Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy.

PURPOSE: Although stereotactic body radiation therapy (SBRT) is an attractive noninvasive approach for liver irradiation, it presents specific challenges associated with respiration-induced liver motion, daily tumor localization due to liver deformation, and poor visualization of target with respect to adjacent normal liver in computed tomography (CT). We aim to identify potential hazards and develop a set of mitigation strategies to improve the safety of our liver SBRT program, using failure mode and effect analysis (FMEA). MATERIALS AND METHODS: A multidisciplinary group consisting of two physicians, three physicists, two dosimetrists, and two therapists was formed. A process map covering ten major stages of the liver SBRT program from the initial diagnosis to posttreatment follow-up was generated. A total of 102 failure modes (FM), together with their causes and effects, were identified. The occurrence (O), severity (S), and lack of detectability (D) were independently scored using a scale from 1 (lowest risk) to 10 (largest risk). The ranking was done using the risk probability number (RPN) defined as the product of average O, S, and D numbers for each mode. Two fault tree analyses were performed. The failure modes with the highest RPN values as well as highest severity score were considered for investigation and a set of mitigation strategies was developed to address these. RESULTS: The median RPN (RPNmed ) values for all modes ranged from of 9 to 105 and the highest median S score (Smed ) was 8. Fourteen FMs were identified to be significant by both RPNmed and Smed (top ten RPNmed ranked and highest Smed FMs) and 12 of them were considered for risk mitigation efforts. The remaining two were omitted due to either sufficient checks already in place, or lack of practical mitigation strategies. Implemented measures consisted of five physics tasks, two physician tasks, and three workflow changes. CONCLUSIONS: The application of FMEA to our liver SBRT program led to the identification of potential FMs and allowed improvement measures to enhance the safety of our clinical practice.

Decreased Risk of Radiation Pneumonitis With Coincident Concurrent Use of Angiotensin-converting Enzyme Inhibitors in Patients Receiving Lung Stereotactic Body Radiation Therapy.

OBJECTIVES: Angiotensin-converting enzyme inhibitors (ACEi) have demonstrated decreased rates of radiation-induced lung injury in animal models and clinical reports have demonstrated decreased pneumonitis in the setting of conventionally fractionated radiation to the lung. We tested the role of ACEi in diminishing rates of symptomatic (grade ≥2) pneumonitis in the setting of lung stereotactic body radiation therapy (SBRT). METHODS: We analyzed patients treated with thoracic SBRT to 48 to 60 Gy in 4 to 5 fractions from 2006 to 2014. We reviewed pretreatment and posttreatment medication profiles to document use of ACEi, angiotensin receptor blockers, bronchodilators, aspirin, PDE-5 inhibitors, nitrates, and endothelin receptor antagonists. Pneumonitis was graded posttreatment based on Common Terminology Criteria for Adverse Events Version 4.0. Univariate and multivariate analysis was performed and time to development of pneumonitis was evaluated by the Kaplan-Meier method. RESULTS: A total of 189 patients were evaluated with a median follow-up of 24.8 months. The overall 1-year rate of symptomatic pneumonitis was 13.2%. The 1-year rate of symptomatic pneumonitis was 4.2% for ACEi users versus 16.3% in nonusers (P=0.03). On univariate analysis, the odds of developing grade 2 or greater pneumonitis were significantly lower for patients on ACEi (P=0.03). On multivariate analysis, after controlling for clinicopathologic characteristics and dosimetric endpoints, there was a significant association between ACEi use and decreased risk of clinical pneumonitis (P=0.04). Angiotensin receptor blockers or other bronchoactive medications did not show significant associations with development of pneumonitis. CONCLUSIONS: Incidental concurrent use of ACEi demonstrated efficacy in diminishing rates of symptomatic pneumonitis in the setting of lung SBRT.

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

OBJECTIVE: To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. METHODS: Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. RESULTS: The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. CONCLUSIONS: There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources.

Observer Evaluation of a Metal Artifact Reduction Algorithm Applied to Head and Neck Cone Beam Computed Tomographic Images.

PURPOSE AND OBJECTIVES: To quantify, through an observer study, the reduction in metal artifacts on cone beam computed tomographic (CBCT) images using a projection-interpolation algorithm, on images containing metal artifacts from dental fillings and implants in patients treated for head and neck (H&N) cancer. METHODS AND MATERIALS: An interpolation-substitution algorithm was applied to H&N CBCT images containing metal artifacts from dental fillings and implants. Image quality with respect to metal artifacts was evaluated subjectively and objectively. First, 6 independent radiation oncologists were asked to rank randomly sorted blinded images (before and after metal artifact reduction) using a 5-point rating scale (1 = severe artifacts; 5 = no artifacts). Second, the standard deviation of different regions of interest (ROI) within each image was calculated and compared with the mean rating scores. RESULTS: The interpolation-substitution technique successfully reduced metal artifacts in 70% of the cases. From a total of 60 images from 15 H&N cancer patients undergoing image guided radiation therapy, the mean rating score on the uncorrected images was 2.3 ± 1.1, versus 3.3 ± 1.0 for the corrected images. The mean difference in ranking score between uncorrected and corrected images was 1.0 (95% confidence interval: 0.9-1.2, P<.05). The standard deviation of each ROI significantly decreased after artifact reduction (P<.01). Moreover, a negative correlation between the mean rating score for each image and the standard deviation of the oral cavity and bilateral cheeks was observed. CONCLUSION: The interpolation-substitution algorithm is efficient and effective for reducing metal artifacts caused by dental fillings and implants on CBCT images, as demonstrated by the statistically significant increase in observer image quality ranking and by the decrease in ROI standard deviation between uncorrected and corrected images.