Marco Virgolin1, Ziyuan Wang2, Tanja Alderliesten2,3, Peter A N Bosman1,4. 1. Centrum Wiskunde and Informatica, Life Sciences and Health Group, Amsterdam, The Netherlands. 2. Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Amsterdam, The Netherlands. 3. Leiden University Medical Center, Department of Radiation Oncology, Leiden, The Netherlands. 4. Delft University of Technology, Algorithmics Group, Delft, The Netherlands.
Abstract
Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: ∼ 8 mm for liver LR, IS, and spleen AP, IS; ∼ 5 mm for liver AP and spleen LR; ∼ 80 % for abdomen sDSC; and ∼ 60 % to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially ( + 5 - mm error for spleen IS, - 10 % sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.
Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: ∼ 8 mm for liver LR, IS, and spleen AP, IS; ∼ 5 mm for liver AP and spleen LR; ∼ 80 % for abdomen sDSC; and ∼ 60 % to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially ( + 5 - mm error for spleen IS, - 10 % sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.
Authors: Marco Virgolin; Irma W E M van Dijk; Jan Wiersma; Cécile M Ronckers; Cees Witteveen; Arjan Bel; Tanja Alderliesten; Peter A N Bosman Journal: Med Phys Date: 2018-03-07 Impact factor: 4.071
Authors: Sophie C Huijskens; Irma W E M van Dijk; Jorrit Visser; Coen R N Rasch; Tanja Alderliesten; Arjan Bel Journal: Radiother Oncol Date: 2017-03-28 Impact factor: 6.280
Authors: Irma W E M van Dijk; Foppe Oldenburger; Mathilde C Cardous-Ubbink; Maud M Geenen; Richard C Heinen; Jan de Kraker; Flora E van Leeuwen; Helena J H van der Pal; Huib N Caron; Caro C E Koning; Leontien C M Kremer Journal: Int J Radiat Oncol Biol Phys Date: 2010-02-04 Impact factor: 7.038
Authors: Ziyuan Wang; Marco Virgolin; Brian V Balgobind; Irma W E M van Dijk; Susan A Smith; Rebecca M Howell; Matthew M Mille; Choonsik Lee; Choonik Lee; Cécile M Ronckers; Peter A N Bosman; Arjan Bel; Tanja Alderliesten Journal: Adv Radiat Oncol Date: 2022-07-04