Biological Imaging for Individualized Therapy in Radiation Oncology: Part II Medical and Clinical Aspects

Gkika, Eleni; Oehlke, Oliver; Bunea, Hatice; Wiedenmann, Nicole; Adebahr, S.; Nestle, Ursula; Zamboglou, Constantinos; Kirste, Simon; Fennell, Jamina Tara; Brunner, Thomas; Gainey, Mark; Baltas, Dimos; Langer, Mathias; Urbach, Horst; Bock, Michael; Meyer, Philipp T.; Grosu, Anca‐Ligia

doi:10.2217/fon-2017-0465

Cited by 7 publications

(2 citation statements)

References 157 publications

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“…In each case, standardization preserves the integrity of critical decision-making information. Consider MRIgRT for prostate cancer (33, 34). Radiology standards exist for MRI acquisition, interpretation, and reporting (35).…”

Section: How Is Biomedical Informatics Relevant To Mrigrt Currently?mentioning

confidence: 99%

“Après Mois, Le Déluge”: Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

et al. 2019

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Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.

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Section: How Is Biomedical Informatics Relevant To Mrigrt Currently?mentioning

confidence: 99%

“Après Mois, Le Déluge”: Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

et al. 2019

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“…In multiparametric MRI for tumor segmentation, different anatomical image contrasts (T1- and T2-weighted) are combined with functional information acquired with perfusion und diffusion measurements. Particularly diffusion-weighted imaging (DWI) has proven to contribute valuable additional information for tumor delineation (16–18). For the DWI images, an echo planar imaging (EPI) pulse sequence is commonly used.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction

et al. 2019

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Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (ktrans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use.

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Imaging Instrumentation and Techniques for Precision Radiotherapy

Parodi¹,

Thieke²

2019

Handbook of Particle Detection and Imaging

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Biological Imaging for Individualized Therapy in Radiation Oncology: Part II Medical and Clinical Aspects

Cited by 7 publications

References 157 publications

“Après Mois, Le Déluge”: Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

“Après Mois, Le Déluge”: Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction

Imaging Instrumentation and Techniques for Precision Radiotherapy

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