Long-Term Results of Conservative Surgery and Radiotherapy for Ductal Carcinoma In Situ Using Lung Density Correction: The University of Michigan Experience

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72520/1/j.1524-4741.2007.00447.x.pd

Monte Carlo vs. Pencil Beam based optimization of stereotactic lung IMRT

<p>Abstract</p> <p>Background</p> <p>The purpose of the present study is to compare finite size pencil beam (fsPB) and Monte Carlo (MC) based optimization of lung intensity-modulated stereotactic radiotherapy (lung IMSRT).</p> <p>Materials and methods</p> <p>A fsPB and a MC algorithm as implemented in a biological IMRT planning system were validated by film measurements in a static lung phantom. Then, they were applied for static lung IMSRT planning based on three different geometrical patient models (one phase static CT, density overwrite one phase static CT, average CT) of the same patient. Both 6 and 15 MV beam energies were used. The resulting treatment plans were compared by how well they fulfilled the prescribed optimization constraints both for the dose distributions calculated on the static patient models and for the accumulated dose, recalculated with MC on each of 8 CTs of a 4DCT set.</p> <p>Results</p> <p>In the phantom measurements, the MC dose engine showed discrepancies < 2%, while the fsPB dose engine showed discrepancies of up to 8% in the presence of lateral electron disequilibrium in the target. In the patient plan optimization, this translates into violations of organ at risk constraints and unpredictable target doses for the fsPB optimized plans. For the 4D MC recalculated dose distribution, MC optimized plans always underestimate the target doses, but the organ at risk doses were comparable. The results depend on the static patient model, and the smallest discrepancy was found for the MC optimized plan on the density overwrite one phase static CT model.</p> <p>Conclusions</p> <p>It is feasible to employ the MC dose engine for optimization of lung IMSRT and the plans are superior to fsPB. Use of static patient models introduces a bias in the MC dose distribution compared to the 4D MC recalculated dose, but this bias is predictable and therefore MC based optimization on static patient models is considered safe.</p

Growth of breast cancer recurrences assessed by consecutive MRI

<p>Abstract</p> <p>Background</p> <p>Women with a personal history of breast cancer have a high risk of developing an ipsi- or contralateral recurrence. We aimed to compare the growth rate of primary breast cancer and recurrences in women who had undergone prior breast magnetic resonance imaging (MRI).</p> <p>Methods</p> <p>Three hundred and sixty-two women were diagnosed with breast cancer and had undergone breast MRI at the time of diagnosis in our institution (2005 - 2009). Among them, 37 had at least one prior breast MRI with the lesion being visible but not diagnosed as cancer. A linear regression of tumour volume measured on MRI scans and time data was performed using a generalized logistic model to calculate growth rates. The primary objective was to compare the tumour growth rate of patients with either primary breast cancer (no history of breast cancer) or ipsi- or contralateral recurrences of breast cancer.</p> <p>Results</p> <p>Twenty women had no history of breast cancer and 17 patients were diagnosed as recurrences (7 and 10 were ipsi- and contralateral, respectively). The tumour growth rate was higher in contralateral recurrences than in ipsilateral recurrences (growth rate [10^-3days^-1] 3.56 vs 1.38, p < .001) or primary cancer (3.56 vs 2.09, p = 0.01). Differences in tumour growth were not significant for other patient-, tumour- or treatment-related characteristics.</p> <p>Conclusions</p> <p>These findings suggest that contralateral breast cancer presents accelerated growth compared to ipsilateral recurrences or primary breast events.</p