Radiotherapy, Intensity-Modulated

Hepatocellular carcinoma is currently one of the most fatal cancers in the world. The routine treatment for this type of cancer consists of surgery, chemotherapy, and finally radiation therapy. Recent advancements in technology have enabled us to deliver highly conformed dose to planning target volume. Two of these methods are Intensity modulated Radiation Therapy (IMRT) and Stereotactic Body Radiation Therapy (SBRT). The difference between these two methods is that in the SBRT high radiation dose per fraction is delivered, but smaller number of fractions which renders better tumor control probability. However, better tumor control comes at the price of complications and radiation induced liver damage.
In this work, we compare the outcome of radiation with regards to the probability of radiation damage to the liver after IMRT and SBRT. For this purpose, we analyzed 10 anonymized patients’ data with liver cancer, and we made two similar treatment plans for them. The difference in two plans is dose per fraction and total dose. After optimizing the treatments and calculating the dose volume histogram, we found the effective volume of the liver being irradiated. Finally, this effective volume and the corresponding dose were used to show that SBRT has the advantage of better tumor control probability at the cost of higher probability of complications.

Simulating Annealing Algorithm (SAA) has been proposed for optimization of the Intensity-Modulated Radiation Therapy (IMRT). Despite the advantage of the SAA to be a global optimizer, the SAA optimization of IMRT is an extensive computational task due to the large scale of the optimization variables, and therefore it requires significant computational resources. In this research we introduce a parallel graphics processing unit (GPU)-based SAA developed in MATLAB platform and compliant with the computational environment for radiotherapy research (CERR) for IMRT treatment planning in order elucidate the performance improvement of the SAA in IMRT optimization. First, we identify the “bottlenecks” of our code, and then we parallelize those on the GPU accordingly. Performance tests were conducted on four different GPU cards in comparison to a serial version of the algorithm executed on a CPU. A gradual increase of the speedup factor as a function of the number of beamlets was found for all four GPUs. A maximum speedup factor of 33.48 was achieved for a prostate case, and 30.51 for a lung cancer case when the K40m card and the maximum number of beams was utilized for each case. At the same time, the two optimized IMRT plans that were created (prostate and lung cancer plans) were met the IMRT optimization goals.