Management of independent motion between multiple targets in lung cancer radiation therapy.

PURPOSE To quantify interfractional independent motions between multiple primary targets in radiation therapy (RT) of lung cancer and to study the dosimetric benefits of an online adaptive replanning method to account for these variations. METHODS AND MATERIALS Ninety-five on-treatment diagnostic-quality computed tomography (CT) scans acquired for 9 lung cancer patients treated with image-guided RT (IGRT) using a CT-on-rails (CTVision, Siemens) were analyzed. On each on-treatment CT set, contours of the targets (gross tumor volume, clinical target volume, or involved nodes), and organs at risk were generated by populating the planning contours using an autosegmentation tool (ABAS, Elekta) with manual editing. For each patient, an intensity modulated RT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2 Gy per fraction. Three plans were generated and compared for each on-treatment CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes, and segment weights of the original plan to conform to the on-treatment anatomy and a new fully reoptimized plan based on the on-treatment CT. RESULTS The interfractional deviations of the distance between centers of masses of the targets from the planning CTs varied from -1.0 to 0.8 cm with an average -0.09 ± 0.41 cm (1 standard deviation). The average combined CTV receiving at least 100% of the prescribed dose (V100) were 99.0 ± 0.7%, 97.8 ± 2.8%, 99.0 ± 0.6%, and 99.1 ± 0.6%, and the lung V20Gy 928 ± 332 cm3, 944 ± 315 cm3, 917 ± 300 cm3, and 891 ± 295 cm3 for the original, repositioning, adaptive, and reoptimized plans, respectively. Wilcoxon signed-rank tests showed that the adaptive plans were statistically significantly better than the repositioning plans and comparable with the reoptimized plans. CONCLUSION Interfractional, relative volume changes and independent motions between multiple primary targets during lung cancer RT, which cannot be accounted for by the current IGRT repositioning exist, but can be corrected by the online adaptive replanning method.

[1]  I. Lax,et al.  The role of radiotherapy in treatment of stage I non-small cell lung cancer. , 2003, Lung cancer.

[2]  J. Urbanic,et al.  Comparison of accelerated hypofractionation and stereotactic body radiotherapy for Stage 1 and node negative Stage 2 non-small cell lung cancer (NSCLC). , 2014, Lung cancer.

[3]  Sreeram Narayanan,et al.  An on-line replanning scheme for interfractional variationsa). , 2008, Medical physics.

[4]  He Wang,et al.  Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes. , 2005, International journal of radiation oncology, biology, physics.

[5]  Jeffrey D Bradley,et al.  Patterns of Failure after Stereotactic Body Radiation Therapy or Lobar Resection for Clinical Stage I Non–Small-Cell Lung Cancer , 2013, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[6]  S. Goyal,et al.  Adaptive radiotherapy in lung cancer: dosimetric benefits and clinical outcome. , 2014, The British journal of radiology.

[7]  X Allen Li,et al.  Characterization and management of interfractional anatomic changes for pancreatic cancer radiotherapy. , 2012, International journal of radiation oncology, biology, physics.

[8]  Philippe Lambin,et al.  Should patient setup in lung cancer be based on the primary tumor? An analysis of tumor coverage and normal tissue dose using repeated positron emission tomography/computed tomography imaging. , 2012, International journal of radiation oncology, biology, physics.

[9]  P. Lambin,et al.  Transition from a simple to a more advanced dose calculation algorithm for radiotherapy of non-small cell lung cancer (NSCLC): implications for clinical implementation in an individualized dose-escalation protocol. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[10]  D. Tremblay,et al.  Characterization of lung tumors motion baseline using cone-beam computed tomography. , 2012, Medical physics.

[11]  P. Lambin,et al.  Volume or position changes of primary lung tumor during (chemo-)radiotherapy cannot be used as a surrogate for mediastinal lymph node changes: the case for optimal mediastinal lymph node imaging during radiotherapy. , 2011, International journal of radiation oncology, biology, physics.

[12]  Randall K Ten Haken,et al.  Using fluorodeoxyglucose positron emission tomography to assess tumor volume during radiotherapy for non-small-cell lung cancer and its potential impact on adaptive dose escalation and normal tissue sparing. , 2009, International journal of radiation oncology, biology, physics.

[13]  Geoffrey D. Hugo,et al.  Dose escalation for locally advanced lung cancer using adaptive radiation therapy with simultaneous integrated volume-adapted boost. , 2013, International journal of radiation oncology, biology, physics.

[14]  Ergun E Ahunbay,et al.  An on-line replanning method for head and neck adaptive radiotherapy. , 2009, Medical physics.

[15]  X. Li,et al.  Adaptive replanning to account for lumpectomy cavity change in sequential boost after whole-breast irradiation. , 2014, International journal of radiation oncology, biology, physics.

[16]  Ping Xia,et al.  An algorithm for shifting MLC shapes to adjust for daily prostate movement during concurrent treatment with pelvic lymph nodesa). , 2007, Medical physics.

[17]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[18]  Ergun E Ahunbay,et al.  An on-line replanning method for head and neck adaptive radiotherapya). , 2009, Medical physics.

[19]  Rebecca L. Siegel Mph,et al.  Cancer statistics, 2016 , 2016 .

[20]  Carri Glide-Hurst,et al.  Comparison of IGRT registration strategies for optimal coverage of primary lung tumors and involved nodes based on multiple four-dimensional CT scans obtained throughout the radiotherapy course. , 2012, International journal of radiation oncology, biology, physics.

[21]  Dirk Verellen,et al.  Innovations in image-guided radiotherapy , 2008, Nature Reviews Cancer.

[22]  Ergun E Ahunbay,et al.  Online adaptive replanning method for prostate radiotherapy. , 2010, International journal of radiation oncology, biology, physics.

[23]  Geoffrey D Hugo,et al.  Tumor, lymph node, and lymph node-to-tumor displacements over a radiotherapy series: analysis of interfraction and intrafraction variations using active breathing control (ABC) in lung cancer. , 2012, International journal of radiation oncology, biology, physics.

[24]  Geoffrey D Hugo,et al.  Localization accuracy of the clinical target volume during image-guided radiotherapy of lung cancer. , 2011, International journal of radiation oncology, biology, physics.

[25]  X. Li,et al.  Assessment and management of interfractional variations in daily diagnostic-quality-CT guided prostate-bed irradiation after prostatectomy. , 2014, Medical physics.

[26]  He Wang,et al.  An automatic CT-guided adaptive radiation therapy technique by online modification of multileaf collimator leaf positions for prostate cancer. , 2005, International journal of radiation oncology, biology, physics.

[27]  M. Partridge,et al.  Adaptive radiotherapy for locally advanced non-small-cell lung cancer does not underdose the microscopic disease and has the potential to increase tumor control. , 2011, International journal of radiation oncology, biology, physics.