1
|
Validation and comparison of radiograph-based organ dose reconstruction approaches for Wilms’ tumor radiation treatment plans. Adv Radiat Oncol 2022; 7:101015. [PMID: 36060631 PMCID: PMC9429523 DOI: 10.1016/j.adro.2022.101015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 06/22/2022] [Indexed: 11/22/2022] Open
|
2
|
Virgolin M, Wang Z, Balgobind BV, van Dijk IWEM, Wiersma J, Kroon PS, Janssens GO, van Herk M, Hodgson DC, Zadravec Zaletel L, Rasch CRN, Bel A, Bosman PAN, Alderliesten T. Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. Phys Med Biol 2020; 65:245021. [PMID: 32580177 DOI: 10.1088/1361-6560/ab9fcc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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 [Formula: see text], ≤ 2.9 Gy for [Formula: see text], and ≤ 13% for [Formula: see text] and [Formula: see text], 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.
Collapse
Affiliation(s)
- M Virgolin
- Life Sciences and Health Group, Centrum Wiskunde & Informatica, The Netherlands. shared first authorship, the two authors contributed equally to this work
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
3
|
Virgolin M, Wang Z, Alderliesten T, Bosman PAN. Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction. J Med Imaging (Bellingham) 2020; 7:046501. [PMID: 32743017 PMCID: PMC7390892 DOI: 10.1117/1.jmi.7.4.046501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 07/15/2020] [Indexed: 11/14/2022] Open
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.
Collapse
Affiliation(s)
- Marco Virgolin
- Centrum Wiskunde and Informatica, Life Sciences and Health Group, Amsterdam, The Netherlands
| | - Ziyuan Wang
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - Tanja Alderliesten
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Amsterdam, The Netherlands
- Leiden University Medical Center, Department of Radiation Oncology, Leiden, The Netherlands
| | - Peter A N Bosman
- Centrum Wiskunde and Informatica, Life Sciences and Health Group, Amsterdam, The Netherlands
- Delft University of Technology, Algorithmics Group, Delft, The Netherlands
| |
Collapse
|
4
|
Wang Z, Virgolin M, Bosman PAN, Crama KF, Balgobind BV, Bel A, Alderliesten T. Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients. J Med Imaging (Bellingham) 2020; 7:015001. [PMID: 32042857 DOI: 10.1117/1.jmi.7.1.015001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 01/17/2020] [Indexed: 01/10/2023] Open
Abstract
Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average < 5 mm difference in plan isocenter/borders, and < 0.9 Gy in organ mean dose (prescribed dose: 14.4 Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation.
Collapse
Affiliation(s)
- Ziyuan Wang
- University of Amsterdam, Amsterdam UMC, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - Marco Virgolin
- Centrum Wiskunde and Informatica, Life Sciences and Health Group, Amsterdam, The Netherlands
| | - Peter A N Bosman
- Centrum Wiskunde and Informatica, Life Sciences and Health Group, Amsterdam, The Netherlands
| | - Koen F Crama
- University of Amsterdam, Amsterdam UMC, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - Brian V Balgobind
- University of Amsterdam, Amsterdam UMC, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - Arjan Bel
- University of Amsterdam, Amsterdam UMC, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - Tanja Alderliesten
- University of Amsterdam, Amsterdam UMC, Department of Radiation Oncology, Amsterdam, The Netherlands
| |
Collapse
|
5
|
Wang Z, Balgobind BV, Virgolin M, van Dijk IWEM, Wiersma J, Ronckers CM, Bosman PAN, Bel A, Alderliesten T. 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? JOURNAL OF RADIOLOGICAL PROTECTION : OFFICIAL JOURNAL OF THE SOCIETY FOR RADIOLOGICAL PROTECTION 2019; 39:598-619. [PMID: 30965301 DOI: 10.1088/1361-6498/ab1796] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In retrospective radiation treatment (RT) dosimetry, a surrogate anatomy is often used for patients without 3D CT. To gain insight in what the crucial aspects in a surrogate anatomy are to enable accurate dose reconstruction, we investigated the relation of patient characteristics and internal anatomical features with deviations in reconstructed organ dose using surrogate patient's CT scans. Abdominal CT scans of 35 childhood cancer patients (age: 2.1-5.6 yr; 17 boys, 18 girls) undergoing RT during 2004-2016 were included. Based on whether an intact right or left kidney is present in the CT scan, two groups were formed each containing 24 patients. From each group, four CTs associated with Wilms' tumor RT plans with an anterior-posterior-posterior-anterior field setup were selected as references. For each reference, a 2D digitally reconstructed radiograph was computed from the reference CT to simulate a 2D radiographic image and dose reconstruction was performed on the other CTs in the respective group. Deviations in organ mean dose (DEmean) of the reconstructions versus the references were calculated, as were deviations in patient characteristics (i.e. age, height, weight) and in anatomical features including organ volume, location (in 3D), and spatial overlaps. Per reference, the Pearson's correlation coefficient between deviations in DEmean and patient characteristics/features were studied. Deviation in organ locations and DEmean for the liver, spleen, and right kidney were moderately correlated (R2 > 0.5) for 8/8, 5/8, and 3/4 reference plans, respectively. Deviations in organ volume or spatial overlap and DEmean for the right and left kidney were weakly correlated (0.3 < R2 < 0.5) in 4/4 and 1/4 reference plans. No correlations (R2 < 0.3) were found between deviations in age or height and DEmean. Therefore, the performance of organ dose reconstruction using surrogate patients' CT scans is primarily related to deviation in organ location, followed by volume and spatial overlap. Further, results were plan dependent.
Collapse
Affiliation(s)
- Ziyuan Wang
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | | | | | | | | | | | | | | | | |
Collapse
|
6
|
Huijskens SC, van Dijk IWEM, Visser J, Balgobind BV, Te Lindert D, Rasch CRN, Alderliesten T, Bel A. Abdominal organ position variation in children during image-guided radiotherapy. Radiat Oncol 2018; 13:173. [PMID: 30208936 PMCID: PMC6136223 DOI: 10.1186/s13014-018-1108-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/20/2018] [Indexed: 12/03/2022] Open
Abstract
Background Interfractional organ position variation might differ for abdominal organs and this could have consequences for defining safety margins. Therefore, the purpose of this study is to quantify interfractional position variations of abdominal organs in children in order to investigate possible correlations between abdominal organs and determine whether position variation is location-dependent. Methods For 20 children (2.2–17.8 years), we retrospectively analyzed 113 CBCTs acquired during the treatment course, which were registered to the reference CT to assess interfractional position variation of the liver, spleen, kidneys, and both diaphragm domes. Organ position variation was assessed in three orthogonal directions and relative to the bony anatomy. Diaphragm dome position variation was assessed in the cranial-caudal (CC) direction only. We investigated possible correlations between position variations of the organs (Spearman’s correlation test, ρ), and tested if organ position variations in the CC direction are related to the diaphragm dome position variations (linear regression analysis, R2) (both tests: significance level p < 0.05). Differences of variations of systematic (∑) and random errors (σ) between organs were tested (Bonferroni significance level p < 0.004). Results In all directions, correlations between liver and spleen position variations, and between right and left kidney position variations were weak (ρ ≤ 0.43). In the CC direction, the position variations of the right and left diaphragm domes were significantly, and stronger, correlated with position variations of the liver (R2 = 0.55) and spleen (R2 = 0.63), respectively, compared to the right (R2 = 0.00) and left kidney (R2 = 0.25). Differences in ∑ and σ between all organs were small and insignificant. Conclusions No (strong) correlations between interfractional position variations of abdominal organs in children were observed. From present results, we concluded that diaphragm dome position variations could be more representative for superiorly located abdominal (liver, spleen) organ position variations than for inferiorly located (kidneys) organ position variations. Differences of systematic and random errors between abdominal organs were small, suggesting that for margin definitions, there was insufficient evidence of a dependence of organ position variation on anatomical location. Electronic supplementary material The online version of this article (10.1186/s13014-018-1108-9) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Sophie C Huijskens
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.
| | - Irma W E M van Dijk
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Jorrit Visser
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Brian V Balgobind
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - D Te Lindert
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Coen R N Rasch
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Tanja Alderliesten
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Arjan Bel
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| |
Collapse
|