@article {1433, title = {A column generation heuristic for {VMAT} planning with adaptive {CVaR} constraints}, journal = {Physics in Medicine {\&} Biology}, volume = {64}, year = {2019}, month = {oct}, pages = {205024}, abstract = {

In this study we develop an efficient computational procedure that generates medically acceptable treatment plans for volumetric modulated arc therapy with constant gantry speed. Our proposed method is a column generation heuristic based on a mixed integer linear programming model, where the objective function contains minimization of total monitor unit of the treatment plan and dose-volume requirements are included as conditional value-at-risk constraints. Our heuristic generates a full treatment arc for the restricted master problem and calibrates the right hand side parameters of the conditional value-at-risk constraints in the first phase. In the second phase, this initial solution is improved by performing column generation. This is a fully automated procedure and produces treatment plans in a single call without any human intervention. We evaluate its performance on real prostate cancer data by comparing the quality of the generated plans with those obtained by a widely used commercial treatment planning system. Our analysis shows that the results are promising, and the generated plans satisfy the prescription restrictions and require fewer monitor units on average compared to the ones obtained using Eclipse.

}, doi = {10.1088/1361-6560/ab416e}, url = {https://doi.org/10.1088/1361-6560/ab416e}, author = {P{\i}nar Dursun and Z C Ta{\c s}k{\i}n and I. Kuban Altinel and Hatice Bilge and Nazmiye D{\"o}nmez Kesen and Murat Okutan and Ethem Nezih Oral} } @article {1432, title = {The determination of optimal treatment plans for Volumetric Modulated Arc Therapy (VMAT)}, journal = {European Journal of Operational Research}, volume = {272}, year = {2019}, pages = {372-388}, abstract = {

The success of radiation therapy depends on the ability to deliver the proper amount of radiation to cancerous cells while protecting healthy tissues. As a natural consequence, any new treatment technology improves quality standards concerning primarily this issue. Similar to the widely used Intensity Modulated Radiation Therapy (IMRT), the radiation resource is outside of the patient{\textquoteright}s body and the beam is shaped by a multi-leaf collimator mounted on the linear accelerator{\textquoteright}s head during the state-of-the-art Volumetric Modulated Arc Therapy (VMAT) as well. However, unlike IMRT, the gantry of the accelerator may rotate along one or more arcs and deliver radiation continuously. This property makes VMAT powerful in obtaining high conformal plans in terms of dose distribution; but the apertures are interdependent and optimal treatment planning problem cannot be decomposed into simpler independent subproblems as a consequence. In this work, we consider optimal treatment planning problem for VMAT. First, we formulate a mixed-integer linear program minimizing total radiation dose intensity subject to clinical requirements embedded within the constraints. Then, we develop efficient solution procedures combining Benders decomposition with certain acceleration strategies. We investigate their performance on a large set of test instances obtained from an anonymous real prostate cancer data.

}, keywords = {algorithms, Benders decomposition, integer programming, Radiation therapy, VMAT}, issn = {0377-2217}, doi = {https://doi.org/10.1016/j.ejor.2018.06.023}, url = {https://www.sciencedirect.com/science/article/pii/S0377221718305514}, author = {P{\i}nar Dursun and Z C Ta{\c s}k{\i}n and I. Kuban Altinel} } @article {1429, title = {Using branch-and-price to determine optimal treatment plans for volumetric modulated arc therapy (VMAT)}, journal = {Computers \& Operations Research}, volume = {110}, year = {2019}, pages = {1-17}, abstract = {

Volumetric Modulated Arc Therapy (VMAT) is the state-of-the-art technique for external radiation therapy treatment. In this method, radiation can be delivered continuously on one or more arcs during the rotation of the gantry of the linear accelerator. This property makes VMAT powerful in obtaining high conformal plans in terms of dose distribution within short treatment times. However, the apertures composed by the leaves of the multileaf collimator (MLC) system that shapes continuously the radiation are interdependent, which makes treatment planning hard. We propose a mixed integer linear programming model for VMAT planning problem and exact branch-and-price algorithms to solve it. The objective of the model is to minimize total radiation that is delivered to the patient, and pricing subproblem is decomposable by rows of the MLC and can be solved as a shortest path problem. We generate a large set of test instances from a real data set and evaluate the performance of the proposed branch-and-price algorithm. Computational results reveal that new algorithms are efficient and capable of finding optimal solutions for large problem instances.

}, keywords = {Branch-and-price, Column generation, integer programming, Radiation therapy, Shortest path problem, VMAT}, issn = {0305-0548}, doi = {https://doi.org/10.1016/j.cor.2019.05.018}, url = {https://www.sciencedirect.com/science/article/pii/S0305054819301315}, author = {P{\i}nar Dursun and Z C Ta{\c s}k{\i}n and I. Kuban Altinel} }