Evaluation of target coverage and margins adequacy during CyberKnife Lung Optimized Treatment

PURPOSE Evaluation of target coverage and verification of safety margins, in motion management strategies implemented by Lung Optimized Treatment (LOT) module in CyberKnife system. METHODS Three fiducial-less motion management strategies provided by LOT can be selected according to tumor visibility in the X ray images acquired during treatment. In 2-view modality the tumor is visible in both X ray images and full motion tracking is performed. In 1-view modality the tumor is visible in a single X ray image, therefore, motion tracking is combined with an internal target volume (ITV)-based margin expansion. In 0-view modality the lesion is not visible, consequently the treatment relies entirely on an ITV-based approach. Data from 30 patients treated in 2-view modality were selected providing information on the three-dimensional tumor motion in correspondence to each X ray image. Treatments in 1-view and 0-view modalities were simulated by processing log files and planning volumes. Planning target volume (PTV) margins were defined according to the tracking modality: end-exhale clinical target volume (CTV) + 3 mm in 2-view and ITV + 5 mm in 0-view. In the 1-view scenario, the ITV encompasses only tumor motion along the non-visible direction. Then, non-uniform ITV to PTV margins were applied: 3 mm and 5 mm in the visible and non-visible direction, respectively. We defined the coverage of each voxel of the CTV as the percentage of X ray images where such voxel was included in the PTV. In 2-view modality coverage was calculated as the intersection between the CTV centred on the imaged target position and the PTV centred on the predicted target position, as recorded in log files. In 1-view modality, coverage was calculated as the intersection between the CTV centred on the imaged target position and the PTV centred on the projected predictor data. In 0-view modality coverage was calculated as the intersection between the CTV centred on the imaged target position and the non-moving PTV. Similar to dose-volume histogram, CTV coverage-volume histograms (defined as CVH) were derived for each patient and treatment modality. The geometric coverages of the 90% and 95% of CTV volume (C90, C95, respectively) were evaluated. Patient-specific optimal margins (ensuring C95 ≥ 95%) were computed retrospectively. RESULTS The median ± interquartile-rage of C90 and C95 for upper lobe lesions was 99.1 ± 0.6% and 99.0 ± 3.1%, whereas they were 98.9 ± 4.2% and 97.8 ± 7.5% for lower and middle lobe tumors. In 2-view, 1-view and 0-view modality, adopted margins ensured C95 ≥ 95% in 70%, 85% and 63% of cases and C95 ≥ 90% in 90%, 88% and 83% of cases, respectively. In 2-view, 1-view and 0-view a reduction in margins still ensured C95 ≥ 95% in 33%, 78% and 59% of cases, respectively. CONCLUSIONS CTV coverage analysis provided an a-posteriori evaluation of the treatment geometric accuracy and allowed a quantitative verification of the adequacy of the PTV margins applied in CyberKnife LOT treatments offering guidance in the selection of CTV margins.

[1]  Hong-yuan Liu,et al.  Target margin design for real-time lung tumor tracking stereotactic body radiation therapy using CyberKnife Xsight Lung Tracking System , 2017, Scientific Reports.

[2]  Jakub Cvek,et al.  Analysis of Lung Tumor Motion in a Large Sample: Patterns and Factors Influencing Precise Delineation of Internal Target Volume. , 2016, International journal of radiation oncology, biology, physics.

[3]  Yoichi M Ito,et al.  Evaluation of the motion of lung tumors during stereotactic body radiation therapy (SBRT) with four-dimensional computed tomography (4DCT) using real-time tumor-tracking radiotherapy system (RTRT). , 2016, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[4]  T Ackerly,et al.  Variation in Lung Tumour Breathing Motion between Planning Four-dimensional Computed Tomography and Stereotactic Ablative Radiotherapy Delivery and its Dosimetric Implications: Any Role for Four-dimensional Set-up Verification? , 2016, Clinical oncology (Royal College of Radiologists (Great Britain)).

[5]  J. Winter,et al.  Accuracy of Robotic Radiosurgical Liver Treatment Throughout the Respiratory Cycle. , 2015, International journal of radiation oncology, biology, physics.

[6]  Peter Balter,et al.  Stereotactic ablative radiotherapy versus lobectomy for operable stage I non-small-cell lung cancer: a pooled analysis of two randomised trials. , 2015, The Lancet. Oncology.

[7]  Influence of different image-guided tracking methods upon the local efficacy of CyberKnife treatment in lung tumors , 2015, Thoracic cancer.

[8]  J. Pouliot,et al.  Comparison between target margins derived from 4DCT scans and real-time tumor motion tracking: insights from lung tumor patients treated with robotic radiosurgery. , 2015, Medical physics.

[9]  A. Sánchez-Reyes,et al.  Retrospective evaluation of CTV to PTV margins using CyberKnife in patients with thoracic tumors , 2014, Journal of applied clinical medical physics.

[10]  Nagarajan Kandasamy,et al.  Plastimatch—An Open-Source Software for Radiotherapy Imaging , 2014 .

[11]  Jean-François Carrier,et al.  Predictive parameters of CyberKnife fiducial-less (XSight Lung) applicability for treatment of early non-small cell lung cancer: a single-center experience. , 2013, International journal of radiation oncology, biology, physics.

[12]  Pietro Cerveri,et al.  Real-time tumor tracking with an artificial neural networks-based method: a feasibility study. , 2013, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[13]  Suresh Senan,et al.  Patterns of disease recurrence after stereotactic ablative radiotherapy for early stage non-small-cell lung cancer: a retrospective analysis. , 2012, The Lancet. Oncology.

[14]  Sonja Dieterich,et al.  Mitigating errors in external respiratory surrogate-based models of tumor position. , 2012, International journal of radiation oncology, biology, physics.

[15]  Shafak Aluwini,et al.  Outcome of four-dimensional stereotactic radiotherapy for centrally located lung tumors. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  Huanmei Wu,et al.  Correlation and prediction uncertainties in the cyberknife synchrony respiratory tracking system. , 2011, Medical physics.

[17]  Marco Riboldi,et al.  Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study , 2010, Technology in cancer research & treatment.

[18]  Andrea Bezjak,et al.  Stereotactic body radiation therapy for inoperable early stage lung cancer. , 2010, JAMA.

[19]  R. V. van Klaveren,et al.  Stereotactic radiotherapy with real-time tumor tracking for non-small cell lung cancer: clinical outcome. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[20]  M. Hoogeman,et al.  Clinical accuracy of the respiratory tumor tracking system of the cyberknife: assessment by analysis of log files. , 2009, International journal of radiation oncology, biology, physics.

[21]  Dwight E Heron,et al.  Four-dimensional computed tomography-based interfractional reproducibility study of lung tumor intrafractional motion. , 2008, International journal of radiation oncology, biology, physics.

[22]  Hiroki Shirato,et al.  Accuracy of tumor motion compensation algorithm from a robotic respiratory tracking system: a simulation study. , 2007, Medical physics.

[23]  C. Maurer,et al.  Xsight Lung Tracking System: A Fiducial-Less Method for Respiratory Motion Tracking , 2007 .

[24]  Jeffrey D Bradley,et al.  A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms. , 2006, Medical physics.

[25]  David A. Jaffray,et al.  Respiration correlated cone-beam computed tomography and 4DCT for evaluating target motion in Stereotactic Lung Radiation Therapy , 2006, Acta oncologica.

[26]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.