Volume 26, Issue 142 (11-2016)                   J Mazandaran Univ Med Sci 2016, 26(142): 40-49 | Back to browse issues page

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Hasanabdali M, Khoshgard K, Sedighi Pashaki A, Rezaei M, Khoshghadam A, Gholami M H et al . Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients. J Mazandaran Univ Med Sci 2016; 26 (142) :40-49
URL: http://jmums.mazums.ac.ir/article-1-8788-en.html
Abstract:   (6257 Views)

Background and purpose: Breast cancer is the most common type of cancer among women. In radiation therapy for breast cancer it is important to prevent damage to normal tissues particularly to lung tissue. In this study, we investigated the incidence of damage in patients with breast cancer by clinical and dosimetric parameters to identify the predictive factors.

Materials and methods: An experimental study was carried out in which 52 patients with breast cancer (stages II/III) who had mastectomy and/or modified radical mastectomy and 8 cycles of chemotherapy were studied in Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done for delivering 50 Gy dose to PTV in 25 fractions. The risk of damage to the lung tissue was calculated using the Lyman model and its relationship with clinical and dosimetric parameters was evaluated. Finally, based on the results, an appropriate model was obtained for predicting the risk of damage to the lung tissue.

Results: The results showed that MLD and IV19 parameters were significantly associated with the risk of damage to lung tissue. In contrast, CLD parameter did not represent any significant relation with incidence of damage. Based on results, the univariate logistic regression model was proposed to predict the risk of damage to the lung tissue in breast cancer patients.

Conclusion: In treatment planning of breast cancer a possible reduction in CLD parameter is suggested to reduce the lung absorbed dose. Validation parameters of the proposed model showed that this model could provide a good estimation of damage to the lung tissue during breast cancer radiotherapy. Therefore, using this model before radiotherapy can result in less lung injury and consequently enhance the patients’ quality of life.

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Type of Study: Research(Original) |

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