Democratising artificial intelligence in healthcare: community-driven approaches for ethical solutions.

The rapid advancement and widespread adoption of artificial intelligence (AI) has ushered in a new era of possibilities in healthcare, ranging from clinical task automation to disease detection. AI algorithms have the potential to analyse medical data, enhance diagnostic accuracy, personalise treatment plans and predict patient outcomes among other possibilities. With a surge in AI's popularity, its developments are outpacing policy and regulatory frameworks, leading to concerns about ethical considerations and collaborative development.

Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING): A data governance risk tool.

Introduction: Digitisation of patient records, coupled with a moral imperative to use routinely collected data for research, necessitate effective data governance that both facilitates evidence-based research and minimises associated risks. The Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING) provides the first quantitative risk-measure for assessing the data-related risks of clinical research projects.

Methods: GOSLING employs a self-assessment designed to standardise risk assessment, considering various domains, including data type, security measures, and public co-production.

Simulation as a tool to model potential workflow enhancements in radiotherapy treatment pathways - A systematic review.

This systematic review aimed to synthesize and summarize the use of simulation of radiotherapy pathways. The objective was to establish the suitability of those simulations in modeling the potential introduction of processes and technologies to speed up radiotherapy pathways. A systematic literature search was carried out using PubMed and Scopus databases to evaluate the use of simulation in radiotherapy pathways.

Sub-regional analysis of the parotid glands: model development for predicting late xerostomia with radiomics features in head and neck cancer patients.

Background: The irradiation of sub-regions of the parotid has been linked to xerostomia development in patients with head and neck cancer (HNC). In this study, we compared the xerostomia classification performance of radiomics features calculated on clinically relevant and de novo sub-regions of the parotid glands of HNC patients.

Material And Methods: All patients ( = 117) were treated with TomoTherapy in 30-35 fractions of 2-2.

Assessing the generalisability of radiomics features previously identified as predictive of radiation-induced sticky saliva and xerostomia.

Background And Purpose: While core to the scientific approach, reproducibility of experimental results is challenging in radiomics studies. A recent publication identified radiomics features that are predictive of late irradiation-induced toxicity in head and neck cancer (HNC) patients. In this study, we assessed the generalisability of these findings.

Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features.

Background And Purpose: The images acquired during radiotherapy for image-guidance purposes could be used to monitor patient-specific response to irradiation and improve treatment personalisation. We investigated whether the kinetics of radiomics features from daily mega-voltage CT image-guidance scans (MVCT) improve prediction of moderate-to-severe xerostomia compared to dose/volume parameters in radiotherapy of head-and-neck cancer (HNC).

Materials And Methods: All included HNC patients (N = 117) received 30 or more fractions of radiotherapy with daily MVCTs.

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.

Introduction: Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma.

Methods: A retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma.

Multi-scale segmentation in GBM treatment using diffusion tensor imaging.

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype.

Associations between voxel-level accumulated dose and rectal toxicity in prostate radiotherapy.

Background And Purpose: Associations between dose and rectal toxicity in prostate radiotherapy are generally poorly understood. Evaluating spatial dose distributions to the rectal wall (RW) may lead to improvements in dose-toxicity modelling by incorporating geometric information, masked by dose-volume histograms. Furthermore, predictive power may be strengthened by incorporating the effects of interfraction motion into delivered dose calculations.

Versatile magnetic microdiscs for the radio enhancement and mechanical disruption of glioblastoma cancer cells.

This study describes the use of highly versatile, lithographically defined magnetic microdiscs. Gold covered magnetic microdiscs are used in both radiosensitizing cancer cells, acting as intracellular emitters of secondary electrons during radiotherapy, and as well as inducing mechanical damage by exerting a mechanical torque when exposed to a rotating magnetic field. This study reveals that lithographically defined microdiscs with a uniform size of 2 microns in diameter highly increase the DNA damage and reduce the glioblastoma colony formation potential compared to conventional radiation therapy.

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps.

Objectives: Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through and maps can be used for planning of treatment.

A Phase I Study of Pegylated Arginine Deiminase (Pegargiminase), Cisplatin, and Pemetrexed in Argininosuccinate Synthetase 1-Deficient Recurrent High-grade Glioma.

Purpose: Patients with recurrent high-grade gliomas (HGG) are usually managed with alkylating chemotherapy ± bevacizumab. However, prognosis remains very poor. Preclinically, we showed that HGGs are a target for arginine depletion with pegargiminase (ADI-PEG20) due to epimutations of argininosuccinate synthetase () and/or argininosuccinate lyase ().

Anatomical change during radiotherapy for head and neck cancer, and its effect on delivered dose to the spinal cord.

Background And Purpose: The impact of weight loss and anatomical change during head and neck (H&N) radiotherapy on spinal cord dosimetry is poorly understood, limiting evidence-based adaptive management strategies.

Materials And Methods: 133 H&N patients treated with daily mega-voltage CT image-guidance (MVCT-IG) on TomoTherapy, were selected. Elastix software was used to deform planning scan SC contours to MVCT-IG scans, and accumulate dose.

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary 'VoxTox' research programme.

The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.

Accumulated dose to the rectum, measured using dose-volume histograms and dose-surface maps, is different from planned dose in all patients treated with radiotherapy for prostate cancer.

Objective: We sought to calculate accumulated dose (DA) to the rectum in patients treated with radiotherapy for prostate cancer. We were particularly interested in whether dose-surface maps (DSMs) provide additional information to dose-volume histograms (DVHs).

Methods: Manual rectal contours were obtained for kilovoltage and daily megavoltage CT scans for 10 participants from the VoxTox study (380 scans).

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task.

Normal tissue complication probability (NTCP) parameters for breast fibrosis: pooled results from two randomised trials.

Introduction: The dose-volume effect of radiation therapy on breast tissue is poorly understood. We estimate NTCP parameters for breast fibrosis after external beam radiotherapy.

Materials And Methods: We pooled individual patient data of 5856 patients from 2 trials including whole breast irradiation followed with or without a boost.

Hadron therapy information sharing prototype.

The European PARTNER project developed a prototypical system for sharing hadron therapy data. This system allows doctors and patients to record and report treatment-related events during and after hadron therapy. It presents doctors and statisticians with an integrated view of adverse events across institutions, using open-source components for data federation, semantics, and analysis.

Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy.

Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult.

Evaluation of poly (ADP-ribose) polymerase inhibitor ABT-888 combined with radiotherapy and temozolomide in glioblastoma.

Background: The cytotoxicity of radiotherapy and chemotherapy can be enhanced by modulating DNA repair. PARP is a family of enzymes required for an efficient base-excision repair of DNA single-strand breaks and inhibition of PARP can prevent the repair of these lesions. The current study investigates the trimodal combination of ABT-888, a potent inhibitor of PARP1-2, ionizing radiation and temozolomide(TMZ)-based chemotherapy in glioblastoma (GBM) cells.

Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation.

Model predictive control (MPC), originally developed in the community of industrial process control, is a potentially effective approach to optimal scheduling of cancer therapy. The basis of MPC is usually a state-space model (a system of ordinary differential equations), whereby existing studies usually assume that the entire states can be directly measured. This paper aims to demonstrate that when the system states are not fully measurable, in conjunction with model parameter discrepancy, MPC is still a useful method for cancer treatment.