Journées de l'optimisation 2018

HEC Montréal, Québec, Canada, 7 — 9 mai 2018

Horaire Auteurs Mon horaire
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MB10 Optimization in radiotherapy centers

7 mai 2018 15h30 – 17h10

Salle: Sony (48)

Présidée par Nadia Lahrichi

4 présentations

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    15h30 - 15h55

    Mixed electron-photon radiation therapy treatment plan optimization using the column generation method

    • Marc-Andre Renaud, prés., McGill University
    • Monica Serban, McGill University
    • Jan Seuntjens, McGill University

    Column generation is well suited for mixed-modality optimisation in radiation therapy as the aperture shaping and modality selection problem can be solved rapidly. We demonstrate that the column generation method applied to mixed photon-electron planning can efficiently generate treatment plans, and investigate its behaviour under different aperture addition schemes.

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    15h55 - 16h20

    Data-driven appointment scheduling using service time predictive model

    • Dina Ben Tayeb, prés.,
    • Louis-Martin Rousseau, Polytechnique Montréal
    • Nadia Lahrichi, Polytechnique Montréal

    Our work concerns a data-driven approach based on the real data of the Centre Intégré de Cancérologie de Laval (CICL) to develop an efficient patient planning. The study is divided into two main stages. First, we elaborate a predictive model of patient treatment time using data mining and regression tools. Then, based on the predicted service times, new schedule grids are constructed and compared using different assignment rules. The proposed schedule in this study proves its performance with the reduction in waiting time and the augmentation of patients seen per day.

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    16h20 - 16h45

    A column generation-based heuristic optimizing dose-volume objectives for volumetric-modulated arc therapy

    • Mehdi Mahnam, prés., Polytechnique Montréal
    • Michel Gendreau, Polytechnique Montréal
    • Nadia Lahrichi, Polytechnique Montréal
    • Louis-Martin Rousseau, Polytechnique Montréal

    Volumetric-modulated arc therapy (VMAT) treatment planning is an efficient treatment technique with a high degree of flexibility in terms of dose rate, gantry speed, and aperture shapes during rotation around the patient. However, the dynamic nature of VMAT results in a large-scale nonconvex optimization problem. Determining the priority of the tissues, voxels, and objectives to obtain clinically acceptable treatment plans poses additional challenges for VMAT optimization. The main purpose of this work is to develop an automatic planning approach integrating direct aperture optimization and re-planning for VMAT, adjusting the model parameters during the algorithm and decreasing the use of trial-and-error in the search for clinically acceptable plans. The proposed algorithm is based on column generation technique which sequentially generates the apertures by solving subproblems and optimizes the corresponding intensities in the master model. In the present work, we modify the weight vector of the penalty function based on the dose-volume histogram (DVH) during the CG iterations. We evaluate the efficiency of the algorithm and the treatment quality using a clinical prostate case and a challenging head-and-neck case.

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    16h45 - 17h10

    An active-reactive methodology for an online multi-appointments chemotherapy scheduling problem

    • Pedram Hooshangitabrizi, prés., Ph.D. Candidate
    • Ivan Contreras, Concordia University
    • Nadia Bhuiyan, Concordia University

    We study a real-world scheduling problem which accommodates requests of patients for chemotherapy treatments in a major metropolitan hospital in Montreal. To solve the problem, an effective and efficient online methodology is proposed which systematically combines two well-defined linear mixed integer programming formulations to handle occurring expected and unexpected events. Using the historical data provided by the oncology clinic, several computational experiments and sensitivity analyses are conducted to draw managerial insights.

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