Materials: Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses., Results: Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R 2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|., Conclusions: With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Published
2024
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