Modelling for Radiation Treatment Outcome

Modelling of tumour control probability (TCP) and normal tissue complication probability (NTCP) has been continuously used to estimate the therapeutic window of radiotherapy. In recent years, available data on tumour and normal tissue biology and from multimodal imaging have increased substantially, in particular, due to image-guided radiotherapy (see previous chapters of this book) and novel high-throughput sequencing technologies. Accordingly, more complex modelling algorithms are applied and issues of data quality, structured modelling procedures, and model validation need to be addressed. This chapter outlines general modelling principles in the era of big data, provides definitions of classical TCP and NTCP models, and presents two applications of outcome modelling in radiotherapy: the model-based approach for assigning patients to photon or proton-beam therapy and radiomics analyses based on clinical imaging data.</p

Modelling for Radiation Treatment Outcome

Modelling of tumour control probability (TCP) and normal tissue complication probability (NTCP) has been continuously used to estimate the therapeutic window of radiotherapy. In recent years, available data on tumour and normal tissue biology and from multimodal imaging have increased substantially, in particular, due to image-guided radiotherapy (see previous chapters of this book) and novel high-throughput sequencing technologies. Accordingly, more complex modelling algorithms are applied and issues of data quality, structured modelling procedures, and model validation need to be addressed. This chapter outlines general modelling principles in the era of big data, provides definitions of classical TCP and NTCP models, and presents two applications of outcome modelling in radiotherapy: the model-based approach for assigning patients to photon or proton-beam therapy and radiomics analyses based on clinical imaging data.</p

Modelling for Radiation Treatment Outcome

Modelling of tumour control probability (TCP) and normal tissue complication probability (NTCP) has been continuously used to estimate the therapeutic window of radiotherapy. In recent years, available data on tumour and normal tissue biology and from multimodal imaging have increased substantially, in particular, due to image-guided radiotherapy (see previous chapters of this book) and novel high-throughput sequencing technologies. Accordingly, more complex modelling algorithms are applied and issues of data quality, structured modelling procedures, and model validation need to be addressed. This chapter outlines general modelling principles in the era of big data, provides definitions of classical TCP and NTCP models, and presents two applications of outcome modelling in radiotherapy: the model-based approach for assigning patients to photon or proton-beam therapy and radiomics analyses based on clinical imaging data.</p

Modelling for Radiation Treatment Outcome

Modelling of tumour control probability (TCP) and normal tissue complication probability (NTCP) has been continuously used to estimate the therapeutic window of radiotherapy. In recent years, available data on tumour and normal tissue biology and from multimodal imaging have increased substantially, in particular, due to image-guided radiotherapy (see previous chapters of this book) and novel high-throughput sequencing technologies. Accordingly, more complex modelling algorithms are applied and issues of data quality, structured modelling procedures, and model validation need to be addressed. This chapter outlines general modelling principles in the era of big data, provides definitions of classical TCP and NTCP models, and presents two applications of outcome modelling in radiotherapy: the model-based approach for assigning patients to photon or proton-beam therapy and radiomics analyses based on clinical imaging data.</p

Standardised convolutional filtering for radiomics

The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a preliminary version of a reference manual on the use of convolutional image filters in radiomics. Filters, such as wavelets or Laplacian of Gaussian filters, play an important part in emphasising specific image characteristics such as edges and blobs. Features derived from filter response maps have been found to be poorly reproducible. This reference manual forms the basis of ongoing work on standardising convolutional filters in radiomics, and will be updated as this work progresses.Comment: 62 pages. For additional information see https://theibsi.github.io

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco- regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints

Definition and validation of a radiomics signature for loco-regional tumour control in patients with locally advanced head and neck squamous cell carcinoma

Purpose: To develop and validate a CT-based radiomics signature for the prognosis of loco-regional tumour control (LRC) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated by primary radiochemotherapy (RCTx) based on retrospective data from 6 partner sites of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Material and methods: Pre-treatment CT images of 318 patients with locally advanced HNSCC were col-lected. Four-hundred forty-six features were extracted from each primary tumour volume and then fil-tered through stability analysis and clustering. First, a baseline signature was developed from demographic and tumour-associated clinical parameters. This signature was then supplemented by CT imaging features. A final signature was derived using repeated 3-fold cross-validation on the discovery cohort. Performance in external validation was assessed by the concordance index (C-Index). Furthermore, calibration and patient stratification in groups with low and high risk for loco-regional recurrence were analysed. Results: For the clinical baseline signature, only the primary tumour volume was selected. The final sig-nature combined the tumour volume with two independent radiomics features. It achieved moderatel

Mesenchymal Stem Cells Induce T-Cell Tolerance and Protect the Preterm Brain after Global Hypoxia-Ischemia

Hypoxic-ischemic encephalopathy (HIE) in preterm infants is a severe disease for which no curative treatment is available. Cerebral inflammation and invasion of activated peripheral immune cells have been shown to play a pivotal role in the etiology of white matter injury, which is the clinical hallmark of HIE in preterm infants. The objective of this study was to assess the neuroprotective and anti-inflammatory effects of intravenously delivered mesenchymal stem cells (MSC) in an ovine model of HIE. In this translational animal model, global hypoxia-ischemia (HI) was induced in instrumented preterm sheep by transient umbilical cord occlusion, which closely mimics the clinical insult. Intravenous administration of 2 x 106MSC/kg reduced microglial proliferation, diminished loss of oligodendrocytes and reduced demyelination, as determined by histology and Diffusion Tensor Imaging (DTI), in the preterm brain after global HI. These anti-inflammatory and neuroprotective effects of MSC were paralleled by reduced electrographic seizure activity in the ischemic preterm brain. Furthermore, we showed that MSC induced persistent peripheral T-cell tolerance in vivo and reduced invasion of T-cells into the preterm brain following global HI. These findings show in a preclinical animal model that intravenously administered MSC reduced cerebral inflammation, protected against white matter injury and established functional improvement in the preterm brain following global HI. Moreover, we provide evidence that induction of T-cell tolerance by MSC might play an important role in the neuroprotective effects of MSC in HIE. This is the first study to describe a marked neuroprotective effect of MSC in a translational animal model of HIE

The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking