PURPOSE: To assess the predictive or comparative value of volumetric changes, measured on daily megavoltage computed tomography during radiotherapy for lung cancer. PATIENTS AND METHODS: We included 80 patients with locally advanced non-small-cell lung cancer treated with image-guided intensity-modulated radiotherapy. The radiotherapy was combined with concurrent chemotherapy, combined with induction chemotherapy, or given as primary treatment. Patients entered two parallel studies with moderately hypofractionated radiotherapy. Tumor volume contouring was done on the daily acquired images. A regression coefficient was derived from the volumetric changes on megavoltage computed tomography, and its predictive value was validated. Logarithmic or polynomial fits were applied to the intratreatment changes to compare the different treatment schedules radiobiologically. RESULTS: Regardless of the treatment type, a high regression coefficient during radiotherapy predicted for a significantly prolonged cause-specific local progression free-survival (p = 0.05). Significant differences were found in the response during radiotherapy. The significant difference in volumetric treatment response between radiotherapy with concurrent chemotherapy and radiotherapy plus induction chemotherapy translated to a superior long-term local progression-free survival for concurrent chemotherapy (p = 0.03). An enhancement ratio of 1.3 was measured for the used platinum/taxane doublet in comparison with radiotherapy alone. CONCLUSION: Contouring on daily megavoltage computed tomography images during radiotherapy enabled us to predict the efficacy of a given treatment. The significant differences in volumetric response between treatment strategies makes it a possible tool for future schedule comparison.
PURPOSE: To assess the predictive or comparative value of volumetric changes, measured on daily megavoltage computed tomography during radiotherapy for lung cancer. PATIENTS AND METHODS: We included 80 patients with locally advanced non-small-cell lung cancer treated with image-guided intensity-modulated radiotherapy. The radiotherapy was combined with concurrent chemotherapy, combined with induction chemotherapy, or given as primary treatment. Patients entered two parallel studies with moderately hypofractionated radiotherapy. Tumor volume contouring was done on the daily acquired images. A regression coefficient was derived from the volumetric changes on megavoltage computed tomography, and its predictive value was validated. Logarithmic or polynomial fits were applied to the intratreatment changes to compare the different treatment schedules radiobiologically. RESULTS: Regardless of the treatment type, a high regression coefficient during radiotherapy predicted for a significantly prolonged cause-specific local progression free-survival (p = 0.05). Significant differences were found in the response during radiotherapy. The significant difference in volumetric treatment response between radiotherapy with concurrent chemotherapy and radiotherapy plus induction chemotherapy translated to a superior long-term local progression-free survival for concurrent chemotherapy (p = 0.03). An enhancement ratio of 1.3 was measured for the used platinum/taxane doublet in comparison with radiotherapy alone. CONCLUSION: Contouring on daily megavoltage computed tomography images during radiotherapy enabled us to predict the efficacy of a given treatment. The significant differences in volumetric response between treatment strategies makes it a possible tool for future schedule comparison.
Authors: Salma K Jabbour; Sinae Kim; Syed A Haider; Xiaoting Xu; Alson Wu; Sujani Surakanti; Joseph Aisner; John Langenfeld; Ning J Yue; Bruce G Haffty; Wei Zou Journal: Int J Radiat Oncol Biol Phys Date: 2015-04-15 Impact factor: 7.038
Authors: C G Vos; M Dahele; J R van Sörnsen de Koste; S Senan; I Bahce; M A Paul; E Thunnissen; E F Smit; K J Hartemink Journal: Strahlenther Onkol Date: 2013-12-22 Impact factor: 3.621
Authors: Yao Ding; John D Hazle; Abdallah S R Mohamed; Steven J Frank; Brian P Hobbs; Rivka R Colen; G Brandon Gunn; Jihong Wang; Jayashree Kalpathy-Cramer; Adam S Garden; Stephen Y Lai; David I Rosenthal; Clifton D Fuller Journal: NMR Biomed Date: 2015-10-09 Impact factor: 4.044
Authors: Geoffrey D Hugo; Elisabeth Weiss; William C Sleeman; Salim Balik; Paul J Keall; Jun Lu; Jeffrey F Williamson Journal: Med Phys Date: 2017-02-02 Impact factor: 4.071