Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy

Rossi, L.; Bijman, R.; Schillemans, W.; Aluwini, S.; Cavedon, Carlo; Witte, Marnix G.; Incrocci, Luca; Heijmen, B.

doi:10.1016/j.radonc.2018.07.027

Cited by 96 publications

(94 citation statements)

References 36 publications

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“…In light of this, further work should be done to understand the spatial rectal dose-volume toxicity relationship, for the safe implementation of pelvic MRgRT. 31,33,34 Our results suggest that localized volumes of the rectal wall, in the path of a single beam, could exceed acceptable dose-volume criteria for a single fraction when unplanned air cavities occur. However, with no fully validated model defining the link between rectal dose and late toxicity, the clinical implications of hotspots in the rectal wall exceeding current rectal dose constraints during MRgRT cannot be defined here.…”

Section: A Implications Of Unplanned Gas In the Rectal Wallmentioning

confidence: 77%

“…Therefore, DVH parameters may not be the most appropriate means to assess the implications of dosimetric changes caused by unplanned gas during MRgRT in the clinic. In light of this, further work should be done to understand the spatial rectal dose‐volume toxicity relationship, for the safe implementation of pelvic MRgRT …”

Section: Discussionmentioning

confidence: 99%

“…29 The rectal dose-volume toxicity relationship is not fully understood. Although delivering [30][31][32][33][34][35][36][37][38][39][40] Gy to the rectum is generally accepted to be safe during EBRT, doses in the region of 100 Gy are commonly seen in the rectum during prostate brachytherapy, indicating that we do not fully understand the doses that the rectal wall can tolerate on small volumes. 27,30 Furthermore, there is nothing to suggest that volumes smaller than 1 cc, that would not be revealed by DVH parameters, do not influence the risk of rectal toxicity.…”

Section: A Implications Of Unplanned Gas In the Rectal Wallmentioning

confidence: 99%

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Assessing localized dosimetric effects due to unplanned gas cavities during pelvic MR‐guided radiotherapy using Monte Carlo simulations

et al. 2019

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Purpose It has been proposed that beam modulation and opposing beam configurations can cancel effects of the Electron Return Effect (ERE) during MR‐guided radiotherapy (MRgRT). However, this may not always be the case for unplanned gas cavities outside of the target in the pelvic region. We evaluate dosimetric effects, including effects in the rectal wall, due to unplanned spherical air cavities during MRgRT. Methods Nine virtual cuboid water phantoms containing spherical air cavities (0.5–7.5 cm diameter) and a reference phantom without air were created. Monte Carlo dose calculations of 7 MV photons under the influence of a 1.5 T transverse magnetic field were produced using Monaco 5.19.02 Treatment Planning System (TPS) (Elekta AB, Stockholm, Sweden). Cavities in the path of a single and multiple beam plans were considered. Dose distributions of phantoms with and without air cavities were compared (ΔD%) using a spherical coordinate system originating in the center of the cavity. Effects in the rectal wall were quantified by comparing dose volume histogram (DVH) parameters for solid and gaseous filling from simulated rectal wall structures. Results Max(ΔD%) of ~70% and 20% were observed around large cavities in the path of a single and multiple beam plans, respectively. Approximately 45 cm3 of phantom surrounding the largest cavity in a single beam received dose changes of >10%. Dmean in the rectal wall was unchanged when comparing gaseous and solid filling in the path of a single beam; however, D1cc and Dmax increased by up to ~45% and ~63%, respectively. Conclusions Unplanned gas cavities in the path of a single beam during pelvic MRgRT with a 1.5 T transverse magnetic field cause dose changes which may impact toxicity in the rectal wall, depending on local dose and fractionation. Effects are reduced but not eliminated with a five‐beam plan.

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Section: A Implications Of Unplanned Gas In the Rectal Wallmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: A Implications Of Unplanned Gas In the Rectal Wallmentioning

confidence: 99%

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Assessing localized dosimetric effects due to unplanned gas cavities during pelvic MR‐guided radiotherapy using Monte Carlo simulations

et al. 2019

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“…Multiple manuscripts have been published using radiomics to predict radiation response, in some cases with prediction power outperforming standard clinical variables (77)(78)(79)(80)(81)(82), though not in all (83). Radiomicsbased statistical approaches can predict various radiation normal tissue complication probabilities including radiation pneumonitis, xerostomia, and rectal wall toxicity (84)(85)(86)(87)(88)(89). Radiomics data, coupled with genomic data and increasingly computable clinical record data, may escort radiation oncology into a new epoch of truly personalized radiation plans based on patient-specific knowledge.…”

Section: Tumor Control Probability and Normal Tissue Complication Promentioning

confidence: 99%

Artificial intelligence in radiation oncology treatment planning: a brief overview

Kiser¹,

Fuller²,

Reed³

2019

J Med Artif Intell

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Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.

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“…Quantitative analysis of medical images could provide information about intensity, shape, size or volume, and texture of tumor or organs at risk that is distinct or complementary to that provided by other data sources (5). Recently, the combination of quantitative analysis of radiological images with Machine Learning (ML) methods, also known as "radiomics, " has been applied also to predict side effects of RT such as lung-injury following Stereotactic Body RT (SBRT) for lung cancer (6), gastrointestinal and genitourinary toxicities (7) and xerostomia (8).…”

Section: Introductionmentioning

confidence: 99%

Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

et al. 2020

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Results: The SVM model with seven features was preferred for RIF prediction and scored sensitivity 0.83 (95% CI 0.80-0.86), specificity 0.75 (95% CI 0.71-0.77) andAvanzo et al. Radiomics-and BED-Based Machine Learning of FibrosisAUC of the score function 0.86 (0.85-0.88) on cross-validation. The selected features included cluster shade and Run Length Non-uniformity of breast 3D-BED, kurtosis and cluster shade from PTV 3D-RED, and 10th percentile of PTV 3D-BED. Conclusion:Textures extracted from 3D-BED and 3D-RED in the breast and PTV can predict late RIF and may help better select patient candidates to exclusive PBI.

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Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy

Cited by 96 publications

References 36 publications

Assessing localized dosimetric effects due to unplanned gas cavities during pelvic MR‐guided radiotherapy using Monte Carlo simulations

Assessing localized dosimetric effects due to unplanned gas cavities during pelvic MR‐guided radiotherapy using Monte Carlo simulations

Artificial intelligence in radiation oncology treatment planning: a brief overview

Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

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