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    Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

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    Authors
    Virgolin, M.
    Wang, Z.
    Balgobind, B. V.
    van Dijk, I.
    Wiersma, J.
    Kroon, P. S.
    Janssens, G. O.
    van Herk, Marcel
    Hodgson, D. C.
    Zaletel, L. Z.
    Rasch, C. R. N.
    Bel, A.
    Bosman, P. A. N.
    Alderliesten, T.
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    Affiliation
    Life Sciences and Health Group, Centrum Wiskunde en Informatica, Amsterdam, Noord-Holland, NETHERLANDS.
    Issue Date
    2020
    
    Metadata
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    Abstract
    To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n=142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤1.7 Gy for Dmean, ≤2.9 Gy for D2cc, and ≤13% for V5Gyand V10Gy, were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, we proposed a novel organ dose reconstruction method that uses ML models to predict dose-volume metric values given patient and plan features. Our approach is not only accurate, but also efficient, as the setup of a surrogate is no longer needed.
    Citation
    Virgolin M, Wang Z, Balgobind B, van Dijk I, Wiersma J, Kroon PS, et al. Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. Phys Med Biol. 2020;65(24).
    Journal
    Physics in Medicine and Biology
    URI
    http://hdl.handle.net/10541/623734
    DOI
    10.1088/1361-6560/ab9fcc
    PubMed ID
    32580177
    Additional Links
    https://dx.doi.org/10.1088/1361-6560/ab9fcc
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1088/1361-6560/ab9fcc
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