Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients

Abstract. Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average <5  mm difference in plan isocenter/borders, and <0.9  Gy in organ mean dose (prescribed dose: 14.4 Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation.

[1]  Tanja Alderliesten,et al.  Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients’ CT scans? , 2018, Medical physics.

[2]  C. Koning,et al.  Dose-effect relationships for adverse events after cranial radiation therapy in long-term childhood cancer survivors. , 2013, International journal of radiation oncology, biology, physics.

[3]  B Heijmen,et al.  SU-E-T-208: Automated Routine 3D Secondary Patient Dose Calculation Prior to and During Fractionated Treatment. , 2013, Medical physics.

[4]  D Mason,et al.  SU-E-T-33: Pydicom: An Open Source DICOM Library , 2011 .

[5]  W P Segars,et al.  Realistic reference adult and paediatric phantom series for internal and external dosimetry. , 2012, Radiation protection dosimetry.

[6]  Vladimir Varchena Pediatric phantoms , 2002, Pediatric Radiology.

[7]  Peter A. N. Bosman,et al.  Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients , 2019, Medical Imaging.

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[9]  Jean Chavaudra,et al.  Retrospective reconstructions of active bone marrow dose-volume histograms. , 2014, International journal of radiation oncology, biology, physics.

[10]  M. Ibrahim Sezan,et al.  A Peak Detection Algorithm and its Application to Histogram-Based Image Data Reduction , 1990, Comput. Vis. Graph. Image Process..

[11]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[12]  Kevin C Oeffinger,et al.  Aging and risk of severe, disabling, life-threatening, and fatal events in the childhood cancer survivor study. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  Wesley E Bolch,et al.  Assessment of different patient‐to‐phantom matching criteria applied in Monte Carlo–based computed tomography dosimetry , 2017, Medical physics.

[14]  G. Sturgeon,et al.  Generating patient-specific dosimetry phantoms with whole-body diffeomorphic image registration , 2011, 2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC).

[15]  X George Xu,et al.  An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history , 2014, Physics in medicine and biology.

[16]  Dieter Röhrich,et al.  Estimated risk of radiation-induced cancer following paediatric cranio-spinal irradiation with electron, photon and proton therapy , 2014, Acta oncologica.

[17]  David I Thwaites,et al.  Back to the future: the history and development of the clinical linear accelerator , 2006, Physics in medicine and biology.

[18]  Tanja Alderliesten,et al.  Magnitude and variability of respiratory-induced diaphragm motion in children during image-guided radiotherapy. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  Carlos Vázquez,et al.  Automatic spine and pelvis detection in frontal X-rays using deep neural networks for patch displacement learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[20]  Wesley E Bolch,et al.  The UF/NCI family of hybrid computational phantoms representing the current US population of male and female children, adolescents, and adults—application to CT dosimetry , 2014, Physics in medicine and biology.

[21]  Boštjan Likar,et al.  A review of methods for quantitative evaluation of axial vertebral rotation , 2009, European Spine Journal.

[22]  Andrew Nisbet,et al.  Cardiac exposures in breast cancer radiotherapy: 1950s-1990s. , 2007, International journal of radiation oncology, biology, physics.

[23]  Dieter Harms,et al.  Revised International Society of Paediatric Oncology (SIOP) working classification of renal tumors of childhood. , 2002, Medical and pediatric oncology.

[24]  Te Vuong,et al.  Past, present, and future of radiotherapy for the benefit of patients , 2013, Nature Reviews Clinical Oncology.

[25]  Steven L Simon,et al.  Radiation dose to the esophagus from breast cancer radiation therapy, 1943-1996: an international population-based study of 414 patients. , 2013, International journal of radiation oncology, biology, physics.

[26]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[27]  P. Voûte,et al.  Radiotherapy in the SIOP (International Society of Pediatric Oncology) nephroblastoma studies: a review. , 1994, Medical and pediatric oncology.

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

[29]  Fran Laurie,et al.  Feasibility and accuracy of UF/NCI phantoms and Monte Carlo retrospective dosimetry in children treated on National Wilms Tumor Study protocols , 2018, Pediatric blood & cancer.

[30]  Kevin C Oeffinger,et al.  Chronic health conditions in adult survivors of childhood cancer. , 2006, The New England journal of medicine.

[31]  Cees Witteveen,et al.  On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors , 2018, Medical physics.

[32]  Kevin C Oeffinger,et al.  Health status of adult long-term survivors of childhood cancer: a report from the Childhood Cancer Survivor Study. , 2003, JAMA.

[33]  V. Cassola,et al.  Standing adult human phantoms based on 10th, 50th and 90th mass and height percentiles of male and female Caucasian populations , 2011, Physics in medicine and biology.

[34]  Stephanie Lamart,et al.  Reconstruction of organ dose for external radiotherapy patients in retrospective epidemiologic studies , 2015, Physics in medicine and biology.

[35]  Marilyn Stovall,et al.  Dose Reconstruction for Therapeutic and Diagnostic Radiation Exposures: Use in Epidemiological Studies , 2006, Radiation research.

[36]  W P Segars,et al.  The development of a population of 4D pediatric XCAT phantoms for imaging research and optimization. , 2015, Medical physics.

[37]  Michael B Sharpe,et al.  Individualized 3D reconstruction of normal tissue dose for patients with long-term follow-up: a step toward understanding dose risk for late toxicity. , 2012, International journal of radiation oncology, biology, physics.

[38]  Marco Virgolin,et al.  How do patient characteristics and anatomical features correlate to accuracy of organ dose reconstruction for Wilms’ tumor radiation treatment plans when using a surrogate patient’s CT scan? , 2019, Journal of radiological protection : official journal of the Society for Radiological Protection.

[39]  N Graf,et al.  Clear cell sarcomas of the kidney registered on International Society of Pediatric Oncology (SIOP) 93-01 and SIOP 2001 protocols: a report of the SIOP Renal Tumour Study Group. , 2013, European journal of cancer.

[40]  N Breslow,et al.  Radiation therapy of Wilms' tumor: results according to dose, field, post-operative timing and histology. , 1978, International journal of radiation oncology, biology, physics.

[41]  John P. Gibbons,et al.  Khan's The Physics of Radiation Therapy , 2014 .

[42]  Jean Chavaudra,et al.  A review of uncertainties in radiotherapy dose reconstruction and their impacts on dose–response relationships , 2017, Journal of radiological protection : official journal of the Society for Radiological Protection.

[43]  R. Dhaliwal,et al.  The use of computed tomography in radiation therapy treatment planning. , 1975, Journal belge de radiologie.

[44]  Wayne D. Newhauser,et al.  A Review of Radiotherapy-Induced Late Effects Research after Advanced Technology Treatments , 2016, Front. Oncol..

[45]  Wendy Leisenring,et al.  Radiation-Related New Primary Solid Cancers in the Childhood Cancer Survivor Study: Comparative Radiation Dose Response and Modification of Treatment Effects. , 2016, International journal of radiation oncology, biology, physics.

[46]  Y. T. Cheung,et al.  Chronic Health Conditions and Neurocognitive Function in Aging Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study. , 2018, Journal of the National Cancer Institute.

[47]  R L Siddon,et al.  Solution to treatment planning problems using coordinate transformations. , 1981, Medical physics.

[48]  Preetha Rajaraman,et al.  Second solid cancers after radiation therapy: a systematic review of the epidemiologic studies of the radiation dose-response relationship. , 2013, International journal of radiation oncology, biology, physics.