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Xiu Z, Muzi M, Huang J, Wolsztynski E. Patient-Adaptive Population-Based Modeling of Arterial Input Functions. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:132-147. [PMID: 36094987 PMCID: PMC10008518 DOI: 10.1109/tmi.2022.3205940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Kinetic modeling of dynamic PET data requires knowledge of tracer concentration in blood plasma, described by the arterial input function (AIF). Arterial blood sampling is the gold standard for AIF measurement, but is invasive and labour intensive. A number of methods have been proposed to accurately estimate the AIF directly from blood sampling and/or imaging data. Here we consider fitting a patient-adaptive mixture of historical population time course profiles to estimate individual AIFs. Travel time of a tracer atom from the injection site to the right ventricle of the heart is modeled as a realization from a Gamma distribution, and the time this atom spends in circulation before being sampled is represented by a subject-specific linear mixture of population profiles. These functions are estimated from independent population data. Individual AIFs are obtained by projection onto this basis of population profile components. The model incorporates knowledge of injection duration into the fit, allowing for varying injection protocols. Analyses of arterial sampling data from 18F-FDG, 15O-H2O and 18F-FLT clinical studies show that the proposed model can outperform reference techniques. The statistically significant gain achieved by using population data to train the basis components, instead of fitting these from the single individual sampling data, is measured on the FDG cohort. Kinetic analyses of simulated data demonstrate the reliability and potential benefit of this approach in estimating physiological parameters. These results are further supported by numerical simulations that demonstrate convergence and stability of the proposed technique under varying training population sizes and noise levels.
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Elamir AM, Stanescu T, Shessel A, Tadic T, Yeung I, Letourneau D, Kim J, Lukovic J, Dawson LA, Wong R, Barry A, Brierley J, Gallinger S, Knox J, O'Kane G, Dhani N, Hosni A, Taylor E. Simulated dose painting of hypoxic sub-volumes in pancreatic cancer stereotactic body radiotherapy. Phys Med Biol 2021; 66. [PMID: 34438383 DOI: 10.1088/1361-6560/ac215c] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/26/2021] [Indexed: 12/26/2022]
Abstract
Dose painting of hypoxic tumour sub-volumes using positron-emission tomography (PET) has been shown to improve tumour controlin silicoin several sites, predominantly head and neck and lung cancers. Pancreatic cancer presents a more stringent challenge, given its proximity to critical gastro-intestinal organs-at-risk (OARs), anatomic motion, and impediments to reliable PET hypoxia quantification. A radiobiological model was developed to estimate clonogen survival fraction (SF), using18F-fluoroazomycin arabinoside PET (FAZA PET) images from ten patients with unresectable pancreatic ductal adenocarcinoma to quantify oxygen enhancement effects. For each patient, four simulated five-fraction stereotactic body radiotherapy (SBRT) plans were generated: (1) a standard SBRT plan aiming to cover the planning target volume with 40 Gy, (2) dose painting plans delivering escalated doses to a maximum of three FAZA-avid hypoxic sub-volumes, (3) dose painting plans with simulated spacer separating the duodenum and pancreatic head, and (4), plans with integrated boosts to geometric contractions of the gross tumour volume (GTV). All plans saturated at least one OAR dose limit. SF was calculated for each plan and sensitivity of SF to simulated hypoxia quantification errors was evaluated. Dose painting resulted in a 55% reduction in SF as compared to standard SBRT; 78% with spacer. Integrated boosts to hypoxia-blind geometric contractions resulted in a 41% reduction in SF. The reduction in SF for dose-painting plans persisted for all hypoxia quantification parameters studied, including registration and rigid motion errors that resulted in shifts and rotations of the GTV and hypoxic sub-volumes by as much as 1 cm and 10 degrees. Although proximity to OARs ultimately limited dose escalation, with estimated SFs (∼10-5) well above levels required to completely ablate a ∼10 cm3tumour, dose painting robustly reduced clonogen survival when accounting for expected treatment and imaging uncertainties and thus, may improve local response and associated morbidity.
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Affiliation(s)
- Ahmed M Elamir
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Teodor Stanescu
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Andrea Shessel
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ivan Yeung
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Stronach Regional Cancer Centre, Southlake Regional Health Centre, Newmarket, Canada
| | - Daniel Letourneau
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - John Kim
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Jelena Lukovic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Laura A Dawson
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Rebecca Wong
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Aisling Barry
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - James Brierley
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Steven Gallinger
- Ontario Institute for Cancer Research, PanCuRx Translational Research Initiative, Toronto, Canada.,Department of Surgery, University of Toronto, Toronto, Canada
| | - Jennifer Knox
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Center, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | - Grainne O'Kane
- Ontario Institute for Cancer Research, PanCuRx Translational Research Initiative, Toronto, Canada.,Division of Medical Oncology and Hematology, Princess Margaret Cancer Center, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | - Neesha Dhani
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Center, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | - Ali Hosni
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Edward Taylor
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
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Sanduleanu S, Jochems A, Upadhaya T, Even AJG, Leijenaar RTH, Dankers FJWM, Klaassen R, Woodruff HC, Hatt M, Kaanders HJAM, Hamming-Vrieze O, van Laarhoven HWM, Subramiam RM, Huang SH, O'Sullivan B, Bratman SV, Dubois LJ, Miclea RL, Di Perri D, Geets X, Crispin-Ortuzar M, Apte A, Deasy JO, Oh JH, Lee NY, Humm JL, Schöder H, De Ruysscher D, Hoebers F, Lambin P. Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures. Radiother Oncol 2020; 153:97-105. [PMID: 33137396 DOI: 10.1016/j.radonc.2020.10.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. MATERIAL AND METHODS A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. RESULTS A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). CONCLUSION The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.
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Affiliation(s)
- Sebastian Sanduleanu
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands.
| | - Arthur Jochems
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Taman Upadhaya
- Laboratory of Medical Information Processing (LaTIM), INSERM, UMR 1101, Univ Brest, France; Department of Radiation Oncology, University of California, 1600 Divisadero Street, CA 94115, San Francisco, United States
| | - Aniek J G Even
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Ralph T H Leijenaar
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Frank J W M Dankers
- Department of Radiation Oncology, Radboud University Nijmegen Medical Centre, The Netherlands
| | - Remy Klaassen
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Henry C Woodruff
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands; Department of Radiology and Nuclear Imaging, GROW - school for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Mathieu Hatt
- Laboratory of Medical Information Processing (LaTIM), INSERM, UMR 1101, Univ Brest, France
| | - Hans J A M Kaanders
- Department of Radiation Oncology, Radboud University Nijmegen Medical Centre, The Netherlands
| | - Olga Hamming-Vrieze
- Department of Radiation Oncology, Antoni van Leeuwenhoek - Netherlands Cancer institute, Amsterdam, The Netherlands
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Rathan M Subramiam
- Boston University School of Medicine, United States; Division of Nuclear Medicine, Russell H Morgan Department of Radiology and Radiologic Sciences, Johns Hopkins Medical Institutions, Baltimore, United States
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Canada
| | - Brian O'Sullivan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Canada
| | - Scott V Bratman
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Canada
| | - Ludwig J Dubois
- Department of Precision Medicine, The M-LAB, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Razvan L Miclea
- Department of Radiology and Nuclear Imaging, GROW - school for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Dario Di Perri
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Belgium; Department of Radiation Oncology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Xavier Geets
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Belgium; Department of Radiation Oncology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Mireia Crispin-Ortuzar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States; Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Nancy Y Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - John L Humm
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (Maastro), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Philippe Lambin
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands; Department of Radiology and Nuclear Imaging, GROW - school for Oncology, Maastricht University Medical Centre+, The Netherlands
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Analysis of hypoxia in human glioblastoma tumors with dynamic 18F-FMISO PET imaging. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:981-993. [PMID: 31520369 DOI: 10.1007/s13246-019-00797-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 08/27/2019] [Accepted: 08/31/2019] [Indexed: 02/07/2023]
Abstract
Gliomas are the most common type of primary brain tumors and are classified as grade IV. Necrosis and hypoxia are essential diagnostic features which result in poor prognosis of gliomas. The aim of this study was to report quantitative temporal analyses aiming at determining the hypoxic regions in glioblastoma multiforme and to suggest an optimal time for the clinical single scan of hypoxia. Nine subjects were imaged with PET and 18F-FMISO in dynamic mode for 15 min followed with static scans at 2, 3 and 4 h post-injection. Spectral analysis, tumor-to-blood ratio (TBR) and tumor-to-normal tissue ratio (TNR) were used to delimit perfused and hypoxic tumor regions. TBR and TNR images were further scaled by thresholding at 1.2, 1.4, 2 and 2.5 levels. The images showed a varying tumor volume with time. TBR produced broader images of the tumor than TNR considering the same thresholds on intensity. Spectral analysis reliably determined hypoxia with different degrees of perfusion. By comparing TBR and TNR with spectral analysis images, weak to moderate correlation coefficients were found for most thresholding values and imaging times (range: 0 to 0.69). Hypoxic volume (HV) estimated from the net uptake rate (Ki) were changing among imaging times. The minimum HV changes were found between 3 h and 4 h, confirming that after 3 h, there was a very low exchange of 81F-FMISO between blood and tumor. On the other hand, hypoxia started to dominate the perfused tissue at 90 min, suggesting this time is suitable for a single scan acquisition irrespective of tumor status being highly hypoxic or perfused. At this time, TBR and TNR were respectively found in the nine subjects as 1.72 ± 0.22 and 1.74 ± 0.19.
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Press RH, Shu HKG, Shim H, Mountz JM, Kurland BF, Wahl RL, Jones EF, Hylton NM, Gerstner ER, Nordstrom RJ, Henderson L, Kurdziel KA, Vikram B, Jacobs MA, Holdhoff M, Taylor E, Jaffray DA, Schwartz LH, Mankoff DA, Kinahan PE, Linden HM, Lambin P, Dilling TJ, Rubin DL, Hadjiiski L, Buatti JM. The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective. Int J Radiat Oncol Biol Phys 2018; 102:1219-1235. [PMID: 29966725 PMCID: PMC6348006 DOI: 10.1016/j.ijrobp.2018.06.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 05/25/2018] [Accepted: 06/14/2018] [Indexed: 02/07/2023]
Abstract
Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
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Affiliation(s)
- Robert H. Press
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hui-Kuo G. Shu
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hyunsuk Shim
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - James M. Mountz
- Dept. of Radiology, University of Pittsburgh, Pittsburgh, PA
| | | | | | - Ella F. Jones
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA
| | - Nola M. Hylton
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA
| | - Elizabeth R. Gerstner
- Dept. of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | - Lori Henderson
- Cancer Imaging Program, National Cancer Institute, Bethesda, MD
| | | | - Bhadrasain Vikram
- Radiation Research Program/Division of Cancer Treatment & Diagnosis, National Cancer Institute, Bethesda, MD
| | - Michael A. Jacobs
- Dept. of Radiology and Radiological Science, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore MD
| | - Matthias Holdhoff
- Brain Cancer Program, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore MD
| | - Edward Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - David A. Jaffray
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | | | - David A. Mankoff
- Dept. of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | | | - Philippe Lambin
- Dept. of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Thomas J. Dilling
- Dept. of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | | | | | - John M. Buatti
- Dept. of Radiation Oncology, University of Iowa, Iowa City, IA
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