Quantitative imaging for radiotherapy purposes

Highlights • Quantitative imaging (QI) is promising for radiotherapy.• The key points from the QI track of the 2nd ESTRO Physics Workshop are discussed.• QI biomarkers may be used to assess the state of tumours throughout treatment.• Next steps for using QI in daily radiotherapy routine are identified.• QI biomarkers are mainly studied in exploratory studies; larger studies are desired.

[1]  N. Schwenzer,et al.  Assessment of image quality of a radiotherapy-specific hardware solution for PET/MRI in head and neck cancer patients , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[2]  P. Carmeliet,et al.  Angiogenesis in cancer and other diseases , 2000, Nature.

[3]  J. Sonke,et al.  Radiation-Induced Lung Density Changes on CT Scan for NSCLC: No Impact of Dose-Escalation Level or Volume. , 2018, International journal of radiation oncology, biology, physics.

[4]  D. Rubin,et al.  Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. , 2016, Radiology.

[5]  Maria A Schmidt,et al.  Radiotherapy planning using MRI , 2015, Physics in medicine and biology.

[6]  Vincent Gregoire,et al.  Molecular imaging-based dose painting: a novel paradigm for radiation therapy prescription. , 2011, Seminars in radiation oncology.

[7]  Johan Bussink,et al.  PET–CT for radiotherapy treatment planning and response monitoring in solid tumors , 2011, Nature Reviews Clinical Oncology.

[8]  Yue Cao,et al.  The promise of dynamic contrast-enhanced imaging in radiation therapy. , 2011, Seminars in radiation oncology.

[9]  R. Bammer Basic principles of diffusion-weighted imaging. , 2003, European journal of radiology.

[10]  Steven P Sourbron,et al.  Classic models for dynamic contrast‐enhanced MRI , 2013, NMR in biomedicine.

[11]  W. Oyen,et al.  Imaging-Based Treatment Adaptation in Radiation Oncology , 2015, The Journal of Nuclear Medicine.

[12]  E. Yorke,et al.  FDG-PET maximum standardized uptake value is prognostic for recurrence and survival after stereotactic body radiotherapy for non-small cell lung cancer. , 2015, Lung cancer.

[13]  G. Poste Bring on the biomarkers , 2011, Nature.

[14]  Jing Yan,et al.  Evaluating early response of cervical cancer under concurrent chemo-radiotherapy by intravoxel incoherent motion MR imaging , 2016, BMC Cancer.

[15]  H. Johannesen,et al.  Repeated diffusion MRI reveals earliest time point for stratification of radiotherapy response in brain metastases , 2017, Physics in medicine and biology.

[16]  Giuseppe Lucio Cascini,et al.  Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC). , 2007, Lung cancer.

[17]  D. Collins,et al.  Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. , 2007, AJR. American journal of roentgenology.

[18]  Evis Sala,et al.  Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy. , 2007, The Lancet. Oncology.

[19]  P. Luijten,et al.  Towards intrinsic R2* imaging in the prostate at 3 and 7tesla. , 2017, Magnetic resonance imaging.

[20]  P. Tofts Quantitative MRI of the Brain , 2003 .

[21]  X Allen Li,et al.  Variability of target and normal structure delineation using multimodality imaging for radiation therapy of pancreatic cancer. , 2014, International journal of radiation oncology, biology, physics.

[22]  U Pietrzyk,et al.  An interactive technique for three-dimensional image registration: validation for PET, SPECT, MRI and CT brain studies. , 1994, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[23]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[24]  Jérémie F. Cohen,et al.  STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. , 2015, Radiology.

[25]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[26]  P. Choyke,et al.  Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. , 2009, Neoplasia.

[27]  C. Thng,et al.  Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI , 2011, Journal of magnetic resonance imaging : JMRI.

[28]  W. W. Hansen,et al.  Nuclear Induction , 2011 .

[29]  Colin G Orton,et al.  Point/counterpoint: it is not appropriate to "deform" dose along with deformable image registration in adaptive radiotherapy. , 2012, Medical physics.

[30]  H. Lyng,et al.  Potentials and challenges of diffusion-weighted magnetic resonance imaging in radiotherapy , 2018, Clinical and translational radiation oncology.

[31]  J. Stoker,et al.  Evaluation of Six Diffusion-weighted MRI Models for Assessing Effects of Neoadjuvant Chemoradiation in Pancreatic Cancer Patients. , 2018, International journal of radiation oncology, biology, physics.

[32]  H. Barnhart,et al.  The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions , 2015, Statistical methods in medical research.

[33]  Astrid M. E. Engberg,et al.  Feasibility of Multiparametric Imaging with PET/MR in Head and Neck Squamous Cell Carcinoma , 2017, The Journal of Nuclear Medicine.

[34]  S Leibfarth,et al.  Automatic delineation of tumor volumes by co-segmentation of combined PET/MR data , 2015, Physics in medicine and biology.

[35]  Jayashree Kalpathy-Cramer,et al.  Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. , 2014, Translational oncology.

[36]  Paul Kinahan,et al.  The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective. , 2018, International journal of radiation oncology, biology, physics.

[37]  Xiaodong Wu,et al.  Comparative study with new accuracy metrics for target volume contouring in PET image guided radiation therapy. , 2012, IEEE transactions on medical imaging.

[38]  V S Khoo,et al.  New developments in MRI for target volume delineation in radiotherapy. , 2006, The British journal of radiology.

[39]  Bernhard Sattler,et al.  PET/CT (and CT) instrumentation, image reconstruction and data transfer for radiotherapy planning. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[40]  D. Jaffray Image-guided radiotherapy: from current concept to future perspectives , 2012, Nature Reviews Clinical Oncology.

[41]  Stuart A. Taylor,et al.  Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.

[42]  P. Hubbard,et al.  Biomimetic phantom for the validation of diffusion magnetic resonance imaging , 2015, Magnetic resonance in medicine.

[43]  Mark S. Bolding,et al.  Portable perfusion phantom for quantitative DCE‐MRI of the abdomen , 2017, Medical physics.

[44]  Maaike R Moman,et al.  Dynamic contrast-enhanced CT for prostate cancer: relationship between image noise, voxel size, and repeatability. , 2010, Radiology.

[45]  Ruijiang Li,et al.  Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy. , 2016, International journal of radiation oncology, biology, physics.

[46]  M. Kessler Image registration and data fusion in radiation therapy. , 2006, The British journal of radiology.

[47]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[48]  D. Binns,et al.  Effect of PET/CT on Management of Patients with Non–Small Cell Lung Cancer: Results of a Prospective Study with 5-Year Survival Data , 2012, The Journal of Nuclear Medicine.

[49]  Steinar Lundgren,et al.  Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE‐MRI , 2009, Journal of magnetic resonance imaging : JMRI.

[50]  A Nisbet,et al.  The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. , 2014, The British journal of radiology.

[51]  A. Nederveen,et al.  Quantitative assessment of biliary stent artifacts on MR images: Potential implications for target delineation in radiotherapy. , 2016, Medical physics.

[52]  V. Kiselev Fundamentals of diffusion MRI physics , 2017, NMR in biomedicine.

[53]  Bram Stieltjes,et al.  Prediction of treatment response in head and neck carcinomas using IVIM-DWI: Evaluation of lymph node metastasis. , 2014, European journal of radiology.

[54]  Martin O. Leach,et al.  Changes in multimodality functional imaging parameters early during chemoradiation predict treatment response in patients with locally advanced head and neck cancer , 2017, European Journal of Nuclear Medicine and Molecular Imaging.

[55]  Tove Grönroos,et al.  Imaging perfusion and hypoxia with PET to predict radiotherapy response in head-and-neck cancer. , 2004, International Journal of Radiation Oncology, Biology, Physics.

[56]  James B. Mitchell,et al.  Magnetic resonance imaging of the tumor microenvironment in radiotherapy: perfusion, hypoxia, and metabolism. , 2014, Seminars in radiation oncology.

[57]  Amy C. Dwyer,et al.  Models and methods for analyzing DCE-MRI: a review. , 2014, Medical physics.

[58]  F. Koetsveld,et al.  Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[59]  Aaron D Ward,et al.  Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. , 2014, Medical physics.

[60]  Mattias P. Heinrich,et al.  Advances and challenges in deformable image registration: From image fusion to complex motion modelling , 2016, Medical Image Anal..

[61]  A. Rubinstein,et al.  Research: increasing value, reducing waste , 2014, The Lancet.

[62]  R. Boellaard Standards for PET Image Acquisition and Quantitative Data Analysis , 2009, Journal of Nuclear Medicine.

[63]  E. D. de Vries,et al.  Prognostic versus predictive value of biomarkers in oncology. , 2008, European journal of cancer.

[64]  C C Ling,et al.  Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. , 2000, International journal of radiation oncology, biology, physics.

[65]  C. Ménard,et al.  A prospective study of DWI, DCE-MRI and FDG PET imaging for target delineation in brachytherapy for cervical cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[66]  D. Collins,et al.  Diffusion-weighted MRI in the body: applications and challenges in oncology. , 2007, AJR. American journal of roentgenology.

[67]  D. Le Bihan,et al.  Artifacts and pitfalls in diffusion MRI , 2006, Journal of magnetic resonance imaging : JMRI.

[68]  P. Mildenberger,et al.  Introduction to the DICOM standard , 2002, European Radiology.

[69]  Roberto Orecchia,et al.  Current concepts on imaging in radiotherapy , 2008, European Journal of Nuclear Medicine and Molecular Imaging.

[70]  Arjan Bel,et al.  Visibility and artifacts of gold fiducial markers used for image guided radiation therapy of pancreatic cancer on MRI. , 2015, Medical physics.

[71]  Davy Sinnaeve The Stejskal–Tanner equation generalized for any gradient shape—an overview of most pulse sequences measuring free diffusion , 2012 .

[72]  Arvid Lundervold,et al.  ssessment of 3 D DCE-MRI of the kidneys using non-rigid image registration nd segmentation of voxel time courses rank , 2009 .

[73]  M. Dewhirst,et al.  Imaging tumor hypoxia to advance radiation oncology. , 2014, Antioxidants & redox signaling.

[74]  Wiro J Niessen,et al.  Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time? , 2012, Magnetic resonance in medicine.

[75]  Timothy E. Schultheiss,et al.  It is not appropriate to “deform” dose along with deformable image registration in adaptive radiotherapy: Point/Counterpoint , 2012 .

[76]  Bram Stieltjes,et al.  Flow‐compensated intravoxel incoherent motion diffusion imaging , 2015, Magnetic resonance in medicine.

[77]  N. Burnet,et al.  Defining the tumour and target volumes for radiotherapy , 2004, Cancer imaging : the official publication of the International Cancer Imaging Society.

[78]  N. Obuchowski,et al.  The QIBA Profile for FDG PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy. , 2020, Radiology.

[79]  M. Alber,et al.  Prospective Evaluation of a Tumor Control Probability Model Based on Dynamic 18F-FMISO PET for Head and Neck Cancer Radiotherapy , 2019, The Journal of Nuclear Medicine.

[80]  Jan-Jakob Sonke,et al.  Adaptive and innovative Radiation Treatment FOR improving Cancer treatment outcomE (ARTFORCE); a randomized controlled phase II trial for individualized treatment of head and neck cancer , 2013, BMC Cancer.

[81]  Philippe Lambin,et al.  Functional MRI for radiotherapy dose painting. , 2012, Magnetic resonance imaging.

[82]  Ralph Sinkus,et al.  Apparent diffusion coefficient from magnetic resonance imaging as a biomarker in oncology drug development. , 2012, European journal of cancer.

[83]  Maaike R Moman,et al.  Pathologic validation of a model based on diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging for tumor delineation in the prostate peripheral zone. , 2012, International journal of radiation oncology, biology, physics.

[84]  B. Erickson,et al.  Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. , 2014, Medical physics.

[85]  Karin Haustermans,et al.  The value of magnetic resonance imaging for radiotherapy planning. , 2014, Seminars in radiation oncology.

[86]  L. Astrakas,et al.  Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping , 2010, Pediatric Radiology.

[87]  G. Brix,et al.  Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[88]  Marco van Vulpen,et al.  Validation of functional imaging with pathology for tumor delineation in the prostate. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[89]  David J Collins,et al.  Hypoxia in prostate cancer: correlation of BOLD-MRI with pimonidazole immunohistochemistry-initial observations. , 2007, International journal of radiation oncology, biology, physics.

[90]  L. Schad,et al.  MR tissue characterization of intracranial tumors by means of texture analysis. , 1993, Magnetic resonance imaging.

[91]  Louis B Harrison,et al.  Impact of tumor hypoxia and anemia on radiation therapy outcomes. , 2002, The oncologist.

[92]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[93]  C. Jack,et al.  Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom , 2018, Magnetic resonance in medicine.

[94]  Daan Christiaens,et al.  Integrated and efficient diffusion-relaxometry using ZEBRA , 2018, Scientific Reports.

[95]  Anna Vilanova,et al.  Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool , 2018, Physics and imaging in radiation oncology.

[96]  S. Bhide,et al.  Prospective, longitudinal, multi-modal functional imaging for radical chemo-IMRT treatment of locally advanced head and neck cancer: the INSIGHT study , 2015, Radiation oncology.

[97]  G. Clarke,et al.  Measuring signal-to-noise ratio in partially parallel imaging MRI. , 2011, Medical physics.

[98]  L. Vargova,et al.  Dynamic changes in water ADC, energy metabolism, extracellular space volume, and tortuosity in neonatal rat brain during global ischemia , 1996, Magnetic resonance in medicine.

[99]  Brian D Ross,et al.  Predicting and monitoring cancer treatment response with diffusion‐weighted MRI , 2010, Journal of magnetic resonance imaging : JMRI.

[100]  M. Recio,et al.  Tumor staging using 3.0 T multiparametric MRI in prostate cancer: impact on treatment decisions for radical radiotherapy , 2015, SpringerPlus.

[101]  Yue Cao,et al.  Clinical applications for diffusion magnetic resonance imaging in radiotherapy. , 2014, Seminars in radiation oncology.

[102]  Issam El-Naqa,et al.  Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer , 2017, Scientific Reports.

[103]  Silvia D. Chang,et al.  Combined diffusion‐weighted and dynamic contrast‐enhanced MRI for prostate cancer diagnosis—Correlation with biopsy and histopathology , 2006, Journal of magnetic resonance imaging : JMRI.

[104]  C. Panje,et al.  Guidance of treatment decisions in risk-adapted primary radiotherapy for prostate cancer using multiparametric magnetic resonance imaging: a single center experience , 2015, Radiation oncology.

[105]  K. Redalen,et al.  Personalized radiotherapy: concepts, biomarkers and trial design. , 2015, The British journal of radiology.

[106]  Eric J. W. Visser,et al.  FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 , 2014, European Journal of Nuclear Medicine and Molecular Imaging.

[107]  A R Padhani,et al.  Diffusion-weighted MRI: a new functional clinical technique for tumour imaging. , 2006, The British journal of radiology.

[108]  W. V. van Elmpt,et al.  CT characteristics allow identification of patient-specific susceptibility for radiation-induced lung damage. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[109]  Bradley J Beattie,et al.  Monitoring early response to chemoradiotherapy with 18F-FMISO dynamic PET in head and neck cancer , 2017, European Journal of Nuclear Medicine and Molecular Imaging.