Vitali Moiseenko

‘Big data' approaches carry promise for advancing our understanding of stereotactic body radiation therapy (SBRT) (also termed stereotactic ablative radiotherapy, SABR) and is guiding the design of clinical trials using hypofractionated radiotherapy. However, the field of big data in radiotherapy, or in combination with other therapies, is still in its infancy and will likely benefit from multidisciplinary collaborative teams including physicians, physicists, radiobiologists, biostatisticians, bioinformaticists and other data scientists analyzing shared data. We herein review opportunities to use the Big data (including dosimetry, clinical factors, imaging and biomarkers/genomics) to improve SBRT outcomes.

(2016). Clinical evaluation of QUANTEC guidelines to predict the risk of cardiac mortality in breast cancer patients. Acta Oncologica: Vol. 55, No. 12, pp. 1506-1510.

In order to model the track structure of clinical mega-voltage photon beams in a reasonable time, it is necessary to use a multi-scale approach incorporating a track-structure algorithm for the regions of interest and a condensed history algorithm for the rest of the geometry. This paper introduces a multi-scale Monte Carlo system, which is used to hand off particle trajectory information between the two algorithms. Since condensed history algorithms ignore electrons with energy below a fixed threshold and those electrons are important to the track structure on the micrometre scale, it is necessary to hand over all charged particles to the track-structure algorithm only in a volume that extends beyond the scoring volume. Additionally, the system is validated against experimental results for radio-isotope gamma spectra.

Purpose: Xerostomia is one of the most likely late toxic effects of radiotherapy treatment in patients with head‐and‐neck cancers. Modern treatment techniques can incorporate knowledge of complication risk into treatment plans. To this end, the authors attempt to quantify the regional radiotherapy dose‐dependence of salivary output loss and recovery in a prospective study.Methods: Salivary output was collected from patients undergoing radiotherapy treatment for head‐and‐neck cancers at the BC Cancer Agency between February 2008 and May 2013. Regional dose‐dependence (i.e., dose susceptibility) of loss and recovery is quantified using nonparametric (Spearman's rank correlation coefficients, local linear regression) and parametric (least‐sum of squares, least‐median of squares) techniques.Results: Salivary flow recovery was seen in 79 of 102 patients considered (p < 0.0001, Wilcoxon sign rank test). Output loss was strongly correlated with left‐ and right parotid combined dose φ = min (D_L, 45 Gy) + min (D_R, 45 Gy), and can be accurately predicted. Median early loss (three months) was 72% of baseline, while median overall loss (1 yr) was 56% of baseline. Fitting an exponential model to whole parotid yields dose sensitivities A_3m = 0.0604 Gy⁻¹ and A_1y = 0.0379 Gy⁻¹. Recovery was not significantly associated with dose. Hints of lateral organ sub‐segment dose–response dimorphism were observed.Conclusions: Sub‐segmentation appears to predict neither loss nor recovery with any greater precision than whole parotid mean dose, though it is not any worse. Sparing the parotid to a combined dose φ of <50 Gy is recommended for a patient to keep ≈40% of baseline function and thus avoid severe xerostomia at 12 months post‐treatment. It seems unlikely that a population's mean recovery will exceed 20%–30% of baseline output at 1 yr after radiotherapy treatment using current (whole‐organ based) clinical guidelines.