Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy
Balgobind, B. V.
van Dijk, I.
Kroon, P. S.
Janssens, G. O.
van Herk, Marcel
Hodgson, D. C.
Zaletel, L. Z.
Rasch, C. R. N.
Bosman, P. A. N.
AffiliationLife Sciences and Health Group, Centrum Wiskunde en Informatica, Amsterdam, Noord-Holland, NETHERLANDS.
MetadataShow full item record
AbstractTo 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.
CitationVirgolin 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).
JournalPhysics in Medicine and Biology
- Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients' CT scans?
- Authors: Wang Z, van Dijk IWEM, Wiersma J, Ronckers CM, Oldenburger F, Balgobind BV, Bosman PAN, Bel A, Alderliesten T
- Issue date: 2018 Jun
- How do patient characteristics and anatomical features correlate to accuracy of organ dose reconstruction for Wilms' tumor radiation treatment plans when using a surrogate patient's CT scan?
- Authors: Wang Z, Balgobind BV, Virgolin M, van Dijk IWEM, Wiersma J, Ronckers CM, Bosman PAN, Bel A, Alderliesten T
- Issue date: 2019 Jun
- On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors.
- Authors: Virgolin M, van Dijk IWEM, Wiersma J, Ronckers CM, Witteveen C, Bel A, Alderliesten T, Bosman PAN
- Issue date: 2018 Apr
- Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.
- Authors: Shiradkar R, Podder TK, Algohary A, Viswanath S, Ellis RJ, Madabhushi A
- Issue date: 2016 Nov 10
- Individualized 3D reconstruction of normal tissue dose for patients with long-term follow-up: a step toward understanding dose risk for late toxicity.
- Authors: Ng A, Brock KK, Sharpe MB, Moseley JL, Craig T, Hodgson DC
- Issue date: 2012 Nov 15