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1.
Front Oncol ; 10: 1597, 2020.
Article in English | MEDLINE | ID: mdl-33042802

ABSTRACT

Background: A rectal sub-region (SRR) has been previously identified by voxel-wise analysis in the inferior-anterior part of the rectum as highly predictive of rectal bleeding (RB) in prostate cancer radiotherapy. Translating the SRR to patient-specific radiotherapy planning is challenging as new constraints have to be defined. A recent geometry-based model proposed to optimize the planning by determining the achievable mean doses (AMDs) to the organs at risk (OARs), taking into account the overlap between the planning target volume (PTV) and OAR. The aim of this study was to quantify the SRR dose sparing by using the AMD model in the planning, while preserving the dose to the prostate. Material and Methods: Three-dimensional volumetric modulated arc therapy (VMAT) planning dose distributions for 60 patients were computed following four different strategies, delivering 78 Gy to the prostate, while meeting the genitourinary group dose constraints to the OAR: (i) a standard plan corresponding to the standard practice for rectum sparing (STDpl), (ii) a plan adding constraints to SRR (SRRpl), (iii) a plan using the AMD model applied to the rectum only (AMD_RECTpl), and (iv) a final plan using the AMD model applied to both the rectum and the SRR (AMD_RECT_SRRpl). After PTV dose normalization, plans were compared with regard to dose distributions, quality, and estimated risk of RB using a normal tissue complication probability model. Results: AMD_RECT_SRRpl showed the largest SRR dose sparing, with significant mean dose reductions of 7.7, 3, and 2.3 Gy, with respect to the STDpl, SRRpl, and AMD_RECTpl, respectively. AMD_RECT_SRRpl also decreased the mean rectal dose by 3.6 Gy relative to STDpl and by 3.3 Gy relative to SRRpl. The absolute risk of grade ≥1 RB decreased from 22.8% using STDpl planning to 17.6% using AMD_RECT_SRRpl considering SRR volume. AMD_RECT_SRRpl plans, however, showed slightly less dose homogeneity and significant increase of the number of monitor units, compared to the three other strategies. Conclusion: Compared to a standard prostate planning, applying dose constraints to a patient-specific SRR by using the achievable mean dose model decreased the mean dose by 7.7 Gy to the SRR and may decrease the relative risk of RB by 22%.

2.
Bull Environ Contam Toxicol ; 88(4): 571-6, 2012 Apr.
Article in English | MEDLINE | ID: mdl-22246475

ABSTRACT

The seasonal accumulation of cadmium, copper, lead, mercury and zinc was determined in sediments, water, and black-chinned tilapia (Sarotherodon melanotheron; muscle, brain, kidney and liver tissues) collected monthly from Biétri Bay. The mean water concentration of metals (in mg L(-1)) ranged from 0.01 to 0.30 (mercury), 0.02-0.26 (cadmium), 2.40-4.80 (lead), 9.05-9.68 (copper), and 12.05-19.87 (zinc). The seasonal variations showed a significant difference in the levels of mercury, cadmium and lead among season. The highest mercury (0.30 ± 0.02 µg L(-1)), cadmium (0.26 ± 0.02 mg L(-1)) and lead (4.80 ± 1.03 mg L(-1)) levels were observed during dry season, while the lowest levels (0.21 ± 0.01, 0.02 ± 0.01 and 2.40 ± 0.02 mg L(-1), respectively mercury, cadmium and lead) were measured during rainy season. The average cadmium (0.58 ± 0.36 mg L(-1)), copper (42.15 ± 19.40 mg L(-1)), lead (58.47 ± 38.10 mg kg(-1)), mercury (0.79 ± 0.47 µg kg(-1)) and zinc (187.58 ± 76.99 mg kg(-1)) concentrations determined in Biétri Bay sediments showed a similar trend as in water. The seasonal variations of mercury, cadmium and lead in tissues revealed that these metals were higher concentrated during dry and swelling seasons. The levels of zinc and copper followed by lead were higher in the tissues. The order of tissues metals concentrations was: kidney > liver > brain > muscle.


Subject(s)
Bays/chemistry , Metals, Heavy/analysis , Tilapia/metabolism , Water Pollutants, Chemical/analysis , Animals , Cote d'Ivoire , Environmental Monitoring , Geologic Sediments/chemistry , Metals, Heavy/metabolism , Seasons , Water Pollutants, Chemical/metabolism
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