Image-based data mining applies to data collected from children

Wilson, Lydia; Bryce-Atkinson, Abigail; Green, Andrew; Li, Yimei; Merchant, Thomas E.; Herk, Marcel van; Osorio, Eliana Vásquez; Faught, Austin M.; Aznar, Marianne C.

doi:10.1016/j.ejmp.2022.05.003

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…Alternatively (and arguably preferably in terms of workflow) automated tools would be incorporated into many TPS’s, but this would require collaboration with vendors and a commitment on their part to provide dedicated solutions for this unique, but small, patient group where commercial incentive may be limited. Alongside the development of such tools, retrospective analysis techniques, such as, image-based data mining could help identify particular (sub-)structures of importance [29] , [30] . In this survey, the mandible, TMJ, and dentition were the most common delineated bones across paediatric radiotherapy centres – likely reflective of the fact that there are guidance and contouring atlases available for these bones.…”

Section: Discussionmentioning

confidence: 99%

The need for consensus on delineation and dose constraints of dentofacial structures in paediatric radiotherapy: Outcomes of a SIOP Europe survey

Davey,

Pan,

Bryce-Atkinson

et al. 2023

Clinical and Translational Radiation Oncology

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Section: Discussionmentioning

confidence: 99%

The need for consensus on delineation and dose constraints of dentofacial structures in paediatric radiotherapy: Outcomes of a SIOP Europe survey

Davey,

Pan,

Bryce-Atkinson

et al. 2023

Clinical and Translational Radiation Oncology

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“…Furthermore, recent studies have shown that the toxicity observed can depend on which subregion of a segmented organ was irradiated ( 2 – 4 , 16 – 37 ). Functionally distinct subregions within a single OAR contour may not be accounted for in current treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Impact of radiation dose distribution on nutritional supplementation needs in head and neck cancer radiotherapy: a voxel-based machine learning approach

Madhavan,

Gamez,

Garces

et al. 2024

Front. Oncol.

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ObjectivesTo investigate the relationship between nutritional supplementation and radiation dose to the pharyngeal constrictor muscles and larynx for head and neck (HN) cancer patients undergoing radiotherapy.MethodsWe retrospectively analyzed radiotherapy (RT) dose for 231 HN cancer patients, focusing on the pharyngeal constrictors and larynx. We defined nutritional supplementation as feeding tube utilization or >10% weight loss from baseline within 90 days after radiotherapy completion. Using deformable image registration (DIR), we mapped each patient’s anatomical structures to a reference coordinate system, and corresponding deformations were applied to dose matrices. Voxel doses were utilized as features for ridge logistic regression models, optimized through 5-fold cross-validation. Model performance was assessed with area under the curve of a receiver operating curve (AUC) and F1 score. We built and compared models using 1) pharyngeal constrictor voxels, 2) larynx voxels, 3) clinical factors and mean regional dose metrics, and 4) clinical factors and dose-volume histogram metrics. Test set AUCs were compared among the models, and feature importance was evaluated.ResultsDIR of the pharyngeal constrictors and larynx yielded mean Dice coefficients of 0.80 and 0.84, respectively. Pharyngeal constrictors voxels and larynx voxel models had AUC of 0.88 and 0.82, respectively. Voxel-based dose modeling identified the superior to middle regions of the pharyngeal constrictors and the superior region of larynx as most predictive of feeding tube use/weight loss. Univariate analysis found treatment setting, treatment laterality, chemotherapy, baseline dysphagia, weight, and socioeconomic status predictive of outcome. An aggregated model using mean doses of pharyngeal constrictors and larynx subregions had an AUC of 0.87 and the model using conventional DVH metrics had an AUC of 0.85 with p-value of 0.04. Feature importance calculations from the regional dose model indicated that mean doses to the superior-middle pharyngeal constrictor muscles followed by mean dose to the superior larynx were most predictive of nutritional supplementation.ConclusionsMachine learning modeling of voxel-level doses enables identification of subregions within organs that correlate with toxicity. For HN radiotherapy, doses to the superior-middle pharyngeal constrictors are most predictive of feeding tube use/weight loss followed by the doses to superior portion of the larynx.

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