Purpose: This study aimed to investigate changes in target coverage using magnetic resonance–guided online adaptive radiotherapy (MRgoART) for kidney tumors and to evaluate the suitable timing of treatment. Materials and Methods: Among patients treated with 3-fraction MRgoART for kidney cancer, 18 tumors located within 1 cm of the gastrointestinal tract were selected. Stereotactic radiosurgery planning with a prescription dose of 26 Gy was performed using pretreatment simulation and three MRgoART timings with an adapt-to-shape method. The best MRgoART plan was defined as the plan achieving the highest percentage of planning target volume (PTV) coverage of 26 Gy. In clinical scenario simulation, MRgoART plans were evaluated in the order of actual treatment. Waiting for the next timing was done when the PTV coverage of 26 Gy did not achieve 95%–99% or did not increase by 5% or more compared to the pretreatment plan. Results: The median percentages of PTV receiving 26 Gy in pretreatment and the first, second, and third MRgoART were 82% (range, 19%), 63% (range, 7% to 99%), 88% (range, 31% to 99%), and 95% (range, 3% to 99%), respectively. Comparing pretreatment simulation plans with the best MRgoART plans showed a significant difference (p = 0.025). In the clinical scenario simulation, 16 of the 18 planning series, including nine plans with 95%–99% PTV coverage of 26 Gy and seven plans with increased PTV coverage by 5% or more, would be irradiated at a good timing. Conclusion MRgoART revealed dose coverage differences at each MRgoART timing. Waiting for optimal irradiation timing could be an option in case of suboptimal timing.

Purpose: This study aimed to investigate changes in target coverage using magnetic resonance–guided online adaptive radiotherapy (MRgoART) for kidney tumors and to evaluate the suitable timing of treatment. Materials and Methods: Among patients treated with 3-fraction MRgoART for kidney cancer, 18 tumors located within 1 cm of the gastrointestinal tract were selected. Stereotactic radiosurgery planning with a prescription dose of 26 Gy was performed using pretreatment simulation and three MRgoART timings with an adapt-to-shape method. The best MRgoART plan was defined as the plan achieving the highest percentage of planning target volume (PTV) coverage of 26 Gy. In clinical scenario simulation, MRgoART plans were evaluated in the order of actual treatment. Waiting for the next timing was done when the PTV coverage of 26 Gy did not achieve 95%–99% or did not increase by 5% or more compared to the pretreatment plan. Results: The median percentages of PTV receiving 26 Gy in pretreatment and the first, second, and third MRgoART were 82% (range, 19%), 63% (range, 7% to 99%), 88% (range, 31% to 99%), and 95% (range, 3% to 99%), respectively. Comparing pretreatment simulation plans with the best MRgoART plans showed a significant difference (p = 0.025). In the clinical scenario simulation, 16 of the 18 planning series, including nine plans with 95%–99% PTV coverage of 26 Gy and seven plans with increased PTV coverage by 5% or more, would be irradiated at a good timing. Conclusion MRgoART revealed dose coverage differences at each MRgoART timing. Waiting for optimal irradiation timing could be an option in case of suboptimal timing.

Purpose: This study aimed to investigate changes in target coverage using magnetic resonance–guided online adaptive radiotherapy (MRgoART) for kidney tumors and to evaluate the suitable timing of treatment. Materials and Methods: Among patients treated with 3-fraction MRgoART for kidney cancer, 18 tumors located within 1 cm of the gastrointestinal tract were selected. Stereotactic radiosurgery planning with a prescription dose of 26 Gy was performed using pretreatment simulation and three MRgoART timings with an adapt-to-shape method. The best MRgoART plan was defined as the plan achieving the highest percentage of planning target volume (PTV) coverage of 26 Gy. In clinical scenario simulation, MRgoART plans were evaluated in the order of actual treatment. Waiting for the next timing was done when the PTV coverage of 26 Gy did not achieve 95%–99% or did not increase by 5% or more compared to the pretreatment plan. Results: The median percentages of PTV receiving 26 Gy in pretreatment and the first, second, and third MRgoART were 82% (range, 19%), 63% (range, 7% to 99%), 88% (range, 31% to 99%), and 95% (range, 3% to 99%), respectively. Comparing pretreatment simulation plans with the best MRgoART plans showed a significant difference (p = 0.025). In the clinical scenario simulation, 16 of the 18 planning series, including nine plans with 95%–99% PTV coverage of 26 Gy and seven plans with increased PTV coverage by 5% or more, would be irradiated at a good timing. Conclusion MRgoART revealed dose coverage differences at each MRgoART timing. Waiting for optimal irradiation timing could be an option in case of suboptimal timing.

Purpose: This study aimed to investigate changes in target coverage using magnetic resonance–guided online adaptive radiotherapy (MRgoART) for kidney tumors and to evaluate the suitable timing of treatment. Materials and Methods: Among patients treated with 3-fraction MRgoART for kidney cancer, 18 tumors located within 1 cm of the gastrointestinal tract were selected. Stereotactic radiosurgery planning with a prescription dose of 26 Gy was performed using pretreatment simulation and three MRgoART timings with an adapt-to-shape method. The best MRgoART plan was defined as the plan achieving the highest percentage of planning target volume (PTV) coverage of 26 Gy. In clinical scenario simulation, MRgoART plans were evaluated in the order of actual treatment. Waiting for the next timing was done when the PTV coverage of 26 Gy did not achieve 95%–99% or did not increase by 5% or more compared to the pretreatment plan. Results: The median percentages of PTV receiving 26 Gy in pretreatment and the first, second, and third MRgoART were 82% (range, 19%), 63% (range, 7% to 99%), 88% (range, 31% to 99%), and 95% (range, 3% to 99%), respectively. Comparing pretreatment simulation plans with the best MRgoART plans showed a significant difference (p = 0.025). In the clinical scenario simulation, 16 of the 18 planning series, including nine plans with 95%–99% PTV coverage of 26 Gy and seven plans with increased PTV coverage by 5% or more, would be irradiated at a good timing. Conclusion MRgoART revealed dose coverage differences at each MRgoART timing. Waiting for optimal irradiation timing could be an option in case of suboptimal timing.

Purpose: This study aimed to investigate changes in target coverage using magnetic resonance–guided online adaptive radiotherapy (MRgoART) for kidney tumors and to evaluate the suitable timing of treatment. Materials and Methods: Among patients treated with 3-fraction MRgoART for kidney cancer, 18 tumors located within 1 cm of the gastrointestinal tract were selected. Stereotactic radiosurgery planning with a prescription dose of 26 Gy was performed using pretreatment simulation and three MRgoART timings with an adapt-to-shape method. The best MRgoART plan was defined as the plan achieving the highest percentage of planning target volume (PTV) coverage of 26 Gy. In clinical scenario simulation, MRgoART plans were evaluated in the order of actual treatment. Waiting for the next timing was done when the PTV coverage of 26 Gy did not achieve 95%–99% or did not increase by 5% or more compared to the pretreatment plan. Results: The median percentages of PTV receiving 26 Gy in pretreatment and the first, second, and third MRgoART were 82% (range, 19%), 63% (range, 7% to 99%), 88% (range, 31% to 99%), and 95% (range, 3% to 99%), respectively. Comparing pretreatment simulation plans with the best MRgoART plans showed a significant difference (p = 0.025). In the clinical scenario simulation, 16 of the 18 planning series, including nine plans with 95%–99% PTV coverage of 26 Gy and seven plans with increased PTV coverage by 5% or more, would be irradiated at a good timing. Conclusion MRgoART revealed dose coverage differences at each MRgoART timing. Waiting for optimal irradiation timing could be an option in case of suboptimal timing.

No study has described Streptococcus dysgalactiae subsp. equisimilis (SDSE) isolates that cause repetitive infections (recurrence and reinfection). We compared the microbiological characteristics of SDSE causing repetitive infections with those causing single infections. Three patients with invasive infections were identified based on their medical records, and multiple SDSE isolates were collected at intervals over three weeks, using a laboratory repository. Isolates from 12 patients with single-episode infections served as controls. Six isolates were collected from three patients with first and second episodes of infection. All isolates causing either repetitive or single-episode infection were subjected to emm typing, multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE), and random amplified polymorphic DNA (RAPD) analyses. Amplification of five virulence genes (sicG, prtF1, prtF2, lmb, and cbp), biofilm formation (BF), and cell invasion abilities (CIAs) were measured as virulent phenotypes. We observed close genetic similarities in the data obtained by emm typing, MLST, PFGE, and RAPD in four isolates from two patients, suggesting recurrence, whereas two isolates from one patient indicated genetic differences in these data, suggesting re-infection. The presence of the five virulence genes and the BF and CIA measurements appeared not to contribute to repetitive infections, compared with isolates causing single-episode infection. In conclusion, clinicians encountering patients with repetitive infections should be aware of both possibilities: recurrence with closely related strains and reinfection with different strains.
Biofilms ; DNA ; Electrophoresis, Gel, Pulsed-Field ; Humans ; Medical Records ; Multilocus Sequence Typing ; Phenotype ; Recurrence ; Streptococcus ; Virulence

PURPOSE: Few studies have compared fractional exhaled nitric oxide (FeNO) measurement by NIOX VERO® (NOV) and other devices in children. Moreover, there is no agreement between differences in FeNO values obtained using different devices in adults. Here, we compared FeNO values obtained using NOV and NObreath® (NOB) systems to derive a correction equation for children. METHODS: Eighty-eight participants (age 7–15 years) who were diagnosed with atopic bronchial asthma and visited Sagamihara National Hospital as outpatients between January and April of 2017 were included. We measured FeNO values obtained using NOB and NOV, and analyzed them using Wilcoxon tests and Altman-Bland plots. RESULTS: The median age of the participants was 11.5 years, and the scored Asthma Control Test (ACT) or Childhood ACT (C-ACT) was 25 (interquartile range, 24–25) or 26 (24–27). NOB and NOV values were significantly different (31 [14–52] versus 36 [20–59] ppb; P = 0.020) and strongly correlated (r = 0.92). An equation to convert NOB values into NOV values was derived using linear regression as follows: log NOV = 0.7329 × log NOB + 0.4704; NOB for 20, 40, 58, 80 and 100 ppb corresponded to NOV for 27, 44, 59, 73 and 86 ppb. Thus, NOB < 58 ppb suggested NOB < NOV, whereas NOB > 58 ppb suggested NOB > NOV. CONCLUSIONS: NOB and NOV values were strongly correlated. Participants whose FeNO values were relatively low represented NOB < NOV, whereas those whose FeNO values were relatively high represented NOB > NOV.
Adult ; Asthma ; Child* ; Exhalation ; Humans ; Linear Models ; Nitric Oxide* ; Outpatients