Monte Carlo skin dose simulation in intraoperative radiotherapy of breast cancer using spherical applicators

Moradi, F.; Ung, Ngie Min; Khandaker, Mayeen Uddin; Mahdiraji, Ghafour Amouzad; Saad, Marniza; Malik, Rozita Abdul; Bustam, Anita Zarina; Zaili, Zainor; Bradley, D.A.

doi:10.1088/1361-6560/aa7fe6

Cited by 36 publications

(51 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of source‐to‐source variations, dose rate measurements performed with another INTRABEAM source were found to differ by 15% to 5% as a function of depth in water, corresponding to a relative difference (i.e., the difference in the PDDs) of up to 10%. This is similar to previously reported source‐to‐source output variations . However, while source output variations (both day‐to‐day and source‐to‐source) would change the absolute doses reported in this work, the relative differences between the dose determination methods (i.e., TARGIT, Zeiss,

C_{Q}

, film) would be unaffected.…”

Section: Discussionsupporting

confidence: 90%

An investigation into the INTRABEAM miniature x‐ray source dosimetry using ionization chamber and radiochromic film measurements

et al. 2018

View full text Add to dashboard Cite

show abstract

C_{Q}

, film) would be unaffected.…”

Section: Discussionsupporting

confidence: 90%

An investigation into the INTRABEAM miniature x‐ray source dosimetry using ionization chamber and radiochromic film measurements

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Unlike previous studies, this study has a larger sample size of 15 XRSs and presents novel DDRs. While it is beyond the scope of this paper to speculate on the reasons for the observed differences, it has been previously suggested that the dose‐rate differences are attributed to the variability in target size, and small changes in other x‐ray generation structures (i.e., electron source) . Future work, in collaboration with Carl Zeiss Meditec, could correlate target thickness to the output characteristics.…”

Section: Discussionmentioning

confidence: 94%

Evaluation of the dosimetric impact of manufacturing variations for the INTRABEAM x‐ray source

Shaikh¹,

Joiner

Nalichowski

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

Introduction INTRABEAM x‐ray sources (XRSs) have distinct output characteristics due to subtle variations between the ideal and manufactured products. The objective of this study is to intercompare 15 XRSs and to dosimetrically quantify the impact of manufacturing variations on the delivered dose. Methods and Materials The normality of the XRS datasets was evaluated with the Shapiro–Wilk test, the accuracy of the calibrated depth–dose curves (DDCs) was validated with ionization chamber measurements, and the shape of each DDC was evaluated using depth–dose ratios (DDRs). For 20 Gy prescribed to the spherical applicator surface, the dose was computed at 5‐mm and 10‐mm depths from the spherical applicator surface for all XRSs. Results At 5‐, 10‐, 20‐, and 30‐mm depths from the source, the coefficient of variation (CV) of the XRS output for 40 kVp was 4.4%, 2.8%, 2.0%, and 3.1% and for 50 kVp was 4.2%, 3.8%, 3.8%, and 3.4%, respectively. At a 20‐mm depth from the source, the 40‐kVp energy had a mean output in Gy/Minute = 0.36, standard deviation (SD) = 0.0072, minimum output = 0.34, and maximum output = 0.37 and a 50‐kVp energy had a mean output = 0.56, SD = 0.021, minimum output = 0.52, and maximum output = 0.60. We noted the maximum DRR values of 2.8% and 2.5% for 40 kVp and 50 kVp, respectively. For all XRSs, the maximum dosimetric effect of these variations within a 10‐mm depth of the applicator surface is ≤ 2.5%. The CV increased as depth increased and as applicator size decreased. Conclusion The American Association of Physicist in Medicine Task Group‐167 requires that the impurities in radionuclides used for brachytherapy produce ≤ 5.0% dosimetric variations. Because of differences in an XRS output and DDC, we have demonstrated the dosimetric variations within a 10‐mm depth of the applicator surface to be ≤ 2.5%.

show abstract

“…Monte Carlo simulation of a breast phantom with realistic tissue compositions and skin layers, demonstrated that in vivo dose depends on the size of applicator as well as the amount of breast tissue between the applicator and the skin. In the study of Fogg et al., the ratios between average doses measured with TLDs at 5 mm and at 15 mm from the point of insertion were 1.47, 1.22, 1.24, and 1.11 for the 3.5, 4.0, 4.5, and 5.0 cm applicators, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…However, given that the dose rate is checked and, if necessary, readjusted before each treatment, these are unlikely to affect measured dose. Small differences in the various structures involved in the generation of x rays, including the electron source, the beam deflector as well as the gold target, such as between manufactured and ideal target thickness, could change photon spectra and doses between different sources, thus explaining a change of dose …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of skin dose in low‐kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry

et al. 2019

View full text Add to dashboard Cite

Purpose The purpose of this study was to implement a machine learning model to predict skin dose from targeted intraoperative (TARGIT) treatment resulting in timely adoption of strategies to limit excessive skin dose. Methods A total of 283 patients affected by invasive breast carcinoma underwent TARGIT with a prescribed dose of 6 Gy at 1 cm, after lumpectomy. Radiochromic films were used to measure the dose to the skin for each patient. Univariate statistical analysis was performed to identify correlation of physical and patient variables with measured dose. After feature selection of predictors of in vivo skin dose, machine learning models stepwise linear regression (SLR), support vector regression (SVR), ensemble with bagging or boosting, and feed forward neural networks were trained on results of in vivo dosimetry to derive models to predict skin dose. Models were evaluated by tenfold cross validation and ranked according to root mean square error (RMSE) and adjusted correlation coefficient of true vs predicted values (adj‐R2). Results The predictors correlated with in vivo dosimetry were the distance of skin from source, depth‐dose in water at depth of the applicator in the breast, use of a replacement source, and irradiation time. The best performing model was SVR, which scored RMSE and adj‐R2, equal to 0.746 [95% confidence intervals (CI), 95% CI 0.737,0.756] and 0.481 (95% CI 0.468,0.494), respectively, on the tenfold cross validation. Conclusion The model trained on results of in vivo dosimetry can be used to predict skin dose during setup of patient for TARGIT and this allows for timely adoption of strategies to prevent of excessive skin dose.

show abstract

Monte Carlo skin dose simulation in intraoperative radiotherapy of breast cancer using spherical applicators

Cited by 36 publications

References 30 publications

An investigation into the INTRABEAM miniature x‐ray source dosimetry using ionization chamber and radiochromic film measurements

An investigation into the INTRABEAM miniature x‐ray source dosimetry using ionization chamber and radiochromic film measurements

Evaluation of the dosimetric impact of manufacturing variations for the INTRABEAM x‐ray source

Prediction of skin dose in low‐kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry

Contact Info

Product

Resources

About